Compare commits

..

69 Commits

Author SHA1 Message Date
SoftFever
6187063190 bump to rc2 2026-03-06 12:12:40 +08:00
SoftFever
b9f93ffd99 Merge branch 'main' into release/v2.3 2026-03-06 12:11:47 +08:00
SoftFever
586118a875 Merge branch 'main' into release/v2.3 2026-03-06 11:14:54 +08:00
SoftFever
ca1f360b83 bump version to 2.3.2-rc 2026-03-01 14:14:33 +08:00
Ian Bassi
4c73ac2fea Fix "Glidlines" "gridlines locale (#12529) 2026-03-01 14:13:54 +08:00
gerchowl
36b764f735 fix: typo "Glidlines" → "Gridlines" in View menu tooltip (#12527) 2026-03-01 14:13:54 +08:00
Rodrigo Faselli
ce1d91024b Fix missing infill layers (fill_surface_by_multilines & fill_surface_trapezoidal bug fix) (#12516)
* FillRectilinear bugfix

* cleaning

* Revert "cleaning"

This reverts commit 0de21ada78.

* Filltrapezoidal is OK

* Filltrapezoidal fix 2
2026-03-01 14:13:54 +08:00
SoftFever
2f3bfd03d5 fix missing translations for Canvas Toolbar Menu 2026-03-01 14:13:53 +08:00
SoftFever
f03c34024d update profile version 2026-03-01 14:13:53 +08:00
SoftFever
6b3f554732 update locale 2026-03-01 14:13:53 +08:00
yw4z
325ced958e Add option for hiding / showing gridlines (#10545)
Might be useful who want to use custom grid line system with textures. or a clean look

### PROBLEM / SCENARIOS
• Custom made textures overlapping with grid system. so it might be confusing for user if want to use specific markings
• User may found its a distracting item and wants a clean look

### SOLUTION
added to view menu
<img width="477" height="434" alt="Screenshot-20251230192707" src="https://github.com/user-attachments/assets/e298d9b2-5f8f-4e33-af22-ea7c84c9b5b8" />

added to canvas menu
<img width="278" height="297" alt="Screenshot-20251230192717" src="https://github.com/user-attachments/assets/a9952408-a361-4e64-ad9a-72e2480c74cf" />

Clean look without gridlines
<img width="1669" height="1157" alt="Screenshot-20250827144140" src="https://github.com/user-attachments/assets/9229f330-9543-4d39-a8fc-01deb9b61336" />

<img width="1669" height="1157" alt="Screenshot-20250827144212" src="https://github.com/user-attachments/assets/ab3848fb-74d7-4618-8bc7-0cdff10a3804" />


Few Examples with custom made textures / markings without gridlines
<img width="1669" height="1157" alt="Screenshot-20250827140008" src="https://github.com/user-attachments/assets/5d7b17ab-e97a-489c-9e4d-99157a37b6b7" />

<img width="1669" height="1157" alt="Screenshot-20250827141904" src="https://github.com/user-attachments/assets/a7e0f360-a85a-489d-9bc2-39286984643a" />
2026-03-01 14:13:53 +08:00
SoftFever
bbbc044e92 update locale and Simplified/Tranditional Chinese translation 2026-03-01 14:13:53 +08:00
Valerii Bokhan
0c666da430 Enhancement: Enabling base patterns (infill) for Organic supports (#12141)
Enhancement: Enabling base patterns for Organic supports
2026-03-01 14:13:53 +08:00
Valerii Bokhan
c924b8ed25 I18n: Preview translations minor fix (Ukrainian) (#12504) 2026-03-01 14:13:53 +08:00
SoftFever
c8dde0f8ca update locale 2026-03-01 14:13:52 +08:00
Ian Bassi
401fb935bc Fix: Init Serialized options (#12489)
Update Tab.cpp
2026-03-01 14:13:52 +08:00
Ian Bassi
7747a6e7a1 Preview translations (#12503)
Updated new preview options missing
+ Ru improvements (thanks @valerii-bokhan )
2026-03-01 14:13:52 +08:00
Ioannis Giannakas
545cdc74ca Fix wipe tower first layer leaving gaps when spacing other than 100pc is specified (#12491)
Co-authored-by: SoftFever <softfeverever@gmail.com>
2026-03-01 14:13:52 +08:00
Heiko Liebscher
b0e19d5577 add new de translations (#12501)
add new translations
2026-03-01 14:13:52 +08:00
Molly K
df216c5105 Simplified Chinese translation improvement (≈ lines after 16500) (second minor update) (#12453)
* SAVE line 20K+ ~ 16573

* fix syntax errors

---------

Co-authored-by: SoftFever <softfeverever@gmail.com>
2026-03-01 14:13:52 +08:00
Alexandre Folle de Menezes
e5d037c044 Fix GUI strings that should start with uppercase (#12499) 2026-03-01 14:13:52 +08:00
SoftFever
6e01d38a5c Skip uploading cache for PR build (#12494) 2026-03-01 14:13:51 +08:00
SoftFever
37790f2a3d Feature/mac_build (#12493)
* fix deps cache miss for Mac PR build
2026-03-01 14:13:51 +08:00
tome9111991
6437fb57e8 Fix: crash in PA calibration pattern generation (#12406)
Fix crash in PA calibration pattern generation

Reset current extruder ID and clear pointers in set_extruders to prevent dangling pointers when extruders are recreated. Also fixed undefined behavior by checking if the vector is empty before taking max_element.

Co-authored-by: SoftFever <softfeverever@gmail.com>
2026-03-01 14:13:51 +08:00
yw4z
a46c8970bf Filament Selection dialog Fixes / Improvements (#12325)
* Update 22.js

* fix printer list empty

* switch to vertical scrolling on custom filaments

* Update 23.css

* add stats

* fix uncommon filament types

* fix setup wizerd styling

---------

Co-authored-by: SoftFever <softfeverever@gmail.com>
2026-03-01 14:13:51 +08:00
Ian Bassi
03bc0af038 "None (allow paint)" to "Painted Only" (#12487)
Co-authored-by: Valerii Bokhan <80919135+valerii-bokhan@users.noreply.github.com>
2026-03-01 14:13:51 +08:00
Felix14_v2
c03fdb4ca0 Update Russian localization (#12001) 2026-03-01 14:13:51 +08:00
SoftFever
1162b101b8 Change bed temperature type to use highest temp by default (#12486)
## Summary
- Change default bed temperature type from "By First filament" to "By Highest Temp"
- Move `bed_temperature_formula` option from develop mode to advanced mode for better accessibility
- Relocate UI control from "Basic Information - Advanced" to "Multimaterial Setup" section where it's more relevant for multi-filament printing
Using the highest temperature of all printed filaments is generally safer for bed adhesion than using the first filament's temperature, especially in multi-material prints where different filaments may require different bed temperatures.

<img width="390" height="239" alt="Screenshot 2026-02-26 at 18 44 56" src="https://github.com/user-attachments/assets/0fe9f8c7-062a-4a7a-8ab3-c52df7e74b26" />
2026-03-01 14:13:51 +08:00
Valerii Bokhan
f91971baf9 Fix: Fixed the values comparison for Float-based config options (#12478) 2026-03-01 14:13:51 +08:00
Argo
937b356725 Wipe tower "infinite out of bed line" fix (#12267)
Fix wipe tower out-of-bed moves

Co-authored-by: Ioannis Giannakas <59056762+igiannakas@users.noreply.github.com>
2026-03-01 14:13:50 +08:00
Boštjan Čadež
721ee0ab00 Fix: macOS 2 finger tap moves moves slider instead of opening context menu (#12375) 2026-03-01 14:13:50 +08:00
SoftFever
f054e3ad9b Fix wipe tower skirt alignment by computing bbx from actual first-layer polygon (#12457) 2026-03-01 14:13:50 +08:00
Ian Bassi
1a57ec13a5 Minor export text change (#12461) 2026-03-01 14:13:50 +08:00
Alexandre Folle de Menezes
c7b65ef498 Fix spacing issues (#12404) 2026-03-01 14:13:50 +08:00
Ioannis Giannakas
31cb77116c Fix wipe tower Y offset for SEMM (#12442) 2026-03-01 14:13:50 +08:00
Ian Bassi
74f75bb90d JD processor upgrade (#12440)
* Update GCodeProcessor.cpp

Co-Authored-By: Rodrigo Faselli <162915171+RF47@users.noreply.github.com>

* Fix

Co-Authored-By: Rodrigo Faselli <162915171+RF47@users.noreply.github.com>

* get_axis_max_jerk_with_jd

* get_get fix

Co-Authored-By: Rodrigo Faselli <162915171+RF47@users.noreply.github.com>

---------

Co-authored-by: Rodrigo Faselli <162915171+RF47@users.noreply.github.com>
2026-03-01 14:13:50 +08:00
Alexandre Folle de Menezes
a3c067cd3b Add translation markers to measure units (#12403)
# Description
Some units were missing the translation markers, others were not in the standard format.
2026-03-01 14:13:49 +08:00
Rodrigo Faselli
90cd00382a Added Junction Deviation support to time estimation (#12417)
# Description

This PR enhances the GCodeProcessor's time estimation by incorporating Junction Deviation (JD) into the jerk calculations, providing more accurate print time predictions for firmwares that use JD (like modern Marlin).

Key Changes:
- Added JD support to time estimation

- Reads machine_max_junction_deviation (machine limits) and default_junction_deviation (print profile)

- When JD is enabled (>0), replaces traditional X/Y jerk values with JD-based calculation:
$Jerk = \sqrt{2.5\cdot JD \cdot acceleration }$

- Falls back to traditional jerk when JD is not used

# Test:
JD:0.0256mm   Accel.: 1000 mm/s²
<img width="2560" height="1392" alt="image" src="https://github.com/user-attachments/assets/f0e95294-bfca-400e-bffc-8d615d051b70" />

Jerk: 8mm/s  (equivalent)
<img width="2560" height="1392" alt="image" src="https://github.com/user-attachments/assets/8508727e-70f6-49ed-ac19-002db73e957b" />

JD:0.0128mm (4mm/s jerk)
<img width="2560" height="1392" alt="image" src="https://github.com/user-attachments/assets/91b04d3b-1b9e-48f4-b4b4-5addda2eff57" />
2026-03-01 14:13:49 +08:00
Sabriel-Koh
deef8b107d fix: filament remap should not "paint" unpainted triangles (#12437)
fix: filament remap should not "paint" unpainted triangles and should update the object extruder UI
2026-03-01 14:13:49 +08:00
Kellwa
478f1fa117 Anycubic Kobra Neo printer profiles (#12341)
* Add files via upload

* Add files via upload

* Add files via upload

* Add files via upload

* fix

---------

Co-authored-by: SoftFever <softfeverever@gmail.com>
2026-03-01 14:13:49 +08:00
mlugo-apx
3f82902ebe Add Elegoo filament profiles to OrcaFilamentLibrary (#12359)
* Add Elegoo filament profiles to OrcaFilamentLibrary

Add 7 Elegoo filament profiles based on manufacturer specifications:
- Elegoo Rapid PETG (high-speed PETG, 240-270°C, 30-600mm/s)
- Elegoo PETG Pro (standard PETG, 230-260°C, 30-270mm/s)
- Elegoo PETG-CF (carbon fiber PETG, 240-270°C, 30-220mm/s)
- Elegoo PLA (standard PLA, 190-230°C, 30-280mm/s)
- Elegoo Rapid PLA+ (high-speed PLA, 190-230°C, 30-600mm/s)
- Elegoo ASA (ASA, 250-280°C, 30-270mm/s)
- Elegoo TPU 95A (flexible TPU, 220-240°C, 30-60mm/s)

All settings sourced from Elegoo's official product specifications.

* Fix dual seam fuzzy painted rev 2 (#11923)

* 2 seam fuzzy 2nd attempt

* Update FuzzySkin.cpp

* Fix debug SVG

* solve bump artifact in extrusion junction joint

* minor fixes

* cleaning

Update FuzzySkin.cpp

* Fix filament override changes not appearing in Unsaved Changes and showing as “Undef category” in preset comparison (#12298)

* Fix an issue that on Windows the Bambu legacy plugin is 01.10.01.09 (#12380)

* tweak legacy library migration from rename to copy in BBLNetworkPlugin (#12400)

* tweak legacy library migration from rename to copy in BBLNetworkPlugin

* Add Pressure Advance visualization support (#11673)

* Add Pressure Advance visualization support

Signed-off-by: minicx <minicx@disroot.org>

* Port Pressure Advance visualization to libvgcode architecture

Adapt PA visualization (originally in commit e3a77259) to work with
the new libvgcode library introduced by upstream PR #10735.

Changes across the libvgcode stack:
- PathVertex: add pressure_advance field
- Types.hpp: add PressureAdvance to EViewType enum
- ViewerImpl: add ColorRange, color mapping, range updates for PA
- LibVGCodeWrapper: pass pressure_advance from MoveVertex to PathVertex

GCodeViewer UI integration:
- Add "Pressure Advance" to view type dropdown
- Add PA color range in legend (3 decimal places)
- Add PA value display in sequential view marker tooltip
- Add PA row in position properties table

The GCodeProcessor PA parsing (M900, M572, SET_PRESSURE_ADVANCE)
is preserved from the original implementation.

* Tag Pressure Advance visualization changes with ORCA comments

Signed-off-by: minicx <minicx@disroot.org>

---------

Signed-off-by: minicx <minicx@disroot.org>
Co-authored-by: Ioannis Giannakas <59056762+igiannakas@users.noreply.github.com>
Co-authored-by: Claude <noreply@anthropic.com>

* Fix time estimation using wrong machine limits due to broken extruder_id indexing (#12411)

The extruder_id*2 offset in get_axis_max_feedrate/get_axis_max_acceleration
was cherry-picked from BambuStudio's per-nozzle limit system, which OrcaSlicer
never ported. Without that system the limit arrays only have 2 values
([0]=Normal, [1]=Stealth), so any extruder_id > 0 or the uninitialized
value (255) would overshoot the array and fall back to values.back(),
always returning stealth-mode limits and producing incorrect time estimates.

Revert to indexing by time mode only (matching v2.3.1 behavior) and simplify
the M201/M203 handlers to write only the two mode slots they actually use.

* Fix machine envelope G-code emitting wrong limits due to broken extruder_id indexing (#12414)

print_machine_envelope() used get_extruder_id(extruder_id)*2 to index
machine limit arrays that only hold [Normal, Stealth] (2 entries).
For multi-extruder setups this went out-of-bounds, causing wrong M201/M203
values in the G-code which then override the estimator's correct limits.

Same class of bug as c6d1c11ebb but on the G-code writer side.

Changes:
- Remove unused extruder_id param from print_machine_envelope()
- Use .values.front() for M201/M203, matching M204/M205 in same function
- Change get_option_value() fallback from .back() to .front() so any
  future out-of-bounds index returns Normal mode instead of Stealth

* update error message when plugin upgrade failed

* Add and update pt-BR translations (#12409)

* Add preference for filament area height to reduce scrolling while using 16+ filaments (#12317)

Fixes https://github.com/OrcaSlicer/OrcaSlicer/issues/12284

Adds a preference for filaments area height since every user has different amount of MM units and external filaments
so users can set a value that fits their setups this way

we might see 3 or 6 filaments for each unit on future. thats why i set value as int

used 10 as default value. its good for 2 multimaterial units ( 8 filaments ) and 1 external filament

min value is 8.  might be good when no external filaments in use

max value is 99. UI works same as 2.3.1 version

<img width="562" height="122" alt="Screenshot-20260215194149" src="https://github.com/user-attachments/assets/309cec36-8b83-48f3-875f-d5f22a9631e7" />

**BEFORE**
Scrollable area fixed for 10 items. that causes a lot of scrolling whe user has 3 or 4 ams units

<img width="411" height="237" alt="Screenshot-20260215194816" src="https://github.com/user-attachments/assets/fc7823c0-d82a-4d1f-bb5b-56e8dd47abd2" />

**AFTER**
value 10. - 2 multimaterial units ( 8 filaments ) and 1 external filament
<img width="1002" height="250" alt="Screenshot-20260215195243" src="https://github.com/user-attachments/assets/e3238cd1-788e-4ed2-b048-89c63bd323db" />

value 18  - 4 multimaterial units ( 16 filaments ) and 1 external filament
<img width="1001" height="355" alt="Screenshot-20260215195127" src="https://github.com/user-attachments/assets/afe0305e-fcb4-4a51-b8dc-e70a063aa391" />

* Fix: Correct range checking for int and float Config Options + QoL changes in tooltips (#11915)

* Fix float number not working properly for option min/max (#11211)

* ConfigOptionDef: min/max values type are changed from INT to FLOAT.

(cherry picked from commit f277bc80c22e0c9a067481a4301922e2c96aed47)

* Fix infinite loop and crash when `fuzzy_skin_point_distance` = 0 (SoftFever/OrcaSlicer#11069)

* Fix Linux build issue

* Fix float comparison due to precision loss

* Fix: Range check added for coInt options; Ranges and defaults added in tooltips

---------

Co-authored-by: Noisyfox <timemanager.rick@gmail.com>
Co-authored-by: SoftFever <softfeverever@gmail.com>

* Update machine profile for OpenEYE Peacock V2 (#12333)

* OpenEYE Peacock V2 machine profile: Add printer_agent key to enable AMS filament synchronization (Klipper/Moonraker)

Signed-off-by: Sezgin AÇIKGÖZ <sezginacikgoz@mail.com>

* Optimize retraction settings in printer profiles to reduce stringing and improve print quality

Signed-off-by: Sezgin AÇIKGÖZ <sezginacikgoz@mail.com>

---------

Signed-off-by: Sezgin AÇIKGÖZ <sezginacikgoz@mail.com>

* Fix preheat regression bugs  (#12438)

Fix preheat regression bugs
revert 769fc137c7

* Fix the issue where `resources/profiles/OrcaFilamentLibrary.json` is not updated accordingly.

---------

Signed-off-by: minicx <minicx@disroot.org>
Signed-off-by: Sezgin AÇIKGÖZ <sezginacikgoz@mail.com>
Co-authored-by: Rodrigo Faselli <162915171+RF47@users.noreply.github.com>
Co-authored-by: Kiss Lorand <50251547+kisslorand@users.noreply.github.com>
Co-authored-by: SoftFever <softfeverever@gmail.com>
Co-authored-by: minicx <39405619+loss-and-quick@users.noreply.github.com>
Co-authored-by: Ioannis Giannakas <59056762+igiannakas@users.noreply.github.com>
Co-authored-by: Claude <noreply@anthropic.com>
Co-authored-by: Alexandre Folle de Menezes <afmenez@terra.com.br>
Co-authored-by: yw4z <ywsyildiz@gmail.com>
Co-authored-by: Valerii Bokhan <80919135+valerii-bokhan@users.noreply.github.com>
Co-authored-by: Noisyfox <timemanager.rick@gmail.com>
Co-authored-by: Sezgin AÇIKGÖZ <sezginacikgoz@mail.com>
2026-03-01 14:13:49 +08:00
Derrick
1c42f67000 Fix Intra-layer "As Object List" not working. (#12368)
Added checks in ByLayer print sequence to handle ordering for Intra-layer ordering option. Prevents new ordering setup when "As object list" is enabled.
2026-03-01 14:13:49 +08:00
SoftFever
b45c7a8112 Fix preheat regression bugs (#12438)
Fix preheat regression bugs
revert 769fc137c7
2026-03-01 14:13:49 +08:00
Sezgin AÇIKGÖZ
da8be684dc Update machine profile for OpenEYE Peacock V2 (#12333)
* OpenEYE Peacock V2 machine profile: Add printer_agent key to enable AMS filament synchronization (Klipper/Moonraker)

Signed-off-by: Sezgin AÇIKGÖZ <sezginacikgoz@mail.com>

* Optimize retraction settings in printer profiles to reduce stringing and improve print quality

Signed-off-by: Sezgin AÇIKGÖZ <sezginacikgoz@mail.com>

---------

Signed-off-by: Sezgin AÇIKGÖZ <sezginacikgoz@mail.com>
2026-03-01 14:13:49 +08:00
Valerii Bokhan
dc27345b05 Fix: Correct range checking for int and float Config Options + QoL changes in tooltips (#11915)
* Fix float number not working properly for option min/max (#11211)

* ConfigOptionDef: min/max values type are changed from INT to FLOAT.

(cherry picked from commit f277bc80c22e0c9a067481a4301922e2c96aed47)

* Fix infinite loop and crash when `fuzzy_skin_point_distance` = 0 (SoftFever/OrcaSlicer#11069)

* Fix Linux build issue

* Fix float comparison due to precision loss

* Fix: Range check added for coInt options; Ranges and defaults added in tooltips

---------

Co-authored-by: Noisyfox <timemanager.rick@gmail.com>
Co-authored-by: SoftFever <softfeverever@gmail.com>
2026-03-01 14:13:49 +08:00
yw4z
10cfab0501 Add preference for filament area height to reduce scrolling while using 16+ filaments (#12317)
Fixes https://github.com/OrcaSlicer/OrcaSlicer/issues/12284

Adds a preference for filaments area height since every user has different amount of MM units and external filaments
so users can set a value that fits their setups this way

we might see 3 or 6 filaments for each unit on future. thats why i set value as int

used 10 as default value. its good for 2 multimaterial units ( 8 filaments ) and 1 external filament

min value is 8.  might be good when no external filaments in use

max value is 99. UI works same as 2.3.1 version

<img width="562" height="122" alt="Screenshot-20260215194149" src="https://github.com/user-attachments/assets/309cec36-8b83-48f3-875f-d5f22a9631e7" />

**BEFORE**
Scrollable area fixed for 10 items. that causes a lot of scrolling whe user has 3 or 4 ams units

<img width="411" height="237" alt="Screenshot-20260215194816" src="https://github.com/user-attachments/assets/fc7823c0-d82a-4d1f-bb5b-56e8dd47abd2" />

**AFTER**
value 10. - 2 multimaterial units ( 8 filaments ) and 1 external filament
<img width="1002" height="250" alt="Screenshot-20260215195243" src="https://github.com/user-attachments/assets/e3238cd1-788e-4ed2-b048-89c63bd323db" />

value 18  - 4 multimaterial units ( 16 filaments ) and 1 external filament
<img width="1001" height="355" alt="Screenshot-20260215195127" src="https://github.com/user-attachments/assets/afe0305e-fcb4-4a51-b8dc-e70a063aa391" />
2026-03-01 14:13:49 +08:00
Alexandre Folle de Menezes
4146066f76 Add and update pt-BR translations (#12409) 2026-03-01 14:13:49 +08:00
SoftFever
8aa43d1ef7 update error message when plugin upgrade failed 2026-03-01 14:13:49 +08:00
SoftFever
04bf8b6bb9 Fix machine envelope G-code emitting wrong limits due to broken extruder_id indexing (#12414)
print_machine_envelope() used get_extruder_id(extruder_id)*2 to index
machine limit arrays that only hold [Normal, Stealth] (2 entries).
For multi-extruder setups this went out-of-bounds, causing wrong M201/M203
values in the G-code which then override the estimator's correct limits.

Same class of bug as c6d1c11ebb but on the G-code writer side.

Changes:
- Remove unused extruder_id param from print_machine_envelope()
- Use .values.front() for M201/M203, matching M204/M205 in same function
- Change get_option_value() fallback from .back() to .front() so any
  future out-of-bounds index returns Normal mode instead of Stealth
2026-03-01 14:13:49 +08:00
SoftFever
84b5906ffa Fix time estimation using wrong machine limits due to broken extruder_id indexing (#12411)
The extruder_id*2 offset in get_axis_max_feedrate/get_axis_max_acceleration
was cherry-picked from BambuStudio's per-nozzle limit system, which OrcaSlicer
never ported. Without that system the limit arrays only have 2 values
([0]=Normal, [1]=Stealth), so any extruder_id > 0 or the uninitialized
value (255) would overshoot the array and fall back to values.back(),
always returning stealth-mode limits and producing incorrect time estimates.

Revert to indexing by time mode only (matching v2.3.1 behavior) and simplify
the M201/M203 handlers to write only the two mode slots they actually use.
2026-03-01 14:13:49 +08:00
minicx
318b7742cc Add Pressure Advance visualization support (#11673)
* Add Pressure Advance visualization support

Signed-off-by: minicx <minicx@disroot.org>

* Port Pressure Advance visualization to libvgcode architecture

Adapt PA visualization (originally in commit e3a77259) to work with
the new libvgcode library introduced by upstream PR #10735.

Changes across the libvgcode stack:
- PathVertex: add pressure_advance field
- Types.hpp: add PressureAdvance to EViewType enum
- ViewerImpl: add ColorRange, color mapping, range updates for PA
- LibVGCodeWrapper: pass pressure_advance from MoveVertex to PathVertex

GCodeViewer UI integration:
- Add "Pressure Advance" to view type dropdown
- Add PA color range in legend (3 decimal places)
- Add PA value display in sequential view marker tooltip
- Add PA row in position properties table

The GCodeProcessor PA parsing (M900, M572, SET_PRESSURE_ADVANCE)
is preserved from the original implementation.

* Tag Pressure Advance visualization changes with ORCA comments

Signed-off-by: minicx <minicx@disroot.org>

---------

Signed-off-by: minicx <minicx@disroot.org>
Co-authored-by: Ioannis Giannakas <59056762+igiannakas@users.noreply.github.com>
Co-authored-by: Claude <noreply@anthropic.com>
2026-03-01 14:13:49 +08:00
SoftFever
b68fd2dc21 tweak legacy library migration from rename to copy in BBLNetworkPlugin (#12400)
* tweak legacy library migration from rename to copy in BBLNetworkPlugin
2026-03-01 14:13:49 +08:00
SoftFever
f2cc134a8f Fix an issue that on Windows the Bambu legacy plugin is 01.10.01.09 (#12380) 2026-03-01 14:13:49 +08:00
Kiss Lorand
31d9c9d087 Fix filament override changes not appearing in Unsaved Changes and showing as “Undef category” in preset comparison (#12298) 2026-03-01 14:13:49 +08:00
Rodrigo Faselli
c9ffc5a699 Fix dual seam fuzzy painted rev 2 (#11923)
* 2 seam fuzzy 2nd attempt

* Update FuzzySkin.cpp

* Fix debug SVG

* solve bump artifact in extrusion junction joint

* minor fixes

* cleaning

Update FuzzySkin.cpp
2026-03-01 14:13:49 +08:00
SoftFever
d37be2f4f5 Reapply "Switch to self hosted mac runner (#12024)" with proper fix (#12278)
* Reapply "Switch to self hosted mac runner (#12024)" with proper fix

This reverts commit 783f9926e3.

* use conditional logic for self-hosted runners

* improve readbility
2026-03-01 14:13:49 +08:00
SoftFever
4cc4da8868 fix flatpak CI build cache 2026-03-01 14:13:49 +08:00
SoftFever
9f5214d14d tweak: adjust max volumetric speed for PETG profiles to improve print quality (#12329)
* tweak: adjust max volumetric speed for PETG profiles to improve print quality
2026-03-01 14:13:48 +08:00
Argo
7e63fce706 Fix 3MF import crash for silent-mode machine limits with legacy vector sizes (#12289)
Normalize printer_options_with_variant_2 (stride=2) machine limit vectors during preset merge to handle legacy 3MF/projects that store only a single (normal,silent) pair despite multiple printer variants, preventing set_with_restore() size-mismatch crashes.

Crash error message during 3MF import as project:

<img width="833" height="274" alt="image" src="https://github.com/user-attachments/assets/f92148a9-98c6-47b7-a0ab-d5ac8b1f2be4" />

3MF attached that causes the bug. 
[cube.3mf.zip](https://github.com/user-attachments/files/25318309/cube.3mf.zip)
2026-02-15 20:50:17 +08:00
Matthias Blaicher
380f4b4a18 Fix EGL/GLX mismatch causing blank 3D preview on Linux (#12308)
- Add configurable GLEW_USE_EGL option (default OFF) to match wxWidgets
- Explicitly set wxUSE_GLCANVAS_EGL=OFF for vendored wxWidgets build
- Add compile-time check to detect EGL/GLX backend mismatch between
  GLEW and wxWidgets, preventing silent rendering failures

The bug occurred when GLEW was compiled with EGL support (using
eglGetProcAddress) but wxWidgets created GLX contexts. This mismatch
caused OpenGL function pointers to fail loading, resulting in blank
3D model preview.

Co-authored-by: SoftFever <softfeverever@gmail.com>
2026-02-15 16:27:07 +08:00
SoftFever
240cf9ab5d bump version to 2.3.2-beta2 2026-02-15 14:31:57 +08:00
Branden Cash
05cb8b4d89 feat(MoonrakerPrinterAgent): support Happy Hare as alternative to AFC for filament sync (#12307)
# Description


# Screenshots/Recordings/Graphs


https://github.com/user-attachments/assets/5558b4be-24eb-4f2d-83fd-8482560a0014

<img width="445" height="285" alt="Screenshot 2026-02-14 at 7 31 57 PM" src="https://github.com/user-attachments/assets/e71fee66-05da-4f9c-8123-0f52e93f0ebb" />


## Tests

Removed configured filaments and pressed the sync button. Observed the filaments configured in my system were populated.
2026-02-15 14:31:29 +08:00
SoftFever
897a3e915f bump profile version to "02.03.02.40" (#12309) 2026-02-15 14:26:33 +08:00
Ian Bassi
586e96479a Linux: Repaired VFA tower (#12290)
* Update CalibUtils.cpp

* VFA.drc case to vfa.drc
2026-02-15 14:26:33 +08:00
Ian Bassi
0879b2079b Spanish minor update (#12268) 2026-02-15 14:26:33 +08:00
Ian Bassi
c4801250ea Spanish Update (#12254)
* Spanish Update
2026-02-15 14:26:33 +08:00
SoftFever
f0386d981f Revert "Switch to self hosted mac runner (#12024)"
This reverts commit f1212be6bb.
2026-02-14 18:11:06 +08:00
Argo
cd5c8f2ad0 Ported Bambu Studio wipe tower interface features (with improved preheat and cooldown behaviour) - NEW (#12266)
Wipe tower interface features and preheat fixes

Fresh PR branch rebuilt on upstream/main (squash of origin/BBL-studio-wipe-tower-merge) to avoid merge-history issues.
2026-02-13 22:57:55 +08:00
SoftFever
2d0a0568e7 Change SoftFever version from '2.3.2-dev' to '2.3.2-beta' 2026-02-13 19:43:45 +08:00
13505 changed files with 2054591 additions and 2131020 deletions

View File

@@ -5,7 +5,7 @@ Language: Cpp
AccessModifierOffset: -4
AlignAfterOpenBracket: Align
AlignConsecutiveAssignments: true
AlignConsecutiveDeclarations: false
AlignConsecutiveDeclarations: true
AlignEscapedNewlines: DontAlign
AlignOperands: true
AlignTrailingComments: true

View File

@@ -4,7 +4,7 @@
"dockerfile": "Dockerfile",
"args": {
"PLATFORM": "linux/amd64",
"BASE_IMAGE": "mcr.microsoft.com/devcontainers/cpp:ubuntu-24.04"
"BASE_IMAGE": "mcr.microsoft.com/devcontainers/cpp:ubuntu-22.04"
},
"options": ["--platform=linux/amd64"]
},

View File

@@ -11,15 +11,6 @@ body:
For this, please use the [Feature request](https://github.com/OrcaSlicer/OrcaSlicer/issues/new?assignees=&labels=&projects=&template=feature_request.yml) issue type or you can discuss your idea on our [Discord server](https://discord.gg/P4VE9UY9gJ) with others.
Before filing, please check if the issue already exists (either open or closed) by using the search bar on the issues page. If it does, comment there. Even if it's closed, we can reopen it based on your comment.
- type: checkboxes
attributes:
label: Is this issue reproducible in the latest nightly build?
description: >
Please verify this issue still happens in the latest nightly build first. It may already be fixed there:
[Nightly builds](https://github.com/OrcaSlicer/OrcaSlicer/releases/tag/nightly-builds).
options:
- label: I have checked the latest nightly build and the issue is still reproducible
required: true
- type: checkboxes
attributes:
label: Is there an existing issue for this problem?

View File

@@ -19,9 +19,3 @@
<!--
> Please describe the tests that you have conducted to verify the changes made in this PR.
-->
<!--
> A guide for users on how to download the artifacts from this PR.
-->
[How to Download Pull Requests Artifacts for Testing](https://www.orcaslicer.com/wiki/how_to_download_pr_artifacts)

View File

@@ -55,7 +55,7 @@ jobs:
if: ${{ !cancelled() && (github.event_name != 'schedule' || github.repository == 'OrcaSlicer/OrcaSlicer') }}
uses: ./.github/workflows/build_check_cache.yml
with:
os: ${{ vars.SELF_HOSTED && 'orca-lnx-server' || 'ubuntu-24.04' }}
os: ubuntu-24.04
build-deps-only: ${{ inputs.build-deps-only || false }}
secrets: inherit
build_windows:
@@ -63,7 +63,7 @@ jobs:
if: ${{ !cancelled() && (github.event_name != 'schedule' || github.repository == 'OrcaSlicer/OrcaSlicer') }}
uses: ./.github/workflows/build_check_cache.yml
with:
os: ${{ vars.SELF_HOSTED && 'orca-win-server' || 'windows-latest' }}
os: windows-latest
build-deps-only: ${{ inputs.build-deps-only || false }}
force-build: ${{ github.event_name == 'schedule' }}
secrets: inherit
@@ -95,7 +95,7 @@ jobs:
secrets: inherit
unit_tests:
name: Unit Tests
runs-on: ${{ vars.SELF_HOSTED && 'orca-lnx-server' || 'ubuntu-24.04' }}
runs-on: ubuntu-24.04
needs: build_linux
if: ${{ !cancelled() && success() }}
steps:
@@ -107,24 +107,21 @@ jobs:
scripts
tests
- name: Apt-Install Dependencies
if: ${{ !vars.SELF_HOSTED }}
uses: ./.github/actions/apt-install-deps
- name: Restore Test Artifact
uses: actions/download-artifact@v8
uses: actions/download-artifact@v7
with:
name: ${{ github.sha }}-tests
- uses: lukka/get-cmake@latest
with:
cmakeVersion: "~4.3.0" # use most recent 4.3.x version
useLocalCache: true # <--= Use the local cache (default is 'false').
useCloudCache: true
cmakeVersion: "~3.28.0" # use most recent 3.28.x version
- name: Unpackage and Run Unit Tests
timeout-minutes: 20
run: |
tar -xvf build_tests.tar
scripts/run_unit_tests.sh
- name: Upload Test Logs
uses: actions/upload-artifact@v7
uses: actions/upload-artifact@v6
if: ${{ failure() }}
with:
name: unit-test-logs
@@ -134,15 +131,10 @@ jobs:
uses: EnricoMi/publish-unit-test-result-action@v2
with:
files: "ctest_results.xml"
- name: Delete Test Artifact
if: success()
uses: geekyeggo/delete-artifact@v6
with:
name: ${{ github.sha }}-tests
flatpak:
name: "Flatpak"
container:
image: ghcr.io/flathub-infra/flatpak-github-actions:gnome-49
image: ghcr.io/flathub-infra/flatpak-github-actions:gnome-48
options: --privileged
volumes:
- /usr/local/lib/android:/usr/local/lib/android
@@ -158,9 +150,8 @@ jobs:
runner: ubuntu-24.04
- arch: aarch64
runner: ubuntu-24.04-arm
# Don't run scheduled builds on forks; skip entirely on self-hosted runners
if: ${{ !cancelled() && !vars.SELF_HOSTED && (github.event_name != 'schedule' || github.repository == 'OrcaSlicer/OrcaSlicer') }}
# Don't run scheduled builds on forks:
if: ${{ !cancelled() && (github.event_name != 'schedule' || github.repository == 'OrcaSlicer/OrcaSlicer') }}
runs-on: ${{ matrix.variant.runner }}
env:
date:
@@ -179,7 +170,7 @@ jobs:
git_commit_hash="${{ github.event.pull_request.head.sha }}"
else
ver=V$ver_pure
git_commit_hash="${{ github.sha }}"
git_commit_hash=""
fi
echo "ver=$ver" >> $GITHUB_ENV
echo "ver_pure=$ver_pure" >> $GITHUB_ENV
@@ -189,37 +180,32 @@ jobs:
# Manage flatpak-builder cache externally so PRs restore but never upload
- name: Restore flatpak-builder cache
if: github.event_name == 'pull_request'
uses: actions/cache/restore@v5
uses: actions/cache/restore@v4
with:
path: .flatpak-builder
key: flatpak-builder-${{ matrix.variant.arch }}-${{ github.event.pull_request.base.sha }}
restore-keys: flatpak-builder-${{ matrix.variant.arch }}-
- name: Save/restore flatpak-builder cache
if: github.event_name != 'pull_request'
uses: actions/cache@v5
uses: actions/cache@v4
with:
path: .flatpak-builder
key: flatpak-builder-${{ matrix.variant.arch }}-${{ github.sha }}
restore-keys: flatpak-builder-${{ matrix.variant.arch }}-
- name: Disable debug info for faster CI builds
run: |
sed -i '/^build-options:/a\ no-debuginfo: true\n strip: true' \
scripts/flatpak/com.orcaslicer.OrcaSlicer.yml
shell: bash
- name: Inject git commit hash into Flatpak manifest
run: |
sed -i "/name: OrcaSlicer/{n;s|buildsystem: simple|buildsystem: simple\n build-options:\n env:\n git_commit_hash: \"$git_commit_hash\"|}" \
scripts/flatpak/com.orcaslicer.OrcaSlicer.yml
sed -i '0,/^finish-args:/s//build-options:\n no-debuginfo: true\n strip: true\nfinish-args:/' \
scripts/flatpak/io.github.orcaslicer.OrcaSlicer.yml
shell: bash
- uses: flatpak/flatpak-github-actions/flatpak-builder@master
with:
bundle: OrcaSlicer-Linux-flatpak_${{ env.ver }}_${{ matrix.variant.arch }}.flatpak
manifest-path: scripts/flatpak/com.orcaslicer.OrcaSlicer.yml
manifest-path: scripts/flatpak/io.github.orcaslicer.OrcaSlicer.yml
cache: false
arch: ${{ matrix.variant.arch }}
upload-artifact: false
- name: Upload artifacts Flatpak
uses: actions/upload-artifact@v7
uses: actions/upload-artifact@v6
with:
name: OrcaSlicer-Linux-flatpak_${{ env.ver }}_${{ matrix.variant.arch }}.flatpak
path: '/__w/OrcaSlicer/OrcaSlicer/OrcaSlicer-Linux-flatpak_${{ env.ver }}_${{ matrix.variant.arch }}.flatpak'
@@ -233,4 +219,3 @@ jobs:
asset_name: OrcaSlicer-Linux-flatpak_nightly_${{ matrix.variant.arch }}.flatpak
asset_content_type: application/octet-stream
max_releases: 1 # optional, if there are more releases than this matching the asset_name, the oldest ones are going to be deleted

View File

@@ -28,18 +28,18 @@ jobs:
- name: Checkout
uses: actions/checkout@v6
with:
lfs: 'false'
lfs: 'true'
- name: set outputs
id: set_outputs
env:
# Keep macOS cache keys and paths architecture-specific.
cache-os: ${{ runner.os == 'macOS' && format('macos-{0}', inputs.arch) || (runner.os == 'Windows' && 'windows' || 'linux-clang') }}
dep-folder-name: ${{ runner.os == 'macOS' && format('/{0}', inputs.arch) || '/OrcaSlicer_dep' }}
output-cmd: ${{ runner.os == 'Windows' && '$env:GITHUB_OUTPUT' || '"$GITHUB_OUTPUT"'}}
run: |
echo cache-key=${{ env.cache-os }}-cache-orcaslicer_deps-build-${{ hashFiles('deps/**') }} >> ${{ env.output-cmd }}
echo cache-path=${{ github.workspace }}/deps/build${{ env.dep-folder-name }} >> ${{ env.output-cmd }}
- name: set outputs
id: set_outputs
env:
# Keep macOS cache keys and paths architecture-specific.
cache-os: ${{ contains(inputs.os, 'macos') && format('macos-{0}', inputs.arch) || inputs.os }}
dep-folder-name: ${{ contains(inputs.os, 'macos') && format('/{0}', inputs.arch) || '/OrcaSlicer_dep' }}
output-cmd: ${{ inputs.os == 'windows-latest' && '$env:GITHUB_OUTPUT' || '"$GITHUB_OUTPUT"'}}
run: |
echo cache-key=${{ env.cache-os }}-cache-orcaslicer_deps-build-${{ hashFiles('deps/**') }} >> ${{ env.output-cmd }}
echo cache-path=${{ github.workspace }}/deps/build${{ env.dep-folder-name }} >> ${{ env.output-cmd }}
- name: load cache
id: cache_deps

View File

@@ -36,7 +36,7 @@ jobs:
- name: Checkout
uses: actions/checkout@v6
with:
lfs: 'false'
lfs: 'true'
- name: load cached deps
uses: actions/cache@v5
@@ -46,39 +46,35 @@ jobs:
- uses: lukka/get-cmake@latest
with:
cmakeVersion: "~4.3.0" # use most recent 4.3.x version
useLocalCache: true # <--= Use the local cache (default is 'false').
useCloudCache: true
cmakeVersion: "~3.28.0" # use most recent 3.28.x version
- name: setup dev on Windows
if: runner.os == 'Windows'
uses: microsoft/setup-msbuild@v3
if: inputs.os == 'windows-latest'
uses: microsoft/setup-msbuild@v2
- name: Get the date on Ubuntu and macOS
if: runner.os != 'Windows'
if: inputs.os != 'windows-latest'
run: echo "date=$(date +'%Y%m%d')" >> $GITHUB_ENV
shell: bash
- name: Get the date on Windows
if: runner.os == 'Windows'
if: inputs.os == 'windows-latest'
run: echo "date=$(Get-Date -Format 'yyyyMMdd')" | Out-File -Append -FilePath $env:GITHUB_ENV -Encoding utf8
shell: pwsh
# Build Dependencies
- name: Build on Windows
if: runner.os == 'Windows'
if: inputs.os == 'windows-latest'
working-directory: ${{ github.workspace }}
run: |
if (-not "${{ vars.SELF_HOSTED }}") {
choco install strawberryperl
}
choco install strawberryperl
.\build_release_vs.bat deps
.\build_release_vs.bat pack
shell: pwsh
cd ${{ github.workspace }}/deps/build
- name: Build on Mac ${{ inputs.arch }}
if: runner.os == 'macOS'
if: contains(inputs.os, 'macos')
working-directory: ${{ github.workspace }}
run: |
if [ -z "${{ vars.SELF_HOSTED }}" ]; then
@@ -90,42 +86,42 @@ jobs:
- name: Apt-Install Dependencies
if: runner.os == 'Linux' && !vars.SELF_HOSTED
if: inputs.os == 'ubuntu-24.04'
uses: ./.github/actions/apt-install-deps
- name: Build on Ubuntu
if: runner.os == 'Linux'
if: inputs.os == 'ubuntu-20.04' || inputs.os == 'ubuntu-24.04'
working-directory: ${{ github.workspace }}
run: |
mkdir -p ${{ github.workspace }}/deps/build/destdir
./build_linux.sh -drlL
./build_linux.sh -dr
cd deps/build
tar -czvf OrcaSlicer_dep_ubuntu_$(date +"%Y%m%d").tar.gz destdir
# Upload Artifacts
# - name: Upload Mac ${{ inputs.arch }} artifacts
# if: runner.os == 'macOS'
# if: contains(inputs.os, 'macos')
# uses: actions/upload-artifact@v6
# with:
# name: OrcaSlicer_dep_mac_${{ env.date }}
# path: ${{ github.workspace }}/deps/build/OrcaSlicer_dep*.tar.gz
# - name: Upload Windows artifacts
# if: runner.os == 'Windows'
# uses: actions/upload-artifact@v6
# with:
# name: OrcaSlicer_dep_win64_${{ env.date }}
# path: ${{ github.workspace }}/deps/build/OrcaSlicer_dep*.zip
- name: Upload Windows artifacts
if: inputs.os == 'windows-latest'
uses: actions/upload-artifact@v6
with:
name: OrcaSlicer_dep_win64_${{ env.date }}
path: ${{ github.workspace }}/deps/build/OrcaSlicer_dep*.zip
# - name: Upload Ubuntu artifacts
# if: runner.os == 'Linux' && !env.ACT
# env:
# ubuntu-ver: '2404'
# uses: actions/upload-artifact@v6
# with:
# name: OrcaSlicer_dep_ubuntu_${{ env.ubuntu-ver }}_${{ env.date }}
# path: ${{ github.workspace }}/deps/build/OrcaSlicer_dep_ubuntu_*.tar.gz
- name: Upload Ubuntu artifacts
if: ${{ ! env.ACT && inputs.os == 'ubuntu-20.04' || inputs.os == 'ubuntu-24.04' }}
env:
ubuntu-ver: ${{ (inputs.os == 'ubuntu-20.04' && '2004') || (inputs.os == 'ubuntu-24.04' && '2404') || '' }}
uses: actions/upload-artifact@v6
with:
name: OrcaSlicer_dep_ubuntu_${{ env.ubuntu-ver }}_${{ env.date }}
path: ${{ github.workspace }}/deps/build/OrcaSlicer_dep_ubuntu_*.tar.gz
build_orca:
name: Build OrcaSlicer

View File

@@ -26,18 +26,16 @@ jobs:
date:
ver:
ver_pure:
ubuntu-ver: '2404'
ubuntu-ver-str: '_Ubuntu2404'
ORCA_UPDATER_SIG_KEY: ${{ secrets.ORCA_UPDATER_SIG_KEY }}
steps:
- name: Checkout
uses: actions/checkout@v6
with:
lfs: 'false'
lfs: 'true'
- name: load cached deps
if: ${{ !(runner.os == 'macOS' && inputs.macos-combine-only) }}
if: ${{ !(contains(inputs.os, 'macos') && inputs.macos-combine-only) }}
uses: actions/cache@v5
with:
path: ${{ inputs.cache-path }}
@@ -46,12 +44,10 @@ jobs:
- uses: lukka/get-cmake@latest
with:
cmakeVersion: "~4.3.0" # use most recent 4.3.x version
useLocalCache: true # <--= Use the local cache (default is 'false').
useCloudCache: true
cmakeVersion: "~3.28.0" # use most recent 3.28.x version
- name: Get the version and date on Ubuntu and macOS
if: runner.os != 'Windows'
if: inputs.os != 'windows-latest'
run: |
ver_pure=$(grep 'set(SoftFever_VERSION' version.inc | cut -d '"' -f2)
if [[ "${{ github.event_name }}" == "pull_request" ]]; then
@@ -68,7 +64,7 @@ jobs:
shell: bash
- name: Get the version and date on Windows
if: runner.os == 'Windows'
if: inputs.os == 'windows-latest'
run: |
$date = Get-Date -Format 'yyyyMMdd'
$ref = "${{ github.ref }}"
@@ -95,7 +91,7 @@ jobs:
# Mac
- name: Install tools mac
if: runner.os == 'macOS' && !inputs.macos-combine-only
if: contains(inputs.os, 'macos') && !inputs.macos-combine-only
run: |
if [ -z "${{ vars.SELF_HOSTED }}" ]; then
brew install libtool
@@ -104,7 +100,7 @@ jobs:
mkdir -p ${{ github.workspace }}/deps/build/${{ inputs.arch }}
- name: Free disk space
if: runner.os == 'macOS' && !inputs.macos-combine-only && !vars.SELF_HOSTED
if: contains(inputs.os, 'macos') && !inputs.macos-combine-only && !vars.SELF_HOSTED
run: |
df -hI /dev/disk3s1s1
sudo find /Applications -maxdepth 1 -type d -name "Xcode_*.app" ! -name "Xcode_15.4.app" -exec rm -rf {} +
@@ -112,50 +108,57 @@ jobs:
df -hI /dev/disk3s1s1
- name: Build slicer mac
if: runner.os == 'macOS' && !inputs.macos-combine-only
if: contains(inputs.os, 'macos') && !inputs.macos-combine-only
working-directory: ${{ github.workspace }}
run: |
./build_release_macos.sh -s -n -x ${{ !vars.SELF_HOSTED && '-1' || '' }} -a ${{ inputs.arch }} -t 10.15
- name: Pack macOS app bundle ${{ inputs.arch }}
if: runner.os == 'macOS' && !inputs.macos-combine-only
if: contains(inputs.os, 'macos') && !inputs.macos-combine-only
working-directory: ${{ github.workspace }}
run: |
tar -czvf OrcaSlicer_Mac_bundle_${{ inputs.arch }}_${{ github.sha }}.tar.gz -C build/${{ inputs.arch }} OrcaSlicer
- name: Upload macOS app bundle ${{ inputs.arch }}
if: runner.os == 'macOS' && !inputs.macos-combine-only
uses: actions/upload-artifact@v7
if: contains(inputs.os, 'macos') && !inputs.macos-combine-only
uses: actions/upload-artifact@v6
with:
name: OrcaSlicer_Mac_bundle_${{ inputs.arch }}_${{ github.sha }}
path: ${{ github.workspace }}/OrcaSlicer_Mac_bundle_${{ inputs.arch }}_${{ github.sha }}.tar.gz
- name: Download macOS app bundles
if: runner.os == 'macOS' && inputs.macos-combine-only
uses: actions/download-artifact@v8
- name: Download macOS arm64 app bundle
if: contains(inputs.os, 'macos') && inputs.macos-combine-only
uses: actions/download-artifact@v7
with:
pattern: OrcaSlicer_Mac_bundle_*_${{ github.sha }}
path: ${{ github.workspace }}/mac_bundles
name: OrcaSlicer_Mac_bundle_arm64_${{ github.sha }}
path: ${{ github.workspace }}/mac_bundles/arm64
- name: Download macOS x86_64 app bundle
if: contains(inputs.os, 'macos') && inputs.macos-combine-only
uses: actions/download-artifact@v7
with:
name: OrcaSlicer_Mac_bundle_x86_64_${{ github.sha }}
path: ${{ github.workspace }}/mac_bundles/x86_64
- name: Extract macOS app bundles
if: runner.os == 'macOS' && inputs.macos-combine-only
if: contains(inputs.os, 'macos') && inputs.macos-combine-only
working-directory: ${{ github.workspace }}
run: |
mkdir -p build/arm64 build/x86_64
arm_bundle=$(find "${{ github.workspace }}/mac_bundles/OrcaSlicer_Mac_bundle_arm64_${{ github.sha }}" -name '*.tar.gz' -print -quit)
x86_bundle=$(find "${{ github.workspace }}/mac_bundles/OrcaSlicer_Mac_bundle_x86_64_${{ github.sha }}" -name '*.tar.gz' -print -quit)
arm_bundle=$(find "${{ github.workspace }}/mac_bundles/arm64" -name '*.tar.gz' -print -quit)
x86_bundle=$(find "${{ github.workspace }}/mac_bundles/x86_64" -name '*.tar.gz' -print -quit)
tar -xzvf "$arm_bundle" -C "${{ github.workspace }}/build/arm64"
tar -xzvf "$x86_bundle" -C "${{ github.workspace }}/build/x86_64"
- name: Build universal mac app bundle
if: runner.os == 'macOS' && inputs.macos-combine-only
if: contains(inputs.os, 'macos') && inputs.macos-combine-only
working-directory: ${{ github.workspace }}
run: |
./build_release_macos.sh -u -x ${{ !vars.SELF_HOSTED && '-1' || '' }} -a universal -t 10.15
- name: Delete intermediate per-arch artifacts
if: runner.os == 'macOS' && inputs.macos-combine-only
uses: geekyeggo/delete-artifact@v6
if: contains(inputs.os, 'macos') && inputs.macos-combine-only
uses: geekyeggo/delete-artifact@v5
with:
name: |
OrcaSlicer_Mac_bundle_arm64_${{ github.sha }}
@@ -163,7 +166,7 @@ jobs:
# Thanks to RaySajuuk, it's working now
- name: Sign app and notary
if: github.repository == 'OrcaSlicer/OrcaSlicer' && (github.ref == 'refs/heads/main' || startsWith(github.ref, 'refs/heads/release/')) && runner.os == 'macOS' && inputs.macos-combine-only
if: github.repository == 'OrcaSlicer/OrcaSlicer' && (github.ref == 'refs/heads/main' || startsWith(github.ref, 'refs/heads/release/')) && contains(inputs.os, 'macos') && inputs.macos-combine-only
working-directory: ${{ github.workspace }}
env:
BUILD_CERTIFICATE_BASE64: ${{ secrets.BUILD_CERTIFICATE_BASE64 }}
@@ -217,7 +220,7 @@ jobs:
fi
- name: Create DMG without notary
if: github.ref != 'refs/heads/main' && runner.os == 'macOS' && inputs.macos-combine-only
if: github.ref != 'refs/heads/main' && contains(inputs.os, 'macos') && inputs.macos-combine-only
working-directory: ${{ github.workspace }}
run: |
mkdir -p ${{ github.workspace }}/build/universal/OrcaSlicer_dmg
@@ -236,22 +239,22 @@ jobs:
fi
- name: Upload artifacts mac
if: runner.os == 'macOS' && inputs.macos-combine-only
uses: actions/upload-artifact@v7
if: contains(inputs.os, 'macos') && inputs.macos-combine-only
uses: actions/upload-artifact@v6
with:
name: OrcaSlicer_Mac_universal_${{ env.ver }}
path: ${{ github.workspace }}/OrcaSlicer_Mac_universal_${{ env.ver }}.dmg
- name: Upload OrcaSlicer_profile_validator DMG mac
if: runner.os == 'macOS' && inputs.macos-combine-only && !vars.SELF_HOSTED
uses: actions/upload-artifact@v7
if: contains(inputs.os, 'macos') && inputs.macos-combine-only
uses: actions/upload-artifact@v6
with:
name: OrcaSlicer_profile_validator_Mac_universal_DMG_${{ env.ver }}
path: ${{ github.workspace }}/OrcaSlicer_profile_validator_Mac_universal_${{ env.ver }}.dmg
if-no-files-found: ignore
- name: Deploy Mac release
if: github.repository == 'OrcaSlicer/OrcaSlicer' && github.ref == 'refs/heads/main' && runner.os == 'macOS' && inputs.macos-combine-only && !vars.SELF_HOSTED
if: github.repository == 'OrcaSlicer/OrcaSlicer' && github.ref == 'refs/heads/main' && contains(inputs.os, 'macos') && inputs.macos-combine-only
uses: WebFreak001/deploy-nightly@v3.2.0
with:
upload_url: https://uploads.github.com/repos/OrcaSlicer/OrcaSlicer/releases/137995723/assets{?name,label}
@@ -262,7 +265,7 @@ jobs:
max_releases: 1 # optional, if there are more releases than this matching the asset_name, the oldest ones are going to be deleted
- name: Deploy Mac OrcaSlicer_profile_validator DMG release
if: github.repository == 'OrcaSlicer/OrcaSlicer' && github.ref == 'refs/heads/main' && runner.os == 'macOS' && inputs.macos-combine-only && !vars.SELF_HOSTED
if: github.repository == 'OrcaSlicer/OrcaSlicer' && github.ref == 'refs/heads/main' && contains(inputs.os, 'macos') && inputs.macos-combine-only
uses: WebFreak001/deploy-nightly@v3.2.0
with:
upload_url: https://uploads.github.com/repos/OrcaSlicer/OrcaSlicer/releases/137995723/assets{?name,label}
@@ -274,72 +277,71 @@ jobs:
# Windows
- name: setup MSVC
if: runner.os == 'Windows'
uses: microsoft/setup-msbuild@v3
if: inputs.os == 'windows-latest'
uses: microsoft/setup-msbuild@v2
- name: Install nsis
if: runner.os == 'Windows' && !vars.SELF_HOSTED
if: inputs.os == 'windows-latest'
run: |
dir "C:/Program Files (x86)/Windows Kits/10/Include"
choco install nsis
- name: Build slicer Win
if: runner.os == 'Windows'
if: inputs.os == 'windows-latest'
working-directory: ${{ github.workspace }}
# Orca: Removed Netfabb STL fixing service support in favor of CGAL.
# env:
# WindowsSdkDir: 'C:\Program Files (x86)\Windows Kits\10\'
# WindowsSDKVersion: '10.0.26100.0\'
env:
WindowsSdkDir: 'C:\Program Files (x86)\Windows Kits\10\'
WindowsSDKVersion: '10.0.26100.0\'
run: .\build_release_vs.bat slicer
- name: Create installer Win
if: runner.os == 'Windows' && !vars.SELF_HOSTED
if: inputs.os == 'windows-latest'
working-directory: ${{ github.workspace }}/build
run: |
cpack -G NSIS
- name: Pack app
if: runner.os == 'Windows'
if: inputs.os == 'windows-latest'
working-directory: ${{ github.workspace }}/build
shell: cmd
run: '"C:/Program Files/7-Zip/7z.exe" a -tzip OrcaSlicer_Windows_${{ env.ver }}_portable.zip ${{ github.workspace }}/build/OrcaSlicer'
- name: Pack PDB
if: runner.os == 'Windows' && !vars.SELF_HOSTED
if: inputs.os == 'windows-latest'
working-directory: ${{ github.workspace }}/build/src/Release
shell: cmd
run: '"C:/Program Files/7-Zip/7z.exe" a -m0=lzma2 -mx9 Debug_PDB_${{ env.ver }}_for_developers_only.7z *.pdb'
- name: Upload artifacts Win zip
if: runner.os == 'Windows'
uses: actions/upload-artifact@v7
if: inputs.os == 'windows-latest'
uses: actions/upload-artifact@v6
with:
name: OrcaSlicer_Windows_${{ env.ver }}_portable
path: ${{ github.workspace }}/build/OrcaSlicer
- name: Upload artifacts Win installer
if: runner.os == 'Windows' && !vars.SELF_HOSTED
uses: actions/upload-artifact@v7
if: inputs.os == 'windows-latest'
uses: actions/upload-artifact@v6
with:
name: OrcaSlicer_Windows_${{ env.ver }}
path: ${{ github.workspace }}/build/OrcaSlicer*.exe
- name: Upload artifacts Win PDB
if: runner.os == 'Windows' && !vars.SELF_HOSTED
uses: actions/upload-artifact@v7
if: inputs.os == 'windows-latest'
uses: actions/upload-artifact@v6
with:
name: PDB
path: ${{ github.workspace }}/build/src/Release/Debug_PDB_${{ env.ver }}_for_developers_only.7z
- name: Upload OrcaSlicer_profile_validator Win
if: runner.os == 'Windows' && !vars.SELF_HOSTED
uses: actions/upload-artifact@v7
if: inputs.os == 'windows-latest'
uses: actions/upload-artifact@v6
with:
name: OrcaSlicer_profile_validator_Windows_${{ env.ver }}
path: ${{ github.workspace }}/build/src/Release/OrcaSlicer_profile_validator.exe
- name: Deploy Windows release portable
if: github.repository == 'OrcaSlicer/OrcaSlicer' && github.ref == 'refs/heads/main' && runner.os == 'Windows' && !vars.SELF_HOSTED
if: github.repository == 'OrcaSlicer/OrcaSlicer' && github.ref == 'refs/heads/main' && inputs.os == 'windows-latest'
uses: WebFreak001/deploy-nightly@v3.2.0
with:
upload_url: https://uploads.github.com/repos/OrcaSlicer/OrcaSlicer/releases/137995723/assets{?name,label}
@@ -350,7 +352,7 @@ jobs:
max_releases: 1
- name: Deploy Windows release installer
if: github.repository == 'OrcaSlicer/OrcaSlicer' && github.ref == 'refs/heads/main' && runner.os == 'Windows' && !vars.SELF_HOSTED
if: github.repository == 'OrcaSlicer/OrcaSlicer' && github.ref == 'refs/heads/main' && inputs.os == 'windows-latest'
uses: WebFreak001/deploy-nightly@v3.2.0
with:
upload_url: https://uploads.github.com/repos/OrcaSlicer/OrcaSlicer/releases/137995723/assets{?name,label}
@@ -361,7 +363,7 @@ jobs:
max_releases: 1
- name: Deploy Windows OrcaSlicer_profile_validator release
if: github.repository == 'OrcaSlicer/OrcaSlicer' && github.ref == 'refs/heads/main' && runner.os == 'Windows' && !vars.SELF_HOSTED
if: github.repository == 'OrcaSlicer/OrcaSlicer' && github.ref == 'refs/heads/main' && inputs.os == 'windows-latest'
uses: WebFreak001/deploy-nightly@v3.2.0
with:
upload_url: https://uploads.github.com/repos/OrcaSlicer/OrcaSlicer/releases/137995723/assets{?name,label}
@@ -373,17 +375,18 @@ jobs:
# Ubuntu
- name: Apt-Install Dependencies
if: runner.os == 'Linux' && !vars.SELF_HOSTED
if: inputs.os == 'ubuntu-24.04'
uses: ./.github/actions/apt-install-deps
# Tests must built at the same time as the slicer;
# if you untangle them feel free to separate them here too
- name: Build slicer and tests
if: runner.os == 'Linux'
if: inputs.os == 'ubuntu-20.04' || inputs.os == 'ubuntu-24.04'
shell: bash
env:
ubuntu-ver-str: ${{ (inputs.os == 'ubuntu-24.04' && '_Ubuntu2404') || '' }}
run: |
./build_linux.sh -istrlL
./scripts/check_appimage_libs.sh ./build/package ./build/package/bin/orca-slicer
./build_linux.sh -istr
mv -n ./build/OrcaSlicer_Linux_V${{ env.ver_pure }}.AppImage ./build/OrcaSlicer_Linux_AppImage${{ env.ubuntu-ver-str }}_${{ env.ver }}.AppImage
chmod +x ./build/OrcaSlicer_Linux_AppImage${{ env.ubuntu-ver-str }}_${{ env.ver }}.AppImage
tar -cvpf build_tests.tar build/tests
@@ -391,8 +394,8 @@ jobs:
# Use tar because upload-artifacts won't always preserve directory structure
# and doesn't preserve file permissions
- name: Upload Test Artifact
if: runner.os == 'Linux'
uses: actions/upload-artifact@v7
if: inputs.os == 'ubuntu-24.04'
uses: actions/upload-artifact@v6
with:
name: ${{ github.sha }}-tests
overwrite: true
@@ -400,18 +403,8 @@ jobs:
retention-days: 5
if-no-files-found: error
- name: Run external slicer regression tests
if: runner.os == 'Linux'
timeout-minutes: 20
shell: bash
run: |
test_repo_dir="${{ runner.temp }}/orca-test-repo"
rm -rf "$test_repo_dir"
git clone --depth 1 https://github.com/OrcaSlicer/orca-test-repo.git "$test_repo_dir"
python3 "$test_repo_dir/run_test.py" "${{ github.workspace }}/build/package/bin/orca-slicer"
- name: Build orca_custom_preset_tests
if: github.ref == 'refs/heads/main' && runner.os == 'Linux' && !vars.SELF_HOSTED
if: github.ref == 'refs/heads/main' && inputs.os == 'ubuntu-24.04'
working-directory: ${{ github.workspace }}/build/src/Release
shell: bash
run: |
@@ -420,21 +413,28 @@ jobs:
zip -r orca_custom_preset_tests.zip user/
- name: Upload artifacts Ubuntu
if: ${{ ! env.ACT && runner.os == 'Linux' }}
uses: actions/upload-artifact@v7
if: ${{ ! env.ACT && inputs.os == 'ubuntu-20.04' || inputs.os == 'ubuntu-24.04' }}
env:
ubuntu-ver: ${{ (inputs.os == 'ubuntu-20.04' && '2004') || (inputs.os == 'ubuntu-24.04' && '2404') || '' }}
ubuntu-ver-str: ${{ (inputs.os == 'ubuntu-24.04' && '_Ubuntu2404') || '' }}
uses: actions/upload-artifact@v6
with:
name: OrcaSlicer_Linux_ubuntu_${{ env.ubuntu-ver }}_${{ env.ver }}
path: './build/OrcaSlicer_Linux_AppImage${{ env.ubuntu-ver-str }}_${{ env.ver }}.AppImage'
- name: Upload OrcaSlicer_profile_validator Ubuntu
if: ${{ ! env.ACT && runner.os == 'Linux' && !vars.SELF_HOSTED }}
uses: actions/upload-artifact@v7
if: ${{ ! env.ACT && inputs.os == 'ubuntu-20.04' || inputs.os == 'ubuntu-24.04' }}
env:
ubuntu-ver: ${{ (inputs.os == 'ubuntu-20.04' && '2004') || (inputs.os == 'ubuntu-24.04' && '2404') || '' }}
uses: actions/upload-artifact@v6
with:
name: OrcaSlicer_profile_validator_Linux_ubuntu_${{ env.ubuntu-ver }}_${{ env.ver }}
path: './build/src/Release/OrcaSlicer_profile_validator'
- name: Deploy Ubuntu release
if: ${{ github.repository == 'OrcaSlicer/OrcaSlicer' && ! env.ACT && github.ref == 'refs/heads/main' && runner.os == 'Linux' && !vars.SELF_HOSTED }}
if: ${{ github.repository == 'OrcaSlicer/OrcaSlicer' && ! env.ACT && github.ref == 'refs/heads/main' && (inputs.os == 'ubuntu-20.04' || inputs.os == 'ubuntu-24.04') }}
env:
ubuntu-ver-str: ${{ (inputs.os == 'ubuntu-24.04' && '_Ubuntu2404') || '' }}
uses: WebFreak001/deploy-nightly@v3.2.0
with:
upload_url: https://uploads.github.com/repos/OrcaSlicer/OrcaSlicer/releases/137995723/assets{?name,label}
@@ -444,7 +444,7 @@ jobs:
asset_content_type: application/octet-stream
max_releases: 1 # optional, if there are more releases than this matching the asset_name, the oldest ones are going to be deleted
- name: Deploy Ubuntu release
if: ${{ github.repository == 'OrcaSlicer/OrcaSlicer' && ! env.ACT && github.ref == 'refs/heads/main' && runner.os == 'Linux' && !vars.SELF_HOSTED }}
if: ${{ github.repository == 'OrcaSlicer/OrcaSlicer' && ! env.ACT && github.ref == 'refs/heads/main' && inputs.os == 'ubuntu-24.04' }}
uses: rickstaa/action-create-tag@v1
with:
tag: "nightly-builds"
@@ -453,7 +453,9 @@ jobs:
message: "nightly-builds"
- name: Deploy Ubuntu OrcaSlicer_profile_validator release
if: ${{ github.repository == 'OrcaSlicer/OrcaSlicer' && ! env.ACT && github.ref == 'refs/heads/main' && runner.os == 'Linux' && !vars.SELF_HOSTED }}
if: ${{ github.repository == 'OrcaSlicer/OrcaSlicer' && ! env.ACT && github.ref == 'refs/heads/main' && (inputs.os == 'ubuntu-20.04' || inputs.os == 'ubuntu-24.04') }}
env:
ubuntu-ver-str: ${{ (inputs.os == 'ubuntu-24.04' && '_Ubuntu2404') || '' }}
uses: WebFreak001/deploy-nightly@v3.2.0
with:
upload_url: https://uploads.github.com/repos/OrcaSlicer/OrcaSlicer/releases/137995723/assets{?name,label}
@@ -464,7 +466,7 @@ jobs:
max_releases: 1
- name: Deploy orca_custom_preset_tests
if: ${{ github.repository == 'OrcaSlicer/OrcaSlicer' && ! env.ACT && github.ref == 'refs/heads/main' && runner.os == 'Linux' && !vars.SELF_HOSTED }}
if: ${{ github.repository == 'OrcaSlicer/OrcaSlicer' && ! env.ACT && github.ref == 'refs/heads/main' && inputs.os == 'ubuntu-24.04' }}
uses: WebFreak001/deploy-nightly@v3.2.0
with:
upload_url: https://uploads.github.com/repos/OrcaSlicer/OrcaSlicer/releases/137995723/assets{?name,label}

View File

@@ -15,11 +15,12 @@ on:
default: 'warning'
permissions:
pull-requests: write
contents: read
jobs:
check_profiles:
check_translation:
name: Check profiles
runs-on: ubuntu-24.04
steps:
@@ -34,46 +35,6 @@ jobs:
python3 ./scripts/orca_extra_profile_check.py 2>&1 | tee ${{ runner.temp }}/extra_json_check.log
exit ${PIPESTATUS[0]}
- name: Check profile indentation
id: indentation_check
continue-on-error: true
run: |
set +e
python3 - <<'PY' 2>&1 | tee ${{ runner.temp }}/indentation_check.log
import re
from pathlib import Path
import sys
profiles_root = Path("resources/profiles")
invalid_files = []
for file_path in sorted(profiles_root.rglob("*.json")):
try:
for line_number, line in enumerate(file_path.read_text(encoding="utf-8").splitlines(), start=1):
if not line.strip():
continue
leading_ws = re.match(r"^[ \t]*", line).group(0)
if " " in leading_ws:
invalid_files.append((file_path, line_number))
break
except Exception as exc:
print(f"[ERROR] Unable to read {file_path}: {exc}")
invalid_files.append((file_path, 0))
if invalid_files:
for path, line in invalid_files:
if line > 0:
print(f"[ERROR] Space indentation found in {path}:{line}")
else:
print(f"[ERROR] Could not validate indentation in {path}")
print("Use tab indentation in profile JSON files (1 tab per indentation level).")
print("Tip: run python3 ./scripts/orca_filament_lib.py --fix --force to normalize formatting.")
sys.exit(1)
print("All profile JSON files use tab-only indentation.")
PY
exit ${PIPESTATUS[0]}
# download
- name: Download
working-directory: ${{ github.workspace }}
@@ -101,11 +62,11 @@ jobs:
./OrcaSlicer_profile_validator -p ${{ github.workspace }}/resources/profiles -l 2 2>&1 | tee ${{ runner.temp }}/validate_custom.log
exit ${PIPESTATUS[0]}
- name: Prepare comment artifact
if: ${{ always() && github.event_name == 'pull_request' && (steps.extra_json_check.outcome == 'failure' || steps.indentation_check.outcome == 'failure' || steps.validate_system.outcome == 'failure' || steps.validate_custom.outcome == 'failure') }}
- name: Post error comment on PR
if: ${{ always() && github.event_name == 'pull_request' && (steps.extra_json_check.outcome == 'failure' || steps.validate_system.outcome == 'failure' || steps.validate_custom.outcome == 'failure') }}
env:
GH_TOKEN: ${{ github.token }}
run: |
mkdir -p ${{ runner.temp }}/profile-check-results
{
echo "## :x: Profile Validation Errors"
echo ""
@@ -119,15 +80,6 @@ jobs:
echo ""
fi
if [ "${{ steps.indentation_check.outcome }}" = "failure" ]; then
echo "### Indentation Check Failed"
echo ""
echo '```'
head -c 30000 ${{ runner.temp }}/indentation_check.log || echo "No output captured"
echo '```'
echo ""
fi
if [ "${{ steps.validate_system.outcome }}" = "failure" ]; then
echo "### System Profile Validation Failed"
echo ""
@@ -148,20 +100,16 @@ jobs:
echo "---"
echo "*Please fix the above errors and push a new commit.*"
} > ${{ runner.temp }}/profile-check-results/pr_comment.md
} > ${{ runner.temp }}/pr_comment.md
echo "${{ github.event.pull_request.number }}" > ${{ runner.temp }}/profile-check-results/pr_number.txt
- name: Upload comment artifact
if: ${{ always() && github.event_name == 'pull_request' && (steps.extra_json_check.outcome == 'failure' || steps.indentation_check.outcome == 'failure' || steps.validate_system.outcome == 'failure' || steps.validate_custom.outcome == 'failure') }}
uses: actions/upload-artifact@v7
with:
name: profile-check-results
path: ${{ runner.temp }}/profile-check-results/
retention-days: 1
gh pr comment ${{ github.event.pull_request.number }} --body-file ${{ runner.temp }}/pr_comment.md
- name: Fail if any check failed
if: ${{ always() && (steps.extra_json_check.outcome == 'failure' || steps.indentation_check.outcome == 'failure' || steps.validate_system.outcome == 'failure' || steps.validate_custom.outcome == 'failure') }}
if: ${{ always() && (steps.extra_json_check.outcome == 'failure' || steps.validate_system.outcome == 'failure' || steps.validate_custom.outcome == 'failure') }}
run: |
echo "One or more profile checks failed. See above for details."
exit 1

View File

@@ -1,46 +0,0 @@
name: Post profile check comment
# NOTE: The workflow name in the 'workflows' filter below must match the 'name'
# field in check_profiles.yml exactly. If that name changes, update it here too.
on:
workflow_run:
workflows: ["Check profiles"]
types:
- completed
permissions:
pull-requests: write
jobs:
post_comment:
name: Post PR comment
runs-on: ubuntu-24.04
if: ${{ github.event.workflow_run.event == 'pull_request' && github.event.workflow_run.conclusion == 'failure' }}
steps:
- name: Download artifact
id: download
uses: actions/download-artifact@v8
continue-on-error: true
with:
name: profile-check-results
run-id: ${{ github.event.workflow_run.id }}
github-token: ${{ github.token }}
- name: Post comment on PR
if: ${{ steps.download.outcome == 'success' }}
env:
GH_TOKEN: ${{ github.token }}
GH_REPO: ${{ github.repository }}
run: |
if [ ! -f pr_number.txt ] || [ ! -f pr_comment.md ]; then
echo "No comment artifact found, skipping."
exit 0
fi
PR_NUMBER=$(cat pr_number.txt)
if ! [[ "$PR_NUMBER" =~ ^[0-9]+$ ]]; then
echo "Invalid PR number: $PR_NUMBER"
exit 1
fi
gh pr comment "$PR_NUMBER" --body-file pr_comment.md

View File

@@ -1,275 +0,0 @@
name: PR Label Bot
on:
pull_request_target:
types:
- opened
- reopened
issue_comment:
types:
- created
permissions:
contents: read
pull-requests: write
issues: write
jobs:
request-label:
if: github.event_name == 'pull_request_target' && github.event.pull_request.author_association != 'COLLABORATOR' && github.event.pull_request.author_association != 'OWNER' && github.event.pull_request.author_association != 'MEMBER'
permissions:
contents: read
pull-requests: write
issues: write
runs-on: ubuntu-latest
steps:
- name: Ask PR author for label
uses: actions/github-script@v7
with:
script: |
function isPermissionDenied(error) {
return error && error.status === 403 && /Resource not accessible by integration/i.test(error.message || '');
}
const allowedLabels = [
'bug-fix',
'enhancement',
'Localization',
'profile',
'QoL',
'UI/UX',
'dependencies'
];
const pr = context.payload.pull_request;
const labelsList = `${allowedLabels
.slice(0, -1)
.map((label) => `\`${label}\``)
.join(', ')} and \`${allowedLabels[allowedLabels.length - 1]}\`.`;
const examplesText = [
'```',
'/bot add-label bug-fix',
'```',
'```',
'/bot add-label bug-fix, UI/UX',
'```',
'```',
'/bot remove-label bug-fix, UI/UX',
'```'
].join('\n');
try {
await github.rest.issues.createComment({
owner: context.repo.owner,
repo: context.repo.repo,
issue_number: pr.number,
body:
`Hi @${pr.user.login}, you can manage the labels for this PR by \`/bot add-label\` and \`/bot remove-label\`\n\n` +
`Allowed labels are:\n${labelsList}\n\n` +
`Examples:\n${examplesText}`
});
} catch (error) {
if (isPermissionDenied(error)) {
core.warning(
'Skipping PR comment because token cannot write. Enable Actions write permissions, ' +
'or run with a token that has issues:write and pull_requests:write.'
);
return;
}
throw error;
}
apply-label:
if: github.event_name == 'issue_comment'
permissions:
contents: read
pull-requests: write
issues: write
runs-on: ubuntu-latest
steps:
- name: Apply label command from PR author
uses: actions/github-script@v7
with:
script: |
function isPermissionDenied(error) {
return error && error.status === 403 && /Resource not accessible by integration/i.test(error.message || '');
}
const allowedLabels = [
'bug-fix',
'enhancement',
'Localization',
'profile',
'QoL',
'UI/UX',
'dependencies'
];
const issue = context.payload.issue;
if (!issue.pull_request) {
core.info('Ignoring comment that is not on a pull request.');
return;
}
const body = (context.payload.comment.body || '').trim();
const commandLine = body
.split('\n')
.map((line) => line.trim())
.find((line) => /^\/bot\s+(add-label|remove-label)(?:\s*:\s*|\s+)/i.test(line));
if (!commandLine) {
core.info('No /bot add-label or /bot remove-label command found.');
return;
}
const commandMatch = commandLine.match(/^\/bot\s+(add-label|remove-label)(?:\s*:\s*|\s+)(.+)\s*$/i);
if (!commandMatch) {
core.info('Label command format is invalid.');
return;
}
const action = commandMatch[1].toLowerCase() === 'add-label' ? 'add' : 'remove';
let labelsExpr = (commandMatch[2] || '').trim();
if (!labelsExpr) {
core.info('Label command is missing label name.');
return;
}
if (labelsExpr.startsWith('[') && labelsExpr.endsWith(']')) {
labelsExpr = labelsExpr.slice(1, -1).trim();
}
const requestedRawLabels = labelsExpr
.split(',')
.map((part) => part.trim())
.filter(Boolean);
if (!requestedRawLabels.length) {
core.info('No labels were provided in the command.');
return;
}
const { data: pr } = await github.rest.pulls.get({
owner: context.repo.owner,
repo: context.repo.repo,
pull_number: issue.number
});
const commenter = context.payload.comment.user.login;
if (commenter !== pr.user.login) {
core.info('Ignoring command because commenter is not the PR author.');
return;
}
const labelsByLower = new Map(
allowedLabels.map((label) => [label.toLowerCase(), label])
);
const resolvedLabels = [];
const invalidLabels = [];
for (const rawLabel of requestedRawLabels) {
const resolved = labelsByLower.get(rawLabel.toLowerCase());
if (resolved) {
resolvedLabels.push(resolved);
} else {
invalidLabels.push(rawLabel);
}
}
const uniqueRequestedLabels = [...new Set(resolvedLabels)];
if (action === 'add' && uniqueRequestedLabels.length) {
try {
await github.rest.issues.addLabels({
owner: context.repo.owner,
repo: context.repo.repo,
issue_number: issue.number,
labels: uniqueRequestedLabels
});
} catch (error) {
if (isPermissionDenied(error)) {
core.warning(
'Cannot add labels because token cannot write. Enable Actions write permissions, ' +
'or run with a token that has issues:write and pull_requests:write.'
);
return;
}
throw error;
}
core.info(`Added labels: ${uniqueRequestedLabels.join(', ')}`);
}
if (action === 'remove' && uniqueRequestedLabels.length) {
let removedCount = 0;
try {
for (const label of uniqueRequestedLabels) {
try {
await github.rest.issues.removeLabel({
owner: context.repo.owner,
repo: context.repo.repo,
issue_number: issue.number,
name: label
});
removedCount += 1;
} catch (error) {
if (isPermissionDenied(error)) {
core.warning(
'Cannot remove labels because token cannot write. Enable Actions write permissions, ' +
'or run with a token that has issues:write and pull_requests:write.'
);
return;
}
if (error.status === 404) {
core.info(`Label is not currently applied: ${label}`);
continue;
}
throw error;
}
}
core.info(`Removed labels count: ${removedCount}`);
} catch (error) {
throw error;
}
}
if (!uniqueRequestedLabels.length) {
core.info('No valid labels were provided in the command.');
}
if (invalidLabels.length) {
const allowedText = allowedLabels.map((label) => `\`${label}\``).join(', ');
const invalidText = invalidLabels.map((label) => `\`${label}\``).join(', ');
const validText = uniqueRequestedLabels.length
? `\n\nProcessed valid label(s): ${uniqueRequestedLabels.map((label) => `\`${label}\``).join(', ')}`
: '';
try {
await github.rest.issues.createComment({
owner: context.repo.owner,
repo: context.repo.repo,
issue_number: issue.number,
body:
`@${commenter} invalid label(s): ${invalidText}.${validText}\n\n` +
`Allowed labels: ${allowedText}\n\n` +
`Use:\n` +
`- \`/bot add-label label\`\n` +
`- \`/bot remove-label label\`\n` +
`- \`/bot add-label label1, label2\``
});
} catch (error) {
if (isPermissionDenied(error)) {
core.warning('Cannot post invalid-label feedback because token cannot write comments.');
return;
}
throw error;
}
}
const processedLabelsText = uniqueRequestedLabels.length ? uniqueRequestedLabels.join(', ') : '(none)';
core.info(`Processed label command: ${action} ${processedLabelsText}`);

View File

@@ -1,14 +0,0 @@
name: Publish to WinGet
on:
release:
types: [ released ]
jobs:
publish:
runs-on: windows-latest
steps:
- uses: vedantmgoyal9/winget-releaser@main
with:
identifier: SoftFever.OrcaSlicer
version: ${{ github.event.release.tag_name }}
token: ${{ secrets.WINGET_TOKEN }}
installers-regex: '\.exe$'

2
.gitignore vendored
View File

@@ -44,5 +44,3 @@ test.js
/.cache/
.clangd
internal_docs/
*.flatpak
/flatpak-repo/

View File

@@ -1,57 +1,23 @@
# CLAUDE.md
# Repository Guidelines
OrcaSlicer — open-source C++17 3D slicer. wxWidgets GUI, CMake build system.
## Project Structure & Module Organization
OrcaSlicers C++17 sources live in `src/`, split by feature modules and platform adapters. User assets, icons, and printer presets are in `resources/`; translations stay in `localization/`. Tests sit in `tests/`, grouped by domain (`libslic3r/`, `sla_print/`, etc.) with fixtures under `tests/data/`. CMake helpers reside in `cmake/`, and longer references in `doc/` and `SoftFever_doc/`. Automation scripts belong in `scripts/` and `tools/`. Treat everything in `deps/` and `deps_src/` as vendored snapshots—do not modify without mirroring upstream tags.
## Build Commands
## Build, Test, and Development Commands
Use out-of-source builds:
- `cmake -S . -B build -DCMAKE_BUILD_TYPE=Release` configures dependencies and generates build files.
- `cmake --build build --target OrcaSlicer --config Release` compiles the app; add `--parallel` to speed up.
- `cmake --build build --target tests` then `ctest --test-dir build --output-on-failure` runs automated suites.
Platform helpers such as `build_linux.sh`, `build_release_macos.sh`, and `build_release_vs2022.bat` wrap the same flow with toolchain flags. Use `build_release_macos.sh -sx` when reproducing macOS build issues, and `scripts/DockerBuild.sh` for reproducible container builds.
```bash
# macOS
cmake --build build/arm64 --config RelWithDebInfo --target all --
## Coding Style & Naming Conventions
`.clang-format` enforces 4-space indents, a 140-column limit, aligned initializers, and brace wrapping for classes and functions. Run `clang-format -i <file>` before committing; the CMake `clang-format` target is available when LLVM tools are on your PATH. Prefer `CamelCase` for classes, `snake_case` for functions and locals, and `SCREAMING_CASE` for constants, matching conventions in `src/`. Keep headers self-contained and align include order with the IWYU pragmas.
# Linux
cmake --build build --config RelWithDebInfo --target all --
## Testing Guidelines
Unit tests rely on Catch2 (`tests/catch2/`). Name specs after the component under test—for example `tests/libslic3r/TestPlanarHole.cpp`—and tag long-running cases so `ctest -L fast` remains useful. Cover new algorithms with deterministic fixtures or sample G-code stored in `tests/data/`. Document manual printer validation or regression slicer checks in your PR when automated coverage is insufficient.
# Windows (replace %build_type% with Debug/Release/RelWithDebInfo)
cmake --build . --config %build_type% --target ALL_BUILD -- -m
```
## Commit & Pull Request Guidelines
The history favors concise, sentence-style subject lines with optional issue references, e.g., `Fix grid lines origin for multiple plates (#10724)`. Squash fixups locally before opening a PR. Complete `.github/pull_request_template.md`, include reproduction steps or screenshots for UI changes, and mention impacted presets or translations. Link issues via `Closes #NNNN` when applicable, and call out dependency bumps or profile migrations for maintainer review.
## Testing
Catch2 framework. Tests in `tests/` directory.
```bash
cd build && ctest --output-on-failure # all tests
ctest --test-dir ./tests/libslic3r # individual suite
ctest --test-dir ./tests/fff_print
```
## Code Style
- C++17, selective C++20. PascalCase classes, snake_case functions/variables
- `#pragma once` for headers. Smart pointers and RAII preferred
- Parallelization via TBB — be mindful of shared state
## Key Entry Points
- App startup: `src/OrcaSlicer.cpp`
- Slicing pipeline: `src/libslic3r/Print.cpp`
- All print/printer/material settings: `src/libslic3r/PrintConfig.cpp`
- GUI: `src/slic3r/GUI/`
- Core algorithms: `src/libslic3r/` (GCode/, Fill/, Support/, Geometry/, Format/, Arachne/)
- Printer profiles: `resources/profiles/[manufacturer].json`
## Critical Constraints
- **Backward compatibility required** for .3mf project files and printer profiles
- **Cross-platform** — all changes must work on Windows, macOS, and Linux
- Profile/format changes need version migration handling
- Dependencies built separately in `deps/build/`, then linked to main app
## Code review focus areas
- Changes must not cause regressions in existing functionality, defaults, profiles, or project compatibility.
- Features gated by options must not affect existing behavior when those options are disabled.
- Changes should follow the existing code style and architecture. Architectural changes should be justified in code comments and the PR description.
- Add helper functions or utilities only when existing code cannot reasonably be reused. Avoid duplication.
- Keep code concise and clear. Manually simplify AI generated bloated codes before review.
- Include targeted tests or documented verification for behavior changes, especially in slicing logic, profiles, formats, and GUI defaults.
## Security & Configuration Tips
Follow `SECURITY.md` for vulnerability reporting. Keep API tokens and printer credentials out of tracked configs; use `sandboxes/` for experimental settings. When touching third-party code in `deps_src/`, record the upstream commit or release in your PR description and run the relevant platform build script to confirm integration.

235
CLAUDE.md
View File

@@ -1 +1,234 @@
@AGENTS.md
# CLAUDE.md
This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository.
## Overview
OrcaSlicer is an open-source 3D slicer application forked from Bambu Studio, built using C++ with wxWidgets for the GUI and CMake as the build system. The project uses a modular architecture with separate libraries for core slicing functionality, GUI components, and platform-specific code.
## Build Commands
### Building on Windows
**Always use this command to build the project when testing build issues on Windows.**
```bash
cmake --build . --config %build_type% --target ALL_BUILD -- -m
```
### Building on macOS
**Always use this command to build the project when testing build issues on macOS.**
```bash
cmake --build build/arm64 --config RelWithDebInfo --target all --
```
### Building on Linux
**Always use this command to build the project when testing build issues on Linux.**
```bash
cmake --build build/arm64 --config RelWithDebInfo --target all --
```
### Build test:
**Always use this command to build the project when testing build issues on Windows.**
```bash
cmake --build . --config %build_type% --target ALL_BUILD -- -m
```
### Building on macOS
**Always use this command to build the project when testing build issues on macOS.**
```bash
cmake --build build/arm64 --config RelWithDebInfo --target all --
```
### Building on Linux
**Always use this command to build the project when testing build issues on Linux.**
```bash
cmake --build build/arm64 --config RelWithDebInfo --target all --
```
### Build System
- Uses CMake with minimum version 3.13 (maximum 3.31.x on Windows)
- Primary build directory: `build/`
- Dependencies are built in `deps/build/`
- The build process is split into dependency building and main application building
- Windows builds use Visual Studio generators
- macOS builds use Xcode by default, Ninja with -x flag
- Linux builds use Ninja generator
### Testing
Tests are located in the `tests/` directory and use the Catch2 testing framework. Test structure:
- `tests/libslic3r/` - Core library tests (21 test files)
- Geometry processing, algorithms, file formats (STL, 3MF, AMF)
- Polygon operations, clipper utilities, Voronoi diagrams
- `tests/fff_print/` - Fused Filament Fabrication tests (12 test files)
- Slicing algorithms, G-code generation, print mechanics
- Fill patterns, extrusion, support material
- `tests/sla_print/` - Stereolithography tests (4 test files)
- SLA-specific printing algorithms, support generation
- `tests/libnest2d/` - 2D nesting algorithm tests
- `tests/slic3rutils/` - Utility function tests
- `tests/sandboxes/` - Experimental/sandbox test code
Run all tests after building:
```bash
cd build && ctest
```
Run tests with verbose output:
```bash
cd build && ctest --output-on-failure
```
Run individual test suites:
```bash
# From build directory
ctest --test-dir ./tests/libslic3r/libslic3r_tests
ctest --test-dir ./tests/fff_print/fff_print_tests
ctest --test-dir ./tests/sla_print/sla_print_tests
# and so on
```
## Architecture
### Core Libraries
- **libslic3r/**: Core slicing engine and algorithms (platform-independent)
- Main slicing logic, geometry processing, G-code generation
- Key classes: Print, PrintObject, Layer, GCode, Config
- Modular design with specialized subdirectories:
- `GCode/` - G-code generation, cooling, pressure equalization, thumbnails
- `Fill/` - Infill pattern implementations (gyroid, honeycomb, lightning, etc.)
- `Support/` - Tree supports and traditional support generation
- `Geometry/` - Advanced geometry operations, Voronoi diagrams, medial axis
- `Format/` - File I/O for 3MF, AMF, STL, OBJ, STEP formats
- `SLA/` - SLA-specific print processing and support generation
- `Arachne/` - Advanced wall generation using skeletal trapezoidation
- **src/slic3r/**: Main application framework and GUI
- GUI application built with wxWidgets
- Integration between libslic3r core and user interface
- Located in `src/slic3r/GUI/` (not shown in this directory but exists)
### Key Algorithmic Components
- **Arachne Wall Generation**: Variable-width perimeter generation using skeletal trapezoidation
- **Tree Supports**: Organic support generation algorithm
- **Lightning Infill**: Sparse infill optimization for internal structures
- **Adaptive Slicing**: Variable layer height based on geometry
- **Multi-material**: Multi-extruder and soluble support processing
- **G-code Post-processing**: Cooling, fan control, pressure advance, conflict checking
### File Format Support
- **3MF/BBS_3MF**: Native format with extensions for multi-material and metadata
- **STL**: Standard tessellation language for 3D models
- **AMF**: Additive Manufacturing Format with color/material support
- **OBJ**: Wavefront OBJ with material definitions
- **STEP**: CAD format support for precise geometry
- **G-code**: Output format with extensive post-processing capabilities
### External Dependencies
- **Clipper2**: Advanced 2D polygon clipping and offsetting
- **libigl**: Computational geometry library for mesh operations
- **TBB**: Intel Threading Building Blocks for parallelization
- **wxWidgets**: Cross-platform GUI framework
- **OpenGL**: 3D graphics rendering and visualization
- **CGAL**: Computational Geometry Algorithms Library (selective use)
- **OpenVDB**: Volumetric data structures for advanced operations
- **Eigen**: Linear algebra library for mathematical operations
## File Organization
### Resources and Configuration
- `resources/profiles/` - Printer and material profiles organized by manufacturer
- `resources/printers/` - Printer-specific configurations and G-code templates
- `resources/images/` - UI icons, logos, calibration images
- `resources/calib/` - Calibration test patterns and data
- `resources/handy_models/` - Built-in test models (benchy, calibration cubes)
### Internationalization and Localization
- `localization/i18n/` - Source translation files (.pot, .po)
- `resources/i18n/` - Runtime language resources
- Translation managed via `scripts/run_gettext.sh` / `scripts/run_gettext.bat`
### Platform-Specific Code
- `src/libslic3r/Platform.cpp` - Platform abstractions and utilities
- `src/libslic3r/MacUtils.mm` - macOS-specific utilities (Objective-C++)
- Windows-specific build scripts and configurations
- Linux distribution support scripts in `scripts/linux.d/`
### Build and Development Tools
- `cmake/modules/` - Custom CMake find modules and utilities
- `scripts/` - Python utilities for profile generation and validation
- `tools/` - Windows build tools (gettext utilities)
- `deps/` - External dependency build configurations
## Development Workflow
### Code Style and Standards
- **C++17 standard** with selective C++20 features
- **Naming conventions**: PascalCase for classes, snake_case for functions/variables
- **Header guards**: Use `#pragma once`
- **Memory management**: Prefer smart pointers, RAII patterns
- **Thread safety**: Use TBB for parallelization, be mindful of shared state
### Common Development Tasks
#### Adding New Print Settings
1. Define setting in `PrintConfig.cpp` with proper bounds and defaults
2. Add UI controls in appropriate GUI components
3. Update serialization in config save/load
4. Add tooltips and help text for user guidance
5. Test with different printer profiles
#### Modifying Slicing Algorithms
1. Core algorithms live in `libslic3r/` subdirectories
2. Performance-critical code should be profiled and optimized
3. Consider multi-threading implications (TBB integration)
4. Validate changes don't break existing profiles
5. Add regression tests where appropriate
#### GUI Development
1. GUI code resides in `src/slic3r/GUI/` (not visible in current tree)
2. Use existing wxWidgets patterns and custom controls
3. Support both light and dark themes
4. Consider DPI scaling on high-resolution displays
5. Maintain cross-platform compatibility
#### Adding Printer Support
1. Create JSON profile in `resources/profiles/[manufacturer].json`
2. Add printer-specific start/end G-code templates
3. Configure build volume, capabilities, and material compatibility
4. Test thoroughly with actual hardware when possible
5. Follow existing profile structure and naming conventions
### Dependencies and Build System
- **CMake-based** with separate dependency building phase
- **Dependencies** built once in `deps/build/`, then linked to main application
- **Cross-platform** considerations important for all changes
- **Resource files** embedded at build time, platform-specific handling
### Performance Considerations
- **Slicing algorithms** are CPU-intensive, profile before optimizing
- **Memory usage** can be substantial with complex models
- **Multi-threading** extensively used via TBB
- **File I/O** optimized for large 3MF files with embedded textures
- **Real-time preview** requires efficient mesh processing
## Important Development Notes
### Codebase Navigation
- Use search tools extensively - codebase has 500k+ lines
- Key entry points: `src/OrcaSlicer.cpp` for application startup
- Core slicing: `libslic3r/Print.cpp` orchestrates the slicing pipeline
- Configuration: `PrintConfig.cpp` defines all print/printer/material settings
### Compatibility and Stability
- **Backward compatibility** maintained for project files and profiles
- **Cross-platform** support essential (Windows/macOS/Linux)
- **File format** changes require careful version handling
- **Profile migrations** needed when settings change significantly
### Quality and Testing
- **Regression testing** important due to algorithm complexity
- **Performance benchmarks** help catch performance regressions
- **Memory leak** detection important for long-running GUI application
- **Cross-platform** testing required before releases

View File

@@ -1,9 +1,13 @@
if(${CMAKE_VERSION} VERSION_GREATER_EQUAL "4.0")
set(CMAKE_POLICY_VERSION_MINIMUM 3.13 CACHE STRING "" FORCE)
set(CMAKE_POLICY_VERSION_MINIMUM 3.5 CACHE STRING "" FORCE)
endif()
cmake_minimum_required(VERSION 3.13)
# Verify that your CMake version is exactly 3.5 series or higher on windows
if ( (MSVC OR WIN32) AND (${CMAKE_VERSION} VERSION_LESS "3.5") )
message(FATAL_ERROR "CMake current version ${CMAKE_VERSION} is too old. Minimum required is 3.5.")
endif()
# The following line used to be in tests/CMakeLists.txt
# Having it there causes rebuilds of all targets on any CMakeLists.txt change under tests/
@@ -85,9 +89,10 @@ else ()
endif ()
find_package(Git)
if(DEFINED ENV{git_commit_hash} AND NOT "$ENV{git_commit_hash}" STREQUAL "")
message(STATUS "Specified git commit hash: $ENV{git_commit_hash}")
if(GIT_FOUND AND EXISTS "${CMAKE_SOURCE_DIR}/.git")
if(GIT_FOUND AND EXISTS "${CMAKE_SOURCE_DIR}/.git")
if(DEFINED ENV{git_commit_hash} AND NOT "$ENV{git_commit_hash}" STREQUAL "")
message(STATUS "Specified git commit hash: $ENV{git_commit_hash}")
# Convert the given hash to short hash
execute_process(
COMMAND ${GIT_EXECUTABLE} rev-parse --short "$ENV{git_commit_hash}"
@@ -95,20 +100,17 @@ if(DEFINED ENV{git_commit_hash} AND NOT "$ENV{git_commit_hash}" STREQUAL "")
OUTPUT_VARIABLE GIT_COMMIT_HASH
OUTPUT_STRIP_TRAILING_WHITESPACE
)
add_definitions("-DGIT_COMMIT_HASH=\"${GIT_COMMIT_HASH}\"")
else()
# No .git directory (e.g., Flatpak sandbox) — truncate directly
string(SUBSTRING "$ENV{git_commit_hash}" 0 7 GIT_COMMIT_HASH)
# Check current Git commit hash
execute_process(
COMMAND ${GIT_EXECUTABLE} log -1 --format=%h
WORKING_DIRECTORY ${CMAKE_SOURCE_DIR}
OUTPUT_VARIABLE GIT_COMMIT_HASH
OUTPUT_STRIP_TRAILING_WHITESPACE
)
add_definitions("-DGIT_COMMIT_HASH=\"${GIT_COMMIT_HASH}\"")
endif()
add_definitions("-DGIT_COMMIT_HASH=\"${GIT_COMMIT_HASH}\"")
elseif(GIT_FOUND AND EXISTS "${CMAKE_SOURCE_DIR}/.git")
# Check current Git commit hash
execute_process(
COMMAND ${GIT_EXECUTABLE} log -1 --format=%h
WORKING_DIRECTORY ${CMAKE_SOURCE_DIR}
OUTPUT_VARIABLE GIT_COMMIT_HASH
OUTPUT_STRIP_TRAILING_WHITESPACE
)
add_definitions("-DGIT_COMMIT_HASH=\"${GIT_COMMIT_HASH}\"")
endif()
if(DEFINED ENV{SLIC3R_STATIC})
@@ -117,10 +119,11 @@ else()
set(SLIC3R_STATIC_INITIAL 1)
endif()
option(SLIC3R_STATIC "Compile OrcaSlicer with static libraries (Boost, TBB)" ${SLIC3R_STATIC_INITIAL})
option(SLIC3R_GUI "Compile OrcaSlicer with GUI components (OpenGL, wxWidgets)" 1)
option(SLIC3R_STATIC "Compile OrcaSlicer with static libraries (Boost, TBB, glew)" ${SLIC3R_STATIC_INITIAL})
option(SLIC3R_GUI "Compile OrcaSlicer with GUI components (OpenGL, wxWidgets)" 1)
option(SLIC3R_FHS "Assume OrcaSlicer is to be installed in a FHS directory structure" 0)
option(SLIC3R_PROFILE "Compile OrcaSlicer with an invasive Shiny profiler" 0)
option(SLIC3R_WX_STABLE "Build against wxWidgets stable (3.0) as oppsed to dev (3.1) on Linux" 0)
option(SLIC3R_PROFILE "Compile OrcaSlicer with an invasive Shiny profiler" 0)
option(SLIC3R_PCH "Use precompiled headers" 1)
option(SLIC3R_MSVC_COMPILE_PARALLEL "Compile on Visual Studio in parallel" 1)
option(SLIC3R_MSVC_PDB "Generate PDB files on MSVC in Release mode" 1)
@@ -157,7 +160,7 @@ if (APPLE)
if (CMAKE_MACOSX_BUNDLE)
set(CMAKE_INSTALL_RPATH @executable_path/../Frameworks)
endif()
SET(CMAKE_XCODE_ATTRIBUTE_PRODUCT_BUNDLE_IDENTIFIER "com.orcaslicer.OrcaSlicer")
SET(CMAKE_XCODE_ATTRIBUTE_PRODUCT_BUNDLE_IDENTIFIER "com.softfever3d.orca-slicer")
message(STATUS "Orca: IS_CROSS_COMPILE: ${IS_CROSS_COMPILE}")
endif ()
@@ -168,7 +171,10 @@ option(BUILD_TESTS "Build unit tests" OFF)
option(ORCA_TOOLS "Build Orca tools" OFF)
if (FLATPAK)
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -std=gnu++20")
set(SLIC3R_PCH OFF CACHE BOOL "" FORCE)
set(SLIC3R_FHS ON CACHE BOOL "" FORCE)
set(BUILD_TESTS OFF CACHE BOOL "" FORCE)
set(SLIC3R_DESKTOP_INTEGRATION OFF CACHE BOOL "" FORCE)
endif ()
@@ -323,44 +329,43 @@ set(CMAKE_POSITION_INDEPENDENT_CODE ON)
# WIN10SDK_PATH is used to point CMake to the WIN10 SDK installation directory.
# We pick it from environment if it is not defined in another way
# ORCA: Removed Netfabb STL fixing service support in favor of CGAL.
# if(WIN32)
# if(NOT DEFINED WIN10SDK_PATH)
# if(DEFINED ENV{WIN10SDK_PATH})
# set(WIN10SDK_PATH "$ENV{WIN10SDK_PATH}")
# endif()
# endif()
# if(DEFINED WIN10SDK_PATH)
# #BBS: modify win10sdk_path
# if (EXISTS "${WIN10SDK_PATH}/winrt/windows.graphics.printing3d.h")
# set(WIN10SDK_INCLUDE_PATH "${WIN10SDK_PATH}")
# else()
# message("WIN10SDK_PATH is invalid: ${WIN10SDK_PATH}")
# message("${WIN10SDK_PATH}/winrt/windows.graphics.printing3d.h was not found")
# message("STL fixing by the Netfabb service will not be compiled")
# unset(WIN10SDK_PATH)
# endif()
# else()
# # Try to use the default Windows 10 SDK path.
# if (DEFINED ENV{WindowsSdkDir} AND DEFINED ENV{WindowsSDKVersion})
# set(WIN10SDK_INCLUDE_PATH "$ENV{WindowsSdkDir}/Include/$ENV{WindowsSDKVersion}")
# else ()
# set(WIN10SDK_INCLUDE_PATH "C:/Program Files (x86)/Windows Kits/10/Include/10.0.26100.0")
# endif ()
# if (NOT EXISTS "${WIN10SDK_INCLUDE_PATH}/winrt/windows.graphics.printing3d.h")
# message("${WIN10SDK_INCLUDE_PATH}/winrt/windows.graphics.printing3d.h was not found")
# message("STL fixing by the Netfabb service will not be compiled")
# unset(WIN10SDK_INCLUDE_PATH)
# endif()
# endif()
# if(WIN10SDK_INCLUDE_PATH)
# message("Building with Win10 Netfabb STL fixing service support")
# add_definitions(-DHAS_WIN10SDK)
# include_directories(SYSTEM "${WIN10SDK_INCLUDE_PATH}")
# else()
# message("Building without Win10 Netfabb STL fixing service support")
# endif()
# endif()
if(WIN32)
if(NOT DEFINED WIN10SDK_PATH)
if(DEFINED ENV{WIN10SDK_PATH})
set(WIN10SDK_PATH "$ENV{WIN10SDK_PATH}")
endif()
endif()
if(DEFINED WIN10SDK_PATH)
#BBS: modify win10sdk_path
if (EXISTS "${WIN10SDK_PATH}/winrt/windows.graphics.printing3d.h")
set(WIN10SDK_INCLUDE_PATH "${WIN10SDK_PATH}")
else()
message("WIN10SDK_PATH is invalid: ${WIN10SDK_PATH}")
message("${WIN10SDK_PATH}/winrt/windows.graphics.printing3d.h was not found")
message("STL fixing by the Netfabb service will not be compiled")
unset(WIN10SDK_PATH)
endif()
else()
# Try to use the default Windows 10 SDK path.
if (DEFINED ENV{WindowsSdkDir} AND DEFINED ENV{WindowsSDKVersion})
set(WIN10SDK_INCLUDE_PATH "$ENV{WindowsSdkDir}/Include/$ENV{WindowsSDKVersion}")
else ()
set(WIN10SDK_INCLUDE_PATH "C:/Program Files (x86)/Windows Kits/10/Include/10.0.26100.0")
endif ()
if (NOT EXISTS "${WIN10SDK_INCLUDE_PATH}/winrt/windows.graphics.printing3d.h")
message("${WIN10SDK_INCLUDE_PATH}/winrt/windows.graphics.printing3d.h was not found")
message("STL fixing by the Netfabb service will not be compiled")
unset(WIN10SDK_INCLUDE_PATH)
endif()
endif()
if(WIN10SDK_INCLUDE_PATH)
message("Building with Win10 Netfabb STL fixing service support")
add_definitions(-DHAS_WIN10SDK)
include_directories(SYSTEM "${WIN10SDK_INCLUDE_PATH}")
else()
message("Building without Win10 Netfabb STL fixing service support")
endif()
endif()
if (APPLE)
message("OS X SDK Path: ${CMAKE_OSX_SYSROOT}")
@@ -374,6 +379,11 @@ endif ()
if (CMAKE_SYSTEM_NAME STREQUAL "Linux")
find_package(PkgConfig REQUIRED)
if (CMAKE_VERSION VERSION_LESS "3.1")
# Workaround for an old CMake, which does not understand CMAKE_CXX_STANDARD.
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -std=c++11")
endif()
# Boost on Raspberry-Pi does not link to pthreads.
set(THREADS_PREFER_PTHREAD_FLAG ON)
find_package(Threads REQUIRED)
@@ -445,12 +455,9 @@ if (NOT MSVC AND ("${CMAKE_CXX_COMPILER_ID}" STREQUAL "GNU" OR "${CMAKE_CXX_COMP
add_compile_options(-Wno-unknown-pragmas)
endif()
# Compress the debug info with zstd to save space in Flatpak CI builds
if(FLATPAK)
if(("${CMAKE_CXX_COMPILER_ID}" STREQUAL "GNU" AND CMAKE_CXX_COMPILER_VERSION VERSION_GREATER 13.0) OR
("${CMAKE_CXX_COMPILER_ID}" MATCHES "Clang" AND CMAKE_CXX_COMPILER_VERSION VERSION_GREATER_EQUAL 15.0))
add_compile_options(-gz=zstd)
endif()
# Bit of a hack for flatpak building: compress the debug info with zstd to save space in CI
if("${CMAKE_CXX_COMPILER_ID}" STREQUAL "GNU" AND CMAKE_CXX_COMPILER_VERSION VERSION_GREATER 13.0)
add_compile_options(-gz=zstd)
endif()
if("${CMAKE_CXX_COMPILER_ID}" STREQUAL "GNU" AND CMAKE_CXX_COMPILER_VERSION VERSION_GREATER 14)
@@ -484,8 +491,7 @@ if (APPLE)
endif ()
if(MSVC)
# Ignore truncating casts in initializers & constructors
# https://learn.microsoft.com/en-us/cpp/error-messages/compiler-warnings/compiler-warning-level-1-c4305
# 添加编译选项,忽略警告 C4305 (格式转换截断)
add_compile_options(/wd4305)
endif()
@@ -576,9 +582,6 @@ endif()
set(Boost_NO_SYSTEM_PATHS TRUE)
find_package(Boost 1.83.0 REQUIRED COMPONENTS system filesystem thread log log_setup locale regex chrono atomic date_time iostreams program_options nowide)
# Find and configure Eigen3
find_package(Eigen3 5.0.1 REQUIRED)
add_library(boost_libs INTERFACE)
add_library(boost_headeronly INTERFACE)
@@ -663,6 +666,16 @@ endif()
## OPTIONAL packages
# Find eigen3 or use bundled version
if (NOT SLIC3R_STATIC)
find_package(Eigen3 3.3)
endif ()
if (NOT EIGEN3_FOUND)
set(EIGEN3_FOUND 1)
set(EIGEN3_INCLUDE_DIR ${LIBDIR}/eigen/)
endif ()
include_directories(BEFORE SYSTEM ${EIGEN3_INCLUDE_DIR})
# Find expat or use bundled version
# Always use the system libexpat on Linux.
@@ -688,6 +701,16 @@ if(APPLE AND CMAKE_VERSION VERSION_GREATER_EQUAL "4.0")
set(OPENGL_LIBRARIES "-framework OpenGL" CACHE STRING "OpenGL framework" FORCE)
endif()
set(GLEW_ROOT "${CMAKE_PREFIX_PATH}")
message("GLEW_ROOT: ${GLEW_ROOT}")
# Find glew or use bundled version
if (SLIC3R_STATIC AND NOT SLIC3R_STATIC_EXCLUDE_GLEW)
set(GLEW_USE_STATIC_LIBS ON)
set(GLEW_VERBOSE ON)
endif()
find_package(GLEW REQUIRED)
find_package(glfw3 REQUIRED)
# Find the Cereal serialization library
@@ -706,7 +729,7 @@ add_custom_target(gettext_make_pot
COMMAND xgettext --keyword=L --keyword=_L --keyword=_u8L --keyword=L_CONTEXT:1,2c --keyword=_L_PLURAL:1,2 --add-comments=TRN --from-code=UTF-8 --no-location --debug --boost
-f "${BBL_L18N_DIR}/list.txt"
-o "${BBL_L18N_DIR}/OrcaSlicer.pot"
COMMAND hintsToPot ${SLIC3R_RESOURCES_DIR} ${BBL_L18N_DIR}
COMMAND hintsToPot ${SLIC3R_RESOURCES_DIR} ${BBL_L18N_DIR}
WORKING_DIRECTORY ${PROJECT_SOURCE_DIR}
COMMENT "Generate pot file from strings in the source tree"
)
@@ -778,10 +801,8 @@ function(orcaslicer_copy_dlls target config postfix output_dlls)
set(_arch "x64")
elseif ("${CMAKE_SYSTEM_PROCESSOR}" STREQUAL "X86")
set(_arch "x86")
elseif ("${CMAKE_SYSTEM_PROCESSOR}" STREQUAL "ARM64")
set(_arch "arm64")
else ()
message(FATAL_ERROR "Unable to detect architecture: ${CMAKE_SYSTEM_PROCESSOR}")
message(FATAL_ERROR "Unable to detect architecture")
endif ()
get_property(_is_multi GLOBAL PROPERTY GENERATOR_IS_MULTI_CONFIG)
@@ -905,7 +926,7 @@ elseif (SLIC3R_FHS)
install(DIRECTORY ${SLIC3R_RESOURCES_DIR}/ DESTINATION ${SLIC3R_FHS_RESOURCES}
PATTERN "*/udev" EXCLUDE
)
install(FILES src/dev-utils/platform/unix/com.orcaslicer.OrcaSlicer.desktop DESTINATION ${CMAKE_INSTALL_DATAROOTDIR}/applications)
install(FILES src/dev-utils/platform/unix/OrcaSlicer.desktop DESTINATION ${CMAKE_INSTALL_DATAROOTDIR}/applications)
foreach(SIZE 32 128 192)
install(FILES ${SLIC3R_RESOURCES_DIR}/images/OrcaSlicer_${SIZE}px.png
DESTINATION ${CMAKE_INSTALL_DATAROOTDIR}/icons/hicolor/${SIZE}x${SIZE}/apps RENAME OrcaSlicer.png
@@ -914,7 +935,7 @@ elseif (SLIC3R_FHS)
elseif (CMAKE_MACOSX_BUNDLE)
# install(DIRECTORY "${SLIC3R_RESOURCES_DIR}/" DESTINATION "${CMAKE_INSTALL_PREFIX}/OrcaSlicer.app/Contents/resources")
else ()
install(FILES src/dev-utils/platform/unix/com.orcaslicer.OrcaSlicer.desktop DESTINATION ${CMAKE_INSTALL_PREFIX}/resources/applications)
install(FILES src/dev-utils/platform/unix/OrcaSlicer.desktop DESTINATION ${CMAKE_INSTALL_PREFIX}/resources/applications)
install(DIRECTORY "${SLIC3R_RESOURCES_DIR}/" DESTINATION "${CMAKE_INSTALL_PREFIX}/resources")
endif ()

View File

@@ -44,7 +44,7 @@ If you come across any of these in search results, please <b>report them</b> as
# Main features
- **[Advanced Calibration Tools](https://www.orcaslicer.com/wiki/calibration_guide)**
- **[Advanced Calibration Tools](https://www.orcaslicer.com/wiki/Calibration)**
Comprehensive suite: temperature towers, flow rate, retraction & more for optimal performance.
- **[Precise Wall](https://www.orcaslicer.com/wiki/quality_settings_precision#precise-wall) and [Seam Control](https://www.orcaslicer.com/wiki/quality_settings_seam)**
Adjust outer wall spacing and apply scarf seams to enhance print accuracy.
@@ -71,7 +71,7 @@ If you come across any of these in search results, please <b>report them</b> as
The [wiki](https://www.orcaslicer.com/wiki) aims to provide a detailed explanation of the slicer settings, including how to maximize their use and how to calibrate and set up your printer.
- **[Access the wiki here](https://www.orcaslicer.com/wiki)**
- **[Contribute to the wiki](https://www.orcaslicer.com/wiki/how_to_wiki)**
- **[Contribute to the wiki](https://www.orcaslicer.com/wiki/How-to-wiki)**
# Download
@@ -137,32 +137,14 @@ winget install --id=SoftFever.OrcaSlicer -e
![mac_security_setting](./SoftFever_doc/mac_security_setting.png)
</details>
## Linux
## Linux (Ubuntu)
### Flathub (Recommended)
OrcaSlicer is available through FlatHub:
<a href='https://flathub.org/apps/com.orcaslicer.OrcaSlicer'><img width='240' alt='Download on Flathub' src='https://dl.flathub.org/assets/badges/flathub-badge-en.png'/></a>
Install from the command line:
```shell
flatpak install flathub com.orcaslicer.OrcaSlicer
flatpak run com.orcaslicer.OrcaSlicer
```
It can also be installed through graphical software managers (KDE Discover, GNOME Software, etc.) when Flathub is enabled. Search for **OrcaSlicer** in your software center.
### AppImage
1. Download App image from the [releases page](https://github.com/OrcaSlicer/OrcaSlicer/releases).
2. Double click the downloaded file to run it.
3. If you run into trouble executing it, try this command in the terminal:
1. If you run into trouble executing it, try this command in the terminal:
`chmod +x /path_to_appimage/OrcaSlicer_Linux.AppImage`
# How to Compile
All updated build instructions for Windows, macOS, and Linux are now available on the official [OrcaSlicer Wiki - How to build](https://www.orcaslicer.com/wiki/how_to_build) page.
All updated build instructions for Windows, macOS, and Linux are now available on the official [OrcaSlicer Wiki - How to build](https://www.orcaslicer.com/wiki/How-to-build) page.
Please refer to the wiki to ensure you're following the latest and most accurate steps for your platform.
@@ -212,16 +194,20 @@ Thank you! :)
<a href="https://ko-fi.com/G2G5IP3CP"><img src="https://img.shields.io/badge/Support_me_on_Ko--fi-FF5E5B?style=flat&logo=ko-fi&logoColor=white" height="50"></a>
<a href="https://paypal.me/softfever3d"><img src="https://img.shields.io/badge/PayPal-003087?style=flat&logo=paypal&logoColor=fff" height="50"></a>
## Some Background
## Some background
Open-source slicing has always been built on a tradition of collaboration and attribution. [Slic3r](https://github.com/Slic3r/Slic3r), created by Alessandro Ranellucci and the RepRap community, laid the foundation. [PrusaSlicer](https://github.com/prusa3d/PrusaSlicer) by Prusa Research built on Slic3r and acknowledged that heritage. [Bambu Studio](https://github.com/bambulab/BambuStudio) in turn forked from PrusaSlicer, and [SuperSlicer](https://github.com/supermerill/SuperSlicer) by @supermerill extended PrusaSlicer with community-driven enhancements. Each project carried the work of its predecessors forward, crediting those who came before.
OrcaSlicer was originally forked from Bambu Studio, it was previously known as BambuStudio-SoftFever.
OrcaSlicer began in that same spirit, drawing from BambuStudio, PrusaSlicer, and ideas inspired by CuraSlicer and SuperSlicer. But it has since grown far beyond its origins. Through relentless innovation — introducing advanced calibration tools, precise wall and seam control, tree supports, adaptive slicing, and hundreds of other features — OrcaSlicer has become the most widely used and actively developed open-source slicer in the 3D printing community. Many of its innovations have been adopted by other slicers, making it a driving force for the entire industry.
The OrcaSlicer logo was designed by community member [Justin Levine](https://github.com/jal-co).
[Bambu Studio](https://github.com/bambulab/BambuStudio) is forked from [PrusaSlicer](https://github.com/prusa3d/PrusaSlicer) by Prusa Research, which is from [Slic3r](https://github.com/Slic3r/Slic3r) by Alessandro Ranellucci and the RepRap community.
OrcaSlicer incorporates a lot of features from [SuperSlicer](https://github.com/supermerill/SuperSlicer) by @supermerill
OrcaSlicer's logo is designed by community member Justin Levine (@freejstnalxndr).
# License
- **OrcaSlicer** is licensed under the GNU Affero General Public License, version 3.
- **OrcaSlicer** is licensed under the GNU Affero General Public License, version 3. OrcaSlicer is based on Bambu Studio by BambuLab.
- **Bambu Studio** is licensed under the GNU Affero General Public License, version 3. Bambu Studio is based on PrusaSlicer by PrusaResearch.
- **PrusaSlicer** is licensed under the GNU Affero General Public License, version 3. PrusaSlicer is owned by Prusa Research. PrusaSlicer is originally based on Slic3r by Alessandro Ranellucci.
- **Slic3r** is licensed under the GNU Affero General Public License, version 3. Slic3r was created by Alessandro Ranellucci with the help of many other contributors.
- The **GNU Affero General Public License**, version 3 ensures that if you use any part of this software in any way (even behind a web server), your software must be released under the same license.
- OrcaSlicer includes a **pressure advance calibration pattern test** adapted from Andrew Ellis' generator, which is licensed under GNU General Public License, version 3. Ellis' generator is itself adapted from a generator developed by Sineos for Marlin, which is licensed under GNU General Public License, version 3.
- The **Bambu networking plugin** is based on non-free libraries from BambuLab. It is optional to the OrcaSlicer and provides extended functionalities for Bambulab printer users.

View File

@@ -5,7 +5,6 @@
# Based on the GitHub Actions workflow in .github/workflows/build_all.yml
set -e
SECONDS=0
# Colors for output
RED='\033[0;31m'
@@ -199,22 +198,22 @@ echo -e "${GREEN}All required dependencies found${NC}"
# Install runtime and SDK if requested
if [[ "$INSTALL_RUNTIME" == true ]]; then
echo -e "${YELLOW}Installing GNOME runtime and SDK...${NC}"
flatpak install --user -y flathub org.gnome.Platform//49
flatpak install --user -y flathub org.gnome.Sdk//49
flatpak install --user -y flathub org.gnome.Platform//48
flatpak install --user -y flathub org.gnome.Sdk//48
fi
# Check if required runtime is available
if ! flatpak info --user org.gnome.Platform//49 &> /dev/null; then
echo -e "${RED}Error: GNOME Platform 49 runtime is not installed.${NC}"
if ! flatpak info --user org.gnome.Platform//48 &> /dev/null; then
echo -e "${RED}Error: GNOME Platform 48 runtime is not installed.${NC}"
echo "Run with -i flag to install it automatically, or install manually:"
echo "flatpak install --user flathub org.gnome.Platform//49"
echo "flatpak install --user flathub org.gnome.Platform//48"
exit 1
fi
if ! flatpak info --user org.gnome.Sdk//49 &> /dev/null; then
echo -e "${RED}Error: GNOME SDK 49 is not installed.${NC}"
if ! flatpak info --user org.gnome.Sdk//48 &> /dev/null; then
echo -e "${RED}Error: GNOME SDK 48 is not installed.${NC}"
echo "Run with -i flag to install it automatically, or install manually:"
echo "flatpak install --user flathub org.gnome.Sdk//49"
echo "flatpak install --user flathub org.gnome.Sdk//48"
exit 1
fi
@@ -255,8 +254,8 @@ mkdir -p "$BUILD_DIR"
rm -rf "$BUILD_DIR/build-dir"
# Check if flatpak manifest exists
if [[ ! -f "./scripts/flatpak/com.orcaslicer.OrcaSlicer.yml" ]]; then
echo -e "${RED}Error: Flatpak manifest not found at scripts/flatpak/com.orcaslicer.OrcaSlicer.yml${NC}"
if [[ ! -f "./scripts/flatpak/io.github.orcaslicer.OrcaSlicer.yml" ]]; then
echo -e "${RED}Error: Flatpak manifest not found at scripts/flatpak/io.github.orcaslicer.OrcaSlicer.yml${NC}"
exit 1
fi
@@ -316,11 +315,11 @@ if [[ "$DISABLE_ROFILES_FUSE" == true ]]; then
fi
# Use a temp manifest with no-debuginfo if requested
MANIFEST="scripts/flatpak/com.orcaslicer.OrcaSlicer.yml"
MANIFEST="scripts/flatpak/io.github.orcaslicer.OrcaSlicer.yml"
if [[ "$NO_DEBUGINFO" == true ]]; then
MANIFEST="scripts/flatpak/com.orcaslicer.OrcaSlicer.no-debug.yml"
sed '/^build-options:/a\ no-debuginfo: true\n strip: true' \
scripts/flatpak/com.orcaslicer.OrcaSlicer.yml > "$MANIFEST"
MANIFEST="scripts/flatpak/io.github.orcaslicer.OrcaSlicer.no-debug.yml"
sed '0,/^finish-args:/s//build-options:\n no-debuginfo: true\n strip: true\nfinish-args:/' \
scripts/flatpak/io.github.orcaslicer.OrcaSlicer.yml > "$MANIFEST"
echo -e "${YELLOW}Debug info disabled (using temp manifest)${NC}"
fi
@@ -330,19 +329,19 @@ if ! flatpak-builder \
"$MANIFEST"; then
echo -e "${RED}Error: flatpak-builder failed${NC}"
echo -e "${YELLOW}Check the build log above for details${NC}"
rm -f "scripts/flatpak/com.orcaslicer.OrcaSlicer.no-debug.yml"
rm -f "scripts/flatpak/io.github.orcaslicer.OrcaSlicer.no-debug.yml"
exit 1
fi
# Clean up temp manifest
rm -f "scripts/flatpak/com.orcaslicer.OrcaSlicer.no-debug.yml"
rm -f "scripts/flatpak/io.github.orcaslicer.OrcaSlicer.no-debug.yml"
# Create bundle
echo -e "${YELLOW}Creating Flatpak bundle...${NC}"
if ! flatpak build-bundle \
"$BUILD_DIR/repo" \
"$BUNDLE_NAME" \
com.orcaslicer.OrcaSlicer \
io.github.orcaslicer.OrcaSlicer \
--arch="$ARCH"; then
echo -e "${RED}Error: Failed to create Flatpak bundle${NC}"
exit 1
@@ -361,10 +360,10 @@ echo -e "${BLUE}To install the Flatpak:${NC}"
echo -e "flatpak install --user $BUNDLE_NAME"
echo ""
echo -e "${BLUE}To run OrcaSlicer:${NC}"
echo -e "flatpak run com.orcaslicer.OrcaSlicer"
echo -e "flatpak run io.github.orcaslicer.OrcaSlicer"
echo ""
echo -e "${BLUE}To uninstall:${NC}"
echo -e "flatpak uninstall --user com.orcaslicer.OrcaSlicer"
echo -e "flatpak uninstall --user io.github.orcaslicer.OrcaSlicer"
echo ""
if [[ "$FORCE_CLEAN" != true ]]; then
echo -e "${BLUE}Cache Management:${NC}"
@@ -372,6 +371,3 @@ if [[ "$FORCE_CLEAN" != true ]]; then
echo -e "• To force a clean build: $0 -f"
echo -e "• To clean cache manually: rm -rf $CACHE_DIR"
fi
elapsed=$SECONDS
printf "\nBuild completed in %dh %dm %ds\n" $((elapsed/3600)) $((elapsed%3600/60)) $((elapsed%60))

View File

@@ -1,6 +1,5 @@
#!/usr/bin/env bash
set -e # Exit immediately if a command exits with a non-zero status.
SECONDS=0
SCRIPT_NAME=$(basename "$0")
SCRIPT_PATH=$(dirname "$(readlink -f "${0}")")
@@ -8,7 +7,7 @@ SCRIPT_PATH=$(dirname "$(readlink -f "${0}")")
pushd "${SCRIPT_PATH}" > /dev/null
function usage() {
echo "Usage: ./${SCRIPT_NAME} [-1][-b][-c][-d][-D][-e][-F][-g][-h][-i][-j N][-p][-r][-s][-t][-u][-l][-L]"
echo "Usage: ./${SCRIPT_NAME} [-1][-b][-c][-d][-D][-e][-h][-i][-j N][-p][-r][-s][-t][-u][-l][-L]"
echo " -1: limit builds to one core (where possible)"
echo " -j N: limit builds to N cores (where possible)"
echo " -b: build in Debug mode"
@@ -17,8 +16,6 @@ function usage() {
echo " -d: download and build dependencies in ./deps/ (build prerequisite)"
echo " -D: dry run"
echo " -e: build in RelWithDebInfo mode"
echo " -F: rebuild the cached Docker/Podman runner image from scratch when used with -g"
echo " -g: run the requested build steps inside a Docker/Podman Ubuntu 24.04 container similar to the GitHub Actions Linux runner"
echo " -h: prints this help text"
echo " -i: build the Orca Slicer AppImage (optional)"
echo " -p: boost ccache hit rate by disabling precompiled headers (default: ON)"
@@ -30,9 +27,6 @@ function usage() {
echo " -L: use ld.lld as linker (if available)"
echo "For a first use, you want to './${SCRIPT_NAME} -u'"
echo " and then './${SCRIPT_NAME} -dsi'"
echo "For a GitHub Actions-like Linux build locally, use './${SCRIPT_NAME} -g -istrlL'"
echo "Use './${SCRIPT_NAME} -gF -istrlL' to rebuild the cached runner image first."
echo "Set ORCA_CONTAINER_CLI, ORCA_DOCKER_IMAGE, ORCA_DOCKER_BASE_IMAGE, or ORCA_DOCKER_CMAKE_VERSION to override the container runtime, cached image tag, base image, or CMake version."
}
SLIC3R_PRECOMPILED_HEADERS="ON"
@@ -40,83 +34,60 @@ SLIC3R_PRECOMPILED_HEADERS="ON"
unset name
BUILD_DIR=build
BUILD_CONFIG=Release
FORWARDED_ARGS=()
while getopts ":1j:bcCdDeFghiprstulL" opt ; do
while getopts ":1j:bcCdDehiprstulL" opt ; do
case ${opt} in
1 )
export CMAKE_BUILD_PARALLEL_LEVEL=1
FORWARDED_ARGS+=("-1")
;;
j )
export CMAKE_BUILD_PARALLEL_LEVEL=$OPTARG
FORWARDED_ARGS+=("-j" "$OPTARG")
;;
b )
BUILD_DIR=build-dbg
BUILD_CONFIG=Debug
FORWARDED_ARGS+=("-b")
;;
c )
CLEAN_BUILD=1
FORWARDED_ARGS+=("-c")
;;
C )
COLORED_OUTPUT="-DCOLORED_OUTPUT=ON"
FORWARDED_ARGS+=("-C")
;;
d )
BUILD_DEPS="1"
FORWARDED_ARGS+=("-d")
;;
D )
DRY_RUN="1"
FORWARDED_ARGS+=("-D")
;;
e )
BUILD_DIR=build-dbginfo
BUILD_CONFIG=RelWithDebInfo
FORWARDED_ARGS+=("-e")
;;
F )
CLEAN_DOCKER_IMAGE="1"
;;
g )
USE_DOCKER="1"
;;
h ) usage
exit 1
;;
i )
BUILD_IMAGE="1"
FORWARDED_ARGS+=("-i")
;;
p )
SLIC3R_PRECOMPILED_HEADERS="OFF"
FORWARDED_ARGS+=("-p")
;;
r )
SKIP_RAM_CHECK="1"
FORWARDED_ARGS+=("-r")
;;
s )
BUILD_ORCA="1"
FORWARDED_ARGS+=("-s")
;;
t )
BUILD_TESTS="1"
FORWARDED_ARGS+=("-t")
;;
u )
export UPDATE_LIB="1"
FORWARDED_ARGS+=("-u")
;;
l )
USE_CLANG="1"
FORWARDED_ARGS+=("-l")
;;
L )
USE_LLD="1"
FORWARDED_ARGS+=("-L")
;;
* )
echo "Unknown argument '${opt}', aborting."
@@ -130,11 +101,6 @@ if [ ${OPTIND} -eq 1 ] ; then
exit 1
fi
if [[ -n "${CLEAN_DOCKER_IMAGE}" ]] && [[ -z "${USE_DOCKER}" ]] ; then
echo "Error: -F requires -g."
exit 1
fi
function check_available_memory_and_disk() {
FREE_MEM_GB=$(free --gibi --total | grep 'Mem' | rev | cut --delimiter=" " --fields=1 | rev)
MIN_MEM_GB=10
@@ -172,275 +138,6 @@ function print_and_run() {
fi
}
function resolve_container_cli() {
if [[ -n "${ORCA_CONTAINER_CLI}" ]] ; then
if ! command -v "${ORCA_CONTAINER_CLI}" >/dev/null 2>&1 ; then
echo "Error: container runtime '${ORCA_CONTAINER_CLI}' was not found." >&2
exit 1
fi
echo "${ORCA_CONTAINER_CLI}"
return
fi
if command -v docker >/dev/null 2>&1 ; then
echo "docker"
return
fi
if command -v podman >/dev/null 2>&1 ; then
echo "podman"
return
fi
echo "Error: neither docker nor podman is available. Install one of them or set ORCA_CONTAINER_CLI." >&2
exit 1
}
function get_docker_runner_image() {
local base_image
local docker_cmake_version
local recipe_hash
local sanitized_base_image
local sanitized_cmake_version
if [[ -n "${ORCA_DOCKER_IMAGE}" ]] ; then
echo "${ORCA_DOCKER_IMAGE}"
return
fi
base_image="${ORCA_DOCKER_BASE_IMAGE:-ubuntu:24.04}"
docker_cmake_version="${ORCA_DOCKER_CMAKE_VERSION-4.3.0}"
recipe_hash=$(find "${SCRIPT_PATH}/build_linux.sh" "${SCRIPT_PATH}/scripts/linux.d" -type f -print0 | sort -z | xargs -0 cat | sha256sum | cut -c1-12)
sanitized_base_image=$(echo "${base_image}" | tr '/:@' '---' | tr -cs 'A-Za-z0-9_.-' '-')
sanitized_cmake_version=$(echo "${docker_cmake_version:-system}" | tr -cs 'A-Za-z0-9_.-' '-')
echo "orcaslicer-linux-builder:${sanitized_base_image}-cmake-${sanitized_cmake_version}-${recipe_hash}"
}
function docker_runner_dockerfile() {
cat <<'EOF'
ARG BASE_IMAGE=ubuntu:24.04
FROM ${BASE_IMAGE}
ARG CMAKE_VERSION=4.3.0
ENV DEBIAN_FRONTEND=noninteractive
SHELL ["/bin/bash", "-c"]
RUN apt-get update && apt-get install -y sudo ca-certificates curl tar
COPY build_linux.sh /tmp/orcaslicer/build_linux.sh
COPY scripts/linux.d /tmp/orcaslicer/scripts/linux.d
WORKDIR /tmp/orcaslicer
RUN chmod +x ./build_linux.sh
RUN ./build_linux.sh -ur
RUN if [[ -n "${CMAKE_VERSION}" ]] ; then \
case "$(uname -m)" in \
x86_64|amd64) cmake_arch="x86_64" ;; \
aarch64|arm64) cmake_arch="aarch64" ;; \
*) cmake_arch="" ;; \
esac ; \
if [[ -n "${cmake_arch}" ]] ; then \
cmake_root="/opt/cmake-${CMAKE_VERSION}-linux-${cmake_arch}" ; \
if ! curl -fsSL "https://github.com/Kitware/CMake/releases/download/v${CMAKE_VERSION}/cmake-${CMAKE_VERSION}-linux-${cmake_arch}.tar.gz" | tar -xz -C /opt ; then \
echo "Warning: failed to install CMake ${CMAKE_VERSION}; falling back to the distro cmake package." ; \
elif [[ -d "${cmake_root}" ]] ; then \
ln -sf "${cmake_root}/bin/"* /usr/local/bin/ ; \
fi ; \
else \
echo "Skipping GitHub Actions CMake install for unsupported architecture $(uname -m)." ; \
fi ; \
fi
RUN rm -rf /var/lib/apt/lists/* /tmp/orcaslicer
EOF
}
function ensure_docker_runner_image() {
local container_cli
local base_image
local runner_image
local docker_cmake_version
local image_exists="0"
local force_rebuild="0"
local -a build_cmd
container_cli="$1"
runner_image="$2"
base_image="${ORCA_DOCKER_BASE_IMAGE:-ubuntu:24.04}"
docker_cmake_version="${ORCA_DOCKER_CMAKE_VERSION-4.3.0}"
if "${container_cli}" image inspect "${runner_image}" >/dev/null 2>&1 ; then
image_exists="1"
fi
if [[ -n "${CLEAN_DOCKER_IMAGE}" ]] ; then
force_rebuild="1"
if [[ "${image_exists}" == "1" ]] ; then
echo "Removing cached container image ${runner_image} ..."
if [[ -z "${DRY_RUN}" ]] ; then
"${container_cli}" image rm -f "${runner_image}" >/dev/null
else
printf '%q ' "${container_cli}" image rm -f "${runner_image}"
echo
fi
image_exists="0"
fi
fi
if [[ "${image_exists}" == "1" ]] ; then
echo "Using cached container image ${runner_image}"
return
fi
build_cmd=(
"${container_cli}" build --pull
-t "${runner_image}"
--build-arg "BASE_IMAGE=${base_image}"
--build-arg "CMAKE_VERSION=${docker_cmake_version}"
)
if [[ "${force_rebuild}" == "1" ]] ; then
build_cmd+=(--no-cache)
fi
build_cmd+=(-f - "${SCRIPT_PATH}")
printf '%q ' "${build_cmd[@]}"
echo
if [[ -n "${DRY_RUN}" ]] ; then
return
fi
docker_runner_dockerfile | "${build_cmd[@]}"
}
function run_in_docker() {
local container_cli
local runner_image
local container_workspace
local host_uid
local host_gid
local host_user
local -a build_args
local -a container_env
container_cli=$(resolve_container_cli)
runner_image=$(get_docker_runner_image)
host_uid=$(id -u)
host_gid=$(id -g)
host_user="${USER:-orca}"
container_workspace="/__w/OrcaSlicer/OrcaSlicer"
build_args=()
for item in "${FORWARDED_ARGS[@]}" ; do
if [[ "${item}" == "-u" ]] || [[ "${item}" == "-D" ]] ; then
continue
fi
build_args+=("${item}")
done
container_env=(
-e "CI=true"
-e "GITHUB_ACTIONS=true"
-e "GITHUB_WORKSPACE=${container_workspace}"
-e "RUNNER_OS=Linux"
-e "RUNNER_TEMP=/tmp"
-e "HOST_UID=${host_uid}"
-e "HOST_GID=${host_gid}"
-e "HOST_USER=${host_user}"
)
if [[ -n "${CMAKE_BUILD_PARALLEL_LEVEL}" ]] ; then
container_env+=( -e "CMAKE_BUILD_PARALLEL_LEVEL=${CMAKE_BUILD_PARALLEL_LEVEL}" )
fi
if [[ -n "${ORCA_UPDATER_SIG_KEY}" ]] ; then
container_env+=( -e "ORCA_UPDATER_SIG_KEY=${ORCA_UPDATER_SIG_KEY}" )
fi
ensure_docker_runner_image "${container_cli}" "${runner_image}"
printf '%q ' "${container_cli}" run --rm -i \
-v "${SCRIPT_PATH}:${container_workspace}" \
-w "${container_workspace}" \
"${container_env[@]}" \
"${runner_image}" \
bash -s -- "${build_args[@]}"
echo
if [[ -n "${DRY_RUN}" ]] ; then
return
fi
"${container_cli}" run --rm -i \
-v "${SCRIPT_PATH}:${container_workspace}" \
-w "${container_workspace}" \
"${container_env[@]}" \
"${runner_image}" \
bash -s -- "${build_args[@]}" <<'EOF'
set -e
function create_builder_user() {
if [[ "${HOST_UID}" == "0" ]] ; then
HOST_USER=root
return
fi
if getent group "${HOST_GID}" >/dev/null 2>&1 ; then
HOST_GROUP=$(getent group "${HOST_GID}" | cut -d: -f1)
else
HOST_GROUP="orca-builder"
if getent group "${HOST_GROUP}" >/dev/null 2>&1 ; then
HOST_GROUP="orca-builder-${HOST_GID}"
fi
groupadd -g "${HOST_GID}" "${HOST_GROUP}"
fi
if getent passwd "${HOST_UID}" >/dev/null 2>&1 ; then
HOST_USER=$(getent passwd "${HOST_UID}" | cut -d: -f1)
usermod -g "${HOST_GROUP}" "${HOST_USER}"
elif id -u "${HOST_USER}" >/dev/null 2>&1 ; then
usermod -u "${HOST_UID}" -g "${HOST_GROUP}" "${HOST_USER}"
else
useradd -m -u "${HOST_UID}" -g "${HOST_GROUP}" -s /bin/bash "${HOST_USER}"
fi
echo "${HOST_USER} ALL=(ALL) NOPASSWD:ALL" >/etc/sudoers.d/orcaslicer-builder
chmod 0440 /etc/sudoers.d/orcaslicer-builder
}
create_builder_user
mkdir -p "${GITHUB_WORKSPACE}/deps/build/destdir"
chown -R "${HOST_UID}:${HOST_GID}" "${GITHUB_WORKSPACE}/deps/build"
if [[ -d "${GITHUB_WORKSPACE}/build" ]] ; then
chown -R "${HOST_UID}:${HOST_GID}" "${GITHUB_WORKSPACE}/build"
fi
if [[ -d "${GITHUB_WORKSPACE}/build-dbg" ]] ; then
chown -R "${HOST_UID}:${HOST_GID}" "${GITHUB_WORKSPACE}/build-dbg"
fi
if [[ -d "${GITHUB_WORKSPACE}/build-dbginfo" ]] ; then
chown -R "${HOST_UID}:${HOST_GID}" "${GITHUB_WORKSPACE}/build-dbginfo"
fi
sudo -H -u "${HOST_USER}" env \
CMAKE_BUILD_PARALLEL_LEVEL="${CMAKE_BUILD_PARALLEL_LEVEL-}" \
GITHUB_WORKSPACE="${GITHUB_WORKSPACE}" \
ORCA_UPDATER_SIG_KEY="${ORCA_UPDATER_SIG_KEY-}" \
bash -c '
set -e
cd "${GITHUB_WORKSPACE}"
if [[ "$#" -gt 0 ]] ; then
./build_linux.sh "$@"
else
echo "No build steps were requested after container setup."
fi
' bash "$@"
EOF
}
if [[ -n "${USE_DOCKER}" ]] ; then
run_in_docker
popd > /dev/null # ${SCRIPT_PATH}
exit 0
fi
# cmake 4.x compatibility workaround
export CMAKE_POLICY_VERSION_MINIMUM=3.5
@@ -454,8 +151,6 @@ elif [[ "${DISTRIBUTION_LIKE}" == *"debian"* ]] || [[ "${DISTRIBUTION_LIKE}" ==
DISTRIBUTION="debian"
elif [[ "${DISTRIBUTION_LIKE}" == *"arch"* ]] ; then
DISTRIBUTION="arch"
elif [[ "${DISTRIBUTION_LIKE}" == *"suse"* ]] ; then
DISTRIBUTION="suse"
fi
if [ ! -f "./scripts/linux.d/${DISTRIBUTION}" ] ; then
@@ -504,15 +199,6 @@ if [[ -n "${USE_LLD}" ]] ; then
fi
fi
# Auto-detect ccache for faster rebuilds
export CMAKE_CCACHE_ARGS=()
if command -v ccache >/dev/null 2>&1 ; then
echo "ccache found at $(command -v ccache), enabling compiler caching..."
export CMAKE_CCACHE_ARGS=(-DCMAKE_C_COMPILER_LAUNCHER=ccache -DCMAKE_CXX_COMPILER_LAUNCHER=ccache)
else
echo "Note: ccache not found. Install ccache for faster rebuilds."
fi
if [[ -n "${BUILD_DEPS}" ]] ; then
echo "Configuring dependencies..."
read -r -a BUILD_ARGS <<< "${DEPS_EXTRA_BUILD_ARGS}"
@@ -526,7 +212,7 @@ if [[ -n "${BUILD_DEPS}" ]] ; then
fi
print_and_run cmake -S deps -B deps/$BUILD_DIR "${CMAKE_C_CXX_COMPILER_CLANG[@]}" "${CMAKE_LLD_LINKER_ARGS[@]}" -G Ninja "${COLORED_OUTPUT}" "${BUILD_ARGS[@]}"
print_and_run cmake --build deps/$BUILD_DIR -j1
print_and_run cmake --build deps/$BUILD_DIR
fi
if [[ -n "${BUILD_ORCA}" ]] || [[ -n "${BUILD_TESTS}" ]] ; then
@@ -545,7 +231,7 @@ if [[ -n "${BUILD_ORCA}" ]] || [[ -n "${BUILD_TESTS}" ]] ; then
BUILD_ARGS+=(-DORCA_UPDATER_SIG_KEY="${ORCA_UPDATER_SIG_KEY}")
fi
print_and_run cmake -S . -B $BUILD_DIR "${CMAKE_C_CXX_COMPILER_CLANG[@]}" "${CMAKE_LLD_LINKER_ARGS[@]}" "${CMAKE_CCACHE_ARGS[@]}" -G "Ninja Multi-Config" \
print_and_run cmake -S . -B $BUILD_DIR "${CMAKE_C_CXX_COMPILER_CLANG[@]}" "${CMAKE_LLD_LINKER_ARGS[@]}" -G "Ninja Multi-Config" \
-DSLIC3R_PCH=${SLIC3R_PRECOMPILED_HEADERS} \
-DORCA_TOOLS=ON \
"${COLORED_OUTPUT}" \
@@ -580,7 +266,4 @@ if [[ -n "${BUILD_IMAGE}" || -n "${BUILD_ORCA}" ]] ; then
popd > /dev/null # build
fi
elapsed=$SECONDS
printf "\nBuild completed in %dh %dm %ds\n" $((elapsed/3600)) $((elapsed%3600/60)) $((elapsed%60))
popd > /dev/null # ${SCRIPT_PATH}

View File

@@ -2,9 +2,8 @@
set -e
set -o pipefail
SECONDS=0
while getopts ":dpa:snt:xbc:i:1Tuh" opt; do
while getopts ":dpa:snt:xbc:1Tuh" opt; do
case "${opt}" in
d )
export BUILD_TARGET="deps"
@@ -35,9 +34,6 @@ while getopts ":dpa:snt:xbc:i:1Tuh" opt; do
c )
export BUILD_CONFIG="$OPTARG"
;;
i )
export CMAKE_IGNORE_PREFIX_PATH="${CMAKE_IGNORE_PREFIX_PATH:+$CMAKE_IGNORE_PREFIX_PATH;}$OPTARG"
;;
1 )
export CMAKE_BUILD_PARALLEL_LEVEL=1
;;
@@ -57,7 +53,6 @@ while getopts ":dpa:snt:xbc:i:1Tuh" opt; do
echo " -x: Use Ninja Multi-Config CMake generator, default is Xcode"
echo " -b: Build without reconfiguring CMake"
echo " -c: Set CMake build configuration, default is Release"
echo " -i: Add a prefix to ignore during CMake dependency discovery (repeatable), defaults to /opt/local:/usr/local:/opt/homebrew"
echo " -1: Use single job for building"
echo " -T: Build and run tests"
exit 0
@@ -98,10 +93,6 @@ if [ -z "$OSX_DEPLOYMENT_TARGET" ]; then
export OSX_DEPLOYMENT_TARGET="11.3"
fi
if [ -z "$CMAKE_IGNORE_PREFIX_PATH" ]; then
export CMAKE_IGNORE_PREFIX_PATH="/opt/local:/usr/local:/opt/homebrew"
fi
CMAKE_VERSION=$(cmake --version | head -1 | sed 's/[^0-9]*\([0-9]*\).*/\1/')
if [ "$CMAKE_VERSION" -ge 4 ] 2>/dev/null; then
export CMAKE_POLICY_VERSION_MINIMUM=3.5
@@ -117,7 +108,6 @@ echo " - BUILD_CONFIG: $BUILD_CONFIG"
echo " - BUILD_TARGET: $BUILD_TARGET"
echo " - CMAKE_GENERATOR: $SLICER_CMAKE_GENERATOR for Slicer, $DEPS_CMAKE_GENERATOR for deps"
echo " - OSX_DEPLOYMENT_TARGET: $OSX_DEPLOYMENT_TARGET"
echo " - CMAKE_IGNORE_PREFIX_PATH: $CMAKE_IGNORE_PREFIX_PATH"
echo
# if which -s brew; then
@@ -161,7 +151,6 @@ function build_deps() {
-DCMAKE_BUILD_TYPE="$BUILD_CONFIG" \
-DCMAKE_OSX_ARCHITECTURES:STRING="${_ARCH}" \
-DCMAKE_OSX_DEPLOYMENT_TARGET="${OSX_DEPLOYMENT_TARGET}" \
-DCMAKE_IGNORE_PREFIX_PATH="${CMAKE_IGNORE_PREFIX_PATH}" \
${CMAKE_POLICY_COMPAT}
fi
cmake --build . --config "$BUILD_CONFIG" --target deps
@@ -203,7 +192,6 @@ function build_slicer() {
-DCMAKE_BUILD_TYPE="$BUILD_CONFIG" \
-DCMAKE_OSX_ARCHITECTURES="${_ARCH}" \
-DCMAKE_OSX_DEPLOYMENT_TARGET="${OSX_DEPLOYMENT_TARGET}" \
-DCMAKE_IGNORE_PREFIX_PATH="${CMAKE_IGNORE_PREFIX_PATH}" \
${CMAKE_POLICY_COMPAT}
fi
cmake --build . --config "$BUILD_CONFIG" --target "$SLICER_BUILD_TARGET"
@@ -343,6 +331,3 @@ fi
if [ "1." == "$PACK_DEPS". ]; then
pack_deps
fi
elapsed=$SECONDS
printf "\nBuild completed in %dh %dm %ds\n" $((elapsed/3600)) $((elapsed%3600/60)) $((elapsed%60))

View File

@@ -1,7 +1,6 @@
@REM OrcaSlicer build script for Windows with VS auto-detect
@echo off
set WP=%CD%
set _START_TIME=%TIME%
@REM Check for Ninja Multi-Config option (-x)
set USE_NINJA=0
@@ -74,7 +73,7 @@ if "%1"=="pack" (
echo packing deps: OrcaSlicer_dep_win64_!build_date!_vs!VS_VERSION!.zip
%WP%/tools/7z.exe a OrcaSlicer_dep_win64_!build_date!_vs!VS_VERSION!.zip OrcaSlicer_dep
goto :done
exit /b 0
)
set debug=OFF
@@ -121,7 +120,7 @@ if "%USE_NINJA%"=="1" (
)
@echo off
if "%1"=="deps" goto :done
if "%1"=="deps" exit /b 0
:slicer
echo "building Orca Slicer..."
@@ -143,16 +142,3 @@ cd ..
call scripts/run_gettext.bat
cd %build_dir%
cmake --build . --target install --config %build_type%
:done
@echo off
for /f "tokens=1-3 delims=:.," %%a in ("%_START_TIME: =0%") do set /a "_start_s=%%a*3600+%%b*60+%%c"
for /f "tokens=1-3 delims=:.," %%a in ("%TIME: =0%") do set /a "_end_s=%%a*3600+%%b*60+%%c"
set /a "_elapsed=_end_s - _start_s"
if %_elapsed% lss 0 set /a "_elapsed+=86400"
set /a "_hours=_elapsed / 3600"
set /a "_remainder=_elapsed - _hours * 3600"
set /a "_mins=_remainder / 60"
set /a "_secs=_remainder - _mins * 60"
echo.
echo Build completed in %_hours%h %_mins%m %_secs%s

View File

@@ -0,0 +1,86 @@
# - Try to find Eigen3 lib
#
# This module supports requiring a minimum version, e.g. you can do
# find_package(Eigen3 3.1.2)
# to require version 3.1.2 or newer of Eigen3.
#
# Once done this will define
#
# EIGEN3_FOUND - system has eigen lib with correct version
# EIGEN3_INCLUDE_DIR - the eigen include directory
# EIGEN3_VERSION - eigen version
# Copyright (c) 2006, 2007 Montel Laurent, <montel@kde.org>
# Copyright (c) 2008, 2009 Gael Guennebaud, <g.gael@free.fr>
# Copyright (c) 2009 Benoit Jacob <jacob.benoit.1@gmail.com>
# Redistribution and use is allowed according to the terms of the 2-clause BSD license.
if(NOT Eigen3_FIND_VERSION)
if(NOT Eigen3_FIND_VERSION_MAJOR)
set(Eigen3_FIND_VERSION_MAJOR 2)
endif(NOT Eigen3_FIND_VERSION_MAJOR)
if(NOT Eigen3_FIND_VERSION_MINOR)
set(Eigen3_FIND_VERSION_MINOR 91)
endif(NOT Eigen3_FIND_VERSION_MINOR)
if(NOT Eigen3_FIND_VERSION_PATCH)
set(Eigen3_FIND_VERSION_PATCH 0)
endif(NOT Eigen3_FIND_VERSION_PATCH)
set(Eigen3_FIND_VERSION "${Eigen3_FIND_VERSION_MAJOR}.${Eigen3_FIND_VERSION_MINOR}.${Eigen3_FIND_VERSION_PATCH}")
endif(NOT Eigen3_FIND_VERSION)
macro(_eigen3_check_version)
file(READ "${EIGEN3_INCLUDE_DIR}/Eigen/src/Core/util/Macros.h" _eigen3_version_header)
string(REGEX MATCH "define[ \t]+EIGEN_WORLD_VERSION[ \t]+([0-9]+)" _eigen3_world_version_match "${_eigen3_version_header}")
set(EIGEN3_WORLD_VERSION "${CMAKE_MATCH_1}")
string(REGEX MATCH "define[ \t]+EIGEN_MAJOR_VERSION[ \t]+([0-9]+)" _eigen3_major_version_match "${_eigen3_version_header}")
set(EIGEN3_MAJOR_VERSION "${CMAKE_MATCH_1}")
string(REGEX MATCH "define[ \t]+EIGEN_MINOR_VERSION[ \t]+([0-9]+)" _eigen3_minor_version_match "${_eigen3_version_header}")
set(EIGEN3_MINOR_VERSION "${CMAKE_MATCH_1}")
set(EIGEN3_VERSION ${EIGEN3_WORLD_VERSION}.${EIGEN3_MAJOR_VERSION}.${EIGEN3_MINOR_VERSION})
if(${EIGEN3_VERSION} VERSION_LESS ${Eigen3_FIND_VERSION})
set(EIGEN3_VERSION_OK FALSE)
else(${EIGEN3_VERSION} VERSION_LESS ${Eigen3_FIND_VERSION})
set(EIGEN3_VERSION_OK TRUE)
endif(${EIGEN3_VERSION} VERSION_LESS ${Eigen3_FIND_VERSION})
if(NOT EIGEN3_VERSION_OK)
message(STATUS "Eigen3 version ${EIGEN3_VERSION} found in ${EIGEN3_INCLUDE_DIR}, "
"but at least version ${Eigen3_FIND_VERSION} is required")
endif(NOT EIGEN3_VERSION_OK)
endmacro(_eigen3_check_version)
if (EIGEN3_INCLUDE_DIR)
# in cache already
_eigen3_check_version()
set(EIGEN3_FOUND ${EIGEN3_VERSION_OK})
else (EIGEN3_INCLUDE_DIR)
# specific additional paths for some OS
if (WIN32)
set(EIGEN_ADDITIONAL_SEARCH_PATHS ${EIGEN_ADDITIONAL_SEARCH_PATHS} "C:/Program Files/Eigen/include" "C:/Program Files (x86)/Eigen/include")
endif(WIN32)
find_path(EIGEN3_INCLUDE_DIR NAMES signature_of_eigen3_matrix_library
PATHS
${CMAKE_INSTALL_PREFIX}/include
${EIGEN_ADDITIONAL_SEARCH_PATHS}
${KDE4_INCLUDE_DIR}
PATH_SUFFIXES eigen3 eigen
)
if(EIGEN3_INCLUDE_DIR)
_eigen3_check_version()
endif(EIGEN3_INCLUDE_DIR)
include(FindPackageHandleStandardArgs)
find_package_handle_standard_args(Eigen3 DEFAULT_MSG EIGEN3_INCLUDE_DIR EIGEN3_VERSION_OK)
mark_as_advanced(EIGEN3_INCLUDE_DIR)
endif(EIGEN3_INCLUDE_DIR)

View File

@@ -11,7 +11,7 @@
<key>CFBundleIconFile</key>
<string>${MACOSX_BUNDLE_ICON_FILE}</string>
<key>CFBundleIdentifier</key>
<string>com.orcaslicer.OrcaSlicer</string>
<string>com.softfever3d.orca-slicer</string>
<key>CFBundleInfoDictionaryVersion</key>
<string>6.0</string>
<key>CFBundleLongVersionString</key>

View File

@@ -18,7 +18,6 @@ orcaslicer_add_cmake_project(Boost
-DBOOST_EXCLUDE_LIBRARIES:STRING=contract|fiber|numpy|stacktrace|wave|test
-DBOOST_LOCALE_ENABLE_ICU:BOOL=OFF # do not link to libicu, breaks compatibility between distros
-DBUILD_TESTING:BOOL=OFF
-DBOOST_IOSTREAMS_ENABLE_BZIP2:BOOL=OFF # avoid libbz2 soname differences in AppImage builds
-DBOOST_IOSTREAMS_ENABLE_ZSTD:BOOL=OFF
"${_context_abi_line}"
"${_context_arch_line}"

View File

@@ -9,7 +9,7 @@ orcaslicer_add_cmake_project(
# For whatever reason, this keeps downloading forever (repeats downloads if finished)
URL https://github.com/CGAL/cgal/releases/download/v5.6.3/CGAL-5.6.3.zip
URL_HASH SHA256=5d577acb4a9918ccb960491482da7a3838f8d363aff47e14d703f19fd84733d4
DEPENDS dep_Boost dep_Eigen dep_GMP dep_MPFR
DEPENDS dep_Boost dep_GMP dep_MPFR
)
include(GNUInstallDirs)

48
deps/CMakeLists.txt vendored
View File

@@ -134,7 +134,7 @@ if (APPLE)
endif ()
# On developer machines, it can be enabled to speed up compilation and suppress warnings coming from IGL.
# On developer machines, it can be enabled to speed up compilation and suppress warnings coming from IGL.
# FIXME:
# Enabling this option is not safe. IGL will compile itself with its own version of Eigen while
# Slic3r compiles with a different version which will cause runtime errors.
@@ -146,7 +146,7 @@ message(STATUS "OrcaSlicer deps debug build: ${DEP_DEBUG}")
find_package(Git REQUIRED)
# The default command line for patching. Only works for newer
# The default command line for patching. Only works for newer
set(PATCH_CMD ${GIT_EXECUTABLE} apply --verbose --ignore-space-change --whitespace=fix)
if (NOT _is_multi AND NOT CMAKE_BUILD_TYPE)
@@ -166,18 +166,11 @@ function(orcaslicer_add_cmake_project projectname)
endif ()
endif ()
set(_gen "")
set(_build_j "-j${NPROC}")
if (MSVC)
set(_gen CMAKE_GENERATOR "${DEP_MSVC_GEN}" CMAKE_GENERATOR_PLATFORM "${DEP_PLATFORM}")
else()
set(_gen "")
endif()
if ($ENV{CMAKE_BUILD_PARALLEL_LEVEL})
set(_build_j "") # assume environment will control --build parallel setting
elseif(MSVC)
set(_build_j "/m")
else()
set(_build_j "-j${NPROC}")
set(_gen CMAKE_GENERATOR "${DEP_MSVC_GEN}" CMAKE_GENERATOR_PLATFORM "${DEP_PLATFORM}")
set(_build_j "/m")
endif ()
if (NOT IS_CROSS_COMPILE OR NOT APPLE)
@@ -192,14 +185,10 @@ if (NOT IS_CROSS_COMPILE OR NOT APPLE)
-DCMAKE_INSTALL_PREFIX:STRING=${DESTDIR}
-DCMAKE_MODULE_PATH:STRING=${PROJECT_SOURCE_DIR}/../cmake/modules
-DCMAKE_PREFIX_PATH:STRING=${DESTDIR}
-DCMAKE_IGNORE_PREFIX_PATH:STRING=${CMAKE_IGNORE_PREFIX_PATH}
-DCMAKE_DEBUG_POSTFIX:STRING=d
-DCMAKE_C_COMPILER:STRING=${CMAKE_C_COMPILER}
-DCMAKE_CXX_COMPILER:STRING=${CMAKE_CXX_COMPILER}
-DCMAKE_TOOLCHAIN_FILE:STRING=${CMAKE_TOOLCHAIN_FILE}
-DCMAKE_EXE_LINKER_FLAGS:STRING=${CMAKE_EXE_LINKER_FLAGS}
-DCMAKE_SHARED_LINKER_FLAGS:STRING=${CMAKE_SHARED_LINKER_FLAGS}
-DCMAKE_MODULE_LINKER_FLAGS:STRING=${CMAKE_MODULE_LINKER_FLAGS}
-DBUILD_SHARED_LIBS:BOOL=OFF
${_cmake_osx_arch}
"${_configs_line}"
@@ -240,7 +229,6 @@ else()
-DCMAKE_POLICY_VERSION_MINIMUM=3.5
-DCMAKE_INSTALL_PREFIX:STRING=${DESTDIR}
-DCMAKE_PREFIX_PATH:STRING=${DESTDIR}
-DCMAKE_IGNORE_PREFIX_PATH:STRING=${CMAKE_IGNORE_PREFIX_PATH}
-DBUILD_SHARED_LIBS:BOOL=OFF
${_cmake_osx_arch}
"${_configs_line}"
@@ -248,7 +236,7 @@ else()
${P_ARGS_CMAKE_ARGS}
${P_ARGS_UNPARSED_ARGUMENTS}
BUILD_COMMAND ${CMAKE_COMMAND} --build . --config Release -- ${_build_j}
INSTALL_COMMAND ${CMAKE_COMMAND} --build . --target install --config Release
INSTALL_COMMAND ${CMAKE_COMMAND} --build . --target install --config Release
)
endif()
@@ -265,12 +253,8 @@ if (MSVC)
message(STATUS "\nDetected X86 compiler => building X86 deps bundle\n")
set(DEPS_ARCH "x86")
include("deps-windows.cmake")
elseif ("${CMAKE_SYSTEM_PROCESSOR}" STREQUAL "ARM64")
message(STATUS "\nDetected ARM64 compiler => building ARM64 deps bundle\n")
set(DEPS_ARCH "arm64")
include("deps-windows.cmake")
else ()
message(FATAL_ERROR "Unable to detect architecture: ${CMAKE_SYSTEM_PROCESSOR}")
message(FATAL_ERROR "Unable to detect architecture")
endif ()
elseif (APPLE)
message("OS X SDK Path: ${CMAKE_OSX_SYSROOT}")
@@ -312,26 +296,26 @@ if(FLATPAK)
endif()
set(ZLIB_PKG "")
if (NOT ZLIB_FOUND)
if (NOT ZLIB_FOUND)
include(ZLIB/ZLIB.cmake)
set(ZLIB_PKG dep_ZLIB)
endif ()
set(PNG_PKG "")
if (NOT PNG_FOUND)
if (NOT PNG_FOUND)
include(PNG/PNG.cmake)
set(PNG_PKG dep_PNG)
endif ()
set(EXPAT_PKG "")
find_package(EXPAT)
if (NOT EXPAT_FOUND)
if (NOT EXPAT_FOUND)
include(EXPAT/EXPAT.cmake)
set(EXPAT_PKG dep_EXPAT)
endif ()
# The order of includes respects the dependencies between libraries
set(DEP_Boost_COMPONENTS system iostreams filesystem thread log locale regex date_time)
include(Boost/Boost.cmake)
# The order of includes respects the dependencies between libraries
include(Cereal/Cereal.cmake)
include(Qhull/Qhull.cmake)
include(GLEW/GLEW.cmake)
@@ -347,7 +331,6 @@ include(OpenVDB/OpenVDB.cmake)
include(GMP/GMP.cmake)
include(MPFR/MPFR.cmake)
include(Eigen/Eigen.cmake)
include(CGAL/CGAL.cmake)
include(NLopt/NLopt.cmake)
@@ -359,7 +342,7 @@ include(Draco/Draco.cmake)
# I *think* 1.1 is used for *just* md5 hashing?
# 3.1 has everything in the right place, but the md5 funcs used are deprecated
# a grep across the repo shows it is used for other things
# TODO: update openssl and everything that uses <openssl/md5.h>
# TODO: update openssl and everything that uses <openssl/md5.h>
set(OPENSSL_PKG "")
if(NOT OPENSSL_FOUND)
include(OpenSSL/OpenSSL.cmake)
@@ -367,7 +350,7 @@ if(NOT OPENSSL_FOUND)
endif()
# we don't want to load a "wrong" openssl when loading curl
# so, just don't even bother
# so, just don't even bother
# ...i think this is how it works? change if wrong
set(CURL_PKG "")
if (NOT OPENSSL_FOUND OR NOT CURL_FOUND)
@@ -391,7 +374,7 @@ endif()
set(FREETYPE_PKG "")
if(NOT FREETYPE_FOUND)
include(FREETYPE/FREETYPE.cmake)
set(FREETYPE_PKG "dep_FREETYPE")
set(FREETYPE_PKG "dep_FREETYPE")
endif()
execute_process(
@@ -423,7 +406,6 @@ set(_dep_list
dep_OpenVDB
dep_OpenCSG
dep_OpenCV
dep_Eigen
dep_CGAL
dep_GLFW
dep_OCCT

View File

@@ -1,5 +0,0 @@
orcaslicer_add_cmake_project(Eigen
URL https://gitlab.com/libeigen/eigen/-/archive/5.0.1/eigen-5.0.1.zip
URL_HASH SHA256=0dbb1f9e3aaad66f352c03227d8c983f6f0b49e0b07e71a7300f4abcc01aee12
DEPENDS dep_Boost dep_GMP dep_MPFR
)

16
deps/GLFW/GLFW.cmake vendored
View File

@@ -6,20 +6,22 @@ else()
set(_build_static ON)
endif()
set(_glfw_platform_args "")
if(CMAKE_SYSTEM_NAME STREQUAL "Linux")
set(_glfw_platform_args -DGLFW_BUILD_WAYLAND=ON -DGLFW_BUILD_X11=ON)
set(_glfw_use_wayland "-DGLFW_USE_WAYLAND=ON")
else()
set(_glfw_use_wayland "-DGLFW_USE_WAYLAND=FF")
endif()
orcaslicer_add_cmake_project(GLFW
URL https://github.com/glfw/glfw/archive/refs/tags/3.4.zip
URL_HASH SHA256=a133ddc3d3c66143eba9035621db8e0bcf34dba1ee9514a9e23e96afd39fd57a
URL https://github.com/glfw/glfw/archive/refs/tags/3.3.7.zip
URL_HASH SHA256=e02d956935e5b9fb4abf90e2c2e07c9a0526d7eacae8ee5353484c69a2a76cd0
#DEPENDS dep_Boost
CMAKE_ARGS
-DBUILD_SHARED_LIBS=${_build_shared}
-DBUILD_SHARED_LIBS=${_build_shared}
-DGLFW_BUILD_DOCS=OFF
-DGLFW_BUILD_EXAMPLES=OFF
-DGLFW_BUILD_TESTS=OFF
${_glfw_platform_args}
-DGLFW_BUILD_TESTS=OFF
${_glfw_use_wayland}
)
if (MSVC)

2
deps/GMP/GMP.cmake vendored
View File

@@ -65,7 +65,7 @@ else ()
DOWNLOAD_DIR ${DEP_DOWNLOAD_DIR}/GMP
PATCH_COMMAND git apply ${GMP_DIRECTORY_FLAG} --verbose ${CMAKE_CURRENT_LIST_DIR}/0001-GMP_GCC15.patch
BUILD_IN_SOURCE ON
CONFIGURE_COMMAND env "CC=${CMAKE_C_COMPILER}" "CXX=${CMAKE_CXX_COMPILER}" "CFLAGS=${_gmp_ccflags}" "CXXFLAGS=${_gmp_ccflags}" "LDFLAGS=${CMAKE_EXE_LINKER_FLAGS}" ./configure ${_cross_compile_arg} --enable-shared=no --enable-cxx=yes --enable-static=yes "--prefix=${DESTDIR}" ${_gmp_build_tgt}
CONFIGURE_COMMAND env "CFLAGS=${_gmp_ccflags}" "CXXFLAGS=${_gmp_ccflags}" ./configure ${_cross_compile_arg} --enable-shared=no --enable-cxx=yes --enable-static=yes "--prefix=${DESTDIR}" ${_gmp_build_tgt}
BUILD_COMMAND make -j
INSTALL_COMMAND make install
)

View File

@@ -31,7 +31,7 @@ else ()
DOWNLOAD_DIR ${DEP_DOWNLOAD_DIR}/MPFR
BUILD_IN_SOURCE ON
CONFIGURE_COMMAND autoreconf -f -i &&
env "CC=${CMAKE_C_COMPILER}" "CXX=${CMAKE_CXX_COMPILER}" "CFLAGS=${_gmp_ccflags}" "CXXFLAGS=${_gmp_ccflags}" "LDFLAGS=${CMAKE_EXE_LINKER_FLAGS}" ./configure ${_cross_compile_arg} --prefix=${DESTDIR} --enable-shared=no --enable-static=yes --with-gmp=${DESTDIR} ${_gmp_build_tgt}
env "CFLAGS=${_gmp_ccflags}" "CXXFLAGS=${_gmp_ccflags}" ./configure ${_cross_compile_arg} --prefix=${DESTDIR} --enable-shared=no --enable-static=yes --with-gmp=${DESTDIR} ${_gmp_build_tgt}
BUILD_COMMAND make -j
INSTALL_COMMAND make install
DEPENDS dep_GMP

View File

@@ -16,7 +16,6 @@ orcaslicer_add_cmake_project(OCCT
#DEPENDS dep_Boost
DEPENDS ${FREETYPE_PKG}
CMAKE_ARGS
-DCMAKE_CXX_STANDARD=17
-DBUILD_LIBRARY_TYPE=${library_build_type}
-DUSE_TK=OFF
-DUSE_TBB=OFF

View File

@@ -55,8 +55,6 @@ orcaslicer_add_cmake_project(OpenCV
-DWITH_VTK=OFF
-DWITH_JPEG=OFF
-DWITH_WEBP=OFF
-DWITH_TIFF=OFF
-DBUILD_TIFF=OFF
-DENABLE_PRECOMPILED_HEADERS=OFF
-DINSTALL_TESTS=OFF
-DINSTALL_C_EXAMPLES=OFF

View File

@@ -21,7 +21,7 @@ else()
if(APPLE)
set(_conf_cmd export MACOSX_DEPLOYMENT_TARGET=${CMAKE_OSX_DEPLOYMENT_TARGET} && ./Configure -mmacosx-version-min=${CMAKE_OSX_DEPLOYMENT_TARGET})
else()
set(_conf_cmd env "CC=${CMAKE_C_COMPILER}" "LDFLAGS=${CMAKE_EXE_LINKER_FLAGS}" "./config")
set(_conf_cmd "./config")
endif()
set(_cross_comp_prefix_line "")
set(_make_cmd make -j${NPROC})

2
deps/TBB/TBB.cmake vendored
View File

@@ -1,4 +1,4 @@
if (FLATPAK AND "${CMAKE_CXX_COMPILER_ID}" STREQUAL "GNU")
if (FLATPAK)
set(_patch_command ${CMAKE_COMMAND} -E copy ${CMAKE_CURRENT_LIST_DIR}/GNU.cmake ./cmake/compilers/GNU.cmake)
else()
set(_patch_command "")

View File

@@ -36,10 +36,8 @@ if ("${DEPS_ARCH}" STREQUAL "x86")
set(DEP_PLATFORM "Win32")
elseif ("${DEPS_ARCH}" STREQUAL "x64")
set(DEP_PLATFORM "x64")
elseif ("${DEPS_ARCH}" STREQUAL "arm64")
set(DEP_PLATFORM "ARM64")
else ()
message(FATAL_ERROR "Unsupported OS architecture: ${DEPS_ARCH}")
message(FATAL_ERROR "Unsupported OS architecture")
endif ()
if (${DEP_DEBUG})
@@ -66,11 +64,8 @@ if ("${DEPS_ARCH}" STREQUAL "x86")
elseif ("${DEPS_ARCH}" STREQUAL "x64")
set(DEP_WXWIDGETS_TARGET "TARGET_CPU=X64")
set(DEP_WXWIDGETS_LIBDIR "vc_x64_lib")
elseif ("${DEPS_ARCH}" STREQUAL "arm64")
set(DEP_WXWIDGETS_TARGET "TARGET_CPU=ARM64")
set(DEP_WXWIDGETS_LIBDIR "vc_arm64_lib")
else ()
message(FATAL_ERROR "Unsupported OS architecture: ${DEPS_ARCH}")
message(FATAL_ERROR "Unsupported OS architecture")
endif ()
find_package(Git REQUIRED)

View File

@@ -21,13 +21,21 @@ else ()
set(_wx_edge "-DwxUSE_WEBVIEW_EDGE=OFF")
endif ()
set(_wx_opengl_override "")
if(APPLE AND CMAKE_VERSION VERSION_GREATER_EQUAL "4.0")
set(_wx_opengl_override
-DOPENGL_gl_LIBRARY="-framework OpenGL"
-DOPENGL_glu_LIBRARY="-framework OpenGL"
)
endif()
orcaslicer_add_cmake_project(
wxWidgets
GIT_REPOSITORY "https://github.com/SoftFever/Orca-deps-wxWidgets"
GIT_TAG v3.3.2
GIT_SHALLOW ON
DEPENDS ${PNG_PKG} ${ZLIB_PKG} ${EXPAT_PKG} ${JPEG_PKG}
CMAKE_ARGS
${_wx_opengl_override}
-DwxBUILD_PRECOMP=ON
${_wx_toolkit}
"-DCMAKE_DEBUG_POSTFIX:STRING=${_wx_debug_postfix}"
@@ -36,9 +44,10 @@ orcaslicer_add_cmake_project(
${_wx_shared}
-DwxUSE_MEDIACTRL=ON
-DwxUSE_DETECT_SM=OFF
-DwxUSE_UNICODE=ON
-DwxUSE_PRIVATE_FONTS=ON
-DwxUSE_OPENGL=ON
-DwxUSE_GLCANVAS_EGL=ON
-DwxUSE_GLCANVAS_EGL=OFF
-DwxUSE_WEBREQUEST=ON
-DwxUSE_WEBVIEW=ON
${_wx_edge}
@@ -52,31 +61,7 @@ orcaslicer_add_cmake_project(
-DwxUSE_ZLIB=sys
-DwxUSE_LIBJPEG=sys
-DwxUSE_LIBTIFF=OFF
-DwxUSE_LIBWEBP=builtin
-DwxUSE_EXPAT=sys
-DwxUSE_NANOSVG=OFF
)
# wxWidgets 3.3 cmake install doesn't include private headers.
# OrcaSlicer uses some of the private headers (for accessibility support).
# Copy the private headers directory after install.
if(MSVC)
set(_wx_inc_dest ${DESTDIR}/include/wx)
else()
set(_wx_inc_dest ${DESTDIR}/include/wx-3.3/wx)
endif()
ExternalProject_Add_Step(dep_wxWidgets copy_private_headers
DEPENDEES install
COMMENT "Copying wxWidgets private headers"
COMMAND ${CMAKE_COMMAND} -E copy_directory
<SOURCE_DIR>/include/wx/private
${_wx_inc_dest}/private
COMMAND ${CMAKE_COMMAND} -E copy_directory
<SOURCE_DIR>/include/wx/generic/private
${_wx_inc_dest}/generic/private
COMMAND ${CMAKE_COMMAND} -E copy_directory
<SOURCE_DIR>/include/wx/gtk/private
${_wx_inc_dest}/gtk/private
)
if (MSVC)

View File

@@ -32,3 +32,6 @@ add_subdirectory(minilzo)
add_subdirectory(qhull)
add_subdirectory(qoi)
add_subdirectory(semver) # Semver static library
# Eigen header-only library
add_subdirectory(eigen)

View File

@@ -18,6 +18,6 @@ target_include_directories(admesh SYSTEM
)
target_link_libraries(admesh
PUBLIC Eigen3::Eigen
PRIVATE boost_headeronly
PUBLIC eigen
)

View File

@@ -15,6 +15,6 @@ target_include_directories(clipper SYSTEM
)
target_link_libraries(clipper
PUBLIC Eigen3::Eigen
PUBLIC eigen
PRIVATE TBB::tbb TBB::tbbmalloc
)

View File

@@ -0,0 +1,12 @@
cmake_minimum_required(VERSION 3.13)
project(eigen)
add_library(eigen INTERFACE)
target_include_directories(eigen SYSTEM
INTERFACE
${CMAKE_CURRENT_SOURCE_DIR}
)
# Eigen is header-only, so we only need to specify the include directory
# The headers are in the Eigen/ subdirectory structure

View File

@@ -0,0 +1,18 @@
Eigen is primarily MPL2 licensed. See COPYING.MPL2 and these links:
http://www.mozilla.org/MPL/2.0/
http://www.mozilla.org/MPL/2.0/FAQ.html
Some files contain third-party code under BSD or LGPL licenses, whence the other
COPYING.* files here.
All the LGPL code is either LGPL 2.1-only, or LGPL 2.1-or-later.
For this reason, the COPYING.LGPL file contains the LGPL 2.1 text.
If you want to guarantee that the Eigen code that you are #including is licensed
under the MPL2 and possibly more permissive licenses (like BSD), #define this
preprocessor symbol:
EIGEN_MPL2_ONLY
For example, with most compilers, you could add this to your project CXXFLAGS:
-DEIGEN_MPL2_ONLY
This will cause a compilation error to be generated if you #include any code that is
LGPL licensed.

View File

@@ -0,0 +1,19 @@
include(RegexUtils)
test_escape_string_as_regex()
file(GLOB Eigen_directory_files "*")
escape_string_as_regex(ESCAPED_CMAKE_CURRENT_SOURCE_DIR "${CMAKE_CURRENT_SOURCE_DIR}")
foreach(f ${Eigen_directory_files})
if(NOT f MATCHES "\\.txt" AND NOT f MATCHES "${ESCAPED_CMAKE_CURRENT_SOURCE_DIR}/[.].+" AND NOT f MATCHES "${ESCAPED_CMAKE_CURRENT_SOURCE_DIR}/src")
list(APPEND Eigen_directory_files_to_install ${f})
endif()
endforeach(f ${Eigen_directory_files})
install(FILES
${Eigen_directory_files_to_install}
DESTINATION ${INCLUDE_INSTALL_DIR}/Eigen COMPONENT Devel
)
install(DIRECTORY src DESTINATION ${INCLUDE_INSTALL_DIR}/Eigen COMPONENT Devel FILES_MATCHING PATTERN "*.h")

View File

@@ -0,0 +1,46 @@
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#ifndef EIGEN_CHOLESKY_MODULE_H
#define EIGEN_CHOLESKY_MODULE_H
#include "Core"
#include "Jacobi"
#include "src/Core/util/DisableStupidWarnings.h"
/** \defgroup Cholesky_Module Cholesky module
*
*
*
* This module provides two variants of the Cholesky decomposition for selfadjoint (hermitian) matrices.
* Those decompositions are also accessible via the following methods:
* - MatrixBase::llt()
* - MatrixBase::ldlt()
* - SelfAdjointView::llt()
* - SelfAdjointView::ldlt()
*
* \code
* #include <Eigen/Cholesky>
* \endcode
*/
#include "src/Cholesky/LLT.h"
#include "src/Cholesky/LDLT.h"
#ifdef EIGEN_USE_LAPACKE
#ifdef EIGEN_USE_MKL
#include "mkl_lapacke.h"
#else
#include "src/misc/lapacke.h"
#endif
#include "src/Cholesky/LLT_LAPACKE.h"
#endif
#include "src/Core/util/ReenableStupidWarnings.h"
#endif // EIGEN_CHOLESKY_MODULE_H
/* vim: set filetype=cpp et sw=2 ts=2 ai: */

View File

@@ -0,0 +1,48 @@
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#ifndef EIGEN_CHOLMODSUPPORT_MODULE_H
#define EIGEN_CHOLMODSUPPORT_MODULE_H
#include "SparseCore"
#include "src/Core/util/DisableStupidWarnings.h"
extern "C" {
#include <cholmod.h>
}
/** \ingroup Support_modules
* \defgroup CholmodSupport_Module CholmodSupport module
*
* This module provides an interface to the Cholmod library which is part of the <a href="http://www.suitesparse.com">suitesparse</a> package.
* It provides the two following main factorization classes:
* - class CholmodSupernodalLLT: a supernodal LLT Cholesky factorization.
* - class CholmodDecomposiiton: a general L(D)LT Cholesky factorization with automatic or explicit runtime selection of the underlying factorization method (supernodal or simplicial).
*
* For the sake of completeness, this module also propose the two following classes:
* - class CholmodSimplicialLLT
* - class CholmodSimplicialLDLT
* Note that these classes does not bring any particular advantage compared to the built-in
* SimplicialLLT and SimplicialLDLT factorization classes.
*
* \code
* #include <Eigen/CholmodSupport>
* \endcode
*
* In order to use this module, the cholmod headers must be accessible from the include paths, and your binary must be linked to the cholmod library and its dependencies.
* The dependencies depend on how cholmod has been compiled.
* For a cmake based project, you can use our FindCholmod.cmake module to help you in this task.
*
*/
#include "src/CholmodSupport/CholmodSupport.h"
#include "src/Core/util/ReenableStupidWarnings.h"
#endif // EIGEN_CHOLMODSUPPORT_MODULE_H

537
deps_src/eigen/Eigen/Core Normal file
View File

@@ -0,0 +1,537 @@
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
// Copyright (C) 2007-2011 Benoit Jacob <jacob.benoit.1@gmail.com>
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#ifndef EIGEN_CORE_H
#define EIGEN_CORE_H
// first thing Eigen does: stop the compiler from committing suicide
#include "src/Core/util/DisableStupidWarnings.h"
#if defined(__CUDACC__) && !defined(EIGEN_NO_CUDA)
#define EIGEN_CUDACC __CUDACC__
#endif
#if defined(__CUDA_ARCH__) && !defined(EIGEN_NO_CUDA)
#define EIGEN_CUDA_ARCH __CUDA_ARCH__
#endif
#if defined(__CUDACC_VER_MAJOR__) && (__CUDACC_VER_MAJOR__ >= 9)
#define EIGEN_CUDACC_VER ((__CUDACC_VER_MAJOR__ * 10000) + (__CUDACC_VER_MINOR__ * 100))
#elif defined(__CUDACC_VER__)
#define EIGEN_CUDACC_VER __CUDACC_VER__
#else
#define EIGEN_CUDACC_VER 0
#endif
// Handle NVCC/CUDA/SYCL
#if defined(__CUDACC__) || defined(__SYCL_DEVICE_ONLY__)
// Do not try asserts on CUDA and SYCL!
#ifndef EIGEN_NO_DEBUG
#define EIGEN_NO_DEBUG
#endif
#ifdef EIGEN_INTERNAL_DEBUGGING
#undef EIGEN_INTERNAL_DEBUGGING
#endif
#ifdef EIGEN_EXCEPTIONS
#undef EIGEN_EXCEPTIONS
#endif
// All functions callable from CUDA code must be qualified with __device__
#ifdef __CUDACC__
// Do not try to vectorize on CUDA and SYCL!
#ifndef EIGEN_DONT_VECTORIZE
#define EIGEN_DONT_VECTORIZE
#endif
#define EIGEN_DEVICE_FUNC __host__ __device__
// We need cuda_runtime.h to ensure that that EIGEN_USING_STD_MATH macro
// works properly on the device side
#include <cuda_runtime.h>
#else
#define EIGEN_DEVICE_FUNC
#endif
#else
#define EIGEN_DEVICE_FUNC
#endif
// When compiling CUDA device code with NVCC, pull in math functions from the
// global namespace. In host mode, and when device doee with clang, use the
// std versions.
#if defined(__CUDA_ARCH__) && defined(__NVCC__)
#define EIGEN_USING_STD_MATH(FUNC) using ::FUNC;
#else
#define EIGEN_USING_STD_MATH(FUNC) using std::FUNC;
#endif
#if (defined(_CPPUNWIND) || defined(__EXCEPTIONS)) && !defined(__CUDA_ARCH__) && !defined(EIGEN_EXCEPTIONS) && !defined(EIGEN_USE_SYCL)
#define EIGEN_EXCEPTIONS
#endif
#ifdef EIGEN_EXCEPTIONS
#include <new>
#endif
// then include this file where all our macros are defined. It's really important to do it first because
// it's where we do all the alignment settings (platform detection and honoring the user's will if he
// defined e.g. EIGEN_DONT_ALIGN) so it needs to be done before we do anything with vectorization.
#include "src/Core/util/Macros.h"
// Disable the ipa-cp-clone optimization flag with MinGW 6.x or newer (enabled by default with -O3)
// See http://eigen.tuxfamily.org/bz/show_bug.cgi?id=556 for details.
#if EIGEN_COMP_MINGW && EIGEN_GNUC_AT_LEAST(4,6)
#pragma GCC optimize ("-fno-ipa-cp-clone")
#endif
#include <complex>
// this include file manages BLAS and MKL related macros
// and inclusion of their respective header files
#include "src/Core/util/MKL_support.h"
// if alignment is disabled, then disable vectorization. Note: EIGEN_MAX_ALIGN_BYTES is the proper check, it takes into
// account both the user's will (EIGEN_MAX_ALIGN_BYTES,EIGEN_DONT_ALIGN) and our own platform checks
#if EIGEN_MAX_ALIGN_BYTES==0
#ifndef EIGEN_DONT_VECTORIZE
#define EIGEN_DONT_VECTORIZE
#endif
#endif
#if EIGEN_COMP_MSVC
#include <malloc.h> // for _aligned_malloc -- need it regardless of whether vectorization is enabled
#if (EIGEN_COMP_MSVC >= 1500) // 2008 or later
// Remember that usage of defined() in a #define is undefined by the standard.
// a user reported that in 64-bit mode, MSVC doesn't care to define _M_IX86_FP.
#if (defined(_M_IX86_FP) && (_M_IX86_FP >= 2)) || EIGEN_ARCH_x86_64
#define EIGEN_SSE2_ON_MSVC_2008_OR_LATER
#endif
#endif
#else
// Remember that usage of defined() in a #define is undefined by the standard
#if (defined __SSE2__) && ( (!EIGEN_COMP_GNUC) || EIGEN_COMP_ICC || EIGEN_GNUC_AT_LEAST(4,2) )
#define EIGEN_SSE2_ON_NON_MSVC_BUT_NOT_OLD_GCC
#endif
#endif
#ifndef EIGEN_DONT_VECTORIZE
#if defined (EIGEN_SSE2_ON_NON_MSVC_BUT_NOT_OLD_GCC) || defined(EIGEN_SSE2_ON_MSVC_2008_OR_LATER)
// Defines symbols for compile-time detection of which instructions are
// used.
// EIGEN_VECTORIZE_YY is defined if and only if the instruction set YY is used
#define EIGEN_VECTORIZE
#define EIGEN_VECTORIZE_SSE
#define EIGEN_VECTORIZE_SSE2
// Detect sse3/ssse3/sse4:
// gcc and icc defines __SSE3__, ...
// there is no way to know about this on msvc. You can define EIGEN_VECTORIZE_SSE* if you
// want to force the use of those instructions with msvc.
#ifdef __SSE3__
#define EIGEN_VECTORIZE_SSE3
#endif
#ifdef __SSSE3__
#define EIGEN_VECTORIZE_SSSE3
#endif
#ifdef __SSE4_1__
#define EIGEN_VECTORIZE_SSE4_1
#endif
#ifdef __SSE4_2__
#define EIGEN_VECTORIZE_SSE4_2
#endif
#ifdef __AVX__
#define EIGEN_VECTORIZE_AVX
#define EIGEN_VECTORIZE_SSE3
#define EIGEN_VECTORIZE_SSSE3
#define EIGEN_VECTORIZE_SSE4_1
#define EIGEN_VECTORIZE_SSE4_2
#endif
#ifdef __AVX2__
#define EIGEN_VECTORIZE_AVX2
#endif
#ifdef __FMA__
#define EIGEN_VECTORIZE_FMA
#endif
#if defined(__AVX512F__) && defined(EIGEN_ENABLE_AVX512)
#define EIGEN_VECTORIZE_AVX512
#define EIGEN_VECTORIZE_AVX2
#define EIGEN_VECTORIZE_AVX
#define EIGEN_VECTORIZE_FMA
#ifdef __AVX512DQ__
#define EIGEN_VECTORIZE_AVX512DQ
#endif
#ifdef __AVX512ER__
#define EIGEN_VECTORIZE_AVX512ER
#endif
#endif
// include files
// This extern "C" works around a MINGW-w64 compilation issue
// https://sourceforge.net/tracker/index.php?func=detail&aid=3018394&group_id=202880&atid=983354
// In essence, intrin.h is included by windows.h and also declares intrinsics (just as emmintrin.h etc. below do).
// However, intrin.h uses an extern "C" declaration, and g++ thus complains of duplicate declarations
// with conflicting linkage. The linkage for intrinsics doesn't matter, but at that stage the compiler doesn't know;
// so, to avoid compile errors when windows.h is included after Eigen/Core, ensure intrinsics are extern "C" here too.
// notice that since these are C headers, the extern "C" is theoretically needed anyways.
extern "C" {
// In theory we should only include immintrin.h and not the other *mmintrin.h header files directly.
// Doing so triggers some issues with ICC. However old gcc versions seems to not have this file, thus:
#if EIGEN_COMP_ICC >= 1110
#include <immintrin.h>
#else
#include <mmintrin.h>
#include <emmintrin.h>
#include <xmmintrin.h>
#ifdef EIGEN_VECTORIZE_SSE3
#include <pmmintrin.h>
#endif
#ifdef EIGEN_VECTORIZE_SSSE3
#include <tmmintrin.h>
#endif
#ifdef EIGEN_VECTORIZE_SSE4_1
#include <smmintrin.h>
#endif
#ifdef EIGEN_VECTORIZE_SSE4_2
#include <nmmintrin.h>
#endif
#if defined(EIGEN_VECTORIZE_AVX) || defined(EIGEN_VECTORIZE_AVX512)
#include <immintrin.h>
#endif
#endif
} // end extern "C"
#elif defined __VSX__
#define EIGEN_VECTORIZE
#define EIGEN_VECTORIZE_VSX
#include <altivec.h>
// We need to #undef all these ugly tokens defined in <altivec.h>
// => use __vector instead of vector
#undef bool
#undef vector
#undef pixel
#elif defined __ALTIVEC__
#define EIGEN_VECTORIZE
#define EIGEN_VECTORIZE_ALTIVEC
#include <altivec.h>
// We need to #undef all these ugly tokens defined in <altivec.h>
// => use __vector instead of vector
#undef bool
#undef vector
#undef pixel
#elif (defined __ARM_NEON) || (defined __ARM_NEON__)
#define EIGEN_VECTORIZE
#define EIGEN_VECTORIZE_NEON
#include <arm_neon.h>
#elif (defined __s390x__ && defined __VEC__)
#define EIGEN_VECTORIZE
#define EIGEN_VECTORIZE_ZVECTOR
#include <vecintrin.h>
#endif
#endif
#if defined(__F16C__) && !defined(EIGEN_COMP_CLANG)
// We can use the optimized fp16 to float and float to fp16 conversion routines
#define EIGEN_HAS_FP16_C
#endif
#if defined __CUDACC__
#define EIGEN_VECTORIZE_CUDA
#include <vector_types.h>
#if EIGEN_CUDACC_VER >= 70500
#define EIGEN_HAS_CUDA_FP16
#endif
#endif
#if defined EIGEN_HAS_CUDA_FP16
#include <host_defines.h>
#include <cuda_fp16.h>
#endif
#if (defined _OPENMP) && (!defined EIGEN_DONT_PARALLELIZE)
#define EIGEN_HAS_OPENMP
#endif
#ifdef EIGEN_HAS_OPENMP
#include <omp.h>
#endif
// MSVC for windows mobile does not have the errno.h file
#if !(EIGEN_COMP_MSVC && EIGEN_OS_WINCE) && !EIGEN_COMP_ARM
#define EIGEN_HAS_ERRNO
#endif
#ifdef EIGEN_HAS_ERRNO
#include <cerrno>
#endif
#include <cstddef>
#include <cstdlib>
#include <cmath>
#include <cassert>
#include <functional>
#include <iosfwd>
#include <cstring>
#include <string>
#include <limits>
#include <climits> // for CHAR_BIT
// for min/max:
#include <algorithm>
// for std::is_nothrow_move_assignable
#ifdef EIGEN_INCLUDE_TYPE_TRAITS
#include <type_traits>
#endif
// for outputting debug info
#ifdef EIGEN_DEBUG_ASSIGN
#include <iostream>
#endif
// required for __cpuid, needs to be included after cmath
#if EIGEN_COMP_MSVC && EIGEN_ARCH_i386_OR_x86_64 && !EIGEN_OS_WINCE
#include <intrin.h>
#endif
/** \brief Namespace containing all symbols from the %Eigen library. */
namespace Eigen {
inline static const char *SimdInstructionSetsInUse(void) {
#if defined(EIGEN_VECTORIZE_AVX512)
return "AVX512, FMA, AVX2, AVX, SSE, SSE2, SSE3, SSSE3, SSE4.1, SSE4.2";
#elif defined(EIGEN_VECTORIZE_AVX)
return "AVX SSE, SSE2, SSE3, SSSE3, SSE4.1, SSE4.2";
#elif defined(EIGEN_VECTORIZE_SSE4_2)
return "SSE, SSE2, SSE3, SSSE3, SSE4.1, SSE4.2";
#elif defined(EIGEN_VECTORIZE_SSE4_1)
return "SSE, SSE2, SSE3, SSSE3, SSE4.1";
#elif defined(EIGEN_VECTORIZE_SSSE3)
return "SSE, SSE2, SSE3, SSSE3";
#elif defined(EIGEN_VECTORIZE_SSE3)
return "SSE, SSE2, SSE3";
#elif defined(EIGEN_VECTORIZE_SSE2)
return "SSE, SSE2";
#elif defined(EIGEN_VECTORIZE_ALTIVEC)
return "AltiVec";
#elif defined(EIGEN_VECTORIZE_VSX)
return "VSX";
#elif defined(EIGEN_VECTORIZE_NEON)
return "ARM NEON";
#elif defined(EIGEN_VECTORIZE_ZVECTOR)
return "S390X ZVECTOR";
#else
return "None";
#endif
}
} // end namespace Eigen
#if defined EIGEN2_SUPPORT_STAGE40_FULL_EIGEN3_STRICTNESS || defined EIGEN2_SUPPORT_STAGE30_FULL_EIGEN3_API || defined EIGEN2_SUPPORT_STAGE20_RESOLVE_API_CONFLICTS || defined EIGEN2_SUPPORT_STAGE10_FULL_EIGEN2_API || defined EIGEN2_SUPPORT
// This will generate an error message:
#error Eigen2-support is only available up to version 3.2. Please go to "http://eigen.tuxfamily.org/index.php?title=Eigen2" for further information
#endif
namespace Eigen {
// we use size_t frequently and we'll never remember to prepend it with std:: everytime just to
// ensure QNX/QCC support
using std::size_t;
// gcc 4.6.0 wants std:: for ptrdiff_t
using std::ptrdiff_t;
}
/** \defgroup Core_Module Core module
* This is the main module of Eigen providing dense matrix and vector support
* (both fixed and dynamic size) with all the features corresponding to a BLAS library
* and much more...
*
* \code
* #include <Eigen/Core>
* \endcode
*/
#include "src/Core/util/Constants.h"
#include "src/Core/util/Meta.h"
#include "src/Core/util/ForwardDeclarations.h"
#include "src/Core/util/StaticAssert.h"
#include "src/Core/util/XprHelper.h"
#include "src/Core/util/Memory.h"
#include "src/Core/NumTraits.h"
#include "src/Core/MathFunctions.h"
#include "src/Core/GenericPacketMath.h"
#include "src/Core/MathFunctionsImpl.h"
#include "src/Core/arch/Default/ConjHelper.h"
#if defined EIGEN_VECTORIZE_AVX512
#include "src/Core/arch/SSE/PacketMath.h"
#include "src/Core/arch/AVX/PacketMath.h"
#include "src/Core/arch/AVX512/PacketMath.h"
#include "src/Core/arch/AVX512/MathFunctions.h"
#elif defined EIGEN_VECTORIZE_AVX
// Use AVX for floats and doubles, SSE for integers
#include "src/Core/arch/SSE/PacketMath.h"
#include "src/Core/arch/SSE/Complex.h"
#include "src/Core/arch/SSE/MathFunctions.h"
#include "src/Core/arch/AVX/PacketMath.h"
#include "src/Core/arch/AVX/MathFunctions.h"
#include "src/Core/arch/AVX/Complex.h"
#include "src/Core/arch/AVX/TypeCasting.h"
#include "src/Core/arch/SSE/TypeCasting.h"
#elif defined EIGEN_VECTORIZE_SSE
#include "src/Core/arch/SSE/PacketMath.h"
#include "src/Core/arch/SSE/MathFunctions.h"
#include "src/Core/arch/SSE/Complex.h"
#include "src/Core/arch/SSE/TypeCasting.h"
#elif defined(EIGEN_VECTORIZE_ALTIVEC) || defined(EIGEN_VECTORIZE_VSX)
#include "src/Core/arch/AltiVec/PacketMath.h"
#include "src/Core/arch/AltiVec/MathFunctions.h"
#include "src/Core/arch/AltiVec/Complex.h"
#elif defined EIGEN_VECTORIZE_NEON
#include "src/Core/arch/NEON/PacketMath.h"
#include "src/Core/arch/NEON/MathFunctions.h"
#include "src/Core/arch/NEON/Complex.h"
#elif defined EIGEN_VECTORIZE_ZVECTOR
#include "src/Core/arch/ZVector/PacketMath.h"
#include "src/Core/arch/ZVector/MathFunctions.h"
#include "src/Core/arch/ZVector/Complex.h"
#endif
// Half float support
#include "src/Core/arch/CUDA/Half.h"
#include "src/Core/arch/CUDA/PacketMathHalf.h"
#include "src/Core/arch/CUDA/TypeCasting.h"
#if defined EIGEN_VECTORIZE_CUDA
#include "src/Core/arch/CUDA/PacketMath.h"
#include "src/Core/arch/CUDA/MathFunctions.h"
#endif
#include "src/Core/arch/Default/Settings.h"
#include "src/Core/functors/TernaryFunctors.h"
#include "src/Core/functors/BinaryFunctors.h"
#include "src/Core/functors/UnaryFunctors.h"
#include "src/Core/functors/NullaryFunctors.h"
#include "src/Core/functors/StlFunctors.h"
#include "src/Core/functors/AssignmentFunctors.h"
// Specialized functors to enable the processing of complex numbers
// on CUDA devices
#include "src/Core/arch/CUDA/Complex.h"
#include "src/Core/IO.h"
#include "src/Core/DenseCoeffsBase.h"
#include "src/Core/DenseBase.h"
#include "src/Core/MatrixBase.h"
#include "src/Core/EigenBase.h"
#include "src/Core/Product.h"
#include "src/Core/CoreEvaluators.h"
#include "src/Core/AssignEvaluator.h"
#ifndef EIGEN_PARSED_BY_DOXYGEN // work around Doxygen bug triggered by Assign.h r814874
// at least confirmed with Doxygen 1.5.5 and 1.5.6
#include "src/Core/Assign.h"
#endif
#include "src/Core/ArrayBase.h"
#include "src/Core/util/BlasUtil.h"
#include "src/Core/DenseStorage.h"
#include "src/Core/NestByValue.h"
// #include "src/Core/ForceAlignedAccess.h"
#include "src/Core/ReturnByValue.h"
#include "src/Core/NoAlias.h"
#include "src/Core/PlainObjectBase.h"
#include "src/Core/Matrix.h"
#include "src/Core/Array.h"
#include "src/Core/CwiseTernaryOp.h"
#include "src/Core/CwiseBinaryOp.h"
#include "src/Core/CwiseUnaryOp.h"
#include "src/Core/CwiseNullaryOp.h"
#include "src/Core/CwiseUnaryView.h"
#include "src/Core/SelfCwiseBinaryOp.h"
#include "src/Core/Dot.h"
#include "src/Core/StableNorm.h"
#include "src/Core/Stride.h"
#include "src/Core/MapBase.h"
#include "src/Core/Map.h"
#include "src/Core/Ref.h"
#include "src/Core/Block.h"
#include "src/Core/VectorBlock.h"
#include "src/Core/Transpose.h"
#include "src/Core/DiagonalMatrix.h"
#include "src/Core/Diagonal.h"
#include "src/Core/DiagonalProduct.h"
#include "src/Core/Redux.h"
#include "src/Core/Visitor.h"
#include "src/Core/Fuzzy.h"
#include "src/Core/Swap.h"
#include "src/Core/CommaInitializer.h"
#include "src/Core/GeneralProduct.h"
#include "src/Core/Solve.h"
#include "src/Core/Inverse.h"
#include "src/Core/SolverBase.h"
#include "src/Core/PermutationMatrix.h"
#include "src/Core/Transpositions.h"
#include "src/Core/TriangularMatrix.h"
#include "src/Core/SelfAdjointView.h"
#include "src/Core/products/GeneralBlockPanelKernel.h"
#include "src/Core/products/Parallelizer.h"
#include "src/Core/ProductEvaluators.h"
#include "src/Core/products/GeneralMatrixVector.h"
#include "src/Core/products/GeneralMatrixMatrix.h"
#include "src/Core/SolveTriangular.h"
#include "src/Core/products/GeneralMatrixMatrixTriangular.h"
#include "src/Core/products/SelfadjointMatrixVector.h"
#include "src/Core/products/SelfadjointMatrixMatrix.h"
#include "src/Core/products/SelfadjointProduct.h"
#include "src/Core/products/SelfadjointRank2Update.h"
#include "src/Core/products/TriangularMatrixVector.h"
#include "src/Core/products/TriangularMatrixMatrix.h"
#include "src/Core/products/TriangularSolverMatrix.h"
#include "src/Core/products/TriangularSolverVector.h"
#include "src/Core/BandMatrix.h"
#include "src/Core/CoreIterators.h"
#include "src/Core/ConditionEstimator.h"
#include "src/Core/BooleanRedux.h"
#include "src/Core/Select.h"
#include "src/Core/VectorwiseOp.h"
#include "src/Core/Random.h"
#include "src/Core/Replicate.h"
#include "src/Core/Reverse.h"
#include "src/Core/ArrayWrapper.h"
#ifdef EIGEN_USE_BLAS
#include "src/Core/products/GeneralMatrixMatrix_BLAS.h"
#include "src/Core/products/GeneralMatrixVector_BLAS.h"
#include "src/Core/products/GeneralMatrixMatrixTriangular_BLAS.h"
#include "src/Core/products/SelfadjointMatrixMatrix_BLAS.h"
#include "src/Core/products/SelfadjointMatrixVector_BLAS.h"
#include "src/Core/products/TriangularMatrixMatrix_BLAS.h"
#include "src/Core/products/TriangularMatrixVector_BLAS.h"
#include "src/Core/products/TriangularSolverMatrix_BLAS.h"
#endif // EIGEN_USE_BLAS
#ifdef EIGEN_USE_MKL_VML
#include "src/Core/Assign_MKL.h"
#endif
#include "src/Core/GlobalFunctions.h"
#include "src/Core/util/ReenableStupidWarnings.h"
#endif // EIGEN_CORE_H

View File

@@ -0,0 +1,7 @@
#include "Core"
#include "LU"
#include "Cholesky"
#include "QR"
#include "SVD"
#include "Geometry"
#include "Eigenvalues"

View File

@@ -0,0 +1,2 @@
#include "Dense"
#include "Sparse"

View File

@@ -0,0 +1,61 @@
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#ifndef EIGEN_EIGENVALUES_MODULE_H
#define EIGEN_EIGENVALUES_MODULE_H
#include "Core"
#include "src/Core/util/DisableStupidWarnings.h"
#include "Cholesky"
#include "Jacobi"
#include "Householder"
#include "LU"
#include "Geometry"
/** \defgroup Eigenvalues_Module Eigenvalues module
*
*
*
* This module mainly provides various eigenvalue solvers.
* This module also provides some MatrixBase methods, including:
* - MatrixBase::eigenvalues(),
* - MatrixBase::operatorNorm()
*
* \code
* #include <Eigen/Eigenvalues>
* \endcode
*/
#include "src/misc/RealSvd2x2.h"
#include "src/Eigenvalues/Tridiagonalization.h"
#include "src/Eigenvalues/RealSchur.h"
#include "src/Eigenvalues/EigenSolver.h"
#include "src/Eigenvalues/SelfAdjointEigenSolver.h"
#include "src/Eigenvalues/GeneralizedSelfAdjointEigenSolver.h"
#include "src/Eigenvalues/HessenbergDecomposition.h"
#include "src/Eigenvalues/ComplexSchur.h"
#include "src/Eigenvalues/ComplexEigenSolver.h"
#include "src/Eigenvalues/RealQZ.h"
#include "src/Eigenvalues/GeneralizedEigenSolver.h"
#include "src/Eigenvalues/MatrixBaseEigenvalues.h"
#ifdef EIGEN_USE_LAPACKE
#ifdef EIGEN_USE_MKL
#include "mkl_lapacke.h"
#else
#include "src/misc/lapacke.h"
#endif
#include "src/Eigenvalues/RealSchur_LAPACKE.h"
#include "src/Eigenvalues/ComplexSchur_LAPACKE.h"
#include "src/Eigenvalues/SelfAdjointEigenSolver_LAPACKE.h"
#endif
#include "src/Core/util/ReenableStupidWarnings.h"
#endif // EIGEN_EIGENVALUES_MODULE_H
/* vim: set filetype=cpp et sw=2 ts=2 ai: */

View File

@@ -0,0 +1,62 @@
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#ifndef EIGEN_GEOMETRY_MODULE_H
#define EIGEN_GEOMETRY_MODULE_H
#include "Core"
#include "src/Core/util/DisableStupidWarnings.h"
#include "SVD"
#include "LU"
#include <limits>
/** \defgroup Geometry_Module Geometry module
*
* This module provides support for:
* - fixed-size homogeneous transformations
* - translation, scaling, 2D and 3D rotations
* - \link Quaternion quaternions \endlink
* - cross products (\ref MatrixBase::cross, \ref MatrixBase::cross3)
* - orthognal vector generation (\ref MatrixBase::unitOrthogonal)
* - some linear components: \link ParametrizedLine parametrized-lines \endlink and \link Hyperplane hyperplanes \endlink
* - \link AlignedBox axis aligned bounding boxes \endlink
* - \link umeyama least-square transformation fitting \endlink
*
* \code
* #include <Eigen/Geometry>
* \endcode
*/
#include "src/Geometry/OrthoMethods.h"
#include "src/Geometry/EulerAngles.h"
#include "src/Geometry/Homogeneous.h"
#include "src/Geometry/RotationBase.h"
#include "src/Geometry/Rotation2D.h"
#include "src/Geometry/Quaternion.h"
#include "src/Geometry/AngleAxis.h"
#include "src/Geometry/Transform.h"
#include "src/Geometry/Translation.h"
#include "src/Geometry/Scaling.h"
#include "src/Geometry/Hyperplane.h"
#include "src/Geometry/ParametrizedLine.h"
#include "src/Geometry/AlignedBox.h"
#include "src/Geometry/Umeyama.h"
// Use the SSE optimized version whenever possible. At the moment the
// SSE version doesn't compile when AVX is enabled
#if defined EIGEN_VECTORIZE_SSE && !defined EIGEN_VECTORIZE_AVX
#include "src/Geometry/arch/Geometry_SSE.h"
#endif
#include "src/Core/util/ReenableStupidWarnings.h"
#endif // EIGEN_GEOMETRY_MODULE_H
/* vim: set filetype=cpp et sw=2 ts=2 ai: */

View File

@@ -0,0 +1,30 @@
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#ifndef EIGEN_HOUSEHOLDER_MODULE_H
#define EIGEN_HOUSEHOLDER_MODULE_H
#include "Core"
#include "src/Core/util/DisableStupidWarnings.h"
/** \defgroup Householder_Module Householder module
* This module provides Householder transformations.
*
* \code
* #include <Eigen/Householder>
* \endcode
*/
#include "src/Householder/Householder.h"
#include "src/Householder/HouseholderSequence.h"
#include "src/Householder/BlockHouseholder.h"
#include "src/Core/util/ReenableStupidWarnings.h"
#endif // EIGEN_HOUSEHOLDER_MODULE_H
/* vim: set filetype=cpp et sw=2 ts=2 ai: */

View File

@@ -0,0 +1,48 @@
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#ifndef EIGEN_ITERATIVELINEARSOLVERS_MODULE_H
#define EIGEN_ITERATIVELINEARSOLVERS_MODULE_H
#include "SparseCore"
#include "OrderingMethods"
#include "src/Core/util/DisableStupidWarnings.h"
/**
* \defgroup IterativeLinearSolvers_Module IterativeLinearSolvers module
*
* This module currently provides iterative methods to solve problems of the form \c A \c x = \c b, where \c A is a squared matrix, usually very large and sparse.
* Those solvers are accessible via the following classes:
* - ConjugateGradient for selfadjoint (hermitian) matrices,
* - LeastSquaresConjugateGradient for rectangular least-square problems,
* - BiCGSTAB for general square matrices.
*
* These iterative solvers are associated with some preconditioners:
* - IdentityPreconditioner - not really useful
* - DiagonalPreconditioner - also called Jacobi preconditioner, work very well on diagonal dominant matrices.
* - IncompleteLUT - incomplete LU factorization with dual thresholding
*
* Such problems can also be solved using the direct sparse decomposition modules: SparseCholesky, CholmodSupport, UmfPackSupport, SuperLUSupport.
*
\code
#include <Eigen/IterativeLinearSolvers>
\endcode
*/
#include "src/IterativeLinearSolvers/SolveWithGuess.h"
#include "src/IterativeLinearSolvers/IterativeSolverBase.h"
#include "src/IterativeLinearSolvers/BasicPreconditioners.h"
#include "src/IterativeLinearSolvers/ConjugateGradient.h"
#include "src/IterativeLinearSolvers/LeastSquareConjugateGradient.h"
#include "src/IterativeLinearSolvers/BiCGSTAB.h"
#include "src/IterativeLinearSolvers/IncompleteLUT.h"
#include "src/IterativeLinearSolvers/IncompleteCholesky.h"
#include "src/Core/util/ReenableStupidWarnings.h"
#endif // EIGEN_ITERATIVELINEARSOLVERS_MODULE_H

View File

@@ -0,0 +1,33 @@
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#ifndef EIGEN_JACOBI_MODULE_H
#define EIGEN_JACOBI_MODULE_H
#include "Core"
#include "src/Core/util/DisableStupidWarnings.h"
/** \defgroup Jacobi_Module Jacobi module
* This module provides Jacobi and Givens rotations.
*
* \code
* #include <Eigen/Jacobi>
* \endcode
*
* In addition to listed classes, it defines the two following MatrixBase methods to apply a Jacobi or Givens rotation:
* - MatrixBase::applyOnTheLeft()
* - MatrixBase::applyOnTheRight().
*/
#include "src/Jacobi/Jacobi.h"
#include "src/Core/util/ReenableStupidWarnings.h"
#endif // EIGEN_JACOBI_MODULE_H
/* vim: set filetype=cpp et sw=2 ts=2 ai: */

50
deps_src/eigen/Eigen/LU Normal file
View File

@@ -0,0 +1,50 @@
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#ifndef EIGEN_LU_MODULE_H
#define EIGEN_LU_MODULE_H
#include "Core"
#include "src/Core/util/DisableStupidWarnings.h"
/** \defgroup LU_Module LU module
* This module includes %LU decomposition and related notions such as matrix inversion and determinant.
* This module defines the following MatrixBase methods:
* - MatrixBase::inverse()
* - MatrixBase::determinant()
*
* \code
* #include <Eigen/LU>
* \endcode
*/
#include "src/misc/Kernel.h"
#include "src/misc/Image.h"
#include "src/LU/FullPivLU.h"
#include "src/LU/PartialPivLU.h"
#ifdef EIGEN_USE_LAPACKE
#ifdef EIGEN_USE_MKL
#include "mkl_lapacke.h"
#else
#include "src/misc/lapacke.h"
#endif
#include "src/LU/PartialPivLU_LAPACKE.h"
#endif
#include "src/LU/Determinant.h"
#include "src/LU/InverseImpl.h"
// Use the SSE optimized version whenever possible. At the moment the
// SSE version doesn't compile when AVX is enabled
#if defined EIGEN_VECTORIZE_SSE && !defined EIGEN_VECTORIZE_AVX
#include "src/LU/arch/Inverse_SSE.h"
#endif
#include "src/Core/util/ReenableStupidWarnings.h"
#endif // EIGEN_LU_MODULE_H
/* vim: set filetype=cpp et sw=2 ts=2 ai: */

View File

@@ -0,0 +1,35 @@
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#ifndef EIGEN_METISSUPPORT_MODULE_H
#define EIGEN_METISSUPPORT_MODULE_H
#include "SparseCore"
#include "src/Core/util/DisableStupidWarnings.h"
extern "C" {
#include <metis.h>
}
/** \ingroup Support_modules
* \defgroup MetisSupport_Module MetisSupport module
*
* \code
* #include <Eigen/MetisSupport>
* \endcode
* This module defines an interface to the METIS reordering package (http://glaros.dtc.umn.edu/gkhome/views/metis).
* It can be used just as any other built-in method as explained in \link OrderingMethods_Module here. \endlink
*/
#include "src/MetisSupport/MetisSupport.h"
#include "src/Core/util/ReenableStupidWarnings.h"
#endif // EIGEN_METISSUPPORT_MODULE_H

View File

@@ -0,0 +1,73 @@
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#ifndef EIGEN_ORDERINGMETHODS_MODULE_H
#define EIGEN_ORDERINGMETHODS_MODULE_H
#include "SparseCore"
#include "src/Core/util/DisableStupidWarnings.h"
/**
* \defgroup OrderingMethods_Module OrderingMethods module
*
* This module is currently for internal use only
*
* It defines various built-in and external ordering methods for sparse matrices.
* They are typically used to reduce the number of elements during
* the sparse matrix decomposition (LLT, LU, QR).
* Precisely, in a preprocessing step, a permutation matrix P is computed using
* those ordering methods and applied to the columns of the matrix.
* Using for instance the sparse Cholesky decomposition, it is expected that
* the nonzeros elements in LLT(A*P) will be much smaller than that in LLT(A).
*
*
* Usage :
* \code
* #include <Eigen/OrderingMethods>
* \endcode
*
* A simple usage is as a template parameter in the sparse decomposition classes :
*
* \code
* SparseLU<MatrixType, COLAMDOrdering<int> > solver;
* \endcode
*
* \code
* SparseQR<MatrixType, COLAMDOrdering<int> > solver;
* \endcode
*
* It is possible as well to call directly a particular ordering method for your own purpose,
* \code
* AMDOrdering<int> ordering;
* PermutationMatrix<Dynamic, Dynamic, int> perm;
* SparseMatrix<double> A;
* //Fill the matrix ...
*
* ordering(A, perm); // Call AMD
* \endcode
*
* \note Some of these methods (like AMD or METIS), need the sparsity pattern
* of the input matrix to be symmetric. When the matrix is structurally unsymmetric,
* Eigen computes internally the pattern of \f$A^T*A\f$ before calling the method.
* If your matrix is already symmetric (at leat in structure), you can avoid that
* by calling the method with a SelfAdjointView type.
*
* \code
* // Call the ordering on the pattern of the lower triangular matrix A
* ordering(A.selfadjointView<Lower>(), perm);
* \endcode
*/
#ifndef EIGEN_MPL2_ONLY
#include "src/OrderingMethods/Amd.h"
#endif
#include "src/OrderingMethods/Ordering.h"
#include "src/Core/util/ReenableStupidWarnings.h"
#endif // EIGEN_ORDERINGMETHODS_MODULE_H

View File

@@ -0,0 +1,48 @@
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#ifndef EIGEN_PASTIXSUPPORT_MODULE_H
#define EIGEN_PASTIXSUPPORT_MODULE_H
#include "SparseCore"
#include "src/Core/util/DisableStupidWarnings.h"
extern "C" {
#include <pastix_nompi.h>
#include <pastix.h>
}
#ifdef complex
#undef complex
#endif
/** \ingroup Support_modules
* \defgroup PaStiXSupport_Module PaStiXSupport module
*
* This module provides an interface to the <a href="http://pastix.gforge.inria.fr/">PaSTiX</a> library.
* PaSTiX is a general \b supernodal, \b parallel and \b opensource sparse solver.
* It provides the two following main factorization classes:
* - class PastixLLT : a supernodal, parallel LLt Cholesky factorization.
* - class PastixLDLT: a supernodal, parallel LDLt Cholesky factorization.
* - class PastixLU : a supernodal, parallel LU factorization (optimized for a symmetric pattern).
*
* \code
* #include <Eigen/PaStiXSupport>
* \endcode
*
* In order to use this module, the PaSTiX headers must be accessible from the include paths, and your binary must be linked to the PaSTiX library and its dependencies.
* The dependencies depend on how PaSTiX has been compiled.
* For a cmake based project, you can use our FindPaSTiX.cmake module to help you in this task.
*
*/
#include "src/PaStiXSupport/PaStiXSupport.h"
#include "src/Core/util/ReenableStupidWarnings.h"
#endif // EIGEN_PASTIXSUPPORT_MODULE_H

View File

@@ -0,0 +1,35 @@
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#ifndef EIGEN_PARDISOSUPPORT_MODULE_H
#define EIGEN_PARDISOSUPPORT_MODULE_H
#include "SparseCore"
#include "src/Core/util/DisableStupidWarnings.h"
#include <mkl_pardiso.h>
/** \ingroup Support_modules
* \defgroup PardisoSupport_Module PardisoSupport module
*
* This module brings support for the Intel(R) MKL PARDISO direct sparse solvers.
*
* \code
* #include <Eigen/PardisoSupport>
* \endcode
*
* In order to use this module, the MKL headers must be accessible from the include paths, and your binary must be linked to the MKL library and its dependencies.
* See this \ref TopicUsingIntelMKL "page" for more information on MKL-Eigen integration.
*
*/
#include "src/PardisoSupport/PardisoSupport.h"
#include "src/Core/util/ReenableStupidWarnings.h"
#endif // EIGEN_PARDISOSUPPORT_MODULE_H

51
deps_src/eigen/Eigen/QR Normal file
View File

@@ -0,0 +1,51 @@
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#ifndef EIGEN_QR_MODULE_H
#define EIGEN_QR_MODULE_H
#include "Core"
#include "src/Core/util/DisableStupidWarnings.h"
#include "Cholesky"
#include "Jacobi"
#include "Householder"
/** \defgroup QR_Module QR module
*
*
*
* This module provides various QR decompositions
* This module also provides some MatrixBase methods, including:
* - MatrixBase::householderQr()
* - MatrixBase::colPivHouseholderQr()
* - MatrixBase::fullPivHouseholderQr()
*
* \code
* #include <Eigen/QR>
* \endcode
*/
#include "src/QR/HouseholderQR.h"
#include "src/QR/FullPivHouseholderQR.h"
#include "src/QR/ColPivHouseholderQR.h"
#include "src/QR/CompleteOrthogonalDecomposition.h"
#ifdef EIGEN_USE_LAPACKE
#ifdef EIGEN_USE_MKL
#include "mkl_lapacke.h"
#else
#include "src/misc/lapacke.h"
#endif
#include "src/QR/HouseholderQR_LAPACKE.h"
#include "src/QR/ColPivHouseholderQR_LAPACKE.h"
#endif
#include "src/Core/util/ReenableStupidWarnings.h"
#endif // EIGEN_QR_MODULE_H
/* vim: set filetype=cpp et sw=2 ts=2 ai: */

View File

@@ -0,0 +1,40 @@
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#ifndef EIGEN_QTMALLOC_MODULE_H
#define EIGEN_QTMALLOC_MODULE_H
#include "Core"
#if (!EIGEN_MALLOC_ALREADY_ALIGNED)
#include "src/Core/util/DisableStupidWarnings.h"
void *qMalloc(std::size_t size)
{
return Eigen::internal::aligned_malloc(size);
}
void qFree(void *ptr)
{
Eigen::internal::aligned_free(ptr);
}
void *qRealloc(void *ptr, std::size_t size)
{
void* newPtr = Eigen::internal::aligned_malloc(size);
std::memcpy(newPtr, ptr, size);
Eigen::internal::aligned_free(ptr);
return newPtr;
}
#include "src/Core/util/ReenableStupidWarnings.h"
#endif
#endif // EIGEN_QTMALLOC_MODULE_H
/* vim: set filetype=cpp et sw=2 ts=2 ai: */

View File

@@ -0,0 +1,34 @@
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#ifndef EIGEN_SPQRSUPPORT_MODULE_H
#define EIGEN_SPQRSUPPORT_MODULE_H
#include "SparseCore"
#include "src/Core/util/DisableStupidWarnings.h"
#include "SuiteSparseQR.hpp"
/** \ingroup Support_modules
* \defgroup SPQRSupport_Module SuiteSparseQR module
*
* This module provides an interface to the SPQR library, which is part of the <a href="http://www.suitesparse.com">suitesparse</a> package.
*
* \code
* #include <Eigen/SPQRSupport>
* \endcode
*
* In order to use this module, the SPQR headers must be accessible from the include paths, and your binary must be linked to the SPQR library and its dependencies (Cholmod, AMD, COLAMD,...).
* For a cmake based project, you can use our FindSPQR.cmake and FindCholmod.Cmake modules
*
*/
#include "src/CholmodSupport/CholmodSupport.h"
#include "src/SPQRSupport/SuiteSparseQRSupport.h"
#endif

51
deps_src/eigen/Eigen/SVD Normal file
View File

@@ -0,0 +1,51 @@
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#ifndef EIGEN_SVD_MODULE_H
#define EIGEN_SVD_MODULE_H
#include "QR"
#include "Householder"
#include "Jacobi"
#include "src/Core/util/DisableStupidWarnings.h"
/** \defgroup SVD_Module SVD module
*
*
*
* This module provides SVD decomposition for matrices (both real and complex).
* Two decomposition algorithms are provided:
* - JacobiSVD implementing two-sided Jacobi iterations is numerically very accurate, fast for small matrices, but very slow for larger ones.
* - BDCSVD implementing a recursive divide & conquer strategy on top of an upper-bidiagonalization which remains fast for large problems.
* These decompositions are accessible via the respective classes and following MatrixBase methods:
* - MatrixBase::jacobiSvd()
* - MatrixBase::bdcSvd()
*
* \code
* #include <Eigen/SVD>
* \endcode
*/
#include "src/misc/RealSvd2x2.h"
#include "src/SVD/UpperBidiagonalization.h"
#include "src/SVD/SVDBase.h"
#include "src/SVD/JacobiSVD.h"
#include "src/SVD/BDCSVD.h"
#if defined(EIGEN_USE_LAPACKE) && !defined(EIGEN_USE_LAPACKE_STRICT)
#ifdef EIGEN_USE_MKL
#include "mkl_lapacke.h"
#else
#include "src/misc/lapacke.h"
#endif
#include "src/SVD/JacobiSVD_LAPACKE.h"
#endif
#include "src/Core/util/ReenableStupidWarnings.h"
#endif // EIGEN_SVD_MODULE_H
/* vim: set filetype=cpp et sw=2 ts=2 ai: */

View File

@@ -0,0 +1,36 @@
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#ifndef EIGEN_SPARSE_MODULE_H
#define EIGEN_SPARSE_MODULE_H
/** \defgroup Sparse_Module Sparse meta-module
*
* Meta-module including all related modules:
* - \ref SparseCore_Module
* - \ref OrderingMethods_Module
* - \ref SparseCholesky_Module
* - \ref SparseLU_Module
* - \ref SparseQR_Module
* - \ref IterativeLinearSolvers_Module
*
\code
#include <Eigen/Sparse>
\endcode
*/
#include "SparseCore"
#include "OrderingMethods"
#ifndef EIGEN_MPL2_ONLY
#include "SparseCholesky"
#endif
#include "SparseLU"
#include "SparseQR"
#include "IterativeLinearSolvers"
#endif // EIGEN_SPARSE_MODULE_H

View File

@@ -0,0 +1,45 @@
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2008-2013 Gael Guennebaud <gael.guennebaud@inria.fr>
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#ifndef EIGEN_SPARSECHOLESKY_MODULE_H
#define EIGEN_SPARSECHOLESKY_MODULE_H
#include "SparseCore"
#include "OrderingMethods"
#include "src/Core/util/DisableStupidWarnings.h"
/**
* \defgroup SparseCholesky_Module SparseCholesky module
*
* This module currently provides two variants of the direct sparse Cholesky decomposition for selfadjoint (hermitian) matrices.
* Those decompositions are accessible via the following classes:
* - SimplicialLLt,
* - SimplicialLDLt
*
* Such problems can also be solved using the ConjugateGradient solver from the IterativeLinearSolvers module.
*
* \code
* #include <Eigen/SparseCholesky>
* \endcode
*/
#ifdef EIGEN_MPL2_ONLY
#error The SparseCholesky module has nothing to offer in MPL2 only mode
#endif
#include "src/SparseCholesky/SimplicialCholesky.h"
#ifndef EIGEN_MPL2_ONLY
#include "src/SparseCholesky/SimplicialCholesky_impl.h"
#endif
#include "src/Core/util/ReenableStupidWarnings.h"
#endif // EIGEN_SPARSECHOLESKY_MODULE_H

View File

@@ -0,0 +1,69 @@
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#ifndef EIGEN_SPARSECORE_MODULE_H
#define EIGEN_SPARSECORE_MODULE_H
#include "Core"
#include "src/Core/util/DisableStupidWarnings.h"
#include <vector>
#include <map>
#include <cstdlib>
#include <cstring>
#include <algorithm>
/**
* \defgroup SparseCore_Module SparseCore module
*
* This module provides a sparse matrix representation, and basic associated matrix manipulations
* and operations.
*
* See the \ref TutorialSparse "Sparse tutorial"
*
* \code
* #include <Eigen/SparseCore>
* \endcode
*
* This module depends on: Core.
*/
#include "src/SparseCore/SparseUtil.h"
#include "src/SparseCore/SparseMatrixBase.h"
#include "src/SparseCore/SparseAssign.h"
#include "src/SparseCore/CompressedStorage.h"
#include "src/SparseCore/AmbiVector.h"
#include "src/SparseCore/SparseCompressedBase.h"
#include "src/SparseCore/SparseMatrix.h"
#include "src/SparseCore/SparseMap.h"
#include "src/SparseCore/MappedSparseMatrix.h"
#include "src/SparseCore/SparseVector.h"
#include "src/SparseCore/SparseRef.h"
#include "src/SparseCore/SparseCwiseUnaryOp.h"
#include "src/SparseCore/SparseCwiseBinaryOp.h"
#include "src/SparseCore/SparseTranspose.h"
#include "src/SparseCore/SparseBlock.h"
#include "src/SparseCore/SparseDot.h"
#include "src/SparseCore/SparseRedux.h"
#include "src/SparseCore/SparseView.h"
#include "src/SparseCore/SparseDiagonalProduct.h"
#include "src/SparseCore/ConservativeSparseSparseProduct.h"
#include "src/SparseCore/SparseSparseProductWithPruning.h"
#include "src/SparseCore/SparseProduct.h"
#include "src/SparseCore/SparseDenseProduct.h"
#include "src/SparseCore/SparseSelfAdjointView.h"
#include "src/SparseCore/SparseTriangularView.h"
#include "src/SparseCore/TriangularSolver.h"
#include "src/SparseCore/SparsePermutation.h"
#include "src/SparseCore/SparseFuzzy.h"
#include "src/SparseCore/SparseSolverBase.h"
#include "src/Core/util/ReenableStupidWarnings.h"
#endif // EIGEN_SPARSECORE_MODULE_H

View File

@@ -0,0 +1,46 @@
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2012 Désiré Nuentsa-Wakam <desire.nuentsa_wakam@inria.fr>
// Copyright (C) 2012 Gael Guennebaud <gael.guennebaud@inria.fr>
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#ifndef EIGEN_SPARSELU_MODULE_H
#define EIGEN_SPARSELU_MODULE_H
#include "SparseCore"
/**
* \defgroup SparseLU_Module SparseLU module
* This module defines a supernodal factorization of general sparse matrices.
* The code is fully optimized for supernode-panel updates with specialized kernels.
* Please, see the documentation of the SparseLU class for more details.
*/
// Ordering interface
#include "OrderingMethods"
#include "src/SparseLU/SparseLU_gemm_kernel.h"
#include "src/SparseLU/SparseLU_Structs.h"
#include "src/SparseLU/SparseLU_SupernodalMatrix.h"
#include "src/SparseLU/SparseLUImpl.h"
#include "src/SparseCore/SparseColEtree.h"
#include "src/SparseLU/SparseLU_Memory.h"
#include "src/SparseLU/SparseLU_heap_relax_snode.h"
#include "src/SparseLU/SparseLU_relax_snode.h"
#include "src/SparseLU/SparseLU_pivotL.h"
#include "src/SparseLU/SparseLU_panel_dfs.h"
#include "src/SparseLU/SparseLU_kernel_bmod.h"
#include "src/SparseLU/SparseLU_panel_bmod.h"
#include "src/SparseLU/SparseLU_column_dfs.h"
#include "src/SparseLU/SparseLU_column_bmod.h"
#include "src/SparseLU/SparseLU_copy_to_ucol.h"
#include "src/SparseLU/SparseLU_pruneL.h"
#include "src/SparseLU/SparseLU_Utils.h"
#include "src/SparseLU/SparseLU.h"
#endif // EIGEN_SPARSELU_MODULE_H

View File

@@ -0,0 +1,37 @@
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#ifndef EIGEN_SPARSEQR_MODULE_H
#define EIGEN_SPARSEQR_MODULE_H
#include "SparseCore"
#include "OrderingMethods"
#include "src/Core/util/DisableStupidWarnings.h"
/** \defgroup SparseQR_Module SparseQR module
* \brief Provides QR decomposition for sparse matrices
*
* This module provides a simplicial version of the left-looking Sparse QR decomposition.
* The columns of the input matrix should be reordered to limit the fill-in during the
* decomposition. Built-in methods (COLAMD, AMD) or external methods (METIS) can be used to this end.
* See the \link OrderingMethods_Module OrderingMethods\endlink module for the list
* of built-in and external ordering methods.
*
* \code
* #include <Eigen/SparseQR>
* \endcode
*
*
*/
#include "OrderingMethods"
#include "src/SparseCore/SparseColEtree.h"
#include "src/SparseQR/SparseQR.h"
#include "src/Core/util/ReenableStupidWarnings.h"
#endif

View File

@@ -0,0 +1,27 @@
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2009 Gael Guennebaud <gael.guennebaud@inria.fr>
// Copyright (C) 2009 Hauke Heibel <hauke.heibel@googlemail.com>
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#ifndef EIGEN_STDDEQUE_MODULE_H
#define EIGEN_STDDEQUE_MODULE_H
#include "Core"
#include <deque>
#if EIGEN_COMP_MSVC && EIGEN_OS_WIN64 && (EIGEN_MAX_STATIC_ALIGN_BYTES<=16) /* MSVC auto aligns up to 16 bytes in 64 bit builds */
#define EIGEN_DEFINE_STL_DEQUE_SPECIALIZATION(...)
#else
#include "src/StlSupport/StdDeque.h"
#endif
#endif // EIGEN_STDDEQUE_MODULE_H

View File

@@ -0,0 +1,26 @@
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2009 Hauke Heibel <hauke.heibel@googlemail.com>
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#ifndef EIGEN_STDLIST_MODULE_H
#define EIGEN_STDLIST_MODULE_H
#include "Core"
#include <list>
#if EIGEN_COMP_MSVC && EIGEN_OS_WIN64 && (EIGEN_MAX_STATIC_ALIGN_BYTES<=16) /* MSVC auto aligns up to 16 bytes in 64 bit builds */
#define EIGEN_DEFINE_STL_LIST_SPECIALIZATION(...)
#else
#include "src/StlSupport/StdList.h"
#endif
#endif // EIGEN_STDLIST_MODULE_H

View File

@@ -0,0 +1,27 @@
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2009 Gael Guennebaud <gael.guennebaud@inria.fr>
// Copyright (C) 2009 Hauke Heibel <hauke.heibel@googlemail.com>
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#ifndef EIGEN_STDVECTOR_MODULE_H
#define EIGEN_STDVECTOR_MODULE_H
#include "Core"
#include <vector>
#if EIGEN_COMP_MSVC && EIGEN_OS_WIN64 && (EIGEN_MAX_STATIC_ALIGN_BYTES<=16) /* MSVC auto aligns up to 16 bytes in 64 bit builds */
#define EIGEN_DEFINE_STL_VECTOR_SPECIALIZATION(...)
#else
#include "src/StlSupport/StdVector.h"
#endif
#endif // EIGEN_STDVECTOR_MODULE_H

View File

@@ -0,0 +1,64 @@
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#ifndef EIGEN_SUPERLUSUPPORT_MODULE_H
#define EIGEN_SUPERLUSUPPORT_MODULE_H
#include "SparseCore"
#include "src/Core/util/DisableStupidWarnings.h"
#ifdef EMPTY
#define EIGEN_EMPTY_WAS_ALREADY_DEFINED
#endif
typedef int int_t;
#include <slu_Cnames.h>
#include <supermatrix.h>
#include <slu_util.h>
// slu_util.h defines a preprocessor token named EMPTY which is really polluting,
// so we remove it in favor of a SUPERLU_EMPTY token.
// If EMPTY was already defined then we don't undef it.
#if defined(EIGEN_EMPTY_WAS_ALREADY_DEFINED)
# undef EIGEN_EMPTY_WAS_ALREADY_DEFINED
#elif defined(EMPTY)
# undef EMPTY
#endif
#define SUPERLU_EMPTY (-1)
namespace Eigen { struct SluMatrix; }
/** \ingroup Support_modules
* \defgroup SuperLUSupport_Module SuperLUSupport module
*
* This module provides an interface to the <a href="http://crd-legacy.lbl.gov/~xiaoye/SuperLU/">SuperLU</a> library.
* It provides the following factorization class:
* - class SuperLU: a supernodal sequential LU factorization.
* - class SuperILU: a supernodal sequential incomplete LU factorization (to be used as a preconditioner for iterative methods).
*
* \warning This wrapper requires at least versions 4.0 of SuperLU. The 3.x versions are not supported.
*
* \warning When including this module, you have to use SUPERLU_EMPTY instead of EMPTY which is no longer defined because it is too polluting.
*
* \code
* #include <Eigen/SuperLUSupport>
* \endcode
*
* In order to use this module, the superlu headers must be accessible from the include paths, and your binary must be linked to the superlu library and its dependencies.
* The dependencies depend on how superlu has been compiled.
* For a cmake based project, you can use our FindSuperLU.cmake module to help you in this task.
*
*/
#include "src/SuperLUSupport/SuperLUSupport.h"
#include "src/Core/util/ReenableStupidWarnings.h"
#endif // EIGEN_SUPERLUSUPPORT_MODULE_H

View File

@@ -0,0 +1,40 @@
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#ifndef EIGEN_UMFPACKSUPPORT_MODULE_H
#define EIGEN_UMFPACKSUPPORT_MODULE_H
#include "SparseCore"
#include "src/Core/util/DisableStupidWarnings.h"
extern "C" {
#include <umfpack.h>
}
/** \ingroup Support_modules
* \defgroup UmfPackSupport_Module UmfPackSupport module
*
* This module provides an interface to the UmfPack library which is part of the <a href="http://www.suitesparse.com">suitesparse</a> package.
* It provides the following factorization class:
* - class UmfPackLU: a multifrontal sequential LU factorization.
*
* \code
* #include <Eigen/UmfPackSupport>
* \endcode
*
* In order to use this module, the umfpack headers must be accessible from the include paths, and your binary must be linked to the umfpack library and its dependencies.
* The dependencies depend on how umfpack has been compiled.
* For a cmake based project, you can use our FindUmfPack.cmake module to help you in this task.
*
*/
#include "src/UmfPackSupport/UmfPackSupport.h"
#include "src/Core/util/ReenableStupidWarnings.h"
#endif // EIGEN_UMFPACKSUPPORT_MODULE_H

View File

@@ -0,0 +1,673 @@
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2008-2011 Gael Guennebaud <gael.guennebaud@inria.fr>
// Copyright (C) 2009 Keir Mierle <mierle@gmail.com>
// Copyright (C) 2009 Benoit Jacob <jacob.benoit.1@gmail.com>
// Copyright (C) 2011 Timothy E. Holy <tim.holy@gmail.com >
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#ifndef EIGEN_LDLT_H
#define EIGEN_LDLT_H
namespace Eigen {
namespace internal {
template<typename MatrixType, int UpLo> struct LDLT_Traits;
// PositiveSemiDef means positive semi-definite and non-zero; same for NegativeSemiDef
enum SignMatrix { PositiveSemiDef, NegativeSemiDef, ZeroSign, Indefinite };
}
/** \ingroup Cholesky_Module
*
* \class LDLT
*
* \brief Robust Cholesky decomposition of a matrix with pivoting
*
* \tparam _MatrixType the type of the matrix of which to compute the LDL^T Cholesky decomposition
* \tparam _UpLo the triangular part that will be used for the decompositon: Lower (default) or Upper.
* The other triangular part won't be read.
*
* Perform a robust Cholesky decomposition of a positive semidefinite or negative semidefinite
* matrix \f$ A \f$ such that \f$ A = P^TLDL^*P \f$, where P is a permutation matrix, L
* is lower triangular with a unit diagonal and D is a diagonal matrix.
*
* The decomposition uses pivoting to ensure stability, so that L will have
* zeros in the bottom right rank(A) - n submatrix. Avoiding the square root
* on D also stabilizes the computation.
*
* Remember that Cholesky decompositions are not rank-revealing. Also, do not use a Cholesky
* decomposition to determine whether a system of equations has a solution.
*
* This class supports the \link InplaceDecomposition inplace decomposition \endlink mechanism.
*
* \sa MatrixBase::ldlt(), SelfAdjointView::ldlt(), class LLT
*/
template<typename _MatrixType, int _UpLo> class LDLT
{
public:
typedef _MatrixType MatrixType;
enum {
RowsAtCompileTime = MatrixType::RowsAtCompileTime,
ColsAtCompileTime = MatrixType::ColsAtCompileTime,
MaxRowsAtCompileTime = MatrixType::MaxRowsAtCompileTime,
MaxColsAtCompileTime = MatrixType::MaxColsAtCompileTime,
UpLo = _UpLo
};
typedef typename MatrixType::Scalar Scalar;
typedef typename NumTraits<typename MatrixType::Scalar>::Real RealScalar;
typedef Eigen::Index Index; ///< \deprecated since Eigen 3.3
typedef typename MatrixType::StorageIndex StorageIndex;
typedef Matrix<Scalar, RowsAtCompileTime, 1, 0, MaxRowsAtCompileTime, 1> TmpMatrixType;
typedef Transpositions<RowsAtCompileTime, MaxRowsAtCompileTime> TranspositionType;
typedef PermutationMatrix<RowsAtCompileTime, MaxRowsAtCompileTime> PermutationType;
typedef internal::LDLT_Traits<MatrixType,UpLo> Traits;
/** \brief Default Constructor.
*
* The default constructor is useful in cases in which the user intends to
* perform decompositions via LDLT::compute(const MatrixType&).
*/
LDLT()
: m_matrix(),
m_transpositions(),
m_sign(internal::ZeroSign),
m_isInitialized(false)
{}
/** \brief Default Constructor with memory preallocation
*
* Like the default constructor but with preallocation of the internal data
* according to the specified problem \a size.
* \sa LDLT()
*/
explicit LDLT(Index size)
: m_matrix(size, size),
m_transpositions(size),
m_temporary(size),
m_sign(internal::ZeroSign),
m_isInitialized(false)
{}
/** \brief Constructor with decomposition
*
* This calculates the decomposition for the input \a matrix.
*
* \sa LDLT(Index size)
*/
template<typename InputType>
explicit LDLT(const EigenBase<InputType>& matrix)
: m_matrix(matrix.rows(), matrix.cols()),
m_transpositions(matrix.rows()),
m_temporary(matrix.rows()),
m_sign(internal::ZeroSign),
m_isInitialized(false)
{
compute(matrix.derived());
}
/** \brief Constructs a LDLT factorization from a given matrix
*
* This overloaded constructor is provided for \link InplaceDecomposition inplace decomposition \endlink when \c MatrixType is a Eigen::Ref.
*
* \sa LDLT(const EigenBase&)
*/
template<typename InputType>
explicit LDLT(EigenBase<InputType>& matrix)
: m_matrix(matrix.derived()),
m_transpositions(matrix.rows()),
m_temporary(matrix.rows()),
m_sign(internal::ZeroSign),
m_isInitialized(false)
{
compute(matrix.derived());
}
/** Clear any existing decomposition
* \sa rankUpdate(w,sigma)
*/
void setZero()
{
m_isInitialized = false;
}
/** \returns a view of the upper triangular matrix U */
inline typename Traits::MatrixU matrixU() const
{
eigen_assert(m_isInitialized && "LDLT is not initialized.");
return Traits::getU(m_matrix);
}
/** \returns a view of the lower triangular matrix L */
inline typename Traits::MatrixL matrixL() const
{
eigen_assert(m_isInitialized && "LDLT is not initialized.");
return Traits::getL(m_matrix);
}
/** \returns the permutation matrix P as a transposition sequence.
*/
inline const TranspositionType& transpositionsP() const
{
eigen_assert(m_isInitialized && "LDLT is not initialized.");
return m_transpositions;
}
/** \returns the coefficients of the diagonal matrix D */
inline Diagonal<const MatrixType> vectorD() const
{
eigen_assert(m_isInitialized && "LDLT is not initialized.");
return m_matrix.diagonal();
}
/** \returns true if the matrix is positive (semidefinite) */
inline bool isPositive() const
{
eigen_assert(m_isInitialized && "LDLT is not initialized.");
return m_sign == internal::PositiveSemiDef || m_sign == internal::ZeroSign;
}
/** \returns true if the matrix is negative (semidefinite) */
inline bool isNegative(void) const
{
eigen_assert(m_isInitialized && "LDLT is not initialized.");
return m_sign == internal::NegativeSemiDef || m_sign == internal::ZeroSign;
}
/** \returns a solution x of \f$ A x = b \f$ using the current decomposition of A.
*
* This function also supports in-place solves using the syntax <tt>x = decompositionObject.solve(x)</tt> .
*
* \note_about_checking_solutions
*
* More precisely, this method solves \f$ A x = b \f$ using the decomposition \f$ A = P^T L D L^* P \f$
* by solving the systems \f$ P^T y_1 = b \f$, \f$ L y_2 = y_1 \f$, \f$ D y_3 = y_2 \f$,
* \f$ L^* y_4 = y_3 \f$ and \f$ P x = y_4 \f$ in succession. If the matrix \f$ A \f$ is singular, then
* \f$ D \f$ will also be singular (all the other matrices are invertible). In that case, the
* least-square solution of \f$ D y_3 = y_2 \f$ is computed. This does not mean that this function
* computes the least-square solution of \f$ A x = b \f$ is \f$ A \f$ is singular.
*
* \sa MatrixBase::ldlt(), SelfAdjointView::ldlt()
*/
template<typename Rhs>
inline const Solve<LDLT, Rhs>
solve(const MatrixBase<Rhs>& b) const
{
eigen_assert(m_isInitialized && "LDLT is not initialized.");
eigen_assert(m_matrix.rows()==b.rows()
&& "LDLT::solve(): invalid number of rows of the right hand side matrix b");
return Solve<LDLT, Rhs>(*this, b.derived());
}
template<typename Derived>
bool solveInPlace(MatrixBase<Derived> &bAndX) const;
template<typename InputType>
LDLT& compute(const EigenBase<InputType>& matrix);
/** \returns an estimate of the reciprocal condition number of the matrix of
* which \c *this is the LDLT decomposition.
*/
RealScalar rcond() const
{
eigen_assert(m_isInitialized && "LDLT is not initialized.");
return internal::rcond_estimate_helper(m_l1_norm, *this);
}
template <typename Derived>
LDLT& rankUpdate(const MatrixBase<Derived>& w, const RealScalar& alpha=1);
/** \returns the internal LDLT decomposition matrix
*
* TODO: document the storage layout
*/
inline const MatrixType& matrixLDLT() const
{
eigen_assert(m_isInitialized && "LDLT is not initialized.");
return m_matrix;
}
MatrixType reconstructedMatrix() const;
/** \returns the adjoint of \c *this, that is, a const reference to the decomposition itself as the underlying matrix is self-adjoint.
*
* This method is provided for compatibility with other matrix decompositions, thus enabling generic code such as:
* \code x = decomposition.adjoint().solve(b) \endcode
*/
const LDLT& adjoint() const { return *this; };
inline Index rows() const { return m_matrix.rows(); }
inline Index cols() const { return m_matrix.cols(); }
/** \brief Reports whether previous computation was successful.
*
* \returns \c Success if computation was succesful,
* \c NumericalIssue if the factorization failed because of a zero pivot.
*/
ComputationInfo info() const
{
eigen_assert(m_isInitialized && "LDLT is not initialized.");
return m_info;
}
#ifndef EIGEN_PARSED_BY_DOXYGEN
template<typename RhsType, typename DstType>
EIGEN_DEVICE_FUNC
void _solve_impl(const RhsType &rhs, DstType &dst) const;
#endif
protected:
static void check_template_parameters()
{
EIGEN_STATIC_ASSERT_NON_INTEGER(Scalar);
}
/** \internal
* Used to compute and store the Cholesky decomposition A = L D L^* = U^* D U.
* The strict upper part is used during the decomposition, the strict lower
* part correspond to the coefficients of L (its diagonal is equal to 1 and
* is not stored), and the diagonal entries correspond to D.
*/
MatrixType m_matrix;
RealScalar m_l1_norm;
TranspositionType m_transpositions;
TmpMatrixType m_temporary;
internal::SignMatrix m_sign;
bool m_isInitialized;
ComputationInfo m_info;
};
namespace internal {
template<int UpLo> struct ldlt_inplace;
template<> struct ldlt_inplace<Lower>
{
template<typename MatrixType, typename TranspositionType, typename Workspace>
static bool unblocked(MatrixType& mat, TranspositionType& transpositions, Workspace& temp, SignMatrix& sign)
{
using std::abs;
typedef typename MatrixType::Scalar Scalar;
typedef typename MatrixType::RealScalar RealScalar;
typedef typename TranspositionType::StorageIndex IndexType;
eigen_assert(mat.rows()==mat.cols());
const Index size = mat.rows();
bool found_zero_pivot = false;
bool ret = true;
if (size <= 1)
{
transpositions.setIdentity();
if(size==0) sign = ZeroSign;
else if (numext::real(mat.coeff(0,0)) > static_cast<RealScalar>(0) ) sign = PositiveSemiDef;
else if (numext::real(mat.coeff(0,0)) < static_cast<RealScalar>(0)) sign = NegativeSemiDef;
else sign = ZeroSign;
return true;
}
for (Index k = 0; k < size; ++k)
{
// Find largest diagonal element
Index index_of_biggest_in_corner;
mat.diagonal().tail(size-k).cwiseAbs().maxCoeff(&index_of_biggest_in_corner);
index_of_biggest_in_corner += k;
transpositions.coeffRef(k) = IndexType(index_of_biggest_in_corner);
if(k != index_of_biggest_in_corner)
{
// apply the transposition while taking care to consider only
// the lower triangular part
Index s = size-index_of_biggest_in_corner-1; // trailing size after the biggest element
mat.row(k).head(k).swap(mat.row(index_of_biggest_in_corner).head(k));
mat.col(k).tail(s).swap(mat.col(index_of_biggest_in_corner).tail(s));
std::swap(mat.coeffRef(k,k),mat.coeffRef(index_of_biggest_in_corner,index_of_biggest_in_corner));
for(Index i=k+1;i<index_of_biggest_in_corner;++i)
{
Scalar tmp = mat.coeffRef(i,k);
mat.coeffRef(i,k) = numext::conj(mat.coeffRef(index_of_biggest_in_corner,i));
mat.coeffRef(index_of_biggest_in_corner,i) = numext::conj(tmp);
}
if(NumTraits<Scalar>::IsComplex)
mat.coeffRef(index_of_biggest_in_corner,k) = numext::conj(mat.coeff(index_of_biggest_in_corner,k));
}
// partition the matrix:
// A00 | - | -
// lu = A10 | A11 | -
// A20 | A21 | A22
Index rs = size - k - 1;
Block<MatrixType,Dynamic,1> A21(mat,k+1,k,rs,1);
Block<MatrixType,1,Dynamic> A10(mat,k,0,1,k);
Block<MatrixType,Dynamic,Dynamic> A20(mat,k+1,0,rs,k);
if(k>0)
{
temp.head(k) = mat.diagonal().real().head(k).asDiagonal() * A10.adjoint();
mat.coeffRef(k,k) -= (A10 * temp.head(k)).value();
if(rs>0)
A21.noalias() -= A20 * temp.head(k);
}
// In some previous versions of Eigen (e.g., 3.2.1), the scaling was omitted if the pivot
// was smaller than the cutoff value. However, since LDLT is not rank-revealing
// we should only make sure that we do not introduce INF or NaN values.
// Remark that LAPACK also uses 0 as the cutoff value.
RealScalar realAkk = numext::real(mat.coeffRef(k,k));
bool pivot_is_valid = (abs(realAkk) > RealScalar(0));
if(k==0 && !pivot_is_valid)
{
// The entire diagonal is zero, there is nothing more to do
// except filling the transpositions, and checking whether the matrix is zero.
sign = ZeroSign;
for(Index j = 0; j<size; ++j)
{
transpositions.coeffRef(j) = IndexType(j);
ret = ret && (mat.col(j).tail(size-j-1).array()==Scalar(0)).all();
}
return ret;
}
if((rs>0) && pivot_is_valid)
A21 /= realAkk;
else if(rs>0)
ret = ret && (A21.array()==Scalar(0)).all();
if(found_zero_pivot && pivot_is_valid) ret = false; // factorization failed
else if(!pivot_is_valid) found_zero_pivot = true;
if (sign == PositiveSemiDef) {
if (realAkk < static_cast<RealScalar>(0)) sign = Indefinite;
} else if (sign == NegativeSemiDef) {
if (realAkk > static_cast<RealScalar>(0)) sign = Indefinite;
} else if (sign == ZeroSign) {
if (realAkk > static_cast<RealScalar>(0)) sign = PositiveSemiDef;
else if (realAkk < static_cast<RealScalar>(0)) sign = NegativeSemiDef;
}
}
return ret;
}
// Reference for the algorithm: Davis and Hager, "Multiple Rank
// Modifications of a Sparse Cholesky Factorization" (Algorithm 1)
// Trivial rearrangements of their computations (Timothy E. Holy)
// allow their algorithm to work for rank-1 updates even if the
// original matrix is not of full rank.
// Here only rank-1 updates are implemented, to reduce the
// requirement for intermediate storage and improve accuracy
template<typename MatrixType, typename WDerived>
static bool updateInPlace(MatrixType& mat, MatrixBase<WDerived>& w, const typename MatrixType::RealScalar& sigma=1)
{
using numext::isfinite;
typedef typename MatrixType::Scalar Scalar;
typedef typename MatrixType::RealScalar RealScalar;
const Index size = mat.rows();
eigen_assert(mat.cols() == size && w.size()==size);
RealScalar alpha = 1;
// Apply the update
for (Index j = 0; j < size; j++)
{
// Check for termination due to an original decomposition of low-rank
if (!(isfinite)(alpha))
break;
// Update the diagonal terms
RealScalar dj = numext::real(mat.coeff(j,j));
Scalar wj = w.coeff(j);
RealScalar swj2 = sigma*numext::abs2(wj);
RealScalar gamma = dj*alpha + swj2;
mat.coeffRef(j,j) += swj2/alpha;
alpha += swj2/dj;
// Update the terms of L
Index rs = size-j-1;
w.tail(rs) -= wj * mat.col(j).tail(rs);
if(gamma != 0)
mat.col(j).tail(rs) += (sigma*numext::conj(wj)/gamma)*w.tail(rs);
}
return true;
}
template<typename MatrixType, typename TranspositionType, typename Workspace, typename WType>
static bool update(MatrixType& mat, const TranspositionType& transpositions, Workspace& tmp, const WType& w, const typename MatrixType::RealScalar& sigma=1)
{
// Apply the permutation to the input w
tmp = transpositions * w;
return ldlt_inplace<Lower>::updateInPlace(mat,tmp,sigma);
}
};
template<> struct ldlt_inplace<Upper>
{
template<typename MatrixType, typename TranspositionType, typename Workspace>
static EIGEN_STRONG_INLINE bool unblocked(MatrixType& mat, TranspositionType& transpositions, Workspace& temp, SignMatrix& sign)
{
Transpose<MatrixType> matt(mat);
return ldlt_inplace<Lower>::unblocked(matt, transpositions, temp, sign);
}
template<typename MatrixType, typename TranspositionType, typename Workspace, typename WType>
static EIGEN_STRONG_INLINE bool update(MatrixType& mat, TranspositionType& transpositions, Workspace& tmp, WType& w, const typename MatrixType::RealScalar& sigma=1)
{
Transpose<MatrixType> matt(mat);
return ldlt_inplace<Lower>::update(matt, transpositions, tmp, w.conjugate(), sigma);
}
};
template<typename MatrixType> struct LDLT_Traits<MatrixType,Lower>
{
typedef const TriangularView<const MatrixType, UnitLower> MatrixL;
typedef const TriangularView<const typename MatrixType::AdjointReturnType, UnitUpper> MatrixU;
static inline MatrixL getL(const MatrixType& m) { return MatrixL(m); }
static inline MatrixU getU(const MatrixType& m) { return MatrixU(m.adjoint()); }
};
template<typename MatrixType> struct LDLT_Traits<MatrixType,Upper>
{
typedef const TriangularView<const typename MatrixType::AdjointReturnType, UnitLower> MatrixL;
typedef const TriangularView<const MatrixType, UnitUpper> MatrixU;
static inline MatrixL getL(const MatrixType& m) { return MatrixL(m.adjoint()); }
static inline MatrixU getU(const MatrixType& m) { return MatrixU(m); }
};
} // end namespace internal
/** Compute / recompute the LDLT decomposition A = L D L^* = U^* D U of \a matrix
*/
template<typename MatrixType, int _UpLo>
template<typename InputType>
LDLT<MatrixType,_UpLo>& LDLT<MatrixType,_UpLo>::compute(const EigenBase<InputType>& a)
{
check_template_parameters();
eigen_assert(a.rows()==a.cols());
const Index size = a.rows();
m_matrix = a.derived();
// Compute matrix L1 norm = max abs column sum.
m_l1_norm = RealScalar(0);
// TODO move this code to SelfAdjointView
for (Index col = 0; col < size; ++col) {
RealScalar abs_col_sum;
if (_UpLo == Lower)
abs_col_sum = m_matrix.col(col).tail(size - col).template lpNorm<1>() + m_matrix.row(col).head(col).template lpNorm<1>();
else
abs_col_sum = m_matrix.col(col).head(col).template lpNorm<1>() + m_matrix.row(col).tail(size - col).template lpNorm<1>();
if (abs_col_sum > m_l1_norm)
m_l1_norm = abs_col_sum;
}
m_transpositions.resize(size);
m_isInitialized = false;
m_temporary.resize(size);
m_sign = internal::ZeroSign;
m_info = internal::ldlt_inplace<UpLo>::unblocked(m_matrix, m_transpositions, m_temporary, m_sign) ? Success : NumericalIssue;
m_isInitialized = true;
return *this;
}
/** Update the LDLT decomposition: given A = L D L^T, efficiently compute the decomposition of A + sigma w w^T.
* \param w a vector to be incorporated into the decomposition.
* \param sigma a scalar, +1 for updates and -1 for "downdates," which correspond to removing previously-added column vectors. Optional; default value is +1.
* \sa setZero()
*/
template<typename MatrixType, int _UpLo>
template<typename Derived>
LDLT<MatrixType,_UpLo>& LDLT<MatrixType,_UpLo>::rankUpdate(const MatrixBase<Derived>& w, const typename LDLT<MatrixType,_UpLo>::RealScalar& sigma)
{
typedef typename TranspositionType::StorageIndex IndexType;
const Index size = w.rows();
if (m_isInitialized)
{
eigen_assert(m_matrix.rows()==size);
}
else
{
m_matrix.resize(size,size);
m_matrix.setZero();
m_transpositions.resize(size);
for (Index i = 0; i < size; i++)
m_transpositions.coeffRef(i) = IndexType(i);
m_temporary.resize(size);
m_sign = sigma>=0 ? internal::PositiveSemiDef : internal::NegativeSemiDef;
m_isInitialized = true;
}
internal::ldlt_inplace<UpLo>::update(m_matrix, m_transpositions, m_temporary, w, sigma);
return *this;
}
#ifndef EIGEN_PARSED_BY_DOXYGEN
template<typename _MatrixType, int _UpLo>
template<typename RhsType, typename DstType>
void LDLT<_MatrixType,_UpLo>::_solve_impl(const RhsType &rhs, DstType &dst) const
{
eigen_assert(rhs.rows() == rows());
// dst = P b
dst = m_transpositions * rhs;
// dst = L^-1 (P b)
matrixL().solveInPlace(dst);
// dst = D^-1 (L^-1 P b)
// more precisely, use pseudo-inverse of D (see bug 241)
using std::abs;
const typename Diagonal<const MatrixType>::RealReturnType vecD(vectorD());
// In some previous versions, tolerance was set to the max of 1/highest (or rather numeric_limits::min())
// and the maximal diagonal entry * epsilon as motivated by LAPACK's xGELSS:
// RealScalar tolerance = numext::maxi(vecD.array().abs().maxCoeff() * NumTraits<RealScalar>::epsilon(),RealScalar(1) / NumTraits<RealScalar>::highest());
// However, LDLT is not rank revealing, and so adjusting the tolerance wrt to the highest
// diagonal element is not well justified and leads to numerical issues in some cases.
// Moreover, Lapack's xSYTRS routines use 0 for the tolerance.
// Using numeric_limits::min() gives us more robustness to denormals.
RealScalar tolerance = (std::numeric_limits<RealScalar>::min)();
for (Index i = 0; i < vecD.size(); ++i)
{
if(abs(vecD(i)) > tolerance)
dst.row(i) /= vecD(i);
else
dst.row(i).setZero();
}
// dst = L^-T (D^-1 L^-1 P b)
matrixU().solveInPlace(dst);
// dst = P^-1 (L^-T D^-1 L^-1 P b) = A^-1 b
dst = m_transpositions.transpose() * dst;
}
#endif
/** \internal use x = ldlt_object.solve(x);
*
* This is the \em in-place version of solve().
*
* \param bAndX represents both the right-hand side matrix b and result x.
*
* \returns true always! If you need to check for existence of solutions, use another decomposition like LU, QR, or SVD.
*
* This version avoids a copy when the right hand side matrix b is not
* needed anymore.
*
* \sa LDLT::solve(), MatrixBase::ldlt()
*/
template<typename MatrixType,int _UpLo>
template<typename Derived>
bool LDLT<MatrixType,_UpLo>::solveInPlace(MatrixBase<Derived> &bAndX) const
{
eigen_assert(m_isInitialized && "LDLT is not initialized.");
eigen_assert(m_matrix.rows() == bAndX.rows());
bAndX = this->solve(bAndX);
return true;
}
/** \returns the matrix represented by the decomposition,
* i.e., it returns the product: P^T L D L^* P.
* This function is provided for debug purpose. */
template<typename MatrixType, int _UpLo>
MatrixType LDLT<MatrixType,_UpLo>::reconstructedMatrix() const
{
eigen_assert(m_isInitialized && "LDLT is not initialized.");
const Index size = m_matrix.rows();
MatrixType res(size,size);
// P
res.setIdentity();
res = transpositionsP() * res;
// L^* P
res = matrixU() * res;
// D(L^*P)
res = vectorD().real().asDiagonal() * res;
// L(DL^*P)
res = matrixL() * res;
// P^T (LDL^*P)
res = transpositionsP().transpose() * res;
return res;
}
/** \cholesky_module
* \returns the Cholesky decomposition with full pivoting without square root of \c *this
* \sa MatrixBase::ldlt()
*/
template<typename MatrixType, unsigned int UpLo>
inline const LDLT<typename SelfAdjointView<MatrixType, UpLo>::PlainObject, UpLo>
SelfAdjointView<MatrixType, UpLo>::ldlt() const
{
return LDLT<PlainObject,UpLo>(m_matrix);
}
/** \cholesky_module
* \returns the Cholesky decomposition with full pivoting without square root of \c *this
* \sa SelfAdjointView::ldlt()
*/
template<typename Derived>
inline const LDLT<typename MatrixBase<Derived>::PlainObject>
MatrixBase<Derived>::ldlt() const
{
return LDLT<PlainObject>(derived());
}
} // end namespace Eigen
#endif // EIGEN_LDLT_H

View File

@@ -0,0 +1,542 @@
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#ifndef EIGEN_LLT_H
#define EIGEN_LLT_H
namespace Eigen {
namespace internal{
template<typename MatrixType, int UpLo> struct LLT_Traits;
}
/** \ingroup Cholesky_Module
*
* \class LLT
*
* \brief Standard Cholesky decomposition (LL^T) of a matrix and associated features
*
* \tparam _MatrixType the type of the matrix of which we are computing the LL^T Cholesky decomposition
* \tparam _UpLo the triangular part that will be used for the decompositon: Lower (default) or Upper.
* The other triangular part won't be read.
*
* This class performs a LL^T Cholesky decomposition of a symmetric, positive definite
* matrix A such that A = LL^* = U^*U, where L is lower triangular.
*
* While the Cholesky decomposition is particularly useful to solve selfadjoint problems like D^*D x = b,
* for that purpose, we recommend the Cholesky decomposition without square root which is more stable
* and even faster. Nevertheless, this standard Cholesky decomposition remains useful in many other
* situations like generalised eigen problems with hermitian matrices.
*
* Remember that Cholesky decompositions are not rank-revealing. This LLT decomposition is only stable on positive definite matrices,
* use LDLT instead for the semidefinite case. Also, do not use a Cholesky decomposition to determine whether a system of equations
* has a solution.
*
* Example: \include LLT_example.cpp
* Output: \verbinclude LLT_example.out
*
* \b Performance: for best performance, it is recommended to use a column-major storage format
* with the Lower triangular part (the default), or, equivalently, a row-major storage format
* with the Upper triangular part. Otherwise, you might get a 20% slowdown for the full factorization
* step, and rank-updates can be up to 3 times slower.
*
* This class supports the \link InplaceDecomposition inplace decomposition \endlink mechanism.
*
* Note that during the decomposition, only the lower (or upper, as defined by _UpLo) triangular part of A is considered.
* Therefore, the strict lower part does not have to store correct values.
*
* \sa MatrixBase::llt(), SelfAdjointView::llt(), class LDLT
*/
template<typename _MatrixType, int _UpLo> class LLT
{
public:
typedef _MatrixType MatrixType;
enum {
RowsAtCompileTime = MatrixType::RowsAtCompileTime,
ColsAtCompileTime = MatrixType::ColsAtCompileTime,
MaxColsAtCompileTime = MatrixType::MaxColsAtCompileTime
};
typedef typename MatrixType::Scalar Scalar;
typedef typename NumTraits<typename MatrixType::Scalar>::Real RealScalar;
typedef Eigen::Index Index; ///< \deprecated since Eigen 3.3
typedef typename MatrixType::StorageIndex StorageIndex;
enum {
PacketSize = internal::packet_traits<Scalar>::size,
AlignmentMask = int(PacketSize)-1,
UpLo = _UpLo
};
typedef internal::LLT_Traits<MatrixType,UpLo> Traits;
/**
* \brief Default Constructor.
*
* The default constructor is useful in cases in which the user intends to
* perform decompositions via LLT::compute(const MatrixType&).
*/
LLT() : m_matrix(), m_isInitialized(false) {}
/** \brief Default Constructor with memory preallocation
*
* Like the default constructor but with preallocation of the internal data
* according to the specified problem \a size.
* \sa LLT()
*/
explicit LLT(Index size) : m_matrix(size, size),
m_isInitialized(false) {}
template<typename InputType>
explicit LLT(const EigenBase<InputType>& matrix)
: m_matrix(matrix.rows(), matrix.cols()),
m_isInitialized(false)
{
compute(matrix.derived());
}
/** \brief Constructs a LDLT factorization from a given matrix
*
* This overloaded constructor is provided for \link InplaceDecomposition inplace decomposition \endlink when
* \c MatrixType is a Eigen::Ref.
*
* \sa LLT(const EigenBase&)
*/
template<typename InputType>
explicit LLT(EigenBase<InputType>& matrix)
: m_matrix(matrix.derived()),
m_isInitialized(false)
{
compute(matrix.derived());
}
/** \returns a view of the upper triangular matrix U */
inline typename Traits::MatrixU matrixU() const
{
eigen_assert(m_isInitialized && "LLT is not initialized.");
return Traits::getU(m_matrix);
}
/** \returns a view of the lower triangular matrix L */
inline typename Traits::MatrixL matrixL() const
{
eigen_assert(m_isInitialized && "LLT is not initialized.");
return Traits::getL(m_matrix);
}
/** \returns the solution x of \f$ A x = b \f$ using the current decomposition of A.
*
* Since this LLT class assumes anyway that the matrix A is invertible, the solution
* theoretically exists and is unique regardless of b.
*
* Example: \include LLT_solve.cpp
* Output: \verbinclude LLT_solve.out
*
* \sa solveInPlace(), MatrixBase::llt(), SelfAdjointView::llt()
*/
template<typename Rhs>
inline const Solve<LLT, Rhs>
solve(const MatrixBase<Rhs>& b) const
{
eigen_assert(m_isInitialized && "LLT is not initialized.");
eigen_assert(m_matrix.rows()==b.rows()
&& "LLT::solve(): invalid number of rows of the right hand side matrix b");
return Solve<LLT, Rhs>(*this, b.derived());
}
template<typename Derived>
void solveInPlace(const MatrixBase<Derived> &bAndX) const;
template<typename InputType>
LLT& compute(const EigenBase<InputType>& matrix);
/** \returns an estimate of the reciprocal condition number of the matrix of
* which \c *this is the Cholesky decomposition.
*/
RealScalar rcond() const
{
eigen_assert(m_isInitialized && "LLT is not initialized.");
eigen_assert(m_info == Success && "LLT failed because matrix appears to be negative");
return internal::rcond_estimate_helper(m_l1_norm, *this);
}
/** \returns the LLT decomposition matrix
*
* TODO: document the storage layout
*/
inline const MatrixType& matrixLLT() const
{
eigen_assert(m_isInitialized && "LLT is not initialized.");
return m_matrix;
}
MatrixType reconstructedMatrix() const;
/** \brief Reports whether previous computation was successful.
*
* \returns \c Success if computation was succesful,
* \c NumericalIssue if the matrix.appears not to be positive definite.
*/
ComputationInfo info() const
{
eigen_assert(m_isInitialized && "LLT is not initialized.");
return m_info;
}
/** \returns the adjoint of \c *this, that is, a const reference to the decomposition itself as the underlying matrix is self-adjoint.
*
* This method is provided for compatibility with other matrix decompositions, thus enabling generic code such as:
* \code x = decomposition.adjoint().solve(b) \endcode
*/
const LLT& adjoint() const { return *this; };
inline Index rows() const { return m_matrix.rows(); }
inline Index cols() const { return m_matrix.cols(); }
template<typename VectorType>
LLT rankUpdate(const VectorType& vec, const RealScalar& sigma = 1);
#ifndef EIGEN_PARSED_BY_DOXYGEN
template<typename RhsType, typename DstType>
EIGEN_DEVICE_FUNC
void _solve_impl(const RhsType &rhs, DstType &dst) const;
#endif
protected:
static void check_template_parameters()
{
EIGEN_STATIC_ASSERT_NON_INTEGER(Scalar);
}
/** \internal
* Used to compute and store L
* The strict upper part is not used and even not initialized.
*/
MatrixType m_matrix;
RealScalar m_l1_norm;
bool m_isInitialized;
ComputationInfo m_info;
};
namespace internal {
template<typename Scalar, int UpLo> struct llt_inplace;
template<typename MatrixType, typename VectorType>
static Index llt_rank_update_lower(MatrixType& mat, const VectorType& vec, const typename MatrixType::RealScalar& sigma)
{
using std::sqrt;
typedef typename MatrixType::Scalar Scalar;
typedef typename MatrixType::RealScalar RealScalar;
typedef typename MatrixType::ColXpr ColXpr;
typedef typename internal::remove_all<ColXpr>::type ColXprCleaned;
typedef typename ColXprCleaned::SegmentReturnType ColXprSegment;
typedef Matrix<Scalar,Dynamic,1> TempVectorType;
typedef typename TempVectorType::SegmentReturnType TempVecSegment;
Index n = mat.cols();
eigen_assert(mat.rows()==n && vec.size()==n);
TempVectorType temp;
if(sigma>0)
{
// This version is based on Givens rotations.
// It is faster than the other one below, but only works for updates,
// i.e., for sigma > 0
temp = sqrt(sigma) * vec;
for(Index i=0; i<n; ++i)
{
JacobiRotation<Scalar> g;
g.makeGivens(mat(i,i), -temp(i), &mat(i,i));
Index rs = n-i-1;
if(rs>0)
{
ColXprSegment x(mat.col(i).tail(rs));
TempVecSegment y(temp.tail(rs));
apply_rotation_in_the_plane(x, y, g);
}
}
}
else
{
temp = vec;
RealScalar beta = 1;
for(Index j=0; j<n; ++j)
{
RealScalar Ljj = numext::real(mat.coeff(j,j));
RealScalar dj = numext::abs2(Ljj);
Scalar wj = temp.coeff(j);
RealScalar swj2 = sigma*numext::abs2(wj);
RealScalar gamma = dj*beta + swj2;
RealScalar x = dj + swj2/beta;
if (x<=RealScalar(0))
return j;
RealScalar nLjj = sqrt(x);
mat.coeffRef(j,j) = nLjj;
beta += swj2/dj;
// Update the terms of L
Index rs = n-j-1;
if(rs)
{
temp.tail(rs) -= (wj/Ljj) * mat.col(j).tail(rs);
if(gamma != 0)
mat.col(j).tail(rs) = (nLjj/Ljj) * mat.col(j).tail(rs) + (nLjj * sigma*numext::conj(wj)/gamma)*temp.tail(rs);
}
}
}
return -1;
}
template<typename Scalar> struct llt_inplace<Scalar, Lower>
{
typedef typename NumTraits<Scalar>::Real RealScalar;
template<typename MatrixType>
static Index unblocked(MatrixType& mat)
{
using std::sqrt;
eigen_assert(mat.rows()==mat.cols());
const Index size = mat.rows();
for(Index k = 0; k < size; ++k)
{
Index rs = size-k-1; // remaining size
Block<MatrixType,Dynamic,1> A21(mat,k+1,k,rs,1);
Block<MatrixType,1,Dynamic> A10(mat,k,0,1,k);
Block<MatrixType,Dynamic,Dynamic> A20(mat,k+1,0,rs,k);
RealScalar x = numext::real(mat.coeff(k,k));
if (k>0) x -= A10.squaredNorm();
if (x<=RealScalar(0))
return k;
mat.coeffRef(k,k) = x = sqrt(x);
if (k>0 && rs>0) A21.noalias() -= A20 * A10.adjoint();
if (rs>0) A21 /= x;
}
return -1;
}
template<typename MatrixType>
static Index blocked(MatrixType& m)
{
eigen_assert(m.rows()==m.cols());
Index size = m.rows();
if(size<32)
return unblocked(m);
Index blockSize = size/8;
blockSize = (blockSize/16)*16;
blockSize = (std::min)((std::max)(blockSize,Index(8)), Index(128));
for (Index k=0; k<size; k+=blockSize)
{
// partition the matrix:
// A00 | - | -
// lu = A10 | A11 | -
// A20 | A21 | A22
Index bs = (std::min)(blockSize, size-k);
Index rs = size - k - bs;
Block<MatrixType,Dynamic,Dynamic> A11(m,k, k, bs,bs);
Block<MatrixType,Dynamic,Dynamic> A21(m,k+bs,k, rs,bs);
Block<MatrixType,Dynamic,Dynamic> A22(m,k+bs,k+bs,rs,rs);
Index ret;
if((ret=unblocked(A11))>=0) return k+ret;
if(rs>0) A11.adjoint().template triangularView<Upper>().template solveInPlace<OnTheRight>(A21);
if(rs>0) A22.template selfadjointView<Lower>().rankUpdate(A21,typename NumTraits<RealScalar>::Literal(-1)); // bottleneck
}
return -1;
}
template<typename MatrixType, typename VectorType>
static Index rankUpdate(MatrixType& mat, const VectorType& vec, const RealScalar& sigma)
{
return Eigen::internal::llt_rank_update_lower(mat, vec, sigma);
}
};
template<typename Scalar> struct llt_inplace<Scalar, Upper>
{
typedef typename NumTraits<Scalar>::Real RealScalar;
template<typename MatrixType>
static EIGEN_STRONG_INLINE Index unblocked(MatrixType& mat)
{
Transpose<MatrixType> matt(mat);
return llt_inplace<Scalar, Lower>::unblocked(matt);
}
template<typename MatrixType>
static EIGEN_STRONG_INLINE Index blocked(MatrixType& mat)
{
Transpose<MatrixType> matt(mat);
return llt_inplace<Scalar, Lower>::blocked(matt);
}
template<typename MatrixType, typename VectorType>
static Index rankUpdate(MatrixType& mat, const VectorType& vec, const RealScalar& sigma)
{
Transpose<MatrixType> matt(mat);
return llt_inplace<Scalar, Lower>::rankUpdate(matt, vec.conjugate(), sigma);
}
};
template<typename MatrixType> struct LLT_Traits<MatrixType,Lower>
{
typedef const TriangularView<const MatrixType, Lower> MatrixL;
typedef const TriangularView<const typename MatrixType::AdjointReturnType, Upper> MatrixU;
static inline MatrixL getL(const MatrixType& m) { return MatrixL(m); }
static inline MatrixU getU(const MatrixType& m) { return MatrixU(m.adjoint()); }
static bool inplace_decomposition(MatrixType& m)
{ return llt_inplace<typename MatrixType::Scalar, Lower>::blocked(m)==-1; }
};
template<typename MatrixType> struct LLT_Traits<MatrixType,Upper>
{
typedef const TriangularView<const typename MatrixType::AdjointReturnType, Lower> MatrixL;
typedef const TriangularView<const MatrixType, Upper> MatrixU;
static inline MatrixL getL(const MatrixType& m) { return MatrixL(m.adjoint()); }
static inline MatrixU getU(const MatrixType& m) { return MatrixU(m); }
static bool inplace_decomposition(MatrixType& m)
{ return llt_inplace<typename MatrixType::Scalar, Upper>::blocked(m)==-1; }
};
} // end namespace internal
/** Computes / recomputes the Cholesky decomposition A = LL^* = U^*U of \a matrix
*
* \returns a reference to *this
*
* Example: \include TutorialLinAlgComputeTwice.cpp
* Output: \verbinclude TutorialLinAlgComputeTwice.out
*/
template<typename MatrixType, int _UpLo>
template<typename InputType>
LLT<MatrixType,_UpLo>& LLT<MatrixType,_UpLo>::compute(const EigenBase<InputType>& a)
{
check_template_parameters();
eigen_assert(a.rows()==a.cols());
const Index size = a.rows();
m_matrix.resize(size, size);
if (!internal::is_same_dense(m_matrix, a.derived()))
m_matrix = a.derived();
// Compute matrix L1 norm = max abs column sum.
m_l1_norm = RealScalar(0);
// TODO move this code to SelfAdjointView
for (Index col = 0; col < size; ++col) {
RealScalar abs_col_sum;
if (_UpLo == Lower)
abs_col_sum = m_matrix.col(col).tail(size - col).template lpNorm<1>() + m_matrix.row(col).head(col).template lpNorm<1>();
else
abs_col_sum = m_matrix.col(col).head(col).template lpNorm<1>() + m_matrix.row(col).tail(size - col).template lpNorm<1>();
if (abs_col_sum > m_l1_norm)
m_l1_norm = abs_col_sum;
}
m_isInitialized = true;
bool ok = Traits::inplace_decomposition(m_matrix);
m_info = ok ? Success : NumericalIssue;
return *this;
}
/** Performs a rank one update (or dowdate) of the current decomposition.
* If A = LL^* before the rank one update,
* then after it we have LL^* = A + sigma * v v^* where \a v must be a vector
* of same dimension.
*/
template<typename _MatrixType, int _UpLo>
template<typename VectorType>
LLT<_MatrixType,_UpLo> LLT<_MatrixType,_UpLo>::rankUpdate(const VectorType& v, const RealScalar& sigma)
{
EIGEN_STATIC_ASSERT_VECTOR_ONLY(VectorType);
eigen_assert(v.size()==m_matrix.cols());
eigen_assert(m_isInitialized);
if(internal::llt_inplace<typename MatrixType::Scalar, UpLo>::rankUpdate(m_matrix,v,sigma)>=0)
m_info = NumericalIssue;
else
m_info = Success;
return *this;
}
#ifndef EIGEN_PARSED_BY_DOXYGEN
template<typename _MatrixType,int _UpLo>
template<typename RhsType, typename DstType>
void LLT<_MatrixType,_UpLo>::_solve_impl(const RhsType &rhs, DstType &dst) const
{
dst = rhs;
solveInPlace(dst);
}
#endif
/** \internal use x = llt_object.solve(x);
*
* This is the \em in-place version of solve().
*
* \param bAndX represents both the right-hand side matrix b and result x.
*
* This version avoids a copy when the right hand side matrix b is not needed anymore.
*
* \warning The parameter is only marked 'const' to make the C++ compiler accept a temporary expression here.
* This function will const_cast it, so constness isn't honored here.
*
* \sa LLT::solve(), MatrixBase::llt()
*/
template<typename MatrixType, int _UpLo>
template<typename Derived>
void LLT<MatrixType,_UpLo>::solveInPlace(const MatrixBase<Derived> &bAndX) const
{
eigen_assert(m_isInitialized && "LLT is not initialized.");
eigen_assert(m_matrix.rows()==bAndX.rows());
matrixL().solveInPlace(bAndX);
matrixU().solveInPlace(bAndX);
}
/** \returns the matrix represented by the decomposition,
* i.e., it returns the product: L L^*.
* This function is provided for debug purpose. */
template<typename MatrixType, int _UpLo>
MatrixType LLT<MatrixType,_UpLo>::reconstructedMatrix() const
{
eigen_assert(m_isInitialized && "LLT is not initialized.");
return matrixL() * matrixL().adjoint().toDenseMatrix();
}
/** \cholesky_module
* \returns the LLT decomposition of \c *this
* \sa SelfAdjointView::llt()
*/
template<typename Derived>
inline const LLT<typename MatrixBase<Derived>::PlainObject>
MatrixBase<Derived>::llt() const
{
return LLT<PlainObject>(derived());
}
/** \cholesky_module
* \returns the LLT decomposition of \c *this
* \sa SelfAdjointView::llt()
*/
template<typename MatrixType, unsigned int UpLo>
inline const LLT<typename SelfAdjointView<MatrixType, UpLo>::PlainObject, UpLo>
SelfAdjointView<MatrixType, UpLo>::llt() const
{
return LLT<PlainObject,UpLo>(m_matrix);
}
} // end namespace Eigen
#endif // EIGEN_LLT_H

View File

@@ -0,0 +1,99 @@
/*
Copyright (c) 2011, Intel Corporation. All rights reserved.
Redistribution and use in source and binary forms, with or without modification,
are permitted provided that the following conditions are met:
* Redistributions of source code must retain the above copyright notice, this
list of conditions and the following disclaimer.
* Redistributions in binary form must reproduce the above copyright notice,
this list of conditions and the following disclaimer in the documentation
and/or other materials provided with the distribution.
* Neither the name of Intel Corporation nor the names of its contributors may
be used to endorse or promote products derived from this software without
specific prior written permission.
THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR
ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
(INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON
ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
********************************************************************************
* Content : Eigen bindings to LAPACKe
* LLt decomposition based on LAPACKE_?potrf function.
********************************************************************************
*/
#ifndef EIGEN_LLT_LAPACKE_H
#define EIGEN_LLT_LAPACKE_H
namespace Eigen {
namespace internal {
template<typename Scalar> struct lapacke_llt;
#define EIGEN_LAPACKE_LLT(EIGTYPE, BLASTYPE, LAPACKE_PREFIX) \
template<> struct lapacke_llt<EIGTYPE> \
{ \
template<typename MatrixType> \
static inline Index potrf(MatrixType& m, char uplo) \
{ \
lapack_int matrix_order; \
lapack_int size, lda, info, StorageOrder; \
EIGTYPE* a; \
eigen_assert(m.rows()==m.cols()); \
/* Set up parameters for ?potrf */ \
size = convert_index<lapack_int>(m.rows()); \
StorageOrder = MatrixType::Flags&RowMajorBit?RowMajor:ColMajor; \
matrix_order = StorageOrder==RowMajor ? LAPACK_ROW_MAJOR : LAPACK_COL_MAJOR; \
a = &(m.coeffRef(0,0)); \
lda = convert_index<lapack_int>(m.outerStride()); \
\
info = LAPACKE_##LAPACKE_PREFIX##potrf( matrix_order, uplo, size, (BLASTYPE*)a, lda ); \
info = (info==0) ? -1 : info>0 ? info-1 : size; \
return info; \
} \
}; \
template<> struct llt_inplace<EIGTYPE, Lower> \
{ \
template<typename MatrixType> \
static Index blocked(MatrixType& m) \
{ \
return lapacke_llt<EIGTYPE>::potrf(m, 'L'); \
} \
template<typename MatrixType, typename VectorType> \
static Index rankUpdate(MatrixType& mat, const VectorType& vec, const typename MatrixType::RealScalar& sigma) \
{ return Eigen::internal::llt_rank_update_lower(mat, vec, sigma); } \
}; \
template<> struct llt_inplace<EIGTYPE, Upper> \
{ \
template<typename MatrixType> \
static Index blocked(MatrixType& m) \
{ \
return lapacke_llt<EIGTYPE>::potrf(m, 'U'); \
} \
template<typename MatrixType, typename VectorType> \
static Index rankUpdate(MatrixType& mat, const VectorType& vec, const typename MatrixType::RealScalar& sigma) \
{ \
Transpose<MatrixType> matt(mat); \
return llt_inplace<EIGTYPE, Lower>::rankUpdate(matt, vec.conjugate(), sigma); \
} \
};
EIGEN_LAPACKE_LLT(double, double, d)
EIGEN_LAPACKE_LLT(float, float, s)
EIGEN_LAPACKE_LLT(dcomplex, lapack_complex_double, z)
EIGEN_LAPACKE_LLT(scomplex, lapack_complex_float, c)
} // end namespace internal
} // end namespace Eigen
#endif // EIGEN_LLT_LAPACKE_H

View File

@@ -0,0 +1,639 @@
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2008-2010 Gael Guennebaud <gael.guennebaud@inria.fr>
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#ifndef EIGEN_CHOLMODSUPPORT_H
#define EIGEN_CHOLMODSUPPORT_H
namespace Eigen {
namespace internal {
template<typename Scalar> struct cholmod_configure_matrix;
template<> struct cholmod_configure_matrix<double> {
template<typename CholmodType>
static void run(CholmodType& mat) {
mat.xtype = CHOLMOD_REAL;
mat.dtype = CHOLMOD_DOUBLE;
}
};
template<> struct cholmod_configure_matrix<std::complex<double> > {
template<typename CholmodType>
static void run(CholmodType& mat) {
mat.xtype = CHOLMOD_COMPLEX;
mat.dtype = CHOLMOD_DOUBLE;
}
};
// Other scalar types are not yet suppotred by Cholmod
// template<> struct cholmod_configure_matrix<float> {
// template<typename CholmodType>
// static void run(CholmodType& mat) {
// mat.xtype = CHOLMOD_REAL;
// mat.dtype = CHOLMOD_SINGLE;
// }
// };
//
// template<> struct cholmod_configure_matrix<std::complex<float> > {
// template<typename CholmodType>
// static void run(CholmodType& mat) {
// mat.xtype = CHOLMOD_COMPLEX;
// mat.dtype = CHOLMOD_SINGLE;
// }
// };
} // namespace internal
/** Wraps the Eigen sparse matrix \a mat into a Cholmod sparse matrix object.
* Note that the data are shared.
*/
template<typename _Scalar, int _Options, typename _StorageIndex>
cholmod_sparse viewAsCholmod(Ref<SparseMatrix<_Scalar,_Options,_StorageIndex> > mat)
{
cholmod_sparse res;
res.nzmax = mat.nonZeros();
res.nrow = mat.rows();
res.ncol = mat.cols();
res.p = mat.outerIndexPtr();
res.i = mat.innerIndexPtr();
res.x = mat.valuePtr();
res.z = 0;
res.sorted = 1;
if(mat.isCompressed())
{
res.packed = 1;
res.nz = 0;
}
else
{
res.packed = 0;
res.nz = mat.innerNonZeroPtr();
}
res.dtype = 0;
res.stype = -1;
if (internal::is_same<_StorageIndex,int>::value)
{
res.itype = CHOLMOD_INT;
}
else if (internal::is_same<_StorageIndex,long>::value)
{
res.itype = CHOLMOD_LONG;
}
else
{
eigen_assert(false && "Index type not supported yet");
}
// setup res.xtype
internal::cholmod_configure_matrix<_Scalar>::run(res);
res.stype = 0;
return res;
}
template<typename _Scalar, int _Options, typename _Index>
const cholmod_sparse viewAsCholmod(const SparseMatrix<_Scalar,_Options,_Index>& mat)
{
cholmod_sparse res = viewAsCholmod(Ref<SparseMatrix<_Scalar,_Options,_Index> >(mat.const_cast_derived()));
return res;
}
template<typename _Scalar, int _Options, typename _Index>
const cholmod_sparse viewAsCholmod(const SparseVector<_Scalar,_Options,_Index>& mat)
{
cholmod_sparse res = viewAsCholmod(Ref<SparseMatrix<_Scalar,_Options,_Index> >(mat.const_cast_derived()));
return res;
}
/** Returns a view of the Eigen sparse matrix \a mat as Cholmod sparse matrix.
* The data are not copied but shared. */
template<typename _Scalar, int _Options, typename _Index, unsigned int UpLo>
cholmod_sparse viewAsCholmod(const SparseSelfAdjointView<const SparseMatrix<_Scalar,_Options,_Index>, UpLo>& mat)
{
cholmod_sparse res = viewAsCholmod(Ref<SparseMatrix<_Scalar,_Options,_Index> >(mat.matrix().const_cast_derived()));
if(UpLo==Upper) res.stype = 1;
if(UpLo==Lower) res.stype = -1;
return res;
}
/** Returns a view of the Eigen \b dense matrix \a mat as Cholmod dense matrix.
* The data are not copied but shared. */
template<typename Derived>
cholmod_dense viewAsCholmod(MatrixBase<Derived>& mat)
{
EIGEN_STATIC_ASSERT((internal::traits<Derived>::Flags&RowMajorBit)==0,THIS_METHOD_IS_ONLY_FOR_COLUMN_MAJOR_MATRICES);
typedef typename Derived::Scalar Scalar;
cholmod_dense res;
res.nrow = mat.rows();
res.ncol = mat.cols();
res.nzmax = res.nrow * res.ncol;
res.d = Derived::IsVectorAtCompileTime ? mat.derived().size() : mat.derived().outerStride();
res.x = (void*)(mat.derived().data());
res.z = 0;
internal::cholmod_configure_matrix<Scalar>::run(res);
return res;
}
/** Returns a view of the Cholmod sparse matrix \a cm as an Eigen sparse matrix.
* The data are not copied but shared. */
template<typename Scalar, int Flags, typename StorageIndex>
MappedSparseMatrix<Scalar,Flags,StorageIndex> viewAsEigen(cholmod_sparse& cm)
{
return MappedSparseMatrix<Scalar,Flags,StorageIndex>
(cm.nrow, cm.ncol, static_cast<StorageIndex*>(cm.p)[cm.ncol],
static_cast<StorageIndex*>(cm.p), static_cast<StorageIndex*>(cm.i),static_cast<Scalar*>(cm.x) );
}
enum CholmodMode {
CholmodAuto, CholmodSimplicialLLt, CholmodSupernodalLLt, CholmodLDLt
};
/** \ingroup CholmodSupport_Module
* \class CholmodBase
* \brief The base class for the direct Cholesky factorization of Cholmod
* \sa class CholmodSupernodalLLT, class CholmodSimplicialLDLT, class CholmodSimplicialLLT
*/
template<typename _MatrixType, int _UpLo, typename Derived>
class CholmodBase : public SparseSolverBase<Derived>
{
protected:
typedef SparseSolverBase<Derived> Base;
using Base::derived;
using Base::m_isInitialized;
public:
typedef _MatrixType MatrixType;
enum { UpLo = _UpLo };
typedef typename MatrixType::Scalar Scalar;
typedef typename MatrixType::RealScalar RealScalar;
typedef MatrixType CholMatrixType;
typedef typename MatrixType::StorageIndex StorageIndex;
enum {
ColsAtCompileTime = MatrixType::ColsAtCompileTime,
MaxColsAtCompileTime = MatrixType::MaxColsAtCompileTime
};
public:
CholmodBase()
: m_cholmodFactor(0), m_info(Success), m_factorizationIsOk(false), m_analysisIsOk(false)
{
EIGEN_STATIC_ASSERT((internal::is_same<double,RealScalar>::value), CHOLMOD_SUPPORTS_DOUBLE_PRECISION_ONLY);
m_shiftOffset[0] = m_shiftOffset[1] = 0.0;
cholmod_start(&m_cholmod);
}
explicit CholmodBase(const MatrixType& matrix)
: m_cholmodFactor(0), m_info(Success), m_factorizationIsOk(false), m_analysisIsOk(false)
{
EIGEN_STATIC_ASSERT((internal::is_same<double,RealScalar>::value), CHOLMOD_SUPPORTS_DOUBLE_PRECISION_ONLY);
m_shiftOffset[0] = m_shiftOffset[1] = 0.0;
cholmod_start(&m_cholmod);
compute(matrix);
}
~CholmodBase()
{
if(m_cholmodFactor)
cholmod_free_factor(&m_cholmodFactor, &m_cholmod);
cholmod_finish(&m_cholmod);
}
inline StorageIndex cols() const { return internal::convert_index<StorageIndex, Index>(m_cholmodFactor->n); }
inline StorageIndex rows() const { return internal::convert_index<StorageIndex, Index>(m_cholmodFactor->n); }
/** \brief Reports whether previous computation was successful.
*
* \returns \c Success if computation was succesful,
* \c NumericalIssue if the matrix.appears to be negative.
*/
ComputationInfo info() const
{
eigen_assert(m_isInitialized && "Decomposition is not initialized.");
return m_info;
}
/** Computes the sparse Cholesky decomposition of \a matrix */
Derived& compute(const MatrixType& matrix)
{
analyzePattern(matrix);
factorize(matrix);
return derived();
}
/** Performs a symbolic decomposition on the sparsity pattern of \a matrix.
*
* This function is particularly useful when solving for several problems having the same structure.
*
* \sa factorize()
*/
void analyzePattern(const MatrixType& matrix)
{
if(m_cholmodFactor)
{
cholmod_free_factor(&m_cholmodFactor, &m_cholmod);
m_cholmodFactor = 0;
}
cholmod_sparse A = viewAsCholmod(matrix.template selfadjointView<UpLo>());
m_cholmodFactor = cholmod_analyze(&A, &m_cholmod);
this->m_isInitialized = true;
this->m_info = Success;
m_analysisIsOk = true;
m_factorizationIsOk = false;
}
/** Performs a numeric decomposition of \a matrix
*
* The given matrix must have the same sparsity pattern as the matrix on which the symbolic decomposition has been performed.
*
* \sa analyzePattern()
*/
void factorize(const MatrixType& matrix)
{
eigen_assert(m_analysisIsOk && "You must first call analyzePattern()");
cholmod_sparse A = viewAsCholmod(matrix.template selfadjointView<UpLo>());
cholmod_factorize_p(&A, m_shiftOffset, 0, 0, m_cholmodFactor, &m_cholmod);
// If the factorization failed, minor is the column at which it did. On success minor == n.
this->m_info = (m_cholmodFactor->minor == m_cholmodFactor->n ? Success : NumericalIssue);
m_factorizationIsOk = true;
}
/** Returns a reference to the Cholmod's configuration structure to get a full control over the performed operations.
* See the Cholmod user guide for details. */
cholmod_common& cholmod() { return m_cholmod; }
#ifndef EIGEN_PARSED_BY_DOXYGEN
/** \internal */
template<typename Rhs,typename Dest>
void _solve_impl(const MatrixBase<Rhs> &b, MatrixBase<Dest> &dest) const
{
eigen_assert(m_factorizationIsOk && "The decomposition is not in a valid state for solving, you must first call either compute() or symbolic()/numeric()");
const Index size = m_cholmodFactor->n;
EIGEN_UNUSED_VARIABLE(size);
eigen_assert(size==b.rows());
// Cholmod needs column-major stoarge without inner-stride, which corresponds to the default behavior of Ref.
Ref<const Matrix<typename Rhs::Scalar,Dynamic,Dynamic,ColMajor> > b_ref(b.derived());
cholmod_dense b_cd = viewAsCholmod(b_ref);
cholmod_dense* x_cd = cholmod_solve(CHOLMOD_A, m_cholmodFactor, &b_cd, &m_cholmod);
if(!x_cd)
{
this->m_info = NumericalIssue;
return;
}
// TODO optimize this copy by swapping when possible (be careful with alignment, etc.)
dest = Matrix<Scalar,Dest::RowsAtCompileTime,Dest::ColsAtCompileTime>::Map(reinterpret_cast<Scalar*>(x_cd->x),b.rows(),b.cols());
cholmod_free_dense(&x_cd, &m_cholmod);
}
/** \internal */
template<typename RhsDerived, typename DestDerived>
void _solve_impl(const SparseMatrixBase<RhsDerived> &b, SparseMatrixBase<DestDerived> &dest) const
{
eigen_assert(m_factorizationIsOk && "The decomposition is not in a valid state for solving, you must first call either compute() or symbolic()/numeric()");
const Index size = m_cholmodFactor->n;
EIGEN_UNUSED_VARIABLE(size);
eigen_assert(size==b.rows());
// note: cs stands for Cholmod Sparse
Ref<SparseMatrix<typename RhsDerived::Scalar,ColMajor,typename RhsDerived::StorageIndex> > b_ref(b.const_cast_derived());
cholmod_sparse b_cs = viewAsCholmod(b_ref);
cholmod_sparse* x_cs = cholmod_spsolve(CHOLMOD_A, m_cholmodFactor, &b_cs, &m_cholmod);
if(!x_cs)
{
this->m_info = NumericalIssue;
return;
}
// TODO optimize this copy by swapping when possible (be careful with alignment, etc.)
dest.derived() = viewAsEigen<typename DestDerived::Scalar,ColMajor,typename DestDerived::StorageIndex>(*x_cs);
cholmod_free_sparse(&x_cs, &m_cholmod);
}
#endif // EIGEN_PARSED_BY_DOXYGEN
/** Sets the shift parameter that will be used to adjust the diagonal coefficients during the numerical factorization.
*
* During the numerical factorization, an offset term is added to the diagonal coefficients:\n
* \c d_ii = \a offset + \c d_ii
*
* The default is \a offset=0.
*
* \returns a reference to \c *this.
*/
Derived& setShift(const RealScalar& offset)
{
m_shiftOffset[0] = double(offset);
return derived();
}
/** \returns the determinant of the underlying matrix from the current factorization */
Scalar determinant() const
{
using std::exp;
return exp(logDeterminant());
}
/** \returns the log determinant of the underlying matrix from the current factorization */
Scalar logDeterminant() const
{
using std::log;
using numext::real;
eigen_assert(m_factorizationIsOk && "The decomposition is not in a valid state for solving, you must first call either compute() or symbolic()/numeric()");
RealScalar logDet = 0;
Scalar *x = static_cast<Scalar*>(m_cholmodFactor->x);
if (m_cholmodFactor->is_super)
{
// Supernodal factorization stored as a packed list of dense column-major blocs,
// as described by the following structure:
// super[k] == index of the first column of the j-th super node
StorageIndex *super = static_cast<StorageIndex*>(m_cholmodFactor->super);
// pi[k] == offset to the description of row indices
StorageIndex *pi = static_cast<StorageIndex*>(m_cholmodFactor->pi);
// px[k] == offset to the respective dense block
StorageIndex *px = static_cast<StorageIndex*>(m_cholmodFactor->px);
Index nb_super_nodes = m_cholmodFactor->nsuper;
for (Index k=0; k < nb_super_nodes; ++k)
{
StorageIndex ncols = super[k + 1] - super[k];
StorageIndex nrows = pi[k + 1] - pi[k];
Map<const Array<Scalar,1,Dynamic>, 0, InnerStride<> > sk(x + px[k], ncols, InnerStride<>(nrows+1));
logDet += sk.real().log().sum();
}
}
else
{
// Simplicial factorization stored as standard CSC matrix.
StorageIndex *p = static_cast<StorageIndex*>(m_cholmodFactor->p);
Index size = m_cholmodFactor->n;
for (Index k=0; k<size; ++k)
logDet += log(real( x[p[k]] ));
}
if (m_cholmodFactor->is_ll)
logDet *= 2.0;
return logDet;
};
template<typename Stream>
void dumpMemory(Stream& /*s*/)
{}
protected:
mutable cholmod_common m_cholmod;
cholmod_factor* m_cholmodFactor;
double m_shiftOffset[2];
mutable ComputationInfo m_info;
int m_factorizationIsOk;
int m_analysisIsOk;
};
/** \ingroup CholmodSupport_Module
* \class CholmodSimplicialLLT
* \brief A simplicial direct Cholesky (LLT) factorization and solver based on Cholmod
*
* This class allows to solve for A.X = B sparse linear problems via a simplicial LL^T Cholesky factorization
* using the Cholmod library.
* This simplicial variant is equivalent to Eigen's built-in SimplicialLLT class. Therefore, it has little practical interest.
* The sparse matrix A must be selfadjoint and positive definite. The vectors or matrices
* X and B can be either dense or sparse.
*
* \tparam _MatrixType the type of the sparse matrix A, it must be a SparseMatrix<>
* \tparam _UpLo the triangular part that will be used for the computations. It can be Lower
* or Upper. Default is Lower.
*
* \implsparsesolverconcept
*
* This class supports all kind of SparseMatrix<>: row or column major; upper, lower, or both; compressed or non compressed.
*
* \warning Only double precision real and complex scalar types are supported by Cholmod.
*
* \sa \ref TutorialSparseSolverConcept, class CholmodSupernodalLLT, class SimplicialLLT
*/
template<typename _MatrixType, int _UpLo = Lower>
class CholmodSimplicialLLT : public CholmodBase<_MatrixType, _UpLo, CholmodSimplicialLLT<_MatrixType, _UpLo> >
{
typedef CholmodBase<_MatrixType, _UpLo, CholmodSimplicialLLT> Base;
using Base::m_cholmod;
public:
typedef _MatrixType MatrixType;
CholmodSimplicialLLT() : Base() { init(); }
CholmodSimplicialLLT(const MatrixType& matrix) : Base()
{
init();
this->compute(matrix);
}
~CholmodSimplicialLLT() {}
protected:
void init()
{
m_cholmod.final_asis = 0;
m_cholmod.supernodal = CHOLMOD_SIMPLICIAL;
m_cholmod.final_ll = 1;
}
};
/** \ingroup CholmodSupport_Module
* \class CholmodSimplicialLDLT
* \brief A simplicial direct Cholesky (LDLT) factorization and solver based on Cholmod
*
* This class allows to solve for A.X = B sparse linear problems via a simplicial LDL^T Cholesky factorization
* using the Cholmod library.
* This simplicial variant is equivalent to Eigen's built-in SimplicialLDLT class. Therefore, it has little practical interest.
* The sparse matrix A must be selfadjoint and positive definite. The vectors or matrices
* X and B can be either dense or sparse.
*
* \tparam _MatrixType the type of the sparse matrix A, it must be a SparseMatrix<>
* \tparam _UpLo the triangular part that will be used for the computations. It can be Lower
* or Upper. Default is Lower.
*
* \implsparsesolverconcept
*
* This class supports all kind of SparseMatrix<>: row or column major; upper, lower, or both; compressed or non compressed.
*
* \warning Only double precision real and complex scalar types are supported by Cholmod.
*
* \sa \ref TutorialSparseSolverConcept, class CholmodSupernodalLLT, class SimplicialLDLT
*/
template<typename _MatrixType, int _UpLo = Lower>
class CholmodSimplicialLDLT : public CholmodBase<_MatrixType, _UpLo, CholmodSimplicialLDLT<_MatrixType, _UpLo> >
{
typedef CholmodBase<_MatrixType, _UpLo, CholmodSimplicialLDLT> Base;
using Base::m_cholmod;
public:
typedef _MatrixType MatrixType;
CholmodSimplicialLDLT() : Base() { init(); }
CholmodSimplicialLDLT(const MatrixType& matrix) : Base()
{
init();
this->compute(matrix);
}
~CholmodSimplicialLDLT() {}
protected:
void init()
{
m_cholmod.final_asis = 1;
m_cholmod.supernodal = CHOLMOD_SIMPLICIAL;
}
};
/** \ingroup CholmodSupport_Module
* \class CholmodSupernodalLLT
* \brief A supernodal Cholesky (LLT) factorization and solver based on Cholmod
*
* This class allows to solve for A.X = B sparse linear problems via a supernodal LL^T Cholesky factorization
* using the Cholmod library.
* This supernodal variant performs best on dense enough problems, e.g., 3D FEM, or very high order 2D FEM.
* The sparse matrix A must be selfadjoint and positive definite. The vectors or matrices
* X and B can be either dense or sparse.
*
* \tparam _MatrixType the type of the sparse matrix A, it must be a SparseMatrix<>
* \tparam _UpLo the triangular part that will be used for the computations. It can be Lower
* or Upper. Default is Lower.
*
* \implsparsesolverconcept
*
* This class supports all kind of SparseMatrix<>: row or column major; upper, lower, or both; compressed or non compressed.
*
* \warning Only double precision real and complex scalar types are supported by Cholmod.
*
* \sa \ref TutorialSparseSolverConcept
*/
template<typename _MatrixType, int _UpLo = Lower>
class CholmodSupernodalLLT : public CholmodBase<_MatrixType, _UpLo, CholmodSupernodalLLT<_MatrixType, _UpLo> >
{
typedef CholmodBase<_MatrixType, _UpLo, CholmodSupernodalLLT> Base;
using Base::m_cholmod;
public:
typedef _MatrixType MatrixType;
CholmodSupernodalLLT() : Base() { init(); }
CholmodSupernodalLLT(const MatrixType& matrix) : Base()
{
init();
this->compute(matrix);
}
~CholmodSupernodalLLT() {}
protected:
void init()
{
m_cholmod.final_asis = 1;
m_cholmod.supernodal = CHOLMOD_SUPERNODAL;
}
};
/** \ingroup CholmodSupport_Module
* \class CholmodDecomposition
* \brief A general Cholesky factorization and solver based on Cholmod
*
* This class allows to solve for A.X = B sparse linear problems via a LL^T or LDL^T Cholesky factorization
* using the Cholmod library. The sparse matrix A must be selfadjoint and positive definite. The vectors or matrices
* X and B can be either dense or sparse.
*
* This variant permits to change the underlying Cholesky method at runtime.
* On the other hand, it does not provide access to the result of the factorization.
* The default is to let Cholmod automatically choose between a simplicial and supernodal factorization.
*
* \tparam _MatrixType the type of the sparse matrix A, it must be a SparseMatrix<>
* \tparam _UpLo the triangular part that will be used for the computations. It can be Lower
* or Upper. Default is Lower.
*
* \implsparsesolverconcept
*
* This class supports all kind of SparseMatrix<>: row or column major; upper, lower, or both; compressed or non compressed.
*
* \warning Only double precision real and complex scalar types are supported by Cholmod.
*
* \sa \ref TutorialSparseSolverConcept
*/
template<typename _MatrixType, int _UpLo = Lower>
class CholmodDecomposition : public CholmodBase<_MatrixType, _UpLo, CholmodDecomposition<_MatrixType, _UpLo> >
{
typedef CholmodBase<_MatrixType, _UpLo, CholmodDecomposition> Base;
using Base::m_cholmod;
public:
typedef _MatrixType MatrixType;
CholmodDecomposition() : Base() { init(); }
CholmodDecomposition(const MatrixType& matrix) : Base()
{
init();
this->compute(matrix);
}
~CholmodDecomposition() {}
void setMode(CholmodMode mode)
{
switch(mode)
{
case CholmodAuto:
m_cholmod.final_asis = 1;
m_cholmod.supernodal = CHOLMOD_AUTO;
break;
case CholmodSimplicialLLt:
m_cholmod.final_asis = 0;
m_cholmod.supernodal = CHOLMOD_SIMPLICIAL;
m_cholmod.final_ll = 1;
break;
case CholmodSupernodalLLt:
m_cholmod.final_asis = 1;
m_cholmod.supernodal = CHOLMOD_SUPERNODAL;
break;
case CholmodLDLt:
m_cholmod.final_asis = 1;
m_cholmod.supernodal = CHOLMOD_SIMPLICIAL;
break;
default:
break;
}
}
protected:
void init()
{
m_cholmod.final_asis = 1;
m_cholmod.supernodal = CHOLMOD_AUTO;
}
};
} // end namespace Eigen
#endif // EIGEN_CHOLMODSUPPORT_H

View File

@@ -0,0 +1,329 @@
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2009 Gael Guennebaud <gael.guennebaud@inria.fr>
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#ifndef EIGEN_ARRAY_H
#define EIGEN_ARRAY_H
namespace Eigen {
namespace internal {
template<typename _Scalar, int _Rows, int _Cols, int _Options, int _MaxRows, int _MaxCols>
struct traits<Array<_Scalar, _Rows, _Cols, _Options, _MaxRows, _MaxCols> > : traits<Matrix<_Scalar, _Rows, _Cols, _Options, _MaxRows, _MaxCols> >
{
typedef ArrayXpr XprKind;
typedef ArrayBase<Array<_Scalar, _Rows, _Cols, _Options, _MaxRows, _MaxCols> > XprBase;
};
}
/** \class Array
* \ingroup Core_Module
*
* \brief General-purpose arrays with easy API for coefficient-wise operations
*
* The %Array class is very similar to the Matrix class. It provides
* general-purpose one- and two-dimensional arrays. The difference between the
* %Array and the %Matrix class is primarily in the API: the API for the
* %Array class provides easy access to coefficient-wise operations, while the
* API for the %Matrix class provides easy access to linear-algebra
* operations.
*
* See documentation of class Matrix for detailed information on the template parameters
* storage layout.
*
* This class can be extended with the help of the plugin mechanism described on the page
* \ref TopicCustomizing_Plugins by defining the preprocessor symbol \c EIGEN_ARRAY_PLUGIN.
*
* \sa \blank \ref TutorialArrayClass, \ref TopicClassHierarchy
*/
template<typename _Scalar, int _Rows, int _Cols, int _Options, int _MaxRows, int _MaxCols>
class Array
: public PlainObjectBase<Array<_Scalar, _Rows, _Cols, _Options, _MaxRows, _MaxCols> >
{
public:
typedef PlainObjectBase<Array> Base;
EIGEN_DENSE_PUBLIC_INTERFACE(Array)
enum { Options = _Options };
typedef typename Base::PlainObject PlainObject;
protected:
template <typename Derived, typename OtherDerived, bool IsVector>
friend struct internal::conservative_resize_like_impl;
using Base::m_storage;
public:
using Base::base;
using Base::coeff;
using Base::coeffRef;
/**
* The usage of
* using Base::operator=;
* fails on MSVC. Since the code below is working with GCC and MSVC, we skipped
* the usage of 'using'. This should be done only for operator=.
*/
template<typename OtherDerived>
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Array& operator=(const EigenBase<OtherDerived> &other)
{
return Base::operator=(other);
}
/** Set all the entries to \a value.
* \sa DenseBase::setConstant(), DenseBase::fill()
*/
/* This overload is needed because the usage of
* using Base::operator=;
* fails on MSVC. Since the code below is working with GCC and MSVC, we skipped
* the usage of 'using'. This should be done only for operator=.
*/
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Array& operator=(const Scalar &value)
{
Base::setConstant(value);
return *this;
}
/** Copies the value of the expression \a other into \c *this with automatic resizing.
*
* *this might be resized to match the dimensions of \a other. If *this was a null matrix (not already initialized),
* it will be initialized.
*
* Note that copying a row-vector into a vector (and conversely) is allowed.
* The resizing, if any, is then done in the appropriate way so that row-vectors
* remain row-vectors and vectors remain vectors.
*/
template<typename OtherDerived>
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Array& operator=(const DenseBase<OtherDerived>& other)
{
return Base::_set(other);
}
/** This is a special case of the templated operator=. Its purpose is to
* prevent a default operator= from hiding the templated operator=.
*/
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Array& operator=(const Array& other)
{
return Base::_set(other);
}
/** Default constructor.
*
* For fixed-size matrices, does nothing.
*
* For dynamic-size matrices, creates an empty matrix of size 0. Does not allocate any array. Such a matrix
* is called a null matrix. This constructor is the unique way to create null matrices: resizing
* a matrix to 0 is not supported.
*
* \sa resize(Index,Index)
*/
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Array() : Base()
{
Base::_check_template_params();
EIGEN_INITIALIZE_COEFFS_IF_THAT_OPTION_IS_ENABLED
}
#ifndef EIGEN_PARSED_BY_DOXYGEN
// FIXME is it still needed ??
/** \internal */
EIGEN_DEVICE_FUNC
Array(internal::constructor_without_unaligned_array_assert)
: Base(internal::constructor_without_unaligned_array_assert())
{
Base::_check_template_params();
EIGEN_INITIALIZE_COEFFS_IF_THAT_OPTION_IS_ENABLED
}
#endif
#if EIGEN_HAS_RVALUE_REFERENCES
EIGEN_DEVICE_FUNC
Array(Array&& other) EIGEN_NOEXCEPT_IF(std::is_nothrow_move_constructible<Scalar>::value)
: Base(std::move(other))
{
Base::_check_template_params();
}
EIGEN_DEVICE_FUNC
Array& operator=(Array&& other) EIGEN_NOEXCEPT_IF(std::is_nothrow_move_assignable<Scalar>::value)
{
other.swap(*this);
return *this;
}
#endif
#ifndef EIGEN_PARSED_BY_DOXYGEN
template<typename T>
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE explicit Array(const T& x)
{
Base::_check_template_params();
Base::template _init1<T>(x);
}
template<typename T0, typename T1>
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Array(const T0& val0, const T1& val1)
{
Base::_check_template_params();
this->template _init2<T0,T1>(val0, val1);
}
#else
/** \brief Constructs a fixed-sized array initialized with coefficients starting at \a data */
EIGEN_DEVICE_FUNC explicit Array(const Scalar *data);
/** Constructs a vector or row-vector with given dimension. \only_for_vectors
*
* Note that this is only useful for dynamic-size vectors. For fixed-size vectors,
* it is redundant to pass the dimension here, so it makes more sense to use the default
* constructor Array() instead.
*/
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE explicit Array(Index dim);
/** constructs an initialized 1x1 Array with the given coefficient */
Array(const Scalar& value);
/** constructs an uninitialized array with \a rows rows and \a cols columns.
*
* This is useful for dynamic-size arrays. For fixed-size arrays,
* it is redundant to pass these parameters, so one should use the default constructor
* Array() instead. */
Array(Index rows, Index cols);
/** constructs an initialized 2D vector with given coefficients */
Array(const Scalar& val0, const Scalar& val1);
#endif
/** constructs an initialized 3D vector with given coefficients */
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Array(const Scalar& val0, const Scalar& val1, const Scalar& val2)
{
Base::_check_template_params();
EIGEN_STATIC_ASSERT_VECTOR_SPECIFIC_SIZE(Array, 3)
m_storage.data()[0] = val0;
m_storage.data()[1] = val1;
m_storage.data()[2] = val2;
}
/** constructs an initialized 4D vector with given coefficients */
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Array(const Scalar& val0, const Scalar& val1, const Scalar& val2, const Scalar& val3)
{
Base::_check_template_params();
EIGEN_STATIC_ASSERT_VECTOR_SPECIFIC_SIZE(Array, 4)
m_storage.data()[0] = val0;
m_storage.data()[1] = val1;
m_storage.data()[2] = val2;
m_storage.data()[3] = val3;
}
/** Copy constructor */
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Array(const Array& other)
: Base(other)
{ }
private:
struct PrivateType {};
public:
/** \sa MatrixBase::operator=(const EigenBase<OtherDerived>&) */
template<typename OtherDerived>
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Array(const EigenBase<OtherDerived> &other,
typename internal::enable_if<internal::is_convertible<typename OtherDerived::Scalar,Scalar>::value,
PrivateType>::type = PrivateType())
: Base(other.derived())
{ }
EIGEN_DEVICE_FUNC inline Index innerStride() const { return 1; }
EIGEN_DEVICE_FUNC inline Index outerStride() const { return this->innerSize(); }
#ifdef EIGEN_ARRAY_PLUGIN
#include EIGEN_ARRAY_PLUGIN
#endif
private:
template<typename MatrixType, typename OtherDerived, bool SwapPointers>
friend struct internal::matrix_swap_impl;
};
/** \defgroup arraytypedefs Global array typedefs
* \ingroup Core_Module
*
* Eigen defines several typedef shortcuts for most common 1D and 2D array types.
*
* The general patterns are the following:
*
* \c ArrayRowsColsType where \c Rows and \c Cols can be \c 2,\c 3,\c 4 for fixed size square matrices or \c X for dynamic size,
* and where \c Type can be \c i for integer, \c f for float, \c d for double, \c cf for complex float, \c cd
* for complex double.
*
* For example, \c Array33d is a fixed-size 3x3 array type of doubles, and \c ArrayXXf is a dynamic-size matrix of floats.
*
* There are also \c ArraySizeType which are self-explanatory. For example, \c Array4cf is
* a fixed-size 1D array of 4 complex floats.
*
* \sa class Array
*/
#define EIGEN_MAKE_ARRAY_TYPEDEFS(Type, TypeSuffix, Size, SizeSuffix) \
/** \ingroup arraytypedefs */ \
typedef Array<Type, Size, Size> Array##SizeSuffix##SizeSuffix##TypeSuffix; \
/** \ingroup arraytypedefs */ \
typedef Array<Type, Size, 1> Array##SizeSuffix##TypeSuffix;
#define EIGEN_MAKE_ARRAY_FIXED_TYPEDEFS(Type, TypeSuffix, Size) \
/** \ingroup arraytypedefs */ \
typedef Array<Type, Size, Dynamic> Array##Size##X##TypeSuffix; \
/** \ingroup arraytypedefs */ \
typedef Array<Type, Dynamic, Size> Array##X##Size##TypeSuffix;
#define EIGEN_MAKE_ARRAY_TYPEDEFS_ALL_SIZES(Type, TypeSuffix) \
EIGEN_MAKE_ARRAY_TYPEDEFS(Type, TypeSuffix, 2, 2) \
EIGEN_MAKE_ARRAY_TYPEDEFS(Type, TypeSuffix, 3, 3) \
EIGEN_MAKE_ARRAY_TYPEDEFS(Type, TypeSuffix, 4, 4) \
EIGEN_MAKE_ARRAY_TYPEDEFS(Type, TypeSuffix, Dynamic, X) \
EIGEN_MAKE_ARRAY_FIXED_TYPEDEFS(Type, TypeSuffix, 2) \
EIGEN_MAKE_ARRAY_FIXED_TYPEDEFS(Type, TypeSuffix, 3) \
EIGEN_MAKE_ARRAY_FIXED_TYPEDEFS(Type, TypeSuffix, 4)
EIGEN_MAKE_ARRAY_TYPEDEFS_ALL_SIZES(int, i)
EIGEN_MAKE_ARRAY_TYPEDEFS_ALL_SIZES(float, f)
EIGEN_MAKE_ARRAY_TYPEDEFS_ALL_SIZES(double, d)
EIGEN_MAKE_ARRAY_TYPEDEFS_ALL_SIZES(std::complex<float>, cf)
EIGEN_MAKE_ARRAY_TYPEDEFS_ALL_SIZES(std::complex<double>, cd)
#undef EIGEN_MAKE_ARRAY_TYPEDEFS_ALL_SIZES
#undef EIGEN_MAKE_ARRAY_TYPEDEFS
#undef EIGEN_MAKE_ARRAY_TYPEDEFS_LARGE
#define EIGEN_USING_ARRAY_TYPEDEFS_FOR_TYPE_AND_SIZE(TypeSuffix, SizeSuffix) \
using Eigen::Matrix##SizeSuffix##TypeSuffix; \
using Eigen::Vector##SizeSuffix##TypeSuffix; \
using Eigen::RowVector##SizeSuffix##TypeSuffix;
#define EIGEN_USING_ARRAY_TYPEDEFS_FOR_TYPE(TypeSuffix) \
EIGEN_USING_ARRAY_TYPEDEFS_FOR_TYPE_AND_SIZE(TypeSuffix, 2) \
EIGEN_USING_ARRAY_TYPEDEFS_FOR_TYPE_AND_SIZE(TypeSuffix, 3) \
EIGEN_USING_ARRAY_TYPEDEFS_FOR_TYPE_AND_SIZE(TypeSuffix, 4) \
EIGEN_USING_ARRAY_TYPEDEFS_FOR_TYPE_AND_SIZE(TypeSuffix, X) \
#define EIGEN_USING_ARRAY_TYPEDEFS \
EIGEN_USING_ARRAY_TYPEDEFS_FOR_TYPE(i) \
EIGEN_USING_ARRAY_TYPEDEFS_FOR_TYPE(f) \
EIGEN_USING_ARRAY_TYPEDEFS_FOR_TYPE(d) \
EIGEN_USING_ARRAY_TYPEDEFS_FOR_TYPE(cf) \
EIGEN_USING_ARRAY_TYPEDEFS_FOR_TYPE(cd)
} // end namespace Eigen
#endif // EIGEN_ARRAY_H

View File

@@ -0,0 +1,226 @@
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2009 Gael Guennebaud <gael.guennebaud@inria.fr>
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#ifndef EIGEN_ARRAYBASE_H
#define EIGEN_ARRAYBASE_H
namespace Eigen {
template<typename ExpressionType> class MatrixWrapper;
/** \class ArrayBase
* \ingroup Core_Module
*
* \brief Base class for all 1D and 2D array, and related expressions
*
* An array is similar to a dense vector or matrix. While matrices are mathematical
* objects with well defined linear algebra operators, an array is just a collection
* of scalar values arranged in a one or two dimensionnal fashion. As the main consequence,
* all operations applied to an array are performed coefficient wise. Furthermore,
* arrays support scalar math functions of the c++ standard library (e.g., std::sin(x)), and convenient
* constructors allowing to easily write generic code working for both scalar values
* and arrays.
*
* This class is the base that is inherited by all array expression types.
*
* \tparam Derived is the derived type, e.g., an array or an expression type.
*
* This class can be extended with the help of the plugin mechanism described on the page
* \ref TopicCustomizing_Plugins by defining the preprocessor symbol \c EIGEN_ARRAYBASE_PLUGIN.
*
* \sa class MatrixBase, \ref TopicClassHierarchy
*/
template<typename Derived> class ArrayBase
: public DenseBase<Derived>
{
public:
#ifndef EIGEN_PARSED_BY_DOXYGEN
/** The base class for a given storage type. */
typedef ArrayBase StorageBaseType;
typedef ArrayBase Eigen_BaseClassForSpecializationOfGlobalMathFuncImpl;
typedef typename internal::traits<Derived>::StorageKind StorageKind;
typedef typename internal::traits<Derived>::Scalar Scalar;
typedef typename internal::packet_traits<Scalar>::type PacketScalar;
typedef typename NumTraits<Scalar>::Real RealScalar;
typedef DenseBase<Derived> Base;
using Base::RowsAtCompileTime;
using Base::ColsAtCompileTime;
using Base::SizeAtCompileTime;
using Base::MaxRowsAtCompileTime;
using Base::MaxColsAtCompileTime;
using Base::MaxSizeAtCompileTime;
using Base::IsVectorAtCompileTime;
using Base::Flags;
using Base::derived;
using Base::const_cast_derived;
using Base::rows;
using Base::cols;
using Base::size;
using Base::coeff;
using Base::coeffRef;
using Base::lazyAssign;
using Base::operator=;
using Base::operator+=;
using Base::operator-=;
using Base::operator*=;
using Base::operator/=;
typedef typename Base::CoeffReturnType CoeffReturnType;
#endif // not EIGEN_PARSED_BY_DOXYGEN
#ifndef EIGEN_PARSED_BY_DOXYGEN
typedef typename Base::PlainObject PlainObject;
/** \internal Represents a matrix with all coefficients equal to one another*/
typedef CwiseNullaryOp<internal::scalar_constant_op<Scalar>,PlainObject> ConstantReturnType;
#endif // not EIGEN_PARSED_BY_DOXYGEN
#define EIGEN_CURRENT_STORAGE_BASE_CLASS Eigen::ArrayBase
#define EIGEN_DOC_UNARY_ADDONS(X,Y)
# include "../plugins/CommonCwiseUnaryOps.h"
# include "../plugins/MatrixCwiseUnaryOps.h"
# include "../plugins/ArrayCwiseUnaryOps.h"
# include "../plugins/CommonCwiseBinaryOps.h"
# include "../plugins/MatrixCwiseBinaryOps.h"
# include "../plugins/ArrayCwiseBinaryOps.h"
# ifdef EIGEN_ARRAYBASE_PLUGIN
# include EIGEN_ARRAYBASE_PLUGIN
# endif
#undef EIGEN_CURRENT_STORAGE_BASE_CLASS
#undef EIGEN_DOC_UNARY_ADDONS
/** Special case of the template operator=, in order to prevent the compiler
* from generating a default operator= (issue hit with g++ 4.1)
*/
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
Derived& operator=(const ArrayBase& other)
{
internal::call_assignment(derived(), other.derived());
return derived();
}
/** Set all the entries to \a value.
* \sa DenseBase::setConstant(), DenseBase::fill() */
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
Derived& operator=(const Scalar &value)
{ Base::setConstant(value); return derived(); }
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
Derived& operator+=(const Scalar& scalar);
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
Derived& operator-=(const Scalar& scalar);
template<typename OtherDerived>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
Derived& operator+=(const ArrayBase<OtherDerived>& other);
template<typename OtherDerived>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
Derived& operator-=(const ArrayBase<OtherDerived>& other);
template<typename OtherDerived>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
Derived& operator*=(const ArrayBase<OtherDerived>& other);
template<typename OtherDerived>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
Derived& operator/=(const ArrayBase<OtherDerived>& other);
public:
EIGEN_DEVICE_FUNC
ArrayBase<Derived>& array() { return *this; }
EIGEN_DEVICE_FUNC
const ArrayBase<Derived>& array() const { return *this; }
/** \returns an \link Eigen::MatrixBase Matrix \endlink expression of this array
* \sa MatrixBase::array() */
EIGEN_DEVICE_FUNC
MatrixWrapper<Derived> matrix() { return MatrixWrapper<Derived>(derived()); }
EIGEN_DEVICE_FUNC
const MatrixWrapper<const Derived> matrix() const { return MatrixWrapper<const Derived>(derived()); }
// template<typename Dest>
// inline void evalTo(Dest& dst) const { dst = matrix(); }
protected:
EIGEN_DEVICE_FUNC
ArrayBase() : Base() {}
private:
explicit ArrayBase(Index);
ArrayBase(Index,Index);
template<typename OtherDerived> explicit ArrayBase(const ArrayBase<OtherDerived>&);
protected:
// mixing arrays and matrices is not legal
template<typename OtherDerived> Derived& operator+=(const MatrixBase<OtherDerived>& )
{EIGEN_STATIC_ASSERT(std::ptrdiff_t(sizeof(typename OtherDerived::Scalar))==-1,YOU_CANNOT_MIX_ARRAYS_AND_MATRICES); return *this;}
// mixing arrays and matrices is not legal
template<typename OtherDerived> Derived& operator-=(const MatrixBase<OtherDerived>& )
{EIGEN_STATIC_ASSERT(std::ptrdiff_t(sizeof(typename OtherDerived::Scalar))==-1,YOU_CANNOT_MIX_ARRAYS_AND_MATRICES); return *this;}
};
/** replaces \c *this by \c *this - \a other.
*
* \returns a reference to \c *this
*/
template<typename Derived>
template<typename OtherDerived>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived &
ArrayBase<Derived>::operator-=(const ArrayBase<OtherDerived> &other)
{
call_assignment(derived(), other.derived(), internal::sub_assign_op<Scalar,typename OtherDerived::Scalar>());
return derived();
}
/** replaces \c *this by \c *this + \a other.
*
* \returns a reference to \c *this
*/
template<typename Derived>
template<typename OtherDerived>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived &
ArrayBase<Derived>::operator+=(const ArrayBase<OtherDerived>& other)
{
call_assignment(derived(), other.derived(), internal::add_assign_op<Scalar,typename OtherDerived::Scalar>());
return derived();
}
/** replaces \c *this by \c *this * \a other coefficient wise.
*
* \returns a reference to \c *this
*/
template<typename Derived>
template<typename OtherDerived>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived &
ArrayBase<Derived>::operator*=(const ArrayBase<OtherDerived>& other)
{
call_assignment(derived(), other.derived(), internal::mul_assign_op<Scalar,typename OtherDerived::Scalar>());
return derived();
}
/** replaces \c *this by \c *this / \a other coefficient wise.
*
* \returns a reference to \c *this
*/
template<typename Derived>
template<typename OtherDerived>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived &
ArrayBase<Derived>::operator/=(const ArrayBase<OtherDerived>& other)
{
call_assignment(derived(), other.derived(), internal::div_assign_op<Scalar,typename OtherDerived::Scalar>());
return derived();
}
} // end namespace Eigen
#endif // EIGEN_ARRAYBASE_H

View File

@@ -0,0 +1,209 @@
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2009-2010 Gael Guennebaud <gael.guennebaud@inria.fr>
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#ifndef EIGEN_ARRAYWRAPPER_H
#define EIGEN_ARRAYWRAPPER_H
namespace Eigen {
/** \class ArrayWrapper
* \ingroup Core_Module
*
* \brief Expression of a mathematical vector or matrix as an array object
*
* This class is the return type of MatrixBase::array(), and most of the time
* this is the only way it is use.
*
* \sa MatrixBase::array(), class MatrixWrapper
*/
namespace internal {
template<typename ExpressionType>
struct traits<ArrayWrapper<ExpressionType> >
: public traits<typename remove_all<typename ExpressionType::Nested>::type >
{
typedef ArrayXpr XprKind;
// Let's remove NestByRefBit
enum {
Flags0 = traits<typename remove_all<typename ExpressionType::Nested>::type >::Flags,
LvalueBitFlag = is_lvalue<ExpressionType>::value ? LvalueBit : 0,
Flags = (Flags0 & ~(NestByRefBit | LvalueBit)) | LvalueBitFlag
};
};
}
template<typename ExpressionType>
class ArrayWrapper : public ArrayBase<ArrayWrapper<ExpressionType> >
{
public:
typedef ArrayBase<ArrayWrapper> Base;
EIGEN_DENSE_PUBLIC_INTERFACE(ArrayWrapper)
EIGEN_INHERIT_ASSIGNMENT_OPERATORS(ArrayWrapper)
typedef typename internal::remove_all<ExpressionType>::type NestedExpression;
typedef typename internal::conditional<
internal::is_lvalue<ExpressionType>::value,
Scalar,
const Scalar
>::type ScalarWithConstIfNotLvalue;
typedef typename internal::ref_selector<ExpressionType>::non_const_type NestedExpressionType;
using Base::coeffRef;
EIGEN_DEVICE_FUNC
explicit EIGEN_STRONG_INLINE ArrayWrapper(ExpressionType& matrix) : m_expression(matrix) {}
EIGEN_DEVICE_FUNC
inline Index rows() const { return m_expression.rows(); }
EIGEN_DEVICE_FUNC
inline Index cols() const { return m_expression.cols(); }
EIGEN_DEVICE_FUNC
inline Index outerStride() const { return m_expression.outerStride(); }
EIGEN_DEVICE_FUNC
inline Index innerStride() const { return m_expression.innerStride(); }
EIGEN_DEVICE_FUNC
inline ScalarWithConstIfNotLvalue* data() { return m_expression.data(); }
EIGEN_DEVICE_FUNC
inline const Scalar* data() const { return m_expression.data(); }
EIGEN_DEVICE_FUNC
inline const Scalar& coeffRef(Index rowId, Index colId) const
{
return m_expression.coeffRef(rowId, colId);
}
EIGEN_DEVICE_FUNC
inline const Scalar& coeffRef(Index index) const
{
return m_expression.coeffRef(index);
}
template<typename Dest>
EIGEN_DEVICE_FUNC
inline void evalTo(Dest& dst) const { dst = m_expression; }
const typename internal::remove_all<NestedExpressionType>::type&
EIGEN_DEVICE_FUNC
nestedExpression() const
{
return m_expression;
}
/** Forwards the resizing request to the nested expression
* \sa DenseBase::resize(Index) */
EIGEN_DEVICE_FUNC
void resize(Index newSize) { m_expression.resize(newSize); }
/** Forwards the resizing request to the nested expression
* \sa DenseBase::resize(Index,Index)*/
EIGEN_DEVICE_FUNC
void resize(Index rows, Index cols) { m_expression.resize(rows,cols); }
protected:
NestedExpressionType m_expression;
};
/** \class MatrixWrapper
* \ingroup Core_Module
*
* \brief Expression of an array as a mathematical vector or matrix
*
* This class is the return type of ArrayBase::matrix(), and most of the time
* this is the only way it is use.
*
* \sa MatrixBase::matrix(), class ArrayWrapper
*/
namespace internal {
template<typename ExpressionType>
struct traits<MatrixWrapper<ExpressionType> >
: public traits<typename remove_all<typename ExpressionType::Nested>::type >
{
typedef MatrixXpr XprKind;
// Let's remove NestByRefBit
enum {
Flags0 = traits<typename remove_all<typename ExpressionType::Nested>::type >::Flags,
LvalueBitFlag = is_lvalue<ExpressionType>::value ? LvalueBit : 0,
Flags = (Flags0 & ~(NestByRefBit | LvalueBit)) | LvalueBitFlag
};
};
}
template<typename ExpressionType>
class MatrixWrapper : public MatrixBase<MatrixWrapper<ExpressionType> >
{
public:
typedef MatrixBase<MatrixWrapper<ExpressionType> > Base;
EIGEN_DENSE_PUBLIC_INTERFACE(MatrixWrapper)
EIGEN_INHERIT_ASSIGNMENT_OPERATORS(MatrixWrapper)
typedef typename internal::remove_all<ExpressionType>::type NestedExpression;
typedef typename internal::conditional<
internal::is_lvalue<ExpressionType>::value,
Scalar,
const Scalar
>::type ScalarWithConstIfNotLvalue;
typedef typename internal::ref_selector<ExpressionType>::non_const_type NestedExpressionType;
using Base::coeffRef;
EIGEN_DEVICE_FUNC
explicit inline MatrixWrapper(ExpressionType& matrix) : m_expression(matrix) {}
EIGEN_DEVICE_FUNC
inline Index rows() const { return m_expression.rows(); }
EIGEN_DEVICE_FUNC
inline Index cols() const { return m_expression.cols(); }
EIGEN_DEVICE_FUNC
inline Index outerStride() const { return m_expression.outerStride(); }
EIGEN_DEVICE_FUNC
inline Index innerStride() const { return m_expression.innerStride(); }
EIGEN_DEVICE_FUNC
inline ScalarWithConstIfNotLvalue* data() { return m_expression.data(); }
EIGEN_DEVICE_FUNC
inline const Scalar* data() const { return m_expression.data(); }
EIGEN_DEVICE_FUNC
inline const Scalar& coeffRef(Index rowId, Index colId) const
{
return m_expression.derived().coeffRef(rowId, colId);
}
EIGEN_DEVICE_FUNC
inline const Scalar& coeffRef(Index index) const
{
return m_expression.coeffRef(index);
}
EIGEN_DEVICE_FUNC
const typename internal::remove_all<NestedExpressionType>::type&
nestedExpression() const
{
return m_expression;
}
/** Forwards the resizing request to the nested expression
* \sa DenseBase::resize(Index) */
EIGEN_DEVICE_FUNC
void resize(Index newSize) { m_expression.resize(newSize); }
/** Forwards the resizing request to the nested expression
* \sa DenseBase::resize(Index,Index)*/
EIGEN_DEVICE_FUNC
void resize(Index rows, Index cols) { m_expression.resize(rows,cols); }
protected:
NestedExpressionType m_expression;
};
} // end namespace Eigen
#endif // EIGEN_ARRAYWRAPPER_H

View File

@@ -0,0 +1,90 @@
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2007 Michael Olbrich <michael.olbrich@gmx.net>
// Copyright (C) 2006-2010 Benoit Jacob <jacob.benoit.1@gmail.com>
// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#ifndef EIGEN_ASSIGN_H
#define EIGEN_ASSIGN_H
namespace Eigen {
template<typename Derived>
template<typename OtherDerived>
EIGEN_STRONG_INLINE Derived& DenseBase<Derived>
::lazyAssign(const DenseBase<OtherDerived>& other)
{
enum{
SameType = internal::is_same<typename Derived::Scalar,typename OtherDerived::Scalar>::value
};
EIGEN_STATIC_ASSERT_LVALUE(Derived)
EIGEN_STATIC_ASSERT_SAME_MATRIX_SIZE(Derived,OtherDerived)
EIGEN_STATIC_ASSERT(SameType,YOU_MIXED_DIFFERENT_NUMERIC_TYPES__YOU_NEED_TO_USE_THE_CAST_METHOD_OF_MATRIXBASE_TO_CAST_NUMERIC_TYPES_EXPLICITLY)
eigen_assert(rows() == other.rows() && cols() == other.cols());
internal::call_assignment_no_alias(derived(),other.derived());
return derived();
}
template<typename Derived>
template<typename OtherDerived>
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Derived& DenseBase<Derived>::operator=(const DenseBase<OtherDerived>& other)
{
internal::call_assignment(derived(), other.derived());
return derived();
}
template<typename Derived>
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Derived& DenseBase<Derived>::operator=(const DenseBase& other)
{
internal::call_assignment(derived(), other.derived());
return derived();
}
template<typename Derived>
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Derived& MatrixBase<Derived>::operator=(const MatrixBase& other)
{
internal::call_assignment(derived(), other.derived());
return derived();
}
template<typename Derived>
template <typename OtherDerived>
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Derived& MatrixBase<Derived>::operator=(const DenseBase<OtherDerived>& other)
{
internal::call_assignment(derived(), other.derived());
return derived();
}
template<typename Derived>
template <typename OtherDerived>
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Derived& MatrixBase<Derived>::operator=(const EigenBase<OtherDerived>& other)
{
internal::call_assignment(derived(), other.derived());
return derived();
}
template<typename Derived>
template<typename OtherDerived>
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Derived& MatrixBase<Derived>::operator=(const ReturnByValue<OtherDerived>& other)
{
other.derived().evalTo(derived());
return derived();
}
} // end namespace Eigen
#endif // EIGEN_ASSIGN_H

View File

@@ -0,0 +1,935 @@
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2011 Benoit Jacob <jacob.benoit.1@gmail.com>
// Copyright (C) 2011-2014 Gael Guennebaud <gael.guennebaud@inria.fr>
// Copyright (C) 2011-2012 Jitse Niesen <jitse@maths.leeds.ac.uk>
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#ifndef EIGEN_ASSIGN_EVALUATOR_H
#define EIGEN_ASSIGN_EVALUATOR_H
namespace Eigen {
// This implementation is based on Assign.h
namespace internal {
/***************************************************************************
* Part 1 : the logic deciding a strategy for traversal and unrolling *
***************************************************************************/
// copy_using_evaluator_traits is based on assign_traits
template <typename DstEvaluator, typename SrcEvaluator, typename AssignFunc>
struct copy_using_evaluator_traits
{
typedef typename DstEvaluator::XprType Dst;
typedef typename Dst::Scalar DstScalar;
enum {
DstFlags = DstEvaluator::Flags,
SrcFlags = SrcEvaluator::Flags
};
public:
enum {
DstAlignment = DstEvaluator::Alignment,
SrcAlignment = SrcEvaluator::Alignment,
DstHasDirectAccess = (DstFlags & DirectAccessBit) == DirectAccessBit,
JointAlignment = EIGEN_PLAIN_ENUM_MIN(DstAlignment,SrcAlignment)
};
private:
enum {
InnerSize = int(Dst::IsVectorAtCompileTime) ? int(Dst::SizeAtCompileTime)
: int(DstFlags)&RowMajorBit ? int(Dst::ColsAtCompileTime)
: int(Dst::RowsAtCompileTime),
InnerMaxSize = int(Dst::IsVectorAtCompileTime) ? int(Dst::MaxSizeAtCompileTime)
: int(DstFlags)&RowMajorBit ? int(Dst::MaxColsAtCompileTime)
: int(Dst::MaxRowsAtCompileTime),
OuterStride = int(outer_stride_at_compile_time<Dst>::ret),
MaxSizeAtCompileTime = Dst::SizeAtCompileTime
};
// TODO distinguish between linear traversal and inner-traversals
typedef typename find_best_packet<DstScalar,Dst::SizeAtCompileTime>::type LinearPacketType;
typedef typename find_best_packet<DstScalar,InnerSize>::type InnerPacketType;
enum {
LinearPacketSize = unpacket_traits<LinearPacketType>::size,
InnerPacketSize = unpacket_traits<InnerPacketType>::size
};
public:
enum {
LinearRequiredAlignment = unpacket_traits<LinearPacketType>::alignment,
InnerRequiredAlignment = unpacket_traits<InnerPacketType>::alignment
};
private:
enum {
DstIsRowMajor = DstFlags&RowMajorBit,
SrcIsRowMajor = SrcFlags&RowMajorBit,
StorageOrdersAgree = (int(DstIsRowMajor) == int(SrcIsRowMajor)),
MightVectorize = bool(StorageOrdersAgree)
&& (int(DstFlags) & int(SrcFlags) & ActualPacketAccessBit)
&& bool(functor_traits<AssignFunc>::PacketAccess),
MayInnerVectorize = MightVectorize
&& int(InnerSize)!=Dynamic && int(InnerSize)%int(InnerPacketSize)==0
&& int(OuterStride)!=Dynamic && int(OuterStride)%int(InnerPacketSize)==0
&& (EIGEN_UNALIGNED_VECTORIZE || int(JointAlignment)>=int(InnerRequiredAlignment)),
MayLinearize = bool(StorageOrdersAgree) && (int(DstFlags) & int(SrcFlags) & LinearAccessBit),
MayLinearVectorize = bool(MightVectorize) && bool(MayLinearize) && bool(DstHasDirectAccess)
&& (EIGEN_UNALIGNED_VECTORIZE || (int(DstAlignment)>=int(LinearRequiredAlignment)) || MaxSizeAtCompileTime == Dynamic),
/* If the destination isn't aligned, we have to do runtime checks and we don't unroll,
so it's only good for large enough sizes. */
MaySliceVectorize = bool(MightVectorize) && bool(DstHasDirectAccess)
&& (int(InnerMaxSize)==Dynamic || int(InnerMaxSize)>=(EIGEN_UNALIGNED_VECTORIZE?InnerPacketSize:(3*InnerPacketSize)))
/* slice vectorization can be slow, so we only want it if the slices are big, which is
indicated by InnerMaxSize rather than InnerSize, think of the case of a dynamic block
in a fixed-size matrix
However, with EIGEN_UNALIGNED_VECTORIZE and unrolling, slice vectorization is still worth it */
};
public:
enum {
Traversal = int(MayLinearVectorize) && (LinearPacketSize>InnerPacketSize) ? int(LinearVectorizedTraversal)
: int(MayInnerVectorize) ? int(InnerVectorizedTraversal)
: int(MayLinearVectorize) ? int(LinearVectorizedTraversal)
: int(MaySliceVectorize) ? int(SliceVectorizedTraversal)
: int(MayLinearize) ? int(LinearTraversal)
: int(DefaultTraversal),
Vectorized = int(Traversal) == InnerVectorizedTraversal
|| int(Traversal) == LinearVectorizedTraversal
|| int(Traversal) == SliceVectorizedTraversal
};
typedef typename conditional<int(Traversal)==LinearVectorizedTraversal, LinearPacketType, InnerPacketType>::type PacketType;
private:
enum {
ActualPacketSize = int(Traversal)==LinearVectorizedTraversal ? LinearPacketSize
: Vectorized ? InnerPacketSize
: 1,
UnrollingLimit = EIGEN_UNROLLING_LIMIT * ActualPacketSize,
MayUnrollCompletely = int(Dst::SizeAtCompileTime) != Dynamic
&& int(Dst::SizeAtCompileTime) * (int(DstEvaluator::CoeffReadCost)+int(SrcEvaluator::CoeffReadCost)) <= int(UnrollingLimit),
MayUnrollInner = int(InnerSize) != Dynamic
&& int(InnerSize) * (int(DstEvaluator::CoeffReadCost)+int(SrcEvaluator::CoeffReadCost)) <= int(UnrollingLimit)
};
public:
enum {
Unrolling = (int(Traversal) == int(InnerVectorizedTraversal) || int(Traversal) == int(DefaultTraversal))
? (
int(MayUnrollCompletely) ? int(CompleteUnrolling)
: int(MayUnrollInner) ? int(InnerUnrolling)
: int(NoUnrolling)
)
: int(Traversal) == int(LinearVectorizedTraversal)
? ( bool(MayUnrollCompletely) && ( EIGEN_UNALIGNED_VECTORIZE || (int(DstAlignment)>=int(LinearRequiredAlignment)))
? int(CompleteUnrolling)
: int(NoUnrolling) )
: int(Traversal) == int(LinearTraversal)
? ( bool(MayUnrollCompletely) ? int(CompleteUnrolling)
: int(NoUnrolling) )
#if EIGEN_UNALIGNED_VECTORIZE
: int(Traversal) == int(SliceVectorizedTraversal)
? ( bool(MayUnrollInner) ? int(InnerUnrolling)
: int(NoUnrolling) )
#endif
: int(NoUnrolling)
};
#ifdef EIGEN_DEBUG_ASSIGN
static void debug()
{
std::cerr << "DstXpr: " << typeid(typename DstEvaluator::XprType).name() << std::endl;
std::cerr << "SrcXpr: " << typeid(typename SrcEvaluator::XprType).name() << std::endl;
std::cerr.setf(std::ios::hex, std::ios::basefield);
std::cerr << "DstFlags" << " = " << DstFlags << " (" << demangle_flags(DstFlags) << " )" << std::endl;
std::cerr << "SrcFlags" << " = " << SrcFlags << " (" << demangle_flags(SrcFlags) << " )" << std::endl;
std::cerr.unsetf(std::ios::hex);
EIGEN_DEBUG_VAR(DstAlignment)
EIGEN_DEBUG_VAR(SrcAlignment)
EIGEN_DEBUG_VAR(LinearRequiredAlignment)
EIGEN_DEBUG_VAR(InnerRequiredAlignment)
EIGEN_DEBUG_VAR(JointAlignment)
EIGEN_DEBUG_VAR(InnerSize)
EIGEN_DEBUG_VAR(InnerMaxSize)
EIGEN_DEBUG_VAR(LinearPacketSize)
EIGEN_DEBUG_VAR(InnerPacketSize)
EIGEN_DEBUG_VAR(ActualPacketSize)
EIGEN_DEBUG_VAR(StorageOrdersAgree)
EIGEN_DEBUG_VAR(MightVectorize)
EIGEN_DEBUG_VAR(MayLinearize)
EIGEN_DEBUG_VAR(MayInnerVectorize)
EIGEN_DEBUG_VAR(MayLinearVectorize)
EIGEN_DEBUG_VAR(MaySliceVectorize)
std::cerr << "Traversal" << " = " << Traversal << " (" << demangle_traversal(Traversal) << ")" << std::endl;
EIGEN_DEBUG_VAR(SrcEvaluator::CoeffReadCost)
EIGEN_DEBUG_VAR(UnrollingLimit)
EIGEN_DEBUG_VAR(MayUnrollCompletely)
EIGEN_DEBUG_VAR(MayUnrollInner)
std::cerr << "Unrolling" << " = " << Unrolling << " (" << demangle_unrolling(Unrolling) << ")" << std::endl;
std::cerr << std::endl;
}
#endif
};
/***************************************************************************
* Part 2 : meta-unrollers
***************************************************************************/
/************************
*** Default traversal ***
************************/
template<typename Kernel, int Index, int Stop>
struct copy_using_evaluator_DefaultTraversal_CompleteUnrolling
{
// FIXME: this is not very clean, perhaps this information should be provided by the kernel?
typedef typename Kernel::DstEvaluatorType DstEvaluatorType;
typedef typename DstEvaluatorType::XprType DstXprType;
enum {
outer = Index / DstXprType::InnerSizeAtCompileTime,
inner = Index % DstXprType::InnerSizeAtCompileTime
};
EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE void run(Kernel &kernel)
{
kernel.assignCoeffByOuterInner(outer, inner);
copy_using_evaluator_DefaultTraversal_CompleteUnrolling<Kernel, Index+1, Stop>::run(kernel);
}
};
template<typename Kernel, int Stop>
struct copy_using_evaluator_DefaultTraversal_CompleteUnrolling<Kernel, Stop, Stop>
{
EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE void run(Kernel&) { }
};
template<typename Kernel, int Index_, int Stop>
struct copy_using_evaluator_DefaultTraversal_InnerUnrolling
{
EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE void run(Kernel &kernel, Index outer)
{
kernel.assignCoeffByOuterInner(outer, Index_);
copy_using_evaluator_DefaultTraversal_InnerUnrolling<Kernel, Index_+1, Stop>::run(kernel, outer);
}
};
template<typename Kernel, int Stop>
struct copy_using_evaluator_DefaultTraversal_InnerUnrolling<Kernel, Stop, Stop>
{
EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE void run(Kernel&, Index) { }
};
/***********************
*** Linear traversal ***
***********************/
template<typename Kernel, int Index, int Stop>
struct copy_using_evaluator_LinearTraversal_CompleteUnrolling
{
EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE void run(Kernel& kernel)
{
kernel.assignCoeff(Index);
copy_using_evaluator_LinearTraversal_CompleteUnrolling<Kernel, Index+1, Stop>::run(kernel);
}
};
template<typename Kernel, int Stop>
struct copy_using_evaluator_LinearTraversal_CompleteUnrolling<Kernel, Stop, Stop>
{
EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE void run(Kernel&) { }
};
/**************************
*** Inner vectorization ***
**************************/
template<typename Kernel, int Index, int Stop>
struct copy_using_evaluator_innervec_CompleteUnrolling
{
// FIXME: this is not very clean, perhaps this information should be provided by the kernel?
typedef typename Kernel::DstEvaluatorType DstEvaluatorType;
typedef typename DstEvaluatorType::XprType DstXprType;
typedef typename Kernel::PacketType PacketType;
enum {
outer = Index / DstXprType::InnerSizeAtCompileTime,
inner = Index % DstXprType::InnerSizeAtCompileTime,
SrcAlignment = Kernel::AssignmentTraits::SrcAlignment,
DstAlignment = Kernel::AssignmentTraits::DstAlignment
};
EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE void run(Kernel &kernel)
{
kernel.template assignPacketByOuterInner<DstAlignment, SrcAlignment, PacketType>(outer, inner);
enum { NextIndex = Index + unpacket_traits<PacketType>::size };
copy_using_evaluator_innervec_CompleteUnrolling<Kernel, NextIndex, Stop>::run(kernel);
}
};
template<typename Kernel, int Stop>
struct copy_using_evaluator_innervec_CompleteUnrolling<Kernel, Stop, Stop>
{
EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE void run(Kernel&) { }
};
template<typename Kernel, int Index_, int Stop, int SrcAlignment, int DstAlignment>
struct copy_using_evaluator_innervec_InnerUnrolling
{
typedef typename Kernel::PacketType PacketType;
EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE void run(Kernel &kernel, Index outer)
{
kernel.template assignPacketByOuterInner<DstAlignment, SrcAlignment, PacketType>(outer, Index_);
enum { NextIndex = Index_ + unpacket_traits<PacketType>::size };
copy_using_evaluator_innervec_InnerUnrolling<Kernel, NextIndex, Stop, SrcAlignment, DstAlignment>::run(kernel, outer);
}
};
template<typename Kernel, int Stop, int SrcAlignment, int DstAlignment>
struct copy_using_evaluator_innervec_InnerUnrolling<Kernel, Stop, Stop, SrcAlignment, DstAlignment>
{
EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE void run(Kernel &, Index) { }
};
/***************************************************************************
* Part 3 : implementation of all cases
***************************************************************************/
// dense_assignment_loop is based on assign_impl
template<typename Kernel,
int Traversal = Kernel::AssignmentTraits::Traversal,
int Unrolling = Kernel::AssignmentTraits::Unrolling>
struct dense_assignment_loop;
/************************
*** Default traversal ***
************************/
template<typename Kernel>
struct dense_assignment_loop<Kernel, DefaultTraversal, NoUnrolling>
{
EIGEN_DEVICE_FUNC static void EIGEN_STRONG_INLINE run(Kernel &kernel)
{
for(Index outer = 0; outer < kernel.outerSize(); ++outer) {
for(Index inner = 0; inner < kernel.innerSize(); ++inner) {
kernel.assignCoeffByOuterInner(outer, inner);
}
}
}
};
template<typename Kernel>
struct dense_assignment_loop<Kernel, DefaultTraversal, CompleteUnrolling>
{
EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE void run(Kernel &kernel)
{
typedef typename Kernel::DstEvaluatorType::XprType DstXprType;
copy_using_evaluator_DefaultTraversal_CompleteUnrolling<Kernel, 0, DstXprType::SizeAtCompileTime>::run(kernel);
}
};
template<typename Kernel>
struct dense_assignment_loop<Kernel, DefaultTraversal, InnerUnrolling>
{
EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE void run(Kernel &kernel)
{
typedef typename Kernel::DstEvaluatorType::XprType DstXprType;
const Index outerSize = kernel.outerSize();
for(Index outer = 0; outer < outerSize; ++outer)
copy_using_evaluator_DefaultTraversal_InnerUnrolling<Kernel, 0, DstXprType::InnerSizeAtCompileTime>::run(kernel, outer);
}
};
/***************************
*** Linear vectorization ***
***************************/
// The goal of unaligned_dense_assignment_loop is simply to factorize the handling
// of the non vectorizable beginning and ending parts
template <bool IsAligned = false>
struct unaligned_dense_assignment_loop
{
// if IsAligned = true, then do nothing
template <typename Kernel>
EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE void run(Kernel&, Index, Index) {}
};
template <>
struct unaligned_dense_assignment_loop<false>
{
// MSVC must not inline this functions. If it does, it fails to optimize the
// packet access path.
// FIXME check which version exhibits this issue
#if EIGEN_COMP_MSVC
template <typename Kernel>
static EIGEN_DONT_INLINE void run(Kernel &kernel,
Index start,
Index end)
#else
template <typename Kernel>
EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE void run(Kernel &kernel,
Index start,
Index end)
#endif
{
for (Index index = start; index < end; ++index)
kernel.assignCoeff(index);
}
};
template<typename Kernel>
struct dense_assignment_loop<Kernel, LinearVectorizedTraversal, NoUnrolling>
{
EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE void run(Kernel &kernel)
{
const Index size = kernel.size();
typedef typename Kernel::Scalar Scalar;
typedef typename Kernel::PacketType PacketType;
enum {
requestedAlignment = Kernel::AssignmentTraits::LinearRequiredAlignment,
packetSize = unpacket_traits<PacketType>::size,
dstIsAligned = int(Kernel::AssignmentTraits::DstAlignment)>=int(requestedAlignment),
dstAlignment = packet_traits<Scalar>::AlignedOnScalar ? int(requestedAlignment)
: int(Kernel::AssignmentTraits::DstAlignment),
srcAlignment = Kernel::AssignmentTraits::JointAlignment
};
const Index alignedStart = dstIsAligned ? 0 : internal::first_aligned<requestedAlignment>(kernel.dstDataPtr(), size);
const Index alignedEnd = alignedStart + ((size-alignedStart)/packetSize)*packetSize;
unaligned_dense_assignment_loop<dstIsAligned!=0>::run(kernel, 0, alignedStart);
for(Index index = alignedStart; index < alignedEnd; index += packetSize)
kernel.template assignPacket<dstAlignment, srcAlignment, PacketType>(index);
unaligned_dense_assignment_loop<>::run(kernel, alignedEnd, size);
}
};
template<typename Kernel>
struct dense_assignment_loop<Kernel, LinearVectorizedTraversal, CompleteUnrolling>
{
EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE void run(Kernel &kernel)
{
typedef typename Kernel::DstEvaluatorType::XprType DstXprType;
typedef typename Kernel::PacketType PacketType;
enum { size = DstXprType::SizeAtCompileTime,
packetSize =unpacket_traits<PacketType>::size,
alignedSize = (size/packetSize)*packetSize };
copy_using_evaluator_innervec_CompleteUnrolling<Kernel, 0, alignedSize>::run(kernel);
copy_using_evaluator_DefaultTraversal_CompleteUnrolling<Kernel, alignedSize, size>::run(kernel);
}
};
/**************************
*** Inner vectorization ***
**************************/
template<typename Kernel>
struct dense_assignment_loop<Kernel, InnerVectorizedTraversal, NoUnrolling>
{
typedef typename Kernel::PacketType PacketType;
enum {
SrcAlignment = Kernel::AssignmentTraits::SrcAlignment,
DstAlignment = Kernel::AssignmentTraits::DstAlignment
};
EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE void run(Kernel &kernel)
{
const Index innerSize = kernel.innerSize();
const Index outerSize = kernel.outerSize();
const Index packetSize = unpacket_traits<PacketType>::size;
for(Index outer = 0; outer < outerSize; ++outer)
for(Index inner = 0; inner < innerSize; inner+=packetSize)
kernel.template assignPacketByOuterInner<DstAlignment, SrcAlignment, PacketType>(outer, inner);
}
};
template<typename Kernel>
struct dense_assignment_loop<Kernel, InnerVectorizedTraversal, CompleteUnrolling>
{
EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE void run(Kernel &kernel)
{
typedef typename Kernel::DstEvaluatorType::XprType DstXprType;
copy_using_evaluator_innervec_CompleteUnrolling<Kernel, 0, DstXprType::SizeAtCompileTime>::run(kernel);
}
};
template<typename Kernel>
struct dense_assignment_loop<Kernel, InnerVectorizedTraversal, InnerUnrolling>
{
EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE void run(Kernel &kernel)
{
typedef typename Kernel::DstEvaluatorType::XprType DstXprType;
typedef typename Kernel::AssignmentTraits Traits;
const Index outerSize = kernel.outerSize();
for(Index outer = 0; outer < outerSize; ++outer)
copy_using_evaluator_innervec_InnerUnrolling<Kernel, 0, DstXprType::InnerSizeAtCompileTime,
Traits::SrcAlignment, Traits::DstAlignment>::run(kernel, outer);
}
};
/***********************
*** Linear traversal ***
***********************/
template<typename Kernel>
struct dense_assignment_loop<Kernel, LinearTraversal, NoUnrolling>
{
EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE void run(Kernel &kernel)
{
const Index size = kernel.size();
for(Index i = 0; i < size; ++i)
kernel.assignCoeff(i);
}
};
template<typename Kernel>
struct dense_assignment_loop<Kernel, LinearTraversal, CompleteUnrolling>
{
EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE void run(Kernel &kernel)
{
typedef typename Kernel::DstEvaluatorType::XprType DstXprType;
copy_using_evaluator_LinearTraversal_CompleteUnrolling<Kernel, 0, DstXprType::SizeAtCompileTime>::run(kernel);
}
};
/**************************
*** Slice vectorization ***
***************************/
template<typename Kernel>
struct dense_assignment_loop<Kernel, SliceVectorizedTraversal, NoUnrolling>
{
EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE void run(Kernel &kernel)
{
typedef typename Kernel::Scalar Scalar;
typedef typename Kernel::PacketType PacketType;
enum {
packetSize = unpacket_traits<PacketType>::size,
requestedAlignment = int(Kernel::AssignmentTraits::InnerRequiredAlignment),
alignable = packet_traits<Scalar>::AlignedOnScalar || int(Kernel::AssignmentTraits::DstAlignment)>=sizeof(Scalar),
dstIsAligned = int(Kernel::AssignmentTraits::DstAlignment)>=int(requestedAlignment),
dstAlignment = alignable ? int(requestedAlignment)
: int(Kernel::AssignmentTraits::DstAlignment)
};
const Scalar *dst_ptr = kernel.dstDataPtr();
if((!bool(dstIsAligned)) && (UIntPtr(dst_ptr) % sizeof(Scalar))>0)
{
// the pointer is not aligend-on scalar, so alignment is not possible
return dense_assignment_loop<Kernel,DefaultTraversal,NoUnrolling>::run(kernel);
}
const Index packetAlignedMask = packetSize - 1;
const Index innerSize = kernel.innerSize();
const Index outerSize = kernel.outerSize();
const Index alignedStep = alignable ? (packetSize - kernel.outerStride() % packetSize) & packetAlignedMask : 0;
Index alignedStart = ((!alignable) || bool(dstIsAligned)) ? 0 : internal::first_aligned<requestedAlignment>(dst_ptr, innerSize);
for(Index outer = 0; outer < outerSize; ++outer)
{
const Index alignedEnd = alignedStart + ((innerSize-alignedStart) & ~packetAlignedMask);
// do the non-vectorizable part of the assignment
for(Index inner = 0; inner<alignedStart ; ++inner)
kernel.assignCoeffByOuterInner(outer, inner);
// do the vectorizable part of the assignment
for(Index inner = alignedStart; inner<alignedEnd; inner+=packetSize)
kernel.template assignPacketByOuterInner<dstAlignment, Unaligned, PacketType>(outer, inner);
// do the non-vectorizable part of the assignment
for(Index inner = alignedEnd; inner<innerSize ; ++inner)
kernel.assignCoeffByOuterInner(outer, inner);
alignedStart = numext::mini((alignedStart+alignedStep)%packetSize, innerSize);
}
}
};
#if EIGEN_UNALIGNED_VECTORIZE
template<typename Kernel>
struct dense_assignment_loop<Kernel, SliceVectorizedTraversal, InnerUnrolling>
{
EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE void run(Kernel &kernel)
{
typedef typename Kernel::DstEvaluatorType::XprType DstXprType;
typedef typename Kernel::PacketType PacketType;
enum { size = DstXprType::InnerSizeAtCompileTime,
packetSize =unpacket_traits<PacketType>::size,
vectorizableSize = (size/packetSize)*packetSize };
for(Index outer = 0; outer < kernel.outerSize(); ++outer)
{
copy_using_evaluator_innervec_InnerUnrolling<Kernel, 0, vectorizableSize, 0, 0>::run(kernel, outer);
copy_using_evaluator_DefaultTraversal_InnerUnrolling<Kernel, vectorizableSize, size>::run(kernel, outer);
}
}
};
#endif
/***************************************************************************
* Part 4 : Generic dense assignment kernel
***************************************************************************/
// This class generalize the assignment of a coefficient (or packet) from one dense evaluator
// to another dense writable evaluator.
// It is parametrized by the two evaluators, and the actual assignment functor.
// This abstraction level permits to keep the evaluation loops as simple and as generic as possible.
// One can customize the assignment using this generic dense_assignment_kernel with different
// functors, or by completely overloading it, by-passing a functor.
template<typename DstEvaluatorTypeT, typename SrcEvaluatorTypeT, typename Functor, int Version = Specialized>
class generic_dense_assignment_kernel
{
protected:
typedef typename DstEvaluatorTypeT::XprType DstXprType;
typedef typename SrcEvaluatorTypeT::XprType SrcXprType;
public:
typedef DstEvaluatorTypeT DstEvaluatorType;
typedef SrcEvaluatorTypeT SrcEvaluatorType;
typedef typename DstEvaluatorType::Scalar Scalar;
typedef copy_using_evaluator_traits<DstEvaluatorTypeT, SrcEvaluatorTypeT, Functor> AssignmentTraits;
typedef typename AssignmentTraits::PacketType PacketType;
EIGEN_DEVICE_FUNC generic_dense_assignment_kernel(DstEvaluatorType &dst, const SrcEvaluatorType &src, const Functor &func, DstXprType& dstExpr)
: m_dst(dst), m_src(src), m_functor(func), m_dstExpr(dstExpr)
{
#ifdef EIGEN_DEBUG_ASSIGN
AssignmentTraits::debug();
#endif
}
EIGEN_DEVICE_FUNC Index size() const { return m_dstExpr.size(); }
EIGEN_DEVICE_FUNC Index innerSize() const { return m_dstExpr.innerSize(); }
EIGEN_DEVICE_FUNC Index outerSize() const { return m_dstExpr.outerSize(); }
EIGEN_DEVICE_FUNC Index rows() const { return m_dstExpr.rows(); }
EIGEN_DEVICE_FUNC Index cols() const { return m_dstExpr.cols(); }
EIGEN_DEVICE_FUNC Index outerStride() const { return m_dstExpr.outerStride(); }
EIGEN_DEVICE_FUNC DstEvaluatorType& dstEvaluator() { return m_dst; }
EIGEN_DEVICE_FUNC const SrcEvaluatorType& srcEvaluator() const { return m_src; }
/// Assign src(row,col) to dst(row,col) through the assignment functor.
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void assignCoeff(Index row, Index col)
{
m_functor.assignCoeff(m_dst.coeffRef(row,col), m_src.coeff(row,col));
}
/// \sa assignCoeff(Index,Index)
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void assignCoeff(Index index)
{
m_functor.assignCoeff(m_dst.coeffRef(index), m_src.coeff(index));
}
/// \sa assignCoeff(Index,Index)
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void assignCoeffByOuterInner(Index outer, Index inner)
{
Index row = rowIndexByOuterInner(outer, inner);
Index col = colIndexByOuterInner(outer, inner);
assignCoeff(row, col);
}
template<int StoreMode, int LoadMode, typename PacketType>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void assignPacket(Index row, Index col)
{
m_functor.template assignPacket<StoreMode>(&m_dst.coeffRef(row,col), m_src.template packet<LoadMode,PacketType>(row,col));
}
template<int StoreMode, int LoadMode, typename PacketType>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void assignPacket(Index index)
{
m_functor.template assignPacket<StoreMode>(&m_dst.coeffRef(index), m_src.template packet<LoadMode,PacketType>(index));
}
template<int StoreMode, int LoadMode, typename PacketType>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void assignPacketByOuterInner(Index outer, Index inner)
{
Index row = rowIndexByOuterInner(outer, inner);
Index col = colIndexByOuterInner(outer, inner);
assignPacket<StoreMode,LoadMode,PacketType>(row, col);
}
EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE Index rowIndexByOuterInner(Index outer, Index inner)
{
typedef typename DstEvaluatorType::ExpressionTraits Traits;
return int(Traits::RowsAtCompileTime) == 1 ? 0
: int(Traits::ColsAtCompileTime) == 1 ? inner
: int(DstEvaluatorType::Flags)&RowMajorBit ? outer
: inner;
}
EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE Index colIndexByOuterInner(Index outer, Index inner)
{
typedef typename DstEvaluatorType::ExpressionTraits Traits;
return int(Traits::ColsAtCompileTime) == 1 ? 0
: int(Traits::RowsAtCompileTime) == 1 ? inner
: int(DstEvaluatorType::Flags)&RowMajorBit ? inner
: outer;
}
EIGEN_DEVICE_FUNC const Scalar* dstDataPtr() const
{
return m_dstExpr.data();
}
protected:
DstEvaluatorType& m_dst;
const SrcEvaluatorType& m_src;
const Functor &m_functor;
// TODO find a way to avoid the needs of the original expression
DstXprType& m_dstExpr;
};
/***************************************************************************
* Part 5 : Entry point for dense rectangular assignment
***************************************************************************/
template<typename DstXprType,typename SrcXprType, typename Functor>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
void resize_if_allowed(DstXprType &dst, const SrcXprType& src, const Functor &/*func*/)
{
EIGEN_ONLY_USED_FOR_DEBUG(dst);
EIGEN_ONLY_USED_FOR_DEBUG(src);
eigen_assert(dst.rows() == src.rows() && dst.cols() == src.cols());
}
template<typename DstXprType,typename SrcXprType, typename T1, typename T2>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
void resize_if_allowed(DstXprType &dst, const SrcXprType& src, const internal::assign_op<T1,T2> &/*func*/)
{
Index dstRows = src.rows();
Index dstCols = src.cols();
if(((dst.rows()!=dstRows) || (dst.cols()!=dstCols)))
dst.resize(dstRows, dstCols);
eigen_assert(dst.rows() == dstRows && dst.cols() == dstCols);
}
template<typename DstXprType, typename SrcXprType, typename Functor>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void call_dense_assignment_loop(DstXprType& dst, const SrcXprType& src, const Functor &func)
{
typedef evaluator<DstXprType> DstEvaluatorType;
typedef evaluator<SrcXprType> SrcEvaluatorType;
SrcEvaluatorType srcEvaluator(src);
// NOTE To properly handle A = (A*A.transpose())/s with A rectangular,
// we need to resize the destination after the source evaluator has been created.
resize_if_allowed(dst, src, func);
DstEvaluatorType dstEvaluator(dst);
typedef generic_dense_assignment_kernel<DstEvaluatorType,SrcEvaluatorType,Functor> Kernel;
Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());
dense_assignment_loop<Kernel>::run(kernel);
}
template<typename DstXprType, typename SrcXprType>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void call_dense_assignment_loop(DstXprType& dst, const SrcXprType& src)
{
call_dense_assignment_loop(dst, src, internal::assign_op<typename DstXprType::Scalar,typename SrcXprType::Scalar>());
}
/***************************************************************************
* Part 6 : Generic assignment
***************************************************************************/
// Based on the respective shapes of the destination and source,
// the class AssignmentKind determine the kind of assignment mechanism.
// AssignmentKind must define a Kind typedef.
template<typename DstShape, typename SrcShape> struct AssignmentKind;
// Assignement kind defined in this file:
struct Dense2Dense {};
struct EigenBase2EigenBase {};
template<typename,typename> struct AssignmentKind { typedef EigenBase2EigenBase Kind; };
template<> struct AssignmentKind<DenseShape,DenseShape> { typedef Dense2Dense Kind; };
// This is the main assignment class
template< typename DstXprType, typename SrcXprType, typename Functor,
typename Kind = typename AssignmentKind< typename evaluator_traits<DstXprType>::Shape , typename evaluator_traits<SrcXprType>::Shape >::Kind,
typename EnableIf = void>
struct Assignment;
// The only purpose of this call_assignment() function is to deal with noalias() / "assume-aliasing" and automatic transposition.
// Indeed, I (Gael) think that this concept of "assume-aliasing" was a mistake, and it makes thing quite complicated.
// So this intermediate function removes everything related to "assume-aliasing" such that Assignment
// does not has to bother about these annoying details.
template<typename Dst, typename Src>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
void call_assignment(Dst& dst, const Src& src)
{
call_assignment(dst, src, internal::assign_op<typename Dst::Scalar,typename Src::Scalar>());
}
template<typename Dst, typename Src>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
void call_assignment(const Dst& dst, const Src& src)
{
call_assignment(dst, src, internal::assign_op<typename Dst::Scalar,typename Src::Scalar>());
}
// Deal with "assume-aliasing"
template<typename Dst, typename Src, typename Func>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
void call_assignment(Dst& dst, const Src& src, const Func& func, typename enable_if< evaluator_assume_aliasing<Src>::value, void*>::type = 0)
{
typename plain_matrix_type<Src>::type tmp(src);
call_assignment_no_alias(dst, tmp, func);
}
template<typename Dst, typename Src, typename Func>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
void call_assignment(Dst& dst, const Src& src, const Func& func, typename enable_if<!evaluator_assume_aliasing<Src>::value, void*>::type = 0)
{
call_assignment_no_alias(dst, src, func);
}
// by-pass "assume-aliasing"
// When there is no aliasing, we require that 'dst' has been properly resized
template<typename Dst, template <typename> class StorageBase, typename Src, typename Func>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
void call_assignment(NoAlias<Dst,StorageBase>& dst, const Src& src, const Func& func)
{
call_assignment_no_alias(dst.expression(), src, func);
}
template<typename Dst, typename Src, typename Func>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
void call_assignment_no_alias(Dst& dst, const Src& src, const Func& func)
{
enum {
NeedToTranspose = ( (int(Dst::RowsAtCompileTime) == 1 && int(Src::ColsAtCompileTime) == 1)
|| (int(Dst::ColsAtCompileTime) == 1 && int(Src::RowsAtCompileTime) == 1)
) && int(Dst::SizeAtCompileTime) != 1
};
typedef typename internal::conditional<NeedToTranspose, Transpose<Dst>, Dst>::type ActualDstTypeCleaned;
typedef typename internal::conditional<NeedToTranspose, Transpose<Dst>, Dst&>::type ActualDstType;
ActualDstType actualDst(dst);
// TODO check whether this is the right place to perform these checks:
EIGEN_STATIC_ASSERT_LVALUE(Dst)
EIGEN_STATIC_ASSERT_SAME_MATRIX_SIZE(ActualDstTypeCleaned,Src)
EIGEN_CHECK_BINARY_COMPATIBILIY(Func,typename ActualDstTypeCleaned::Scalar,typename Src::Scalar);
Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);
}
template<typename Dst, typename Src>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
void call_assignment_no_alias(Dst& dst, const Src& src)
{
call_assignment_no_alias(dst, src, internal::assign_op<typename Dst::Scalar,typename Src::Scalar>());
}
template<typename Dst, typename Src, typename Func>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
void call_assignment_no_alias_no_transpose(Dst& dst, const Src& src, const Func& func)
{
// TODO check whether this is the right place to perform these checks:
EIGEN_STATIC_ASSERT_LVALUE(Dst)
EIGEN_STATIC_ASSERT_SAME_MATRIX_SIZE(Dst,Src)
EIGEN_CHECK_BINARY_COMPATIBILIY(Func,typename Dst::Scalar,typename Src::Scalar);
Assignment<Dst,Src,Func>::run(dst, src, func);
}
template<typename Dst, typename Src>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
void call_assignment_no_alias_no_transpose(Dst& dst, const Src& src)
{
call_assignment_no_alias_no_transpose(dst, src, internal::assign_op<typename Dst::Scalar,typename Src::Scalar>());
}
// forward declaration
template<typename Dst, typename Src> void check_for_aliasing(const Dst &dst, const Src &src);
// Generic Dense to Dense assignment
// Note that the last template argument "Weak" is needed to make it possible to perform
// both partial specialization+SFINAE without ambiguous specialization
template< typename DstXprType, typename SrcXprType, typename Functor, typename Weak>
struct Assignment<DstXprType, SrcXprType, Functor, Dense2Dense, Weak>
{
EIGEN_DEVICE_FUNC
static EIGEN_STRONG_INLINE void run(DstXprType &dst, const SrcXprType &src, const Functor &func)
{
#ifndef EIGEN_NO_DEBUG
internal::check_for_aliasing(dst, src);
#endif
call_dense_assignment_loop(dst, src, func);
}
};
// Generic assignment through evalTo.
// TODO: not sure we have to keep that one, but it helps porting current code to new evaluator mechanism.
// Note that the last template argument "Weak" is needed to make it possible to perform
// both partial specialization+SFINAE without ambiguous specialization
template< typename DstXprType, typename SrcXprType, typename Functor, typename Weak>
struct Assignment<DstXprType, SrcXprType, Functor, EigenBase2EigenBase, Weak>
{
EIGEN_DEVICE_FUNC
static EIGEN_STRONG_INLINE void run(DstXprType &dst, const SrcXprType &src, const internal::assign_op<typename DstXprType::Scalar,typename SrcXprType::Scalar> &/*func*/)
{
Index dstRows = src.rows();
Index dstCols = src.cols();
if((dst.rows()!=dstRows) || (dst.cols()!=dstCols))
dst.resize(dstRows, dstCols);
eigen_assert(dst.rows() == src.rows() && dst.cols() == src.cols());
src.evalTo(dst);
}
// NOTE The following two functions are templated to avoid their instanciation if not needed
// This is needed because some expressions supports evalTo only and/or have 'void' as scalar type.
template<typename SrcScalarType>
EIGEN_DEVICE_FUNC
static EIGEN_STRONG_INLINE void run(DstXprType &dst, const SrcXprType &src, const internal::add_assign_op<typename DstXprType::Scalar,SrcScalarType> &/*func*/)
{
Index dstRows = src.rows();
Index dstCols = src.cols();
if((dst.rows()!=dstRows) || (dst.cols()!=dstCols))
dst.resize(dstRows, dstCols);
eigen_assert(dst.rows() == src.rows() && dst.cols() == src.cols());
src.addTo(dst);
}
template<typename SrcScalarType>
EIGEN_DEVICE_FUNC
static EIGEN_STRONG_INLINE void run(DstXprType &dst, const SrcXprType &src, const internal::sub_assign_op<typename DstXprType::Scalar,SrcScalarType> &/*func*/)
{
Index dstRows = src.rows();
Index dstCols = src.cols();
if((dst.rows()!=dstRows) || (dst.cols()!=dstCols))
dst.resize(dstRows, dstCols);
eigen_assert(dst.rows() == src.rows() && dst.cols() == src.cols());
src.subTo(dst);
}
};
} // namespace internal
} // end namespace Eigen
#endif // EIGEN_ASSIGN_EVALUATOR_H

View File

@@ -0,0 +1,178 @@
/*
Copyright (c) 2011, Intel Corporation. All rights reserved.
Copyright (C) 2015 Gael Guennebaud <gael.guennebaud@inria.fr>
Redistribution and use in source and binary forms, with or without modification,
are permitted provided that the following conditions are met:
* Redistributions of source code must retain the above copyright notice, this
list of conditions and the following disclaimer.
* Redistributions in binary form must reproduce the above copyright notice,
this list of conditions and the following disclaimer in the documentation
and/or other materials provided with the distribution.
* Neither the name of Intel Corporation nor the names of its contributors may
be used to endorse or promote products derived from this software without
specific prior written permission.
THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR
ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
(INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON
ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
********************************************************************************
* Content : Eigen bindings to Intel(R) MKL
* MKL VML support for coefficient-wise unary Eigen expressions like a=b.sin()
********************************************************************************
*/
#ifndef EIGEN_ASSIGN_VML_H
#define EIGEN_ASSIGN_VML_H
namespace Eigen {
namespace internal {
template<typename Dst, typename Src>
class vml_assign_traits
{
private:
enum {
DstHasDirectAccess = Dst::Flags & DirectAccessBit,
SrcHasDirectAccess = Src::Flags & DirectAccessBit,
StorageOrdersAgree = (int(Dst::IsRowMajor) == int(Src::IsRowMajor)),
InnerSize = int(Dst::IsVectorAtCompileTime) ? int(Dst::SizeAtCompileTime)
: int(Dst::Flags)&RowMajorBit ? int(Dst::ColsAtCompileTime)
: int(Dst::RowsAtCompileTime),
InnerMaxSize = int(Dst::IsVectorAtCompileTime) ? int(Dst::MaxSizeAtCompileTime)
: int(Dst::Flags)&RowMajorBit ? int(Dst::MaxColsAtCompileTime)
: int(Dst::MaxRowsAtCompileTime),
MaxSizeAtCompileTime = Dst::SizeAtCompileTime,
MightEnableVml = StorageOrdersAgree && DstHasDirectAccess && SrcHasDirectAccess && Src::InnerStrideAtCompileTime==1 && Dst::InnerStrideAtCompileTime==1,
MightLinearize = MightEnableVml && (int(Dst::Flags) & int(Src::Flags) & LinearAccessBit),
VmlSize = MightLinearize ? MaxSizeAtCompileTime : InnerMaxSize,
LargeEnough = VmlSize==Dynamic || VmlSize>=EIGEN_MKL_VML_THRESHOLD
};
public:
enum {
EnableVml = MightEnableVml && LargeEnough,
Traversal = MightLinearize ? LinearTraversal : DefaultTraversal
};
};
#define EIGEN_PP_EXPAND(ARG) ARG
#if !defined (EIGEN_FAST_MATH) || (EIGEN_FAST_MATH != 1)
#define EIGEN_VMLMODE_EXPAND_LA , VML_HA
#else
#define EIGEN_VMLMODE_EXPAND_LA , VML_LA
#endif
#define EIGEN_VMLMODE_EXPAND__
#define EIGEN_VMLMODE_PREFIX_LA vm
#define EIGEN_VMLMODE_PREFIX__ v
#define EIGEN_VMLMODE_PREFIX(VMLMODE) EIGEN_CAT(EIGEN_VMLMODE_PREFIX_,VMLMODE)
#define EIGEN_MKL_VML_DECLARE_UNARY_CALL(EIGENOP, VMLOP, EIGENTYPE, VMLTYPE, VMLMODE) \
template< typename DstXprType, typename SrcXprNested> \
struct Assignment<DstXprType, CwiseUnaryOp<scalar_##EIGENOP##_op<EIGENTYPE>, SrcXprNested>, assign_op<EIGENTYPE,EIGENTYPE>, \
Dense2Dense, typename enable_if<vml_assign_traits<DstXprType,SrcXprNested>::EnableVml>::type> { \
typedef CwiseUnaryOp<scalar_##EIGENOP##_op<EIGENTYPE>, SrcXprNested> SrcXprType; \
static void run(DstXprType &dst, const SrcXprType &src, const assign_op<EIGENTYPE,EIGENTYPE> &func) { \
resize_if_allowed(dst, src, func); \
eigen_assert(dst.rows() == src.rows() && dst.cols() == src.cols()); \
if(vml_assign_traits<DstXprType,SrcXprNested>::Traversal==LinearTraversal) { \
VMLOP(dst.size(), (const VMLTYPE*)src.nestedExpression().data(), \
(VMLTYPE*)dst.data() EIGEN_PP_EXPAND(EIGEN_VMLMODE_EXPAND_##VMLMODE) ); \
} else { \
const Index outerSize = dst.outerSize(); \
for(Index outer = 0; outer < outerSize; ++outer) { \
const EIGENTYPE *src_ptr = src.IsRowMajor ? &(src.nestedExpression().coeffRef(outer,0)) : \
&(src.nestedExpression().coeffRef(0, outer)); \
EIGENTYPE *dst_ptr = dst.IsRowMajor ? &(dst.coeffRef(outer,0)) : &(dst.coeffRef(0, outer)); \
VMLOP( dst.innerSize(), (const VMLTYPE*)src_ptr, \
(VMLTYPE*)dst_ptr EIGEN_PP_EXPAND(EIGEN_VMLMODE_EXPAND_##VMLMODE)); \
} \
} \
} \
}; \
#define EIGEN_MKL_VML_DECLARE_UNARY_CALLS_REAL(EIGENOP, VMLOP, VMLMODE) \
EIGEN_MKL_VML_DECLARE_UNARY_CALL(EIGENOP, EIGEN_CAT(EIGEN_VMLMODE_PREFIX(VMLMODE),s##VMLOP), float, float, VMLMODE) \
EIGEN_MKL_VML_DECLARE_UNARY_CALL(EIGENOP, EIGEN_CAT(EIGEN_VMLMODE_PREFIX(VMLMODE),d##VMLOP), double, double, VMLMODE)
#define EIGEN_MKL_VML_DECLARE_UNARY_CALLS_CPLX(EIGENOP, VMLOP, VMLMODE) \
EIGEN_MKL_VML_DECLARE_UNARY_CALL(EIGENOP, EIGEN_CAT(EIGEN_VMLMODE_PREFIX(VMLMODE),c##VMLOP), scomplex, MKL_Complex8, VMLMODE) \
EIGEN_MKL_VML_DECLARE_UNARY_CALL(EIGENOP, EIGEN_CAT(EIGEN_VMLMODE_PREFIX(VMLMODE),z##VMLOP), dcomplex, MKL_Complex16, VMLMODE)
#define EIGEN_MKL_VML_DECLARE_UNARY_CALLS(EIGENOP, VMLOP, VMLMODE) \
EIGEN_MKL_VML_DECLARE_UNARY_CALLS_REAL(EIGENOP, VMLOP, VMLMODE) \
EIGEN_MKL_VML_DECLARE_UNARY_CALLS_CPLX(EIGENOP, VMLOP, VMLMODE)
EIGEN_MKL_VML_DECLARE_UNARY_CALLS(sin, Sin, LA)
EIGEN_MKL_VML_DECLARE_UNARY_CALLS(asin, Asin, LA)
EIGEN_MKL_VML_DECLARE_UNARY_CALLS(sinh, Sinh, LA)
EIGEN_MKL_VML_DECLARE_UNARY_CALLS(cos, Cos, LA)
EIGEN_MKL_VML_DECLARE_UNARY_CALLS(acos, Acos, LA)
EIGEN_MKL_VML_DECLARE_UNARY_CALLS(cosh, Cosh, LA)
EIGEN_MKL_VML_DECLARE_UNARY_CALLS(tan, Tan, LA)
EIGEN_MKL_VML_DECLARE_UNARY_CALLS(atan, Atan, LA)
EIGEN_MKL_VML_DECLARE_UNARY_CALLS(tanh, Tanh, LA)
// EIGEN_MKL_VML_DECLARE_UNARY_CALLS(abs, Abs, _)
EIGEN_MKL_VML_DECLARE_UNARY_CALLS(exp, Exp, LA)
EIGEN_MKL_VML_DECLARE_UNARY_CALLS(log, Ln, LA)
EIGEN_MKL_VML_DECLARE_UNARY_CALLS(log10, Log10, LA)
EIGEN_MKL_VML_DECLARE_UNARY_CALLS(sqrt, Sqrt, _)
EIGEN_MKL_VML_DECLARE_UNARY_CALLS_REAL(square, Sqr, _)
EIGEN_MKL_VML_DECLARE_UNARY_CALLS_CPLX(arg, Arg, _)
EIGEN_MKL_VML_DECLARE_UNARY_CALLS_REAL(round, Round, _)
EIGEN_MKL_VML_DECLARE_UNARY_CALLS_REAL(floor, Floor, _)
EIGEN_MKL_VML_DECLARE_UNARY_CALLS_REAL(ceil, Ceil, _)
#define EIGEN_MKL_VML_DECLARE_POW_CALL(EIGENOP, VMLOP, EIGENTYPE, VMLTYPE, VMLMODE) \
template< typename DstXprType, typename SrcXprNested, typename Plain> \
struct Assignment<DstXprType, CwiseBinaryOp<scalar_##EIGENOP##_op<EIGENTYPE,EIGENTYPE>, SrcXprNested, \
const CwiseNullaryOp<internal::scalar_constant_op<EIGENTYPE>,Plain> >, assign_op<EIGENTYPE,EIGENTYPE>, \
Dense2Dense, typename enable_if<vml_assign_traits<DstXprType,SrcXprNested>::EnableVml>::type> { \
typedef CwiseBinaryOp<scalar_##EIGENOP##_op<EIGENTYPE,EIGENTYPE>, SrcXprNested, \
const CwiseNullaryOp<internal::scalar_constant_op<EIGENTYPE>,Plain> > SrcXprType; \
static void run(DstXprType &dst, const SrcXprType &src, const assign_op<EIGENTYPE,EIGENTYPE> &func) { \
resize_if_allowed(dst, src, func); \
eigen_assert(dst.rows() == src.rows() && dst.cols() == src.cols()); \
VMLTYPE exponent = reinterpret_cast<const VMLTYPE&>(src.rhs().functor().m_other); \
if(vml_assign_traits<DstXprType,SrcXprNested>::Traversal==LinearTraversal) \
{ \
VMLOP( dst.size(), (const VMLTYPE*)src.lhs().data(), exponent, \
(VMLTYPE*)dst.data() EIGEN_PP_EXPAND(EIGEN_VMLMODE_EXPAND_##VMLMODE) ); \
} else { \
const Index outerSize = dst.outerSize(); \
for(Index outer = 0; outer < outerSize; ++outer) { \
const EIGENTYPE *src_ptr = src.IsRowMajor ? &(src.lhs().coeffRef(outer,0)) : \
&(src.lhs().coeffRef(0, outer)); \
EIGENTYPE *dst_ptr = dst.IsRowMajor ? &(dst.coeffRef(outer,0)) : &(dst.coeffRef(0, outer)); \
VMLOP( dst.innerSize(), (const VMLTYPE*)src_ptr, exponent, \
(VMLTYPE*)dst_ptr EIGEN_PP_EXPAND(EIGEN_VMLMODE_EXPAND_##VMLMODE)); \
} \
} \
} \
};
EIGEN_MKL_VML_DECLARE_POW_CALL(pow, vmsPowx, float, float, LA)
EIGEN_MKL_VML_DECLARE_POW_CALL(pow, vmdPowx, double, double, LA)
EIGEN_MKL_VML_DECLARE_POW_CALL(pow, vmcPowx, scomplex, MKL_Complex8, LA)
EIGEN_MKL_VML_DECLARE_POW_CALL(pow, vmzPowx, dcomplex, MKL_Complex16, LA)
} // end namespace internal
} // end namespace Eigen
#endif // EIGEN_ASSIGN_VML_H

View File

@@ -0,0 +1,353 @@
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2009 Gael Guennebaud <gael.guennebaud@inria.fr>
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#ifndef EIGEN_BANDMATRIX_H
#define EIGEN_BANDMATRIX_H
namespace Eigen {
namespace internal {
template<typename Derived>
class BandMatrixBase : public EigenBase<Derived>
{
public:
enum {
Flags = internal::traits<Derived>::Flags,
CoeffReadCost = internal::traits<Derived>::CoeffReadCost,
RowsAtCompileTime = internal::traits<Derived>::RowsAtCompileTime,
ColsAtCompileTime = internal::traits<Derived>::ColsAtCompileTime,
MaxRowsAtCompileTime = internal::traits<Derived>::MaxRowsAtCompileTime,
MaxColsAtCompileTime = internal::traits<Derived>::MaxColsAtCompileTime,
Supers = internal::traits<Derived>::Supers,
Subs = internal::traits<Derived>::Subs,
Options = internal::traits<Derived>::Options
};
typedef typename internal::traits<Derived>::Scalar Scalar;
typedef Matrix<Scalar,RowsAtCompileTime,ColsAtCompileTime> DenseMatrixType;
typedef typename DenseMatrixType::StorageIndex StorageIndex;
typedef typename internal::traits<Derived>::CoefficientsType CoefficientsType;
typedef EigenBase<Derived> Base;
protected:
enum {
DataRowsAtCompileTime = ((Supers!=Dynamic) && (Subs!=Dynamic))
? 1 + Supers + Subs
: Dynamic,
SizeAtCompileTime = EIGEN_SIZE_MIN_PREFER_DYNAMIC(RowsAtCompileTime,ColsAtCompileTime)
};
public:
using Base::derived;
using Base::rows;
using Base::cols;
/** \returns the number of super diagonals */
inline Index supers() const { return derived().supers(); }
/** \returns the number of sub diagonals */
inline Index subs() const { return derived().subs(); }
/** \returns an expression of the underlying coefficient matrix */
inline const CoefficientsType& coeffs() const { return derived().coeffs(); }
/** \returns an expression of the underlying coefficient matrix */
inline CoefficientsType& coeffs() { return derived().coeffs(); }
/** \returns a vector expression of the \a i -th column,
* only the meaningful part is returned.
* \warning the internal storage must be column major. */
inline Block<CoefficientsType,Dynamic,1> col(Index i)
{
EIGEN_STATIC_ASSERT((Options&RowMajor)==0,THIS_METHOD_IS_ONLY_FOR_COLUMN_MAJOR_MATRICES);
Index start = 0;
Index len = coeffs().rows();
if (i<=supers())
{
start = supers()-i;
len = (std::min)(rows(),std::max<Index>(0,coeffs().rows() - (supers()-i)));
}
else if (i>=rows()-subs())
len = std::max<Index>(0,coeffs().rows() - (i + 1 - rows() + subs()));
return Block<CoefficientsType,Dynamic,1>(coeffs(), start, i, len, 1);
}
/** \returns a vector expression of the main diagonal */
inline Block<CoefficientsType,1,SizeAtCompileTime> diagonal()
{ return Block<CoefficientsType,1,SizeAtCompileTime>(coeffs(),supers(),0,1,(std::min)(rows(),cols())); }
/** \returns a vector expression of the main diagonal (const version) */
inline const Block<const CoefficientsType,1,SizeAtCompileTime> diagonal() const
{ return Block<const CoefficientsType,1,SizeAtCompileTime>(coeffs(),supers(),0,1,(std::min)(rows(),cols())); }
template<int Index> struct DiagonalIntReturnType {
enum {
ReturnOpposite = (Options&SelfAdjoint) && (((Index)>0 && Supers==0) || ((Index)<0 && Subs==0)),
Conjugate = ReturnOpposite && NumTraits<Scalar>::IsComplex,
ActualIndex = ReturnOpposite ? -Index : Index,
DiagonalSize = (RowsAtCompileTime==Dynamic || ColsAtCompileTime==Dynamic)
? Dynamic
: (ActualIndex<0
? EIGEN_SIZE_MIN_PREFER_DYNAMIC(ColsAtCompileTime, RowsAtCompileTime + ActualIndex)
: EIGEN_SIZE_MIN_PREFER_DYNAMIC(RowsAtCompileTime, ColsAtCompileTime - ActualIndex))
};
typedef Block<CoefficientsType,1, DiagonalSize> BuildType;
typedef typename internal::conditional<Conjugate,
CwiseUnaryOp<internal::scalar_conjugate_op<Scalar>,BuildType >,
BuildType>::type Type;
};
/** \returns a vector expression of the \a N -th sub or super diagonal */
template<int N> inline typename DiagonalIntReturnType<N>::Type diagonal()
{
return typename DiagonalIntReturnType<N>::BuildType(coeffs(), supers()-N, (std::max)(0,N), 1, diagonalLength(N));
}
/** \returns a vector expression of the \a N -th sub or super diagonal */
template<int N> inline const typename DiagonalIntReturnType<N>::Type diagonal() const
{
return typename DiagonalIntReturnType<N>::BuildType(coeffs(), supers()-N, (std::max)(0,N), 1, diagonalLength(N));
}
/** \returns a vector expression of the \a i -th sub or super diagonal */
inline Block<CoefficientsType,1,Dynamic> diagonal(Index i)
{
eigen_assert((i<0 && -i<=subs()) || (i>=0 && i<=supers()));
return Block<CoefficientsType,1,Dynamic>(coeffs(), supers()-i, std::max<Index>(0,i), 1, diagonalLength(i));
}
/** \returns a vector expression of the \a i -th sub or super diagonal */
inline const Block<const CoefficientsType,1,Dynamic> diagonal(Index i) const
{
eigen_assert((i<0 && -i<=subs()) || (i>=0 && i<=supers()));
return Block<const CoefficientsType,1,Dynamic>(coeffs(), supers()-i, std::max<Index>(0,i), 1, diagonalLength(i));
}
template<typename Dest> inline void evalTo(Dest& dst) const
{
dst.resize(rows(),cols());
dst.setZero();
dst.diagonal() = diagonal();
for (Index i=1; i<=supers();++i)
dst.diagonal(i) = diagonal(i);
for (Index i=1; i<=subs();++i)
dst.diagonal(-i) = diagonal(-i);
}
DenseMatrixType toDenseMatrix() const
{
DenseMatrixType res(rows(),cols());
evalTo(res);
return res;
}
protected:
inline Index diagonalLength(Index i) const
{ return i<0 ? (std::min)(cols(),rows()+i) : (std::min)(rows(),cols()-i); }
};
/**
* \class BandMatrix
* \ingroup Core_Module
*
* \brief Represents a rectangular matrix with a banded storage
*
* \tparam _Scalar Numeric type, i.e. float, double, int
* \tparam _Rows Number of rows, or \b Dynamic
* \tparam _Cols Number of columns, or \b Dynamic
* \tparam _Supers Number of super diagonal
* \tparam _Subs Number of sub diagonal
* \tparam _Options A combination of either \b #RowMajor or \b #ColMajor, and of \b #SelfAdjoint
* The former controls \ref TopicStorageOrders "storage order", and defaults to
* column-major. The latter controls whether the matrix represents a selfadjoint
* matrix in which case either Supers of Subs have to be null.
*
* \sa class TridiagonalMatrix
*/
template<typename _Scalar, int _Rows, int _Cols, int _Supers, int _Subs, int _Options>
struct traits<BandMatrix<_Scalar,_Rows,_Cols,_Supers,_Subs,_Options> >
{
typedef _Scalar Scalar;
typedef Dense StorageKind;
typedef Eigen::Index StorageIndex;
enum {
CoeffReadCost = NumTraits<Scalar>::ReadCost,
RowsAtCompileTime = _Rows,
ColsAtCompileTime = _Cols,
MaxRowsAtCompileTime = _Rows,
MaxColsAtCompileTime = _Cols,
Flags = LvalueBit,
Supers = _Supers,
Subs = _Subs,
Options = _Options,
DataRowsAtCompileTime = ((Supers!=Dynamic) && (Subs!=Dynamic)) ? 1 + Supers + Subs : Dynamic
};
typedef Matrix<Scalar,DataRowsAtCompileTime,ColsAtCompileTime,Options&RowMajor?RowMajor:ColMajor> CoefficientsType;
};
template<typename _Scalar, int Rows, int Cols, int Supers, int Subs, int Options>
class BandMatrix : public BandMatrixBase<BandMatrix<_Scalar,Rows,Cols,Supers,Subs,Options> >
{
public:
typedef typename internal::traits<BandMatrix>::Scalar Scalar;
typedef typename internal::traits<BandMatrix>::StorageIndex StorageIndex;
typedef typename internal::traits<BandMatrix>::CoefficientsType CoefficientsType;
explicit inline BandMatrix(Index rows=Rows, Index cols=Cols, Index supers=Supers, Index subs=Subs)
: m_coeffs(1+supers+subs,cols),
m_rows(rows), m_supers(supers), m_subs(subs)
{
}
/** \returns the number of columns */
inline Index rows() const { return m_rows.value(); }
/** \returns the number of rows */
inline Index cols() const { return m_coeffs.cols(); }
/** \returns the number of super diagonals */
inline Index supers() const { return m_supers.value(); }
/** \returns the number of sub diagonals */
inline Index subs() const { return m_subs.value(); }
inline const CoefficientsType& coeffs() const { return m_coeffs; }
inline CoefficientsType& coeffs() { return m_coeffs; }
protected:
CoefficientsType m_coeffs;
internal::variable_if_dynamic<Index, Rows> m_rows;
internal::variable_if_dynamic<Index, Supers> m_supers;
internal::variable_if_dynamic<Index, Subs> m_subs;
};
template<typename _CoefficientsType,int _Rows, int _Cols, int _Supers, int _Subs,int _Options>
class BandMatrixWrapper;
template<typename _CoefficientsType,int _Rows, int _Cols, int _Supers, int _Subs,int _Options>
struct traits<BandMatrixWrapper<_CoefficientsType,_Rows,_Cols,_Supers,_Subs,_Options> >
{
typedef typename _CoefficientsType::Scalar Scalar;
typedef typename _CoefficientsType::StorageKind StorageKind;
typedef typename _CoefficientsType::StorageIndex StorageIndex;
enum {
CoeffReadCost = internal::traits<_CoefficientsType>::CoeffReadCost,
RowsAtCompileTime = _Rows,
ColsAtCompileTime = _Cols,
MaxRowsAtCompileTime = _Rows,
MaxColsAtCompileTime = _Cols,
Flags = LvalueBit,
Supers = _Supers,
Subs = _Subs,
Options = _Options,
DataRowsAtCompileTime = ((Supers!=Dynamic) && (Subs!=Dynamic)) ? 1 + Supers + Subs : Dynamic
};
typedef _CoefficientsType CoefficientsType;
};
template<typename _CoefficientsType,int _Rows, int _Cols, int _Supers, int _Subs,int _Options>
class BandMatrixWrapper : public BandMatrixBase<BandMatrixWrapper<_CoefficientsType,_Rows,_Cols,_Supers,_Subs,_Options> >
{
public:
typedef typename internal::traits<BandMatrixWrapper>::Scalar Scalar;
typedef typename internal::traits<BandMatrixWrapper>::CoefficientsType CoefficientsType;
typedef typename internal::traits<BandMatrixWrapper>::StorageIndex StorageIndex;
explicit inline BandMatrixWrapper(const CoefficientsType& coeffs, Index rows=_Rows, Index cols=_Cols, Index supers=_Supers, Index subs=_Subs)
: m_coeffs(coeffs),
m_rows(rows), m_supers(supers), m_subs(subs)
{
EIGEN_UNUSED_VARIABLE(cols);
//internal::assert(coeffs.cols()==cols() && (supers()+subs()+1)==coeffs.rows());
}
/** \returns the number of columns */
inline Index rows() const { return m_rows.value(); }
/** \returns the number of rows */
inline Index cols() const { return m_coeffs.cols(); }
/** \returns the number of super diagonals */
inline Index supers() const { return m_supers.value(); }
/** \returns the number of sub diagonals */
inline Index subs() const { return m_subs.value(); }
inline const CoefficientsType& coeffs() const { return m_coeffs; }
protected:
const CoefficientsType& m_coeffs;
internal::variable_if_dynamic<Index, _Rows> m_rows;
internal::variable_if_dynamic<Index, _Supers> m_supers;
internal::variable_if_dynamic<Index, _Subs> m_subs;
};
/**
* \class TridiagonalMatrix
* \ingroup Core_Module
*
* \brief Represents a tridiagonal matrix with a compact banded storage
*
* \tparam Scalar Numeric type, i.e. float, double, int
* \tparam Size Number of rows and cols, or \b Dynamic
* \tparam Options Can be 0 or \b SelfAdjoint
*
* \sa class BandMatrix
*/
template<typename Scalar, int Size, int Options>
class TridiagonalMatrix : public BandMatrix<Scalar,Size,Size,Options&SelfAdjoint?0:1,1,Options|RowMajor>
{
typedef BandMatrix<Scalar,Size,Size,Options&SelfAdjoint?0:1,1,Options|RowMajor> Base;
typedef typename Base::StorageIndex StorageIndex;
public:
explicit TridiagonalMatrix(Index size = Size) : Base(size,size,Options&SelfAdjoint?0:1,1) {}
inline typename Base::template DiagonalIntReturnType<1>::Type super()
{ return Base::template diagonal<1>(); }
inline const typename Base::template DiagonalIntReturnType<1>::Type super() const
{ return Base::template diagonal<1>(); }
inline typename Base::template DiagonalIntReturnType<-1>::Type sub()
{ return Base::template diagonal<-1>(); }
inline const typename Base::template DiagonalIntReturnType<-1>::Type sub() const
{ return Base::template diagonal<-1>(); }
protected:
};
struct BandShape {};
template<typename _Scalar, int _Rows, int _Cols, int _Supers, int _Subs, int _Options>
struct evaluator_traits<BandMatrix<_Scalar,_Rows,_Cols,_Supers,_Subs,_Options> >
: public evaluator_traits_base<BandMatrix<_Scalar,_Rows,_Cols,_Supers,_Subs,_Options> >
{
typedef BandShape Shape;
};
template<typename _CoefficientsType,int _Rows, int _Cols, int _Supers, int _Subs,int _Options>
struct evaluator_traits<BandMatrixWrapper<_CoefficientsType,_Rows,_Cols,_Supers,_Subs,_Options> >
: public evaluator_traits_base<BandMatrixWrapper<_CoefficientsType,_Rows,_Cols,_Supers,_Subs,_Options> >
{
typedef BandShape Shape;
};
template<> struct AssignmentKind<DenseShape,BandShape> { typedef EigenBase2EigenBase Kind; };
} // end namespace internal
} // end namespace Eigen
#endif // EIGEN_BANDMATRIX_H

View File

@@ -0,0 +1,452 @@
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
// Copyright (C) 2006-2010 Benoit Jacob <jacob.benoit.1@gmail.com>
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#ifndef EIGEN_BLOCK_H
#define EIGEN_BLOCK_H
namespace Eigen {
namespace internal {
template<typename XprType, int BlockRows, int BlockCols, bool InnerPanel>
struct traits<Block<XprType, BlockRows, BlockCols, InnerPanel> > : traits<XprType>
{
typedef typename traits<XprType>::Scalar Scalar;
typedef typename traits<XprType>::StorageKind StorageKind;
typedef typename traits<XprType>::XprKind XprKind;
typedef typename ref_selector<XprType>::type XprTypeNested;
typedef typename remove_reference<XprTypeNested>::type _XprTypeNested;
enum{
MatrixRows = traits<XprType>::RowsAtCompileTime,
MatrixCols = traits<XprType>::ColsAtCompileTime,
RowsAtCompileTime = MatrixRows == 0 ? 0 : BlockRows,
ColsAtCompileTime = MatrixCols == 0 ? 0 : BlockCols,
MaxRowsAtCompileTime = BlockRows==0 ? 0
: RowsAtCompileTime != Dynamic ? int(RowsAtCompileTime)
: int(traits<XprType>::MaxRowsAtCompileTime),
MaxColsAtCompileTime = BlockCols==0 ? 0
: ColsAtCompileTime != Dynamic ? int(ColsAtCompileTime)
: int(traits<XprType>::MaxColsAtCompileTime),
XprTypeIsRowMajor = (int(traits<XprType>::Flags)&RowMajorBit) != 0,
IsRowMajor = (MaxRowsAtCompileTime==1&&MaxColsAtCompileTime!=1) ? 1
: (MaxColsAtCompileTime==1&&MaxRowsAtCompileTime!=1) ? 0
: XprTypeIsRowMajor,
HasSameStorageOrderAsXprType = (IsRowMajor == XprTypeIsRowMajor),
InnerSize = IsRowMajor ? int(ColsAtCompileTime) : int(RowsAtCompileTime),
InnerStrideAtCompileTime = HasSameStorageOrderAsXprType
? int(inner_stride_at_compile_time<XprType>::ret)
: int(outer_stride_at_compile_time<XprType>::ret),
OuterStrideAtCompileTime = HasSameStorageOrderAsXprType
? int(outer_stride_at_compile_time<XprType>::ret)
: int(inner_stride_at_compile_time<XprType>::ret),
// FIXME, this traits is rather specialized for dense object and it needs to be cleaned further
FlagsLvalueBit = is_lvalue<XprType>::value ? LvalueBit : 0,
FlagsRowMajorBit = IsRowMajor ? RowMajorBit : 0,
Flags = (traits<XprType>::Flags & (DirectAccessBit | (InnerPanel?CompressedAccessBit:0))) | FlagsLvalueBit | FlagsRowMajorBit,
// FIXME DirectAccessBit should not be handled by expressions
//
// Alignment is needed by MapBase's assertions
// We can sefely set it to false here. Internal alignment errors will be detected by an eigen_internal_assert in the respective evaluator
Alignment = 0
};
};
template<typename XprType, int BlockRows=Dynamic, int BlockCols=Dynamic, bool InnerPanel = false,
bool HasDirectAccess = internal::has_direct_access<XprType>::ret> class BlockImpl_dense;
} // end namespace internal
template<typename XprType, int BlockRows, int BlockCols, bool InnerPanel, typename StorageKind> class BlockImpl;
/** \class Block
* \ingroup Core_Module
*
* \brief Expression of a fixed-size or dynamic-size block
*
* \tparam XprType the type of the expression in which we are taking a block
* \tparam BlockRows the number of rows of the block we are taking at compile time (optional)
* \tparam BlockCols the number of columns of the block we are taking at compile time (optional)
* \tparam InnerPanel is true, if the block maps to a set of rows of a row major matrix or
* to set of columns of a column major matrix (optional). The parameter allows to determine
* at compile time whether aligned access is possible on the block expression.
*
* This class represents an expression of either a fixed-size or dynamic-size block. It is the return
* type of DenseBase::block(Index,Index,Index,Index) and DenseBase::block<int,int>(Index,Index) and
* most of the time this is the only way it is used.
*
* However, if you want to directly maniputate block expressions,
* for instance if you want to write a function returning such an expression, you
* will need to use this class.
*
* Here is an example illustrating the dynamic case:
* \include class_Block.cpp
* Output: \verbinclude class_Block.out
*
* \note Even though this expression has dynamic size, in the case where \a XprType
* has fixed size, this expression inherits a fixed maximal size which means that evaluating
* it does not cause a dynamic memory allocation.
*
* Here is an example illustrating the fixed-size case:
* \include class_FixedBlock.cpp
* Output: \verbinclude class_FixedBlock.out
*
* \sa DenseBase::block(Index,Index,Index,Index), DenseBase::block(Index,Index), class VectorBlock
*/
template<typename XprType, int BlockRows, int BlockCols, bool InnerPanel> class Block
: public BlockImpl<XprType, BlockRows, BlockCols, InnerPanel, typename internal::traits<XprType>::StorageKind>
{
typedef BlockImpl<XprType, BlockRows, BlockCols, InnerPanel, typename internal::traits<XprType>::StorageKind> Impl;
public:
//typedef typename Impl::Base Base;
typedef Impl Base;
EIGEN_GENERIC_PUBLIC_INTERFACE(Block)
EIGEN_INHERIT_ASSIGNMENT_OPERATORS(Block)
typedef typename internal::remove_all<XprType>::type NestedExpression;
/** Column or Row constructor
*/
EIGEN_DEVICE_FUNC
inline Block(XprType& xpr, Index i) : Impl(xpr,i)
{
eigen_assert( (i>=0) && (
((BlockRows==1) && (BlockCols==XprType::ColsAtCompileTime) && i<xpr.rows())
||((BlockRows==XprType::RowsAtCompileTime) && (BlockCols==1) && i<xpr.cols())));
}
/** Fixed-size constructor
*/
EIGEN_DEVICE_FUNC
inline Block(XprType& xpr, Index startRow, Index startCol)
: Impl(xpr, startRow, startCol)
{
EIGEN_STATIC_ASSERT(RowsAtCompileTime!=Dynamic && ColsAtCompileTime!=Dynamic,THIS_METHOD_IS_ONLY_FOR_FIXED_SIZE)
eigen_assert(startRow >= 0 && BlockRows >= 0 && startRow + BlockRows <= xpr.rows()
&& startCol >= 0 && BlockCols >= 0 && startCol + BlockCols <= xpr.cols());
}
/** Dynamic-size constructor
*/
EIGEN_DEVICE_FUNC
inline Block(XprType& xpr,
Index startRow, Index startCol,
Index blockRows, Index blockCols)
: Impl(xpr, startRow, startCol, blockRows, blockCols)
{
eigen_assert((RowsAtCompileTime==Dynamic || RowsAtCompileTime==blockRows)
&& (ColsAtCompileTime==Dynamic || ColsAtCompileTime==blockCols));
eigen_assert(startRow >= 0 && blockRows >= 0 && startRow <= xpr.rows() - blockRows
&& startCol >= 0 && blockCols >= 0 && startCol <= xpr.cols() - blockCols);
}
};
// The generic default implementation for dense block simplu forward to the internal::BlockImpl_dense
// that must be specialized for direct and non-direct access...
template<typename XprType, int BlockRows, int BlockCols, bool InnerPanel>
class BlockImpl<XprType, BlockRows, BlockCols, InnerPanel, Dense>
: public internal::BlockImpl_dense<XprType, BlockRows, BlockCols, InnerPanel>
{
typedef internal::BlockImpl_dense<XprType, BlockRows, BlockCols, InnerPanel> Impl;
typedef typename XprType::StorageIndex StorageIndex;
public:
typedef Impl Base;
EIGEN_INHERIT_ASSIGNMENT_OPERATORS(BlockImpl)
EIGEN_DEVICE_FUNC inline BlockImpl(XprType& xpr, Index i) : Impl(xpr,i) {}
EIGEN_DEVICE_FUNC inline BlockImpl(XprType& xpr, Index startRow, Index startCol) : Impl(xpr, startRow, startCol) {}
EIGEN_DEVICE_FUNC
inline BlockImpl(XprType& xpr, Index startRow, Index startCol, Index blockRows, Index blockCols)
: Impl(xpr, startRow, startCol, blockRows, blockCols) {}
};
namespace internal {
/** \internal Internal implementation of dense Blocks in the general case. */
template<typename XprType, int BlockRows, int BlockCols, bool InnerPanel, bool HasDirectAccess> class BlockImpl_dense
: public internal::dense_xpr_base<Block<XprType, BlockRows, BlockCols, InnerPanel> >::type
{
typedef Block<XprType, BlockRows, BlockCols, InnerPanel> BlockType;
typedef typename internal::ref_selector<XprType>::non_const_type XprTypeNested;
public:
typedef typename internal::dense_xpr_base<BlockType>::type Base;
EIGEN_DENSE_PUBLIC_INTERFACE(BlockType)
EIGEN_INHERIT_ASSIGNMENT_OPERATORS(BlockImpl_dense)
// class InnerIterator; // FIXME apparently never used
/** Column or Row constructor
*/
EIGEN_DEVICE_FUNC
inline BlockImpl_dense(XprType& xpr, Index i)
: m_xpr(xpr),
// It is a row if and only if BlockRows==1 and BlockCols==XprType::ColsAtCompileTime,
// and it is a column if and only if BlockRows==XprType::RowsAtCompileTime and BlockCols==1,
// all other cases are invalid.
// The case a 1x1 matrix seems ambiguous, but the result is the same anyway.
m_startRow( (BlockRows==1) && (BlockCols==XprType::ColsAtCompileTime) ? i : 0),
m_startCol( (BlockRows==XprType::RowsAtCompileTime) && (BlockCols==1) ? i : 0),
m_blockRows(BlockRows==1 ? 1 : xpr.rows()),
m_blockCols(BlockCols==1 ? 1 : xpr.cols())
{}
/** Fixed-size constructor
*/
EIGEN_DEVICE_FUNC
inline BlockImpl_dense(XprType& xpr, Index startRow, Index startCol)
: m_xpr(xpr), m_startRow(startRow), m_startCol(startCol),
m_blockRows(BlockRows), m_blockCols(BlockCols)
{}
/** Dynamic-size constructor
*/
EIGEN_DEVICE_FUNC
inline BlockImpl_dense(XprType& xpr,
Index startRow, Index startCol,
Index blockRows, Index blockCols)
: m_xpr(xpr), m_startRow(startRow), m_startCol(startCol),
m_blockRows(blockRows), m_blockCols(blockCols)
{}
EIGEN_DEVICE_FUNC inline Index rows() const { return m_blockRows.value(); }
EIGEN_DEVICE_FUNC inline Index cols() const { return m_blockCols.value(); }
EIGEN_DEVICE_FUNC
inline Scalar& coeffRef(Index rowId, Index colId)
{
EIGEN_STATIC_ASSERT_LVALUE(XprType)
return m_xpr.coeffRef(rowId + m_startRow.value(), colId + m_startCol.value());
}
EIGEN_DEVICE_FUNC
inline const Scalar& coeffRef(Index rowId, Index colId) const
{
return m_xpr.derived().coeffRef(rowId + m_startRow.value(), colId + m_startCol.value());
}
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE const CoeffReturnType coeff(Index rowId, Index colId) const
{
return m_xpr.coeff(rowId + m_startRow.value(), colId + m_startCol.value());
}
EIGEN_DEVICE_FUNC
inline Scalar& coeffRef(Index index)
{
EIGEN_STATIC_ASSERT_LVALUE(XprType)
return m_xpr.coeffRef(m_startRow.value() + (RowsAtCompileTime == 1 ? 0 : index),
m_startCol.value() + (RowsAtCompileTime == 1 ? index : 0));
}
EIGEN_DEVICE_FUNC
inline const Scalar& coeffRef(Index index) const
{
return m_xpr.coeffRef(m_startRow.value() + (RowsAtCompileTime == 1 ? 0 : index),
m_startCol.value() + (RowsAtCompileTime == 1 ? index : 0));
}
EIGEN_DEVICE_FUNC
inline const CoeffReturnType coeff(Index index) const
{
return m_xpr.coeff(m_startRow.value() + (RowsAtCompileTime == 1 ? 0 : index),
m_startCol.value() + (RowsAtCompileTime == 1 ? index : 0));
}
template<int LoadMode>
inline PacketScalar packet(Index rowId, Index colId) const
{
return m_xpr.template packet<Unaligned>(rowId + m_startRow.value(), colId + m_startCol.value());
}
template<int LoadMode>
inline void writePacket(Index rowId, Index colId, const PacketScalar& val)
{
m_xpr.template writePacket<Unaligned>(rowId + m_startRow.value(), colId + m_startCol.value(), val);
}
template<int LoadMode>
inline PacketScalar packet(Index index) const
{
return m_xpr.template packet<Unaligned>
(m_startRow.value() + (RowsAtCompileTime == 1 ? 0 : index),
m_startCol.value() + (RowsAtCompileTime == 1 ? index : 0));
}
template<int LoadMode>
inline void writePacket(Index index, const PacketScalar& val)
{
m_xpr.template writePacket<Unaligned>
(m_startRow.value() + (RowsAtCompileTime == 1 ? 0 : index),
m_startCol.value() + (RowsAtCompileTime == 1 ? index : 0), val);
}
#ifdef EIGEN_PARSED_BY_DOXYGEN
/** \sa MapBase::data() */
EIGEN_DEVICE_FUNC inline const Scalar* data() const;
EIGEN_DEVICE_FUNC inline Index innerStride() const;
EIGEN_DEVICE_FUNC inline Index outerStride() const;
#endif
EIGEN_DEVICE_FUNC
const typename internal::remove_all<XprTypeNested>::type& nestedExpression() const
{
return m_xpr;
}
EIGEN_DEVICE_FUNC
XprType& nestedExpression() { return m_xpr; }
EIGEN_DEVICE_FUNC
StorageIndex startRow() const
{
return m_startRow.value();
}
EIGEN_DEVICE_FUNC
StorageIndex startCol() const
{
return m_startCol.value();
}
protected:
XprTypeNested m_xpr;
const internal::variable_if_dynamic<StorageIndex, (XprType::RowsAtCompileTime == 1 && BlockRows==1) ? 0 : Dynamic> m_startRow;
const internal::variable_if_dynamic<StorageIndex, (XprType::ColsAtCompileTime == 1 && BlockCols==1) ? 0 : Dynamic> m_startCol;
const internal::variable_if_dynamic<StorageIndex, RowsAtCompileTime> m_blockRows;
const internal::variable_if_dynamic<StorageIndex, ColsAtCompileTime> m_blockCols;
};
/** \internal Internal implementation of dense Blocks in the direct access case.*/
template<typename XprType, int BlockRows, int BlockCols, bool InnerPanel>
class BlockImpl_dense<XprType,BlockRows,BlockCols, InnerPanel,true>
: public MapBase<Block<XprType, BlockRows, BlockCols, InnerPanel> >
{
typedef Block<XprType, BlockRows, BlockCols, InnerPanel> BlockType;
typedef typename internal::ref_selector<XprType>::non_const_type XprTypeNested;
enum {
XprTypeIsRowMajor = (int(traits<XprType>::Flags)&RowMajorBit) != 0
};
public:
typedef MapBase<BlockType> Base;
EIGEN_DENSE_PUBLIC_INTERFACE(BlockType)
EIGEN_INHERIT_ASSIGNMENT_OPERATORS(BlockImpl_dense)
/** Column or Row constructor
*/
EIGEN_DEVICE_FUNC
inline BlockImpl_dense(XprType& xpr, Index i)
: Base(xpr.data() + i * ( ((BlockRows==1) && (BlockCols==XprType::ColsAtCompileTime) && (!XprTypeIsRowMajor))
|| ((BlockRows==XprType::RowsAtCompileTime) && (BlockCols==1) && ( XprTypeIsRowMajor)) ? xpr.innerStride() : xpr.outerStride()),
BlockRows==1 ? 1 : xpr.rows(),
BlockCols==1 ? 1 : xpr.cols()),
m_xpr(xpr),
m_startRow( (BlockRows==1) && (BlockCols==XprType::ColsAtCompileTime) ? i : 0),
m_startCol( (BlockRows==XprType::RowsAtCompileTime) && (BlockCols==1) ? i : 0)
{
init();
}
/** Fixed-size constructor
*/
EIGEN_DEVICE_FUNC
inline BlockImpl_dense(XprType& xpr, Index startRow, Index startCol)
: Base(xpr.data()+xpr.innerStride()*(XprTypeIsRowMajor?startCol:startRow) + xpr.outerStride()*(XprTypeIsRowMajor?startRow:startCol)),
m_xpr(xpr), m_startRow(startRow), m_startCol(startCol)
{
init();
}
/** Dynamic-size constructor
*/
EIGEN_DEVICE_FUNC
inline BlockImpl_dense(XprType& xpr,
Index startRow, Index startCol,
Index blockRows, Index blockCols)
: Base(xpr.data()+xpr.innerStride()*(XprTypeIsRowMajor?startCol:startRow) + xpr.outerStride()*(XprTypeIsRowMajor?startRow:startCol), blockRows, blockCols),
m_xpr(xpr), m_startRow(startRow), m_startCol(startCol)
{
init();
}
EIGEN_DEVICE_FUNC
const typename internal::remove_all<XprTypeNested>::type& nestedExpression() const
{
return m_xpr;
}
EIGEN_DEVICE_FUNC
XprType& nestedExpression() { return m_xpr; }
/** \sa MapBase::innerStride() */
EIGEN_DEVICE_FUNC
inline Index innerStride() const
{
return internal::traits<BlockType>::HasSameStorageOrderAsXprType
? m_xpr.innerStride()
: m_xpr.outerStride();
}
/** \sa MapBase::outerStride() */
EIGEN_DEVICE_FUNC
inline Index outerStride() const
{
return m_outerStride;
}
EIGEN_DEVICE_FUNC
StorageIndex startRow() const
{
return m_startRow.value();
}
EIGEN_DEVICE_FUNC
StorageIndex startCol() const
{
return m_startCol.value();
}
#ifndef __SUNPRO_CC
// FIXME sunstudio is not friendly with the above friend...
// META-FIXME there is no 'friend' keyword around here. Is this obsolete?
protected:
#endif
#ifndef EIGEN_PARSED_BY_DOXYGEN
/** \internal used by allowAligned() */
EIGEN_DEVICE_FUNC
inline BlockImpl_dense(XprType& xpr, const Scalar* data, Index blockRows, Index blockCols)
: Base(data, blockRows, blockCols), m_xpr(xpr)
{
init();
}
#endif
protected:
EIGEN_DEVICE_FUNC
void init()
{
m_outerStride = internal::traits<BlockType>::HasSameStorageOrderAsXprType
? m_xpr.outerStride()
: m_xpr.innerStride();
}
XprTypeNested m_xpr;
const internal::variable_if_dynamic<StorageIndex, (XprType::RowsAtCompileTime == 1 && BlockRows==1) ? 0 : Dynamic> m_startRow;
const internal::variable_if_dynamic<StorageIndex, (XprType::ColsAtCompileTime == 1 && BlockCols==1) ? 0 : Dynamic> m_startCol;
Index m_outerStride;
};
} // end namespace internal
} // end namespace Eigen
#endif // EIGEN_BLOCK_H

View File

@@ -0,0 +1,164 @@
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#ifndef EIGEN_ALLANDANY_H
#define EIGEN_ALLANDANY_H
namespace Eigen {
namespace internal {
template<typename Derived, int UnrollCount>
struct all_unroller
{
typedef typename Derived::ExpressionTraits Traits;
enum {
col = (UnrollCount-1) / Traits::RowsAtCompileTime,
row = (UnrollCount-1) % Traits::RowsAtCompileTime
};
static inline bool run(const Derived &mat)
{
return all_unroller<Derived, UnrollCount-1>::run(mat) && mat.coeff(row, col);
}
};
template<typename Derived>
struct all_unroller<Derived, 0>
{
static inline bool run(const Derived &/*mat*/) { return true; }
};
template<typename Derived>
struct all_unroller<Derived, Dynamic>
{
static inline bool run(const Derived &) { return false; }
};
template<typename Derived, int UnrollCount>
struct any_unroller
{
typedef typename Derived::ExpressionTraits Traits;
enum {
col = (UnrollCount-1) / Traits::RowsAtCompileTime,
row = (UnrollCount-1) % Traits::RowsAtCompileTime
};
static inline bool run(const Derived &mat)
{
return any_unroller<Derived, UnrollCount-1>::run(mat) || mat.coeff(row, col);
}
};
template<typename Derived>
struct any_unroller<Derived, 0>
{
static inline bool run(const Derived & /*mat*/) { return false; }
};
template<typename Derived>
struct any_unroller<Derived, Dynamic>
{
static inline bool run(const Derived &) { return false; }
};
} // end namespace internal
/** \returns true if all coefficients are true
*
* Example: \include MatrixBase_all.cpp
* Output: \verbinclude MatrixBase_all.out
*
* \sa any(), Cwise::operator<()
*/
template<typename Derived>
inline bool DenseBase<Derived>::all() const
{
typedef internal::evaluator<Derived> Evaluator;
enum {
unroll = SizeAtCompileTime != Dynamic
&& SizeAtCompileTime * (Evaluator::CoeffReadCost + NumTraits<Scalar>::AddCost) <= EIGEN_UNROLLING_LIMIT
};
Evaluator evaluator(derived());
if(unroll)
return internal::all_unroller<Evaluator, unroll ? int(SizeAtCompileTime) : Dynamic>::run(evaluator);
else
{
for(Index j = 0; j < cols(); ++j)
for(Index i = 0; i < rows(); ++i)
if (!evaluator.coeff(i, j)) return false;
return true;
}
}
/** \returns true if at least one coefficient is true
*
* \sa all()
*/
template<typename Derived>
inline bool DenseBase<Derived>::any() const
{
typedef internal::evaluator<Derived> Evaluator;
enum {
unroll = SizeAtCompileTime != Dynamic
&& SizeAtCompileTime * (Evaluator::CoeffReadCost + NumTraits<Scalar>::AddCost) <= EIGEN_UNROLLING_LIMIT
};
Evaluator evaluator(derived());
if(unroll)
return internal::any_unroller<Evaluator, unroll ? int(SizeAtCompileTime) : Dynamic>::run(evaluator);
else
{
for(Index j = 0; j < cols(); ++j)
for(Index i = 0; i < rows(); ++i)
if (evaluator.coeff(i, j)) return true;
return false;
}
}
/** \returns the number of coefficients which evaluate to true
*
* \sa all(), any()
*/
template<typename Derived>
inline Eigen::Index DenseBase<Derived>::count() const
{
return derived().template cast<bool>().template cast<Index>().sum();
}
/** \returns true is \c *this contains at least one Not A Number (NaN).
*
* \sa allFinite()
*/
template<typename Derived>
inline bool DenseBase<Derived>::hasNaN() const
{
#if EIGEN_COMP_MSVC || (defined __FAST_MATH__)
return derived().array().isNaN().any();
#else
return !((derived().array()==derived().array()).all());
#endif
}
/** \returns true if \c *this contains only finite numbers, i.e., no NaN and no +/-INF values.
*
* \sa hasNaN()
*/
template<typename Derived>
inline bool DenseBase<Derived>::allFinite() const
{
#if EIGEN_COMP_MSVC || (defined __FAST_MATH__)
return derived().array().isFinite().all();
#else
return !((derived()-derived()).hasNaN());
#endif
}
} // end namespace Eigen
#endif // EIGEN_ALLANDANY_H

View File

@@ -0,0 +1,160 @@
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com>
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#ifndef EIGEN_COMMAINITIALIZER_H
#define EIGEN_COMMAINITIALIZER_H
namespace Eigen {
/** \class CommaInitializer
* \ingroup Core_Module
*
* \brief Helper class used by the comma initializer operator
*
* This class is internally used to implement the comma initializer feature. It is
* the return type of MatrixBase::operator<<, and most of the time this is the only
* way it is used.
*
* \sa \blank \ref MatrixBaseCommaInitRef "MatrixBase::operator<<", CommaInitializer::finished()
*/
template<typename XprType>
struct CommaInitializer
{
typedef typename XprType::Scalar Scalar;
EIGEN_DEVICE_FUNC
inline CommaInitializer(XprType& xpr, const Scalar& s)
: m_xpr(xpr), m_row(0), m_col(1), m_currentBlockRows(1)
{
m_xpr.coeffRef(0,0) = s;
}
template<typename OtherDerived>
EIGEN_DEVICE_FUNC
inline CommaInitializer(XprType& xpr, const DenseBase<OtherDerived>& other)
: m_xpr(xpr), m_row(0), m_col(other.cols()), m_currentBlockRows(other.rows())
{
m_xpr.block(0, 0, other.rows(), other.cols()) = other;
}
/* Copy/Move constructor which transfers ownership. This is crucial in
* absence of return value optimization to avoid assertions during destruction. */
// FIXME in C++11 mode this could be replaced by a proper RValue constructor
EIGEN_DEVICE_FUNC
inline CommaInitializer(const CommaInitializer& o)
: m_xpr(o.m_xpr), m_row(o.m_row), m_col(o.m_col), m_currentBlockRows(o.m_currentBlockRows) {
// Mark original object as finished. In absence of R-value references we need to const_cast:
const_cast<CommaInitializer&>(o).m_row = m_xpr.rows();
const_cast<CommaInitializer&>(o).m_col = m_xpr.cols();
const_cast<CommaInitializer&>(o).m_currentBlockRows = 0;
}
/* inserts a scalar value in the target matrix */
EIGEN_DEVICE_FUNC
CommaInitializer& operator,(const Scalar& s)
{
if (m_col==m_xpr.cols())
{
m_row+=m_currentBlockRows;
m_col = 0;
m_currentBlockRows = 1;
eigen_assert(m_row<m_xpr.rows()
&& "Too many rows passed to comma initializer (operator<<)");
}
eigen_assert(m_col<m_xpr.cols()
&& "Too many coefficients passed to comma initializer (operator<<)");
eigen_assert(m_currentBlockRows==1);
m_xpr.coeffRef(m_row, m_col++) = s;
return *this;
}
/* inserts a matrix expression in the target matrix */
template<typename OtherDerived>
EIGEN_DEVICE_FUNC
CommaInitializer& operator,(const DenseBase<OtherDerived>& other)
{
if (m_col==m_xpr.cols() && (other.cols()!=0 || other.rows()!=m_currentBlockRows))
{
m_row+=m_currentBlockRows;
m_col = 0;
m_currentBlockRows = other.rows();
eigen_assert(m_row+m_currentBlockRows<=m_xpr.rows()
&& "Too many rows passed to comma initializer (operator<<)");
}
eigen_assert((m_col + other.cols() <= m_xpr.cols())
&& "Too many coefficients passed to comma initializer (operator<<)");
eigen_assert(m_currentBlockRows==other.rows());
m_xpr.template block<OtherDerived::RowsAtCompileTime, OtherDerived::ColsAtCompileTime>
(m_row, m_col, other.rows(), other.cols()) = other;
m_col += other.cols();
return *this;
}
EIGEN_DEVICE_FUNC
inline ~CommaInitializer()
#if defined VERIFY_RAISES_ASSERT && (!defined EIGEN_NO_ASSERTION_CHECKING) && defined EIGEN_EXCEPTIONS
EIGEN_EXCEPTION_SPEC(Eigen::eigen_assert_exception)
#endif
{
finished();
}
/** \returns the built matrix once all its coefficients have been set.
* Calling finished is 100% optional. Its purpose is to write expressions
* like this:
* \code
* quaternion.fromRotationMatrix((Matrix3f() << axis0, axis1, axis2).finished());
* \endcode
*/
EIGEN_DEVICE_FUNC
inline XprType& finished() {
eigen_assert(((m_row+m_currentBlockRows) == m_xpr.rows() || m_xpr.cols() == 0)
&& m_col == m_xpr.cols()
&& "Too few coefficients passed to comma initializer (operator<<)");
return m_xpr;
}
XprType& m_xpr; // target expression
Index m_row; // current row id
Index m_col; // current col id
Index m_currentBlockRows; // current block height
};
/** \anchor MatrixBaseCommaInitRef
* Convenient operator to set the coefficients of a matrix.
*
* The coefficients must be provided in a row major order and exactly match
* the size of the matrix. Otherwise an assertion is raised.
*
* Example: \include MatrixBase_set.cpp
* Output: \verbinclude MatrixBase_set.out
*
* \note According the c++ standard, the argument expressions of this comma initializer are evaluated in arbitrary order.
*
* \sa CommaInitializer::finished(), class CommaInitializer
*/
template<typename Derived>
inline CommaInitializer<Derived> DenseBase<Derived>::operator<< (const Scalar& s)
{
return CommaInitializer<Derived>(*static_cast<Derived*>(this), s);
}
/** \sa operator<<(const Scalar&) */
template<typename Derived>
template<typename OtherDerived>
inline CommaInitializer<Derived>
DenseBase<Derived>::operator<<(const DenseBase<OtherDerived>& other)
{
return CommaInitializer<Derived>(*static_cast<Derived *>(this), other);
}
} // end namespace Eigen
#endif // EIGEN_COMMAINITIALIZER_H

View File

@@ -0,0 +1,175 @@
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2016 Rasmus Munk Larsen (rmlarsen@google.com)
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#ifndef EIGEN_CONDITIONESTIMATOR_H
#define EIGEN_CONDITIONESTIMATOR_H
namespace Eigen {
namespace internal {
template <typename Vector, typename RealVector, bool IsComplex>
struct rcond_compute_sign {
static inline Vector run(const Vector& v) {
const RealVector v_abs = v.cwiseAbs();
return (v_abs.array() == static_cast<typename Vector::RealScalar>(0))
.select(Vector::Ones(v.size()), v.cwiseQuotient(v_abs));
}
};
// Partial specialization to avoid elementwise division for real vectors.
template <typename Vector>
struct rcond_compute_sign<Vector, Vector, false> {
static inline Vector run(const Vector& v) {
return (v.array() < static_cast<typename Vector::RealScalar>(0))
.select(-Vector::Ones(v.size()), Vector::Ones(v.size()));
}
};
/**
* \returns an estimate of ||inv(matrix)||_1 given a decomposition of
* \a matrix that implements .solve() and .adjoint().solve() methods.
*
* This function implements Algorithms 4.1 and 5.1 from
* http://www.maths.manchester.ac.uk/~higham/narep/narep135.pdf
* which also forms the basis for the condition number estimators in
* LAPACK. Since at most 10 calls to the solve method of dec are
* performed, the total cost is O(dims^2), as opposed to O(dims^3)
* needed to compute the inverse matrix explicitly.
*
* The most common usage is in estimating the condition number
* ||matrix||_1 * ||inv(matrix)||_1. The first term ||matrix||_1 can be
* computed directly in O(n^2) operations.
*
* Supports the following decompositions: FullPivLU, PartialPivLU, LDLT, and
* LLT.
*
* \sa FullPivLU, PartialPivLU, LDLT, LLT.
*/
template <typename Decomposition>
typename Decomposition::RealScalar rcond_invmatrix_L1_norm_estimate(const Decomposition& dec)
{
typedef typename Decomposition::MatrixType MatrixType;
typedef typename Decomposition::Scalar Scalar;
typedef typename Decomposition::RealScalar RealScalar;
typedef typename internal::plain_col_type<MatrixType>::type Vector;
typedef typename internal::plain_col_type<MatrixType, RealScalar>::type RealVector;
const bool is_complex = (NumTraits<Scalar>::IsComplex != 0);
eigen_assert(dec.rows() == dec.cols());
const Index n = dec.rows();
if (n == 0)
return 0;
// Disable Index to float conversion warning
#ifdef __INTEL_COMPILER
#pragma warning push
#pragma warning ( disable : 2259 )
#endif
Vector v = dec.solve(Vector::Ones(n) / Scalar(n));
#ifdef __INTEL_COMPILER
#pragma warning pop
#endif
// lower_bound is a lower bound on
// ||inv(matrix)||_1 = sup_v ||inv(matrix) v||_1 / ||v||_1
// and is the objective maximized by the ("super-") gradient ascent
// algorithm below.
RealScalar lower_bound = v.template lpNorm<1>();
if (n == 1)
return lower_bound;
// Gradient ascent algorithm follows: We know that the optimum is achieved at
// one of the simplices v = e_i, so in each iteration we follow a
// super-gradient to move towards the optimal one.
RealScalar old_lower_bound = lower_bound;
Vector sign_vector(n);
Vector old_sign_vector;
Index v_max_abs_index = -1;
Index old_v_max_abs_index = v_max_abs_index;
for (int k = 0; k < 4; ++k)
{
sign_vector = internal::rcond_compute_sign<Vector, RealVector, is_complex>::run(v);
if (k > 0 && !is_complex && sign_vector == old_sign_vector) {
// Break if the solution stagnated.
break;
}
// v_max_abs_index = argmax |real( inv(matrix)^T * sign_vector )|
v = dec.adjoint().solve(sign_vector);
v.real().cwiseAbs().maxCoeff(&v_max_abs_index);
if (v_max_abs_index == old_v_max_abs_index) {
// Break if the solution stagnated.
break;
}
// Move to the new simplex e_j, where j = v_max_abs_index.
v = dec.solve(Vector::Unit(n, v_max_abs_index)); // v = inv(matrix) * e_j.
lower_bound = v.template lpNorm<1>();
if (lower_bound <= old_lower_bound) {
// Break if the gradient step did not increase the lower_bound.
break;
}
if (!is_complex) {
old_sign_vector = sign_vector;
}
old_v_max_abs_index = v_max_abs_index;
old_lower_bound = lower_bound;
}
// The following calculates an independent estimate of ||matrix||_1 by
// multiplying matrix by a vector with entries of slowly increasing
// magnitude and alternating sign:
// v_i = (-1)^{i} (1 + (i / (dim-1))), i = 0,...,dim-1.
// This improvement to Hager's algorithm above is due to Higham. It was
// added to make the algorithm more robust in certain corner cases where
// large elements in the matrix might otherwise escape detection due to
// exact cancellation (especially when op and op_adjoint correspond to a
// sequence of backsubstitutions and permutations), which could cause
// Hager's algorithm to vastly underestimate ||matrix||_1.
Scalar alternating_sign(RealScalar(1));
for (Index i = 0; i < n; ++i) {
// The static_cast is needed when Scalar is a complex and RealScalar implements expression templates
v[i] = alternating_sign * static_cast<RealScalar>(RealScalar(1) + (RealScalar(i) / (RealScalar(n - 1))));
alternating_sign = -alternating_sign;
}
v = dec.solve(v);
const RealScalar alternate_lower_bound = (2 * v.template lpNorm<1>()) / (3 * RealScalar(n));
return numext::maxi(lower_bound, alternate_lower_bound);
}
/** \brief Reciprocal condition number estimator.
*
* Computing a decomposition of a dense matrix takes O(n^3) operations, while
* this method estimates the condition number quickly and reliably in O(n^2)
* operations.
*
* \returns an estimate of the reciprocal condition number
* (1 / (||matrix||_1 * ||inv(matrix)||_1)) of matrix, given ||matrix||_1 and
* its decomposition. Supports the following decompositions: FullPivLU,
* PartialPivLU, LDLT, and LLT.
*
* \sa FullPivLU, PartialPivLU, LDLT, LLT.
*/
template <typename Decomposition>
typename Decomposition::RealScalar
rcond_estimate_helper(typename Decomposition::RealScalar matrix_norm, const Decomposition& dec)
{
typedef typename Decomposition::RealScalar RealScalar;
eigen_assert(dec.rows() == dec.cols());
if (dec.rows() == 0) return NumTraits<RealScalar>::infinity();
if (matrix_norm == RealScalar(0)) return RealScalar(0);
if (dec.rows() == 1) return RealScalar(1);
const RealScalar inverse_matrix_norm = rcond_invmatrix_L1_norm_estimate(dec);
return (inverse_matrix_norm == RealScalar(0) ? RealScalar(0)
: (RealScalar(1) / inverse_matrix_norm) / matrix_norm);
}
} // namespace internal
} // namespace Eigen
#endif

File diff suppressed because it is too large Load Diff

View File

@@ -0,0 +1,127 @@
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2008-2014 Gael Guennebaud <gael.guennebaud@inria.fr>
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#ifndef EIGEN_COREITERATORS_H
#define EIGEN_COREITERATORS_H
namespace Eigen {
/* This file contains the respective InnerIterator definition of the expressions defined in Eigen/Core
*/
namespace internal {
template<typename XprType, typename EvaluatorKind>
class inner_iterator_selector;
}
/** \class InnerIterator
* \brief An InnerIterator allows to loop over the element of any matrix expression.
*
* \warning To be used with care because an evaluator is constructed every time an InnerIterator iterator is constructed.
*
* TODO: add a usage example
*/
template<typename XprType>
class InnerIterator
{
protected:
typedef internal::inner_iterator_selector<XprType, typename internal::evaluator_traits<XprType>::Kind> IteratorType;
typedef internal::evaluator<XprType> EvaluatorType;
typedef typename internal::traits<XprType>::Scalar Scalar;
public:
/** Construct an iterator over the \a outerId -th row or column of \a xpr */
InnerIterator(const XprType &xpr, const Index &outerId)
: m_eval(xpr), m_iter(m_eval, outerId, xpr.innerSize())
{}
/// \returns the value of the current coefficient.
EIGEN_STRONG_INLINE Scalar value() const { return m_iter.value(); }
/** Increment the iterator \c *this to the next non-zero coefficient.
* Explicit zeros are not skipped over. To skip explicit zeros, see class SparseView
*/
EIGEN_STRONG_INLINE InnerIterator& operator++() { m_iter.operator++(); return *this; }
/// \returns the column or row index of the current coefficient.
EIGEN_STRONG_INLINE Index index() const { return m_iter.index(); }
/// \returns the row index of the current coefficient.
EIGEN_STRONG_INLINE Index row() const { return m_iter.row(); }
/// \returns the column index of the current coefficient.
EIGEN_STRONG_INLINE Index col() const { return m_iter.col(); }
/// \returns \c true if the iterator \c *this still references a valid coefficient.
EIGEN_STRONG_INLINE operator bool() const { return m_iter; }
protected:
EvaluatorType m_eval;
IteratorType m_iter;
private:
// If you get here, then you're not using the right InnerIterator type, e.g.:
// SparseMatrix<double,RowMajor> A;
// SparseMatrix<double>::InnerIterator it(A,0);
template<typename T> InnerIterator(const EigenBase<T>&,Index outer);
};
namespace internal {
// Generic inner iterator implementation for dense objects
template<typename XprType>
class inner_iterator_selector<XprType, IndexBased>
{
protected:
typedef evaluator<XprType> EvaluatorType;
typedef typename traits<XprType>::Scalar Scalar;
enum { IsRowMajor = (XprType::Flags&RowMajorBit)==RowMajorBit };
public:
EIGEN_STRONG_INLINE inner_iterator_selector(const EvaluatorType &eval, const Index &outerId, const Index &innerSize)
: m_eval(eval), m_inner(0), m_outer(outerId), m_end(innerSize)
{}
EIGEN_STRONG_INLINE Scalar value() const
{
return (IsRowMajor) ? m_eval.coeff(m_outer, m_inner)
: m_eval.coeff(m_inner, m_outer);
}
EIGEN_STRONG_INLINE inner_iterator_selector& operator++() { m_inner++; return *this; }
EIGEN_STRONG_INLINE Index index() const { return m_inner; }
inline Index row() const { return IsRowMajor ? m_outer : index(); }
inline Index col() const { return IsRowMajor ? index() : m_outer; }
EIGEN_STRONG_INLINE operator bool() const { return m_inner < m_end && m_inner>=0; }
protected:
const EvaluatorType& m_eval;
Index m_inner;
const Index m_outer;
const Index m_end;
};
// For iterator-based evaluator, inner-iterator is already implemented as
// evaluator<>::InnerIterator
template<typename XprType>
class inner_iterator_selector<XprType, IteratorBased>
: public evaluator<XprType>::InnerIterator
{
protected:
typedef typename evaluator<XprType>::InnerIterator Base;
typedef evaluator<XprType> EvaluatorType;
public:
EIGEN_STRONG_INLINE inner_iterator_selector(const EvaluatorType &eval, const Index &outerId, const Index &/*innerSize*/)
: Base(eval, outerId)
{}
};
} // end namespace internal
} // end namespace Eigen
#endif // EIGEN_COREITERATORS_H

View File

@@ -0,0 +1,184 @@
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2008-2014 Gael Guennebaud <gael.guennebaud@inria.fr>
// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com>
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#ifndef EIGEN_CWISE_BINARY_OP_H
#define EIGEN_CWISE_BINARY_OP_H
namespace Eigen {
namespace internal {
template<typename BinaryOp, typename Lhs, typename Rhs>
struct traits<CwiseBinaryOp<BinaryOp, Lhs, Rhs> >
{
// we must not inherit from traits<Lhs> since it has
// the potential to cause problems with MSVC
typedef typename remove_all<Lhs>::type Ancestor;
typedef typename traits<Ancestor>::XprKind XprKind;
enum {
RowsAtCompileTime = traits<Ancestor>::RowsAtCompileTime,
ColsAtCompileTime = traits<Ancestor>::ColsAtCompileTime,
MaxRowsAtCompileTime = traits<Ancestor>::MaxRowsAtCompileTime,
MaxColsAtCompileTime = traits<Ancestor>::MaxColsAtCompileTime
};
// even though we require Lhs and Rhs to have the same scalar type (see CwiseBinaryOp constructor),
// we still want to handle the case when the result type is different.
typedef typename result_of<
BinaryOp(
const typename Lhs::Scalar&,
const typename Rhs::Scalar&
)
>::type Scalar;
typedef typename cwise_promote_storage_type<typename traits<Lhs>::StorageKind,
typename traits<Rhs>::StorageKind,
BinaryOp>::ret StorageKind;
typedef typename promote_index_type<typename traits<Lhs>::StorageIndex,
typename traits<Rhs>::StorageIndex>::type StorageIndex;
typedef typename Lhs::Nested LhsNested;
typedef typename Rhs::Nested RhsNested;
typedef typename remove_reference<LhsNested>::type _LhsNested;
typedef typename remove_reference<RhsNested>::type _RhsNested;
enum {
Flags = cwise_promote_storage_order<typename traits<Lhs>::StorageKind,typename traits<Rhs>::StorageKind,_LhsNested::Flags & RowMajorBit,_RhsNested::Flags & RowMajorBit>::value
};
};
} // end namespace internal
template<typename BinaryOp, typename Lhs, typename Rhs, typename StorageKind>
class CwiseBinaryOpImpl;
/** \class CwiseBinaryOp
* \ingroup Core_Module
*
* \brief Generic expression where a coefficient-wise binary operator is applied to two expressions
*
* \tparam BinaryOp template functor implementing the operator
* \tparam LhsType the type of the left-hand side
* \tparam RhsType the type of the right-hand side
*
* This class represents an expression where a coefficient-wise binary operator is applied to two expressions.
* It is the return type of binary operators, by which we mean only those binary operators where
* both the left-hand side and the right-hand side are Eigen expressions.
* For example, the return type of matrix1+matrix2 is a CwiseBinaryOp.
*
* Most of the time, this is the only way that it is used, so you typically don't have to name
* CwiseBinaryOp types explicitly.
*
* \sa MatrixBase::binaryExpr(const MatrixBase<OtherDerived> &,const CustomBinaryOp &) const, class CwiseUnaryOp, class CwiseNullaryOp
*/
template<typename BinaryOp, typename LhsType, typename RhsType>
class CwiseBinaryOp :
public CwiseBinaryOpImpl<
BinaryOp, LhsType, RhsType,
typename internal::cwise_promote_storage_type<typename internal::traits<LhsType>::StorageKind,
typename internal::traits<RhsType>::StorageKind,
BinaryOp>::ret>,
internal::no_assignment_operator
{
public:
typedef typename internal::remove_all<BinaryOp>::type Functor;
typedef typename internal::remove_all<LhsType>::type Lhs;
typedef typename internal::remove_all<RhsType>::type Rhs;
typedef typename CwiseBinaryOpImpl<
BinaryOp, LhsType, RhsType,
typename internal::cwise_promote_storage_type<typename internal::traits<LhsType>::StorageKind,
typename internal::traits<Rhs>::StorageKind,
BinaryOp>::ret>::Base Base;
EIGEN_GENERIC_PUBLIC_INTERFACE(CwiseBinaryOp)
typedef typename internal::ref_selector<LhsType>::type LhsNested;
typedef typename internal::ref_selector<RhsType>::type RhsNested;
typedef typename internal::remove_reference<LhsNested>::type _LhsNested;
typedef typename internal::remove_reference<RhsNested>::type _RhsNested;
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE CwiseBinaryOp(const Lhs& aLhs, const Rhs& aRhs, const BinaryOp& func = BinaryOp())
: m_lhs(aLhs), m_rhs(aRhs), m_functor(func)
{
EIGEN_CHECK_BINARY_COMPATIBILIY(BinaryOp,typename Lhs::Scalar,typename Rhs::Scalar);
// require the sizes to match
EIGEN_STATIC_ASSERT_SAME_MATRIX_SIZE(Lhs, Rhs)
eigen_assert(aLhs.rows() == aRhs.rows() && aLhs.cols() == aRhs.cols());
}
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Index rows() const {
// return the fixed size type if available to enable compile time optimizations
if (internal::traits<typename internal::remove_all<LhsNested>::type>::RowsAtCompileTime==Dynamic)
return m_rhs.rows();
else
return m_lhs.rows();
}
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Index cols() const {
// return the fixed size type if available to enable compile time optimizations
if (internal::traits<typename internal::remove_all<LhsNested>::type>::ColsAtCompileTime==Dynamic)
return m_rhs.cols();
else
return m_lhs.cols();
}
/** \returns the left hand side nested expression */
EIGEN_DEVICE_FUNC
const _LhsNested& lhs() const { return m_lhs; }
/** \returns the right hand side nested expression */
EIGEN_DEVICE_FUNC
const _RhsNested& rhs() const { return m_rhs; }
/** \returns the functor representing the binary operation */
EIGEN_DEVICE_FUNC
const BinaryOp& functor() const { return m_functor; }
protected:
LhsNested m_lhs;
RhsNested m_rhs;
const BinaryOp m_functor;
};
// Generic API dispatcher
template<typename BinaryOp, typename Lhs, typename Rhs, typename StorageKind>
class CwiseBinaryOpImpl
: public internal::generic_xpr_base<CwiseBinaryOp<BinaryOp, Lhs, Rhs> >::type
{
public:
typedef typename internal::generic_xpr_base<CwiseBinaryOp<BinaryOp, Lhs, Rhs> >::type Base;
};
/** replaces \c *this by \c *this - \a other.
*
* \returns a reference to \c *this
*/
template<typename Derived>
template<typename OtherDerived>
EIGEN_STRONG_INLINE Derived &
MatrixBase<Derived>::operator-=(const MatrixBase<OtherDerived> &other)
{
call_assignment(derived(), other.derived(), internal::sub_assign_op<Scalar,typename OtherDerived::Scalar>());
return derived();
}
/** replaces \c *this by \c *this + \a other.
*
* \returns a reference to \c *this
*/
template<typename Derived>
template<typename OtherDerived>
EIGEN_STRONG_INLINE Derived &
MatrixBase<Derived>::operator+=(const MatrixBase<OtherDerived>& other)
{
call_assignment(derived(), other.derived(), internal::add_assign_op<Scalar,typename OtherDerived::Scalar>());
return derived();
}
} // end namespace Eigen
#endif // EIGEN_CWISE_BINARY_OP_H

View File

@@ -0,0 +1,866 @@
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2008-2010 Gael Guennebaud <gael.guennebaud@inria.fr>
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#ifndef EIGEN_CWISE_NULLARY_OP_H
#define EIGEN_CWISE_NULLARY_OP_H
namespace Eigen {
namespace internal {
template<typename NullaryOp, typename PlainObjectType>
struct traits<CwiseNullaryOp<NullaryOp, PlainObjectType> > : traits<PlainObjectType>
{
enum {
Flags = traits<PlainObjectType>::Flags & RowMajorBit
};
};
} // namespace internal
/** \class CwiseNullaryOp
* \ingroup Core_Module
*
* \brief Generic expression of a matrix where all coefficients are defined by a functor
*
* \tparam NullaryOp template functor implementing the operator
* \tparam PlainObjectType the underlying plain matrix/array type
*
* This class represents an expression of a generic nullary operator.
* It is the return type of the Ones(), Zero(), Constant(), Identity() and Random() methods,
* and most of the time this is the only way it is used.
*
* However, if you want to write a function returning such an expression, you
* will need to use this class.
*
* The functor NullaryOp must expose one of the following method:
<table class="manual">
<tr ><td>\c operator()() </td><td>if the procedural generation does not depend on the coefficient entries (e.g., random numbers)</td></tr>
<tr class="alt"><td>\c operator()(Index i)</td><td>if the procedural generation makes sense for vectors only and that it depends on the coefficient index \c i (e.g., linspace) </td></tr>
<tr ><td>\c operator()(Index i,Index j)</td><td>if the procedural generation depends on the matrix coordinates \c i, \c j (e.g., to generate a checkerboard with 0 and 1)</td></tr>
</table>
* It is also possible to expose the last two operators if the generation makes sense for matrices but can be optimized for vectors.
*
* See DenseBase::NullaryExpr(Index,const CustomNullaryOp&) for an example binding
* C++11 random number generators.
*
* A nullary expression can also be used to implement custom sophisticated matrix manipulations
* that cannot be covered by the existing set of natively supported matrix manipulations.
* See this \ref TopicCustomizing_NullaryExpr "page" for some examples and additional explanations
* on the behavior of CwiseNullaryOp.
*
* \sa class CwiseUnaryOp, class CwiseBinaryOp, DenseBase::NullaryExpr
*/
template<typename NullaryOp, typename PlainObjectType>
class CwiseNullaryOp : public internal::dense_xpr_base< CwiseNullaryOp<NullaryOp, PlainObjectType> >::type, internal::no_assignment_operator
{
public:
typedef typename internal::dense_xpr_base<CwiseNullaryOp>::type Base;
EIGEN_DENSE_PUBLIC_INTERFACE(CwiseNullaryOp)
EIGEN_DEVICE_FUNC
CwiseNullaryOp(Index rows, Index cols, const NullaryOp& func = NullaryOp())
: m_rows(rows), m_cols(cols), m_functor(func)
{
eigen_assert(rows >= 0
&& (RowsAtCompileTime == Dynamic || RowsAtCompileTime == rows)
&& cols >= 0
&& (ColsAtCompileTime == Dynamic || ColsAtCompileTime == cols));
}
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Index rows() const { return m_rows.value(); }
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Index cols() const { return m_cols.value(); }
/** \returns the functor representing the nullary operation */
EIGEN_DEVICE_FUNC
const NullaryOp& functor() const { return m_functor; }
protected:
const internal::variable_if_dynamic<Index, RowsAtCompileTime> m_rows;
const internal::variable_if_dynamic<Index, ColsAtCompileTime> m_cols;
const NullaryOp m_functor;
};
/** \returns an expression of a matrix defined by a custom functor \a func
*
* The parameters \a rows and \a cols are the number of rows and of columns of
* the returned matrix. Must be compatible with this MatrixBase type.
*
* This variant is meant to be used for dynamic-size matrix types. For fixed-size types,
* it is redundant to pass \a rows and \a cols as arguments, so Zero() should be used
* instead.
*
* The template parameter \a CustomNullaryOp is the type of the functor.
*
* \sa class CwiseNullaryOp
*/
template<typename Derived>
template<typename CustomNullaryOp>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const CwiseNullaryOp<CustomNullaryOp, typename DenseBase<Derived>::PlainObject>
DenseBase<Derived>::NullaryExpr(Index rows, Index cols, const CustomNullaryOp& func)
{
return CwiseNullaryOp<CustomNullaryOp, PlainObject>(rows, cols, func);
}
/** \returns an expression of a matrix defined by a custom functor \a func
*
* The parameter \a size is the size of the returned vector.
* Must be compatible with this MatrixBase type.
*
* \only_for_vectors
*
* This variant is meant to be used for dynamic-size vector types. For fixed-size types,
* it is redundant to pass \a size as argument, so Zero() should be used
* instead.
*
* The template parameter \a CustomNullaryOp is the type of the functor.
*
* Here is an example with C++11 random generators: \include random_cpp11.cpp
* Output: \verbinclude random_cpp11.out
*
* \sa class CwiseNullaryOp
*/
template<typename Derived>
template<typename CustomNullaryOp>
EIGEN_STRONG_INLINE const CwiseNullaryOp<CustomNullaryOp, typename DenseBase<Derived>::PlainObject>
DenseBase<Derived>::NullaryExpr(Index size, const CustomNullaryOp& func)
{
EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
if(RowsAtCompileTime == 1) return CwiseNullaryOp<CustomNullaryOp, PlainObject>(1, size, func);
else return CwiseNullaryOp<CustomNullaryOp, PlainObject>(size, 1, func);
}
/** \returns an expression of a matrix defined by a custom functor \a func
*
* This variant is only for fixed-size DenseBase types. For dynamic-size types, you
* need to use the variants taking size arguments.
*
* The template parameter \a CustomNullaryOp is the type of the functor.
*
* \sa class CwiseNullaryOp
*/
template<typename Derived>
template<typename CustomNullaryOp>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const CwiseNullaryOp<CustomNullaryOp, typename DenseBase<Derived>::PlainObject>
DenseBase<Derived>::NullaryExpr(const CustomNullaryOp& func)
{
return CwiseNullaryOp<CustomNullaryOp, PlainObject>(RowsAtCompileTime, ColsAtCompileTime, func);
}
/** \returns an expression of a constant matrix of value \a value
*
* The parameters \a rows and \a cols are the number of rows and of columns of
* the returned matrix. Must be compatible with this DenseBase type.
*
* This variant is meant to be used for dynamic-size matrix types. For fixed-size types,
* it is redundant to pass \a rows and \a cols as arguments, so Zero() should be used
* instead.
*
* The template parameter \a CustomNullaryOp is the type of the functor.
*
* \sa class CwiseNullaryOp
*/
template<typename Derived>
EIGEN_STRONG_INLINE const typename DenseBase<Derived>::ConstantReturnType
DenseBase<Derived>::Constant(Index rows, Index cols, const Scalar& value)
{
return DenseBase<Derived>::NullaryExpr(rows, cols, internal::scalar_constant_op<Scalar>(value));
}
/** \returns an expression of a constant matrix of value \a value
*
* The parameter \a size is the size of the returned vector.
* Must be compatible with this DenseBase type.
*
* \only_for_vectors
*
* This variant is meant to be used for dynamic-size vector types. For fixed-size types,
* it is redundant to pass \a size as argument, so Zero() should be used
* instead.
*
* The template parameter \a CustomNullaryOp is the type of the functor.
*
* \sa class CwiseNullaryOp
*/
template<typename Derived>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const typename DenseBase<Derived>::ConstantReturnType
DenseBase<Derived>::Constant(Index size, const Scalar& value)
{
return DenseBase<Derived>::NullaryExpr(size, internal::scalar_constant_op<Scalar>(value));
}
/** \returns an expression of a constant matrix of value \a value
*
* This variant is only for fixed-size DenseBase types. For dynamic-size types, you
* need to use the variants taking size arguments.
*
* The template parameter \a CustomNullaryOp is the type of the functor.
*
* \sa class CwiseNullaryOp
*/
template<typename Derived>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const typename DenseBase<Derived>::ConstantReturnType
DenseBase<Derived>::Constant(const Scalar& value)
{
EIGEN_STATIC_ASSERT_FIXED_SIZE(Derived)
return DenseBase<Derived>::NullaryExpr(RowsAtCompileTime, ColsAtCompileTime, internal::scalar_constant_op<Scalar>(value));
}
/** \deprecated because of accuracy loss. In Eigen 3.3, it is an alias for LinSpaced(Index,const Scalar&,const Scalar&)
*
* \sa LinSpaced(Index,Scalar,Scalar), setLinSpaced(Index,const Scalar&,const Scalar&)
*/
template<typename Derived>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const typename DenseBase<Derived>::RandomAccessLinSpacedReturnType
DenseBase<Derived>::LinSpaced(Sequential_t, Index size, const Scalar& low, const Scalar& high)
{
EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
return DenseBase<Derived>::NullaryExpr(size, internal::linspaced_op<Scalar,PacketScalar>(low,high,size));
}
/** \deprecated because of accuracy loss. In Eigen 3.3, it is an alias for LinSpaced(const Scalar&,const Scalar&)
*
* \sa LinSpaced(Scalar,Scalar)
*/
template<typename Derived>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const typename DenseBase<Derived>::RandomAccessLinSpacedReturnType
DenseBase<Derived>::LinSpaced(Sequential_t, const Scalar& low, const Scalar& high)
{
EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
EIGEN_STATIC_ASSERT_FIXED_SIZE(Derived)
return DenseBase<Derived>::NullaryExpr(Derived::SizeAtCompileTime, internal::linspaced_op<Scalar,PacketScalar>(low,high,Derived::SizeAtCompileTime));
}
/**
* \brief Sets a linearly spaced vector.
*
* The function generates 'size' equally spaced values in the closed interval [low,high].
* When size is set to 1, a vector of length 1 containing 'high' is returned.
*
* \only_for_vectors
*
* Example: \include DenseBase_LinSpaced.cpp
* Output: \verbinclude DenseBase_LinSpaced.out
*
* For integer scalar types, an even spacing is possible if and only if the length of the range,
* i.e., \c high-low is a scalar multiple of \c size-1, or if \c size is a scalar multiple of the
* number of values \c high-low+1 (meaning each value can be repeated the same number of time).
* If one of these two considions is not satisfied, then \c high is lowered to the largest value
* satisfying one of this constraint.
* Here are some examples:
*
* Example: \include DenseBase_LinSpacedInt.cpp
* Output: \verbinclude DenseBase_LinSpacedInt.out
*
* \sa setLinSpaced(Index,const Scalar&,const Scalar&), CwiseNullaryOp
*/
template<typename Derived>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const typename DenseBase<Derived>::RandomAccessLinSpacedReturnType
DenseBase<Derived>::LinSpaced(Index size, const Scalar& low, const Scalar& high)
{
EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
return DenseBase<Derived>::NullaryExpr(size, internal::linspaced_op<Scalar,PacketScalar>(low,high,size));
}
/**
* \copydoc DenseBase::LinSpaced(Index, const Scalar&, const Scalar&)
* Special version for fixed size types which does not require the size parameter.
*/
template<typename Derived>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const typename DenseBase<Derived>::RandomAccessLinSpacedReturnType
DenseBase<Derived>::LinSpaced(const Scalar& low, const Scalar& high)
{
EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
EIGEN_STATIC_ASSERT_FIXED_SIZE(Derived)
return DenseBase<Derived>::NullaryExpr(Derived::SizeAtCompileTime, internal::linspaced_op<Scalar,PacketScalar>(low,high,Derived::SizeAtCompileTime));
}
/** \returns true if all coefficients in this matrix are approximately equal to \a val, to within precision \a prec */
template<typename Derived>
EIGEN_DEVICE_FUNC bool DenseBase<Derived>::isApproxToConstant
(const Scalar& val, const RealScalar& prec) const
{
typename internal::nested_eval<Derived,1>::type self(derived());
for(Index j = 0; j < cols(); ++j)
for(Index i = 0; i < rows(); ++i)
if(!internal::isApprox(self.coeff(i, j), val, prec))
return false;
return true;
}
/** This is just an alias for isApproxToConstant().
*
* \returns true if all coefficients in this matrix are approximately equal to \a value, to within precision \a prec */
template<typename Derived>
EIGEN_DEVICE_FUNC bool DenseBase<Derived>::isConstant
(const Scalar& val, const RealScalar& prec) const
{
return isApproxToConstant(val, prec);
}
/** Alias for setConstant(): sets all coefficients in this expression to \a val.
*
* \sa setConstant(), Constant(), class CwiseNullaryOp
*/
template<typename Derived>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void DenseBase<Derived>::fill(const Scalar& val)
{
setConstant(val);
}
/** Sets all coefficients in this expression to value \a val.
*
* \sa fill(), setConstant(Index,const Scalar&), setConstant(Index,Index,const Scalar&), setZero(), setOnes(), Constant(), class CwiseNullaryOp, setZero(), setOnes()
*/
template<typename Derived>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& DenseBase<Derived>::setConstant(const Scalar& val)
{
return derived() = Constant(rows(), cols(), val);
}
/** Resizes to the given \a size, and sets all coefficients in this expression to the given value \a val.
*
* \only_for_vectors
*
* Example: \include Matrix_setConstant_int.cpp
* Output: \verbinclude Matrix_setConstant_int.out
*
* \sa MatrixBase::setConstant(const Scalar&), setConstant(Index,Index,const Scalar&), class CwiseNullaryOp, MatrixBase::Constant(const Scalar&)
*/
template<typename Derived>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived&
PlainObjectBase<Derived>::setConstant(Index size, const Scalar& val)
{
resize(size);
return setConstant(val);
}
/** Resizes to the given size, and sets all coefficients in this expression to the given value \a val.
*
* \param rows the new number of rows
* \param cols the new number of columns
* \param val the value to which all coefficients are set
*
* Example: \include Matrix_setConstant_int_int.cpp
* Output: \verbinclude Matrix_setConstant_int_int.out
*
* \sa MatrixBase::setConstant(const Scalar&), setConstant(Index,const Scalar&), class CwiseNullaryOp, MatrixBase::Constant(const Scalar&)
*/
template<typename Derived>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived&
PlainObjectBase<Derived>::setConstant(Index rows, Index cols, const Scalar& val)
{
resize(rows, cols);
return setConstant(val);
}
/**
* \brief Sets a linearly spaced vector.
*
* The function generates 'size' equally spaced values in the closed interval [low,high].
* When size is set to 1, a vector of length 1 containing 'high' is returned.
*
* \only_for_vectors
*
* Example: \include DenseBase_setLinSpaced.cpp
* Output: \verbinclude DenseBase_setLinSpaced.out
*
* For integer scalar types, do not miss the explanations on the definition
* of \link LinSpaced(Index,const Scalar&,const Scalar&) even spacing \endlink.
*
* \sa LinSpaced(Index,const Scalar&,const Scalar&), CwiseNullaryOp
*/
template<typename Derived>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& DenseBase<Derived>::setLinSpaced(Index newSize, const Scalar& low, const Scalar& high)
{
EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
return derived() = Derived::NullaryExpr(newSize, internal::linspaced_op<Scalar,PacketScalar>(low,high,newSize));
}
/**
* \brief Sets a linearly spaced vector.
*
* The function fills \c *this with equally spaced values in the closed interval [low,high].
* When size is set to 1, a vector of length 1 containing 'high' is returned.
*
* \only_for_vectors
*
* For integer scalar types, do not miss the explanations on the definition
* of \link LinSpaced(Index,const Scalar&,const Scalar&) even spacing \endlink.
*
* \sa LinSpaced(Index,const Scalar&,const Scalar&), setLinSpaced(Index, const Scalar&, const Scalar&), CwiseNullaryOp
*/
template<typename Derived>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& DenseBase<Derived>::setLinSpaced(const Scalar& low, const Scalar& high)
{
EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
return setLinSpaced(size(), low, high);
}
// zero:
/** \returns an expression of a zero matrix.
*
* The parameters \a rows and \a cols are the number of rows and of columns of
* the returned matrix. Must be compatible with this MatrixBase type.
*
* This variant is meant to be used for dynamic-size matrix types. For fixed-size types,
* it is redundant to pass \a rows and \a cols as arguments, so Zero() should be used
* instead.
*
* Example: \include MatrixBase_zero_int_int.cpp
* Output: \verbinclude MatrixBase_zero_int_int.out
*
* \sa Zero(), Zero(Index)
*/
template<typename Derived>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const typename DenseBase<Derived>::ConstantReturnType
DenseBase<Derived>::Zero(Index rows, Index cols)
{
return Constant(rows, cols, Scalar(0));
}
/** \returns an expression of a zero vector.
*
* The parameter \a size is the size of the returned vector.
* Must be compatible with this MatrixBase type.
*
* \only_for_vectors
*
* This variant is meant to be used for dynamic-size vector types. For fixed-size types,
* it is redundant to pass \a size as argument, so Zero() should be used
* instead.
*
* Example: \include MatrixBase_zero_int.cpp
* Output: \verbinclude MatrixBase_zero_int.out
*
* \sa Zero(), Zero(Index,Index)
*/
template<typename Derived>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const typename DenseBase<Derived>::ConstantReturnType
DenseBase<Derived>::Zero(Index size)
{
return Constant(size, Scalar(0));
}
/** \returns an expression of a fixed-size zero matrix or vector.
*
* This variant is only for fixed-size MatrixBase types. For dynamic-size types, you
* need to use the variants taking size arguments.
*
* Example: \include MatrixBase_zero.cpp
* Output: \verbinclude MatrixBase_zero.out
*
* \sa Zero(Index), Zero(Index,Index)
*/
template<typename Derived>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const typename DenseBase<Derived>::ConstantReturnType
DenseBase<Derived>::Zero()
{
return Constant(Scalar(0));
}
/** \returns true if *this is approximately equal to the zero matrix,
* within the precision given by \a prec.
*
* Example: \include MatrixBase_isZero.cpp
* Output: \verbinclude MatrixBase_isZero.out
*
* \sa class CwiseNullaryOp, Zero()
*/
template<typename Derived>
EIGEN_DEVICE_FUNC bool DenseBase<Derived>::isZero(const RealScalar& prec) const
{
typename internal::nested_eval<Derived,1>::type self(derived());
for(Index j = 0; j < cols(); ++j)
for(Index i = 0; i < rows(); ++i)
if(!internal::isMuchSmallerThan(self.coeff(i, j), static_cast<Scalar>(1), prec))
return false;
return true;
}
/** Sets all coefficients in this expression to zero.
*
* Example: \include MatrixBase_setZero.cpp
* Output: \verbinclude MatrixBase_setZero.out
*
* \sa class CwiseNullaryOp, Zero()
*/
template<typename Derived>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& DenseBase<Derived>::setZero()
{
return setConstant(Scalar(0));
}
/** Resizes to the given \a size, and sets all coefficients in this expression to zero.
*
* \only_for_vectors
*
* Example: \include Matrix_setZero_int.cpp
* Output: \verbinclude Matrix_setZero_int.out
*
* \sa DenseBase::setZero(), setZero(Index,Index), class CwiseNullaryOp, DenseBase::Zero()
*/
template<typename Derived>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived&
PlainObjectBase<Derived>::setZero(Index newSize)
{
resize(newSize);
return setConstant(Scalar(0));
}
/** Resizes to the given size, and sets all coefficients in this expression to zero.
*
* \param rows the new number of rows
* \param cols the new number of columns
*
* Example: \include Matrix_setZero_int_int.cpp
* Output: \verbinclude Matrix_setZero_int_int.out
*
* \sa DenseBase::setZero(), setZero(Index), class CwiseNullaryOp, DenseBase::Zero()
*/
template<typename Derived>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived&
PlainObjectBase<Derived>::setZero(Index rows, Index cols)
{
resize(rows, cols);
return setConstant(Scalar(0));
}
// ones:
/** \returns an expression of a matrix where all coefficients equal one.
*
* The parameters \a rows and \a cols are the number of rows and of columns of
* the returned matrix. Must be compatible with this MatrixBase type.
*
* This variant is meant to be used for dynamic-size matrix types. For fixed-size types,
* it is redundant to pass \a rows and \a cols as arguments, so Ones() should be used
* instead.
*
* Example: \include MatrixBase_ones_int_int.cpp
* Output: \verbinclude MatrixBase_ones_int_int.out
*
* \sa Ones(), Ones(Index), isOnes(), class Ones
*/
template<typename Derived>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const typename DenseBase<Derived>::ConstantReturnType
DenseBase<Derived>::Ones(Index rows, Index cols)
{
return Constant(rows, cols, Scalar(1));
}
/** \returns an expression of a vector where all coefficients equal one.
*
* The parameter \a newSize is the size of the returned vector.
* Must be compatible with this MatrixBase type.
*
* \only_for_vectors
*
* This variant is meant to be used for dynamic-size vector types. For fixed-size types,
* it is redundant to pass \a size as argument, so Ones() should be used
* instead.
*
* Example: \include MatrixBase_ones_int.cpp
* Output: \verbinclude MatrixBase_ones_int.out
*
* \sa Ones(), Ones(Index,Index), isOnes(), class Ones
*/
template<typename Derived>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const typename DenseBase<Derived>::ConstantReturnType
DenseBase<Derived>::Ones(Index newSize)
{
return Constant(newSize, Scalar(1));
}
/** \returns an expression of a fixed-size matrix or vector where all coefficients equal one.
*
* This variant is only for fixed-size MatrixBase types. For dynamic-size types, you
* need to use the variants taking size arguments.
*
* Example: \include MatrixBase_ones.cpp
* Output: \verbinclude MatrixBase_ones.out
*
* \sa Ones(Index), Ones(Index,Index), isOnes(), class Ones
*/
template<typename Derived>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const typename DenseBase<Derived>::ConstantReturnType
DenseBase<Derived>::Ones()
{
return Constant(Scalar(1));
}
/** \returns true if *this is approximately equal to the matrix where all coefficients
* are equal to 1, within the precision given by \a prec.
*
* Example: \include MatrixBase_isOnes.cpp
* Output: \verbinclude MatrixBase_isOnes.out
*
* \sa class CwiseNullaryOp, Ones()
*/
template<typename Derived>
EIGEN_DEVICE_FUNC bool DenseBase<Derived>::isOnes
(const RealScalar& prec) const
{
return isApproxToConstant(Scalar(1), prec);
}
/** Sets all coefficients in this expression to one.
*
* Example: \include MatrixBase_setOnes.cpp
* Output: \verbinclude MatrixBase_setOnes.out
*
* \sa class CwiseNullaryOp, Ones()
*/
template<typename Derived>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& DenseBase<Derived>::setOnes()
{
return setConstant(Scalar(1));
}
/** Resizes to the given \a newSize, and sets all coefficients in this expression to one.
*
* \only_for_vectors
*
* Example: \include Matrix_setOnes_int.cpp
* Output: \verbinclude Matrix_setOnes_int.out
*
* \sa MatrixBase::setOnes(), setOnes(Index,Index), class CwiseNullaryOp, MatrixBase::Ones()
*/
template<typename Derived>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived&
PlainObjectBase<Derived>::setOnes(Index newSize)
{
resize(newSize);
return setConstant(Scalar(1));
}
/** Resizes to the given size, and sets all coefficients in this expression to one.
*
* \param rows the new number of rows
* \param cols the new number of columns
*
* Example: \include Matrix_setOnes_int_int.cpp
* Output: \verbinclude Matrix_setOnes_int_int.out
*
* \sa MatrixBase::setOnes(), setOnes(Index), class CwiseNullaryOp, MatrixBase::Ones()
*/
template<typename Derived>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived&
PlainObjectBase<Derived>::setOnes(Index rows, Index cols)
{
resize(rows, cols);
return setConstant(Scalar(1));
}
// Identity:
/** \returns an expression of the identity matrix (not necessarily square).
*
* The parameters \a rows and \a cols are the number of rows and of columns of
* the returned matrix. Must be compatible with this MatrixBase type.
*
* This variant is meant to be used for dynamic-size matrix types. For fixed-size types,
* it is redundant to pass \a rows and \a cols as arguments, so Identity() should be used
* instead.
*
* Example: \include MatrixBase_identity_int_int.cpp
* Output: \verbinclude MatrixBase_identity_int_int.out
*
* \sa Identity(), setIdentity(), isIdentity()
*/
template<typename Derived>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const typename MatrixBase<Derived>::IdentityReturnType
MatrixBase<Derived>::Identity(Index rows, Index cols)
{
return DenseBase<Derived>::NullaryExpr(rows, cols, internal::scalar_identity_op<Scalar>());
}
/** \returns an expression of the identity matrix (not necessarily square).
*
* This variant is only for fixed-size MatrixBase types. For dynamic-size types, you
* need to use the variant taking size arguments.
*
* Example: \include MatrixBase_identity.cpp
* Output: \verbinclude MatrixBase_identity.out
*
* \sa Identity(Index,Index), setIdentity(), isIdentity()
*/
template<typename Derived>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const typename MatrixBase<Derived>::IdentityReturnType
MatrixBase<Derived>::Identity()
{
EIGEN_STATIC_ASSERT_FIXED_SIZE(Derived)
return MatrixBase<Derived>::NullaryExpr(RowsAtCompileTime, ColsAtCompileTime, internal::scalar_identity_op<Scalar>());
}
/** \returns true if *this is approximately equal to the identity matrix
* (not necessarily square),
* within the precision given by \a prec.
*
* Example: \include MatrixBase_isIdentity.cpp
* Output: \verbinclude MatrixBase_isIdentity.out
*
* \sa class CwiseNullaryOp, Identity(), Identity(Index,Index), setIdentity()
*/
template<typename Derived>
bool MatrixBase<Derived>::isIdentity
(const RealScalar& prec) const
{
typename internal::nested_eval<Derived,1>::type self(derived());
for(Index j = 0; j < cols(); ++j)
{
for(Index i = 0; i < rows(); ++i)
{
if(i == j)
{
if(!internal::isApprox(self.coeff(i, j), static_cast<Scalar>(1), prec))
return false;
}
else
{
if(!internal::isMuchSmallerThan(self.coeff(i, j), static_cast<RealScalar>(1), prec))
return false;
}
}
}
return true;
}
namespace internal {
template<typename Derived, bool Big = (Derived::SizeAtCompileTime>=16)>
struct setIdentity_impl
{
EIGEN_DEVICE_FUNC
static EIGEN_STRONG_INLINE Derived& run(Derived& m)
{
return m = Derived::Identity(m.rows(), m.cols());
}
};
template<typename Derived>
struct setIdentity_impl<Derived, true>
{
EIGEN_DEVICE_FUNC
static EIGEN_STRONG_INLINE Derived& run(Derived& m)
{
m.setZero();
const Index size = numext::mini(m.rows(), m.cols());
for(Index i = 0; i < size; ++i) m.coeffRef(i,i) = typename Derived::Scalar(1);
return m;
}
};
} // end namespace internal
/** Writes the identity expression (not necessarily square) into *this.
*
* Example: \include MatrixBase_setIdentity.cpp
* Output: \verbinclude MatrixBase_setIdentity.out
*
* \sa class CwiseNullaryOp, Identity(), Identity(Index,Index), isIdentity()
*/
template<typename Derived>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& MatrixBase<Derived>::setIdentity()
{
return internal::setIdentity_impl<Derived>::run(derived());
}
/** \brief Resizes to the given size, and writes the identity expression (not necessarily square) into *this.
*
* \param rows the new number of rows
* \param cols the new number of columns
*
* Example: \include Matrix_setIdentity_int_int.cpp
* Output: \verbinclude Matrix_setIdentity_int_int.out
*
* \sa MatrixBase::setIdentity(), class CwiseNullaryOp, MatrixBase::Identity()
*/
template<typename Derived>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& MatrixBase<Derived>::setIdentity(Index rows, Index cols)
{
derived().resize(rows, cols);
return setIdentity();
}
/** \returns an expression of the i-th unit (basis) vector.
*
* \only_for_vectors
*
* \sa MatrixBase::Unit(Index), MatrixBase::UnitX(), MatrixBase::UnitY(), MatrixBase::UnitZ(), MatrixBase::UnitW()
*/
template<typename Derived>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const typename MatrixBase<Derived>::BasisReturnType MatrixBase<Derived>::Unit(Index newSize, Index i)
{
EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
return BasisReturnType(SquareMatrixType::Identity(newSize,newSize), i);
}
/** \returns an expression of the i-th unit (basis) vector.
*
* \only_for_vectors
*
* This variant is for fixed-size vector only.
*
* \sa MatrixBase::Unit(Index,Index), MatrixBase::UnitX(), MatrixBase::UnitY(), MatrixBase::UnitZ(), MatrixBase::UnitW()
*/
template<typename Derived>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const typename MatrixBase<Derived>::BasisReturnType MatrixBase<Derived>::Unit(Index i)
{
EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
return BasisReturnType(SquareMatrixType::Identity(),i);
}
/** \returns an expression of the X axis unit vector (1{,0}^*)
*
* \only_for_vectors
*
* \sa MatrixBase::Unit(Index,Index), MatrixBase::Unit(Index), MatrixBase::UnitY(), MatrixBase::UnitZ(), MatrixBase::UnitW()
*/
template<typename Derived>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const typename MatrixBase<Derived>::BasisReturnType MatrixBase<Derived>::UnitX()
{ return Derived::Unit(0); }
/** \returns an expression of the Y axis unit vector (0,1{,0}^*)
*
* \only_for_vectors
*
* \sa MatrixBase::Unit(Index,Index), MatrixBase::Unit(Index), MatrixBase::UnitY(), MatrixBase::UnitZ(), MatrixBase::UnitW()
*/
template<typename Derived>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const typename MatrixBase<Derived>::BasisReturnType MatrixBase<Derived>::UnitY()
{ return Derived::Unit(1); }
/** \returns an expression of the Z axis unit vector (0,0,1{,0}^*)
*
* \only_for_vectors
*
* \sa MatrixBase::Unit(Index,Index), MatrixBase::Unit(Index), MatrixBase::UnitY(), MatrixBase::UnitZ(), MatrixBase::UnitW()
*/
template<typename Derived>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const typename MatrixBase<Derived>::BasisReturnType MatrixBase<Derived>::UnitZ()
{ return Derived::Unit(2); }
/** \returns an expression of the W axis unit vector (0,0,0,1)
*
* \only_for_vectors
*
* \sa MatrixBase::Unit(Index,Index), MatrixBase::Unit(Index), MatrixBase::UnitY(), MatrixBase::UnitZ(), MatrixBase::UnitW()
*/
template<typename Derived>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const typename MatrixBase<Derived>::BasisReturnType MatrixBase<Derived>::UnitW()
{ return Derived::Unit(3); }
} // end namespace Eigen
#endif // EIGEN_CWISE_NULLARY_OP_H

View File

@@ -0,0 +1,197 @@
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2008-2014 Gael Guennebaud <gael.guennebaud@inria.fr>
// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com>
// Copyright (C) 2016 Eugene Brevdo <ebrevdo@gmail.com>
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#ifndef EIGEN_CWISE_TERNARY_OP_H
#define EIGEN_CWISE_TERNARY_OP_H
namespace Eigen {
namespace internal {
template <typename TernaryOp, typename Arg1, typename Arg2, typename Arg3>
struct traits<CwiseTernaryOp<TernaryOp, Arg1, Arg2, Arg3> > {
// we must not inherit from traits<Arg1> since it has
// the potential to cause problems with MSVC
typedef typename remove_all<Arg1>::type Ancestor;
typedef typename traits<Ancestor>::XprKind XprKind;
enum {
RowsAtCompileTime = traits<Ancestor>::RowsAtCompileTime,
ColsAtCompileTime = traits<Ancestor>::ColsAtCompileTime,
MaxRowsAtCompileTime = traits<Ancestor>::MaxRowsAtCompileTime,
MaxColsAtCompileTime = traits<Ancestor>::MaxColsAtCompileTime
};
// even though we require Arg1, Arg2, and Arg3 to have the same scalar type
// (see CwiseTernaryOp constructor),
// we still want to handle the case when the result type is different.
typedef typename result_of<TernaryOp(
const typename Arg1::Scalar&, const typename Arg2::Scalar&,
const typename Arg3::Scalar&)>::type Scalar;
typedef typename internal::traits<Arg1>::StorageKind StorageKind;
typedef typename internal::traits<Arg1>::StorageIndex StorageIndex;
typedef typename Arg1::Nested Arg1Nested;
typedef typename Arg2::Nested Arg2Nested;
typedef typename Arg3::Nested Arg3Nested;
typedef typename remove_reference<Arg1Nested>::type _Arg1Nested;
typedef typename remove_reference<Arg2Nested>::type _Arg2Nested;
typedef typename remove_reference<Arg3Nested>::type _Arg3Nested;
enum { Flags = _Arg1Nested::Flags & RowMajorBit };
};
} // end namespace internal
template <typename TernaryOp, typename Arg1, typename Arg2, typename Arg3,
typename StorageKind>
class CwiseTernaryOpImpl;
/** \class CwiseTernaryOp
* \ingroup Core_Module
*
* \brief Generic expression where a coefficient-wise ternary operator is
* applied to two expressions
*
* \tparam TernaryOp template functor implementing the operator
* \tparam Arg1Type the type of the first argument
* \tparam Arg2Type the type of the second argument
* \tparam Arg3Type the type of the third argument
*
* This class represents an expression where a coefficient-wise ternary
* operator is applied to three expressions.
* It is the return type of ternary operators, by which we mean only those
* ternary operators where
* all three arguments are Eigen expressions.
* For example, the return type of betainc(matrix1, matrix2, matrix3) is a
* CwiseTernaryOp.
*
* Most of the time, this is the only way that it is used, so you typically
* don't have to name
* CwiseTernaryOp types explicitly.
*
* \sa MatrixBase::ternaryExpr(const MatrixBase<Argument2> &, const
* MatrixBase<Argument3> &, const CustomTernaryOp &) const, class CwiseBinaryOp,
* class CwiseUnaryOp, class CwiseNullaryOp
*/
template <typename TernaryOp, typename Arg1Type, typename Arg2Type,
typename Arg3Type>
class CwiseTernaryOp : public CwiseTernaryOpImpl<
TernaryOp, Arg1Type, Arg2Type, Arg3Type,
typename internal::traits<Arg1Type>::StorageKind>,
internal::no_assignment_operator
{
public:
typedef typename internal::remove_all<Arg1Type>::type Arg1;
typedef typename internal::remove_all<Arg2Type>::type Arg2;
typedef typename internal::remove_all<Arg3Type>::type Arg3;
typedef typename CwiseTernaryOpImpl<
TernaryOp, Arg1Type, Arg2Type, Arg3Type,
typename internal::traits<Arg1Type>::StorageKind>::Base Base;
EIGEN_GENERIC_PUBLIC_INTERFACE(CwiseTernaryOp)
typedef typename internal::ref_selector<Arg1Type>::type Arg1Nested;
typedef typename internal::ref_selector<Arg2Type>::type Arg2Nested;
typedef typename internal::ref_selector<Arg3Type>::type Arg3Nested;
typedef typename internal::remove_reference<Arg1Nested>::type _Arg1Nested;
typedef typename internal::remove_reference<Arg2Nested>::type _Arg2Nested;
typedef typename internal::remove_reference<Arg3Nested>::type _Arg3Nested;
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE CwiseTernaryOp(const Arg1& a1, const Arg2& a2,
const Arg3& a3,
const TernaryOp& func = TernaryOp())
: m_arg1(a1), m_arg2(a2), m_arg3(a3), m_functor(func) {
// require the sizes to match
EIGEN_STATIC_ASSERT_SAME_MATRIX_SIZE(Arg1, Arg2)
EIGEN_STATIC_ASSERT_SAME_MATRIX_SIZE(Arg1, Arg3)
// The index types should match
EIGEN_STATIC_ASSERT((internal::is_same<
typename internal::traits<Arg1Type>::StorageKind,
typename internal::traits<Arg2Type>::StorageKind>::value),
STORAGE_KIND_MUST_MATCH)
EIGEN_STATIC_ASSERT((internal::is_same<
typename internal::traits<Arg1Type>::StorageKind,
typename internal::traits<Arg3Type>::StorageKind>::value),
STORAGE_KIND_MUST_MATCH)
eigen_assert(a1.rows() == a2.rows() && a1.cols() == a2.cols() &&
a1.rows() == a3.rows() && a1.cols() == a3.cols());
}
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Index rows() const {
// return the fixed size type if available to enable compile time
// optimizations
if (internal::traits<typename internal::remove_all<Arg1Nested>::type>::
RowsAtCompileTime == Dynamic &&
internal::traits<typename internal::remove_all<Arg2Nested>::type>::
RowsAtCompileTime == Dynamic)
return m_arg3.rows();
else if (internal::traits<typename internal::remove_all<Arg1Nested>::type>::
RowsAtCompileTime == Dynamic &&
internal::traits<typename internal::remove_all<Arg3Nested>::type>::
RowsAtCompileTime == Dynamic)
return m_arg2.rows();
else
return m_arg1.rows();
}
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Index cols() const {
// return the fixed size type if available to enable compile time
// optimizations
if (internal::traits<typename internal::remove_all<Arg1Nested>::type>::
ColsAtCompileTime == Dynamic &&
internal::traits<typename internal::remove_all<Arg2Nested>::type>::
ColsAtCompileTime == Dynamic)
return m_arg3.cols();
else if (internal::traits<typename internal::remove_all<Arg1Nested>::type>::
ColsAtCompileTime == Dynamic &&
internal::traits<typename internal::remove_all<Arg3Nested>::type>::
ColsAtCompileTime == Dynamic)
return m_arg2.cols();
else
return m_arg1.cols();
}
/** \returns the first argument nested expression */
EIGEN_DEVICE_FUNC
const _Arg1Nested& arg1() const { return m_arg1; }
/** \returns the first argument nested expression */
EIGEN_DEVICE_FUNC
const _Arg2Nested& arg2() const { return m_arg2; }
/** \returns the third argument nested expression */
EIGEN_DEVICE_FUNC
const _Arg3Nested& arg3() const { return m_arg3; }
/** \returns the functor representing the ternary operation */
EIGEN_DEVICE_FUNC
const TernaryOp& functor() const { return m_functor; }
protected:
Arg1Nested m_arg1;
Arg2Nested m_arg2;
Arg3Nested m_arg3;
const TernaryOp m_functor;
};
// Generic API dispatcher
template <typename TernaryOp, typename Arg1, typename Arg2, typename Arg3,
typename StorageKind>
class CwiseTernaryOpImpl
: public internal::generic_xpr_base<
CwiseTernaryOp<TernaryOp, Arg1, Arg2, Arg3> >::type {
public:
typedef typename internal::generic_xpr_base<
CwiseTernaryOp<TernaryOp, Arg1, Arg2, Arg3> >::type Base;
};
} // end namespace Eigen
#endif // EIGEN_CWISE_TERNARY_OP_H

View File

@@ -0,0 +1,103 @@
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2008-2014 Gael Guennebaud <gael.guennebaud@inria.fr>
// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com>
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#ifndef EIGEN_CWISE_UNARY_OP_H
#define EIGEN_CWISE_UNARY_OP_H
namespace Eigen {
namespace internal {
template<typename UnaryOp, typename XprType>
struct traits<CwiseUnaryOp<UnaryOp, XprType> >
: traits<XprType>
{
typedef typename result_of<
UnaryOp(const typename XprType::Scalar&)
>::type Scalar;
typedef typename XprType::Nested XprTypeNested;
typedef typename remove_reference<XprTypeNested>::type _XprTypeNested;
enum {
Flags = _XprTypeNested::Flags & RowMajorBit
};
};
}
template<typename UnaryOp, typename XprType, typename StorageKind>
class CwiseUnaryOpImpl;
/** \class CwiseUnaryOp
* \ingroup Core_Module
*
* \brief Generic expression where a coefficient-wise unary operator is applied to an expression
*
* \tparam UnaryOp template functor implementing the operator
* \tparam XprType the type of the expression to which we are applying the unary operator
*
* This class represents an expression where a unary operator is applied to an expression.
* It is the return type of all operations taking exactly 1 input expression, regardless of the
* presence of other inputs such as scalars. For example, the operator* in the expression 3*matrix
* is considered unary, because only the right-hand side is an expression, and its
* return type is a specialization of CwiseUnaryOp.
*
* Most of the time, this is the only way that it is used, so you typically don't have to name
* CwiseUnaryOp types explicitly.
*
* \sa MatrixBase::unaryExpr(const CustomUnaryOp &) const, class CwiseBinaryOp, class CwiseNullaryOp
*/
template<typename UnaryOp, typename XprType>
class CwiseUnaryOp : public CwiseUnaryOpImpl<UnaryOp, XprType, typename internal::traits<XprType>::StorageKind>, internal::no_assignment_operator
{
public:
typedef typename CwiseUnaryOpImpl<UnaryOp, XprType,typename internal::traits<XprType>::StorageKind>::Base Base;
EIGEN_GENERIC_PUBLIC_INTERFACE(CwiseUnaryOp)
typedef typename internal::ref_selector<XprType>::type XprTypeNested;
typedef typename internal::remove_all<XprType>::type NestedExpression;
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
explicit CwiseUnaryOp(const XprType& xpr, const UnaryOp& func = UnaryOp())
: m_xpr(xpr), m_functor(func) {}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
Index rows() const { return m_xpr.rows(); }
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
Index cols() const { return m_xpr.cols(); }
/** \returns the functor representing the unary operation */
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
const UnaryOp& functor() const { return m_functor; }
/** \returns the nested expression */
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
const typename internal::remove_all<XprTypeNested>::type&
nestedExpression() const { return m_xpr; }
/** \returns the nested expression */
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
typename internal::remove_all<XprTypeNested>::type&
nestedExpression() { return m_xpr; }
protected:
XprTypeNested m_xpr;
const UnaryOp m_functor;
};
// Generic API dispatcher
template<typename UnaryOp, typename XprType, typename StorageKind>
class CwiseUnaryOpImpl
: public internal::generic_xpr_base<CwiseUnaryOp<UnaryOp, XprType> >::type
{
public:
typedef typename internal::generic_xpr_base<CwiseUnaryOp<UnaryOp, XprType> >::type Base;
};
} // end namespace Eigen
#endif // EIGEN_CWISE_UNARY_OP_H

View File

@@ -0,0 +1,128 @@
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2009-2010 Gael Guennebaud <gael.guennebaud@inria.fr>
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#ifndef EIGEN_CWISE_UNARY_VIEW_H
#define EIGEN_CWISE_UNARY_VIEW_H
namespace Eigen {
namespace internal {
template<typename ViewOp, typename MatrixType>
struct traits<CwiseUnaryView<ViewOp, MatrixType> >
: traits<MatrixType>
{
typedef typename result_of<
ViewOp(const typename traits<MatrixType>::Scalar&)
>::type Scalar;
typedef typename MatrixType::Nested MatrixTypeNested;
typedef typename remove_all<MatrixTypeNested>::type _MatrixTypeNested;
enum {
FlagsLvalueBit = is_lvalue<MatrixType>::value ? LvalueBit : 0,
Flags = traits<_MatrixTypeNested>::Flags & (RowMajorBit | FlagsLvalueBit | DirectAccessBit), // FIXME DirectAccessBit should not be handled by expressions
MatrixTypeInnerStride = inner_stride_at_compile_time<MatrixType>::ret,
// need to cast the sizeof's from size_t to int explicitly, otherwise:
// "error: no integral type can represent all of the enumerator values
InnerStrideAtCompileTime = MatrixTypeInnerStride == Dynamic
? int(Dynamic)
: int(MatrixTypeInnerStride) * int(sizeof(typename traits<MatrixType>::Scalar) / sizeof(Scalar)),
OuterStrideAtCompileTime = outer_stride_at_compile_time<MatrixType>::ret == Dynamic
? int(Dynamic)
: outer_stride_at_compile_time<MatrixType>::ret * int(sizeof(typename traits<MatrixType>::Scalar) / sizeof(Scalar))
};
};
}
template<typename ViewOp, typename MatrixType, typename StorageKind>
class CwiseUnaryViewImpl;
/** \class CwiseUnaryView
* \ingroup Core_Module
*
* \brief Generic lvalue expression of a coefficient-wise unary operator of a matrix or a vector
*
* \tparam ViewOp template functor implementing the view
* \tparam MatrixType the type of the matrix we are applying the unary operator
*
* This class represents a lvalue expression of a generic unary view operator of a matrix or a vector.
* It is the return type of real() and imag(), and most of the time this is the only way it is used.
*
* \sa MatrixBase::unaryViewExpr(const CustomUnaryOp &) const, class CwiseUnaryOp
*/
template<typename ViewOp, typename MatrixType>
class CwiseUnaryView : public CwiseUnaryViewImpl<ViewOp, MatrixType, typename internal::traits<MatrixType>::StorageKind>
{
public:
typedef typename CwiseUnaryViewImpl<ViewOp, MatrixType,typename internal::traits<MatrixType>::StorageKind>::Base Base;
EIGEN_GENERIC_PUBLIC_INTERFACE(CwiseUnaryView)
typedef typename internal::ref_selector<MatrixType>::non_const_type MatrixTypeNested;
typedef typename internal::remove_all<MatrixType>::type NestedExpression;
explicit inline CwiseUnaryView(MatrixType& mat, const ViewOp& func = ViewOp())
: m_matrix(mat), m_functor(func) {}
EIGEN_INHERIT_ASSIGNMENT_OPERATORS(CwiseUnaryView)
EIGEN_STRONG_INLINE Index rows() const { return m_matrix.rows(); }
EIGEN_STRONG_INLINE Index cols() const { return m_matrix.cols(); }
/** \returns the functor representing unary operation */
const ViewOp& functor() const { return m_functor; }
/** \returns the nested expression */
const typename internal::remove_all<MatrixTypeNested>::type&
nestedExpression() const { return m_matrix; }
/** \returns the nested expression */
typename internal::remove_reference<MatrixTypeNested>::type&
nestedExpression() { return m_matrix.const_cast_derived(); }
protected:
MatrixTypeNested m_matrix;
ViewOp m_functor;
};
// Generic API dispatcher
template<typename ViewOp, typename XprType, typename StorageKind>
class CwiseUnaryViewImpl
: public internal::generic_xpr_base<CwiseUnaryView<ViewOp, XprType> >::type
{
public:
typedef typename internal::generic_xpr_base<CwiseUnaryView<ViewOp, XprType> >::type Base;
};
template<typename ViewOp, typename MatrixType>
class CwiseUnaryViewImpl<ViewOp,MatrixType,Dense>
: public internal::dense_xpr_base< CwiseUnaryView<ViewOp, MatrixType> >::type
{
public:
typedef CwiseUnaryView<ViewOp, MatrixType> Derived;
typedef typename internal::dense_xpr_base< CwiseUnaryView<ViewOp, MatrixType> >::type Base;
EIGEN_DENSE_PUBLIC_INTERFACE(Derived)
EIGEN_INHERIT_ASSIGNMENT_OPERATORS(CwiseUnaryViewImpl)
EIGEN_DEVICE_FUNC inline Scalar* data() { return &(this->coeffRef(0)); }
EIGEN_DEVICE_FUNC inline const Scalar* data() const { return &(this->coeff(0)); }
EIGEN_DEVICE_FUNC inline Index innerStride() const
{
return derived().nestedExpression().innerStride() * sizeof(typename internal::traits<MatrixType>::Scalar) / sizeof(Scalar);
}
EIGEN_DEVICE_FUNC inline Index outerStride() const
{
return derived().nestedExpression().outerStride() * sizeof(typename internal::traits<MatrixType>::Scalar) / sizeof(Scalar);
}
};
} // end namespace Eigen
#endif // EIGEN_CWISE_UNARY_VIEW_H

View File

@@ -0,0 +1,611 @@
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2007-2010 Benoit Jacob <jacob.benoit.1@gmail.com>
// Copyright (C) 2008-2010 Gael Guennebaud <gael.guennebaud@inria.fr>
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#ifndef EIGEN_DENSEBASE_H
#define EIGEN_DENSEBASE_H
namespace Eigen {
namespace internal {
// The index type defined by EIGEN_DEFAULT_DENSE_INDEX_TYPE must be a signed type.
// This dummy function simply aims at checking that at compile time.
static inline void check_DenseIndex_is_signed() {
EIGEN_STATIC_ASSERT(NumTraits<DenseIndex>::IsSigned,THE_INDEX_TYPE_MUST_BE_A_SIGNED_TYPE);
}
} // end namespace internal
/** \class DenseBase
* \ingroup Core_Module
*
* \brief Base class for all dense matrices, vectors, and arrays
*
* This class is the base that is inherited by all dense objects (matrix, vector, arrays,
* and related expression types). The common Eigen API for dense objects is contained in this class.
*
* \tparam Derived is the derived type, e.g., a matrix type or an expression.
*
* This class can be extended with the help of the plugin mechanism described on the page
* \ref TopicCustomizing_Plugins by defining the preprocessor symbol \c EIGEN_DENSEBASE_PLUGIN.
*
* \sa \blank \ref TopicClassHierarchy
*/
template<typename Derived> class DenseBase
#ifndef EIGEN_PARSED_BY_DOXYGEN
: public DenseCoeffsBase<Derived>
#else
: public DenseCoeffsBase<Derived,DirectWriteAccessors>
#endif // not EIGEN_PARSED_BY_DOXYGEN
{
public:
/** Inner iterator type to iterate over the coefficients of a row or column.
* \sa class InnerIterator
*/
typedef Eigen::InnerIterator<Derived> InnerIterator;
typedef typename internal::traits<Derived>::StorageKind StorageKind;
/**
* \brief The type used to store indices
* \details This typedef is relevant for types that store multiple indices such as
* PermutationMatrix or Transpositions, otherwise it defaults to Eigen::Index
* \sa \blank \ref TopicPreprocessorDirectives, Eigen::Index, SparseMatrixBase.
*/
typedef typename internal::traits<Derived>::StorageIndex StorageIndex;
/** The numeric type of the expression' coefficients, e.g. float, double, int or std::complex<float>, etc. */
typedef typename internal::traits<Derived>::Scalar Scalar;
/** The numeric type of the expression' coefficients, e.g. float, double, int or std::complex<float>, etc.
*
* It is an alias for the Scalar type */
typedef Scalar value_type;
typedef typename NumTraits<Scalar>::Real RealScalar;
typedef DenseCoeffsBase<Derived> Base;
using Base::derived;
using Base::const_cast_derived;
using Base::rows;
using Base::cols;
using Base::size;
using Base::rowIndexByOuterInner;
using Base::colIndexByOuterInner;
using Base::coeff;
using Base::coeffByOuterInner;
using Base::operator();
using Base::operator[];
using Base::x;
using Base::y;
using Base::z;
using Base::w;
using Base::stride;
using Base::innerStride;
using Base::outerStride;
using Base::rowStride;
using Base::colStride;
typedef typename Base::CoeffReturnType CoeffReturnType;
enum {
RowsAtCompileTime = internal::traits<Derived>::RowsAtCompileTime,
/**< The number of rows at compile-time. This is just a copy of the value provided
* by the \a Derived type. If a value is not known at compile-time,
* it is set to the \a Dynamic constant.
* \sa MatrixBase::rows(), MatrixBase::cols(), ColsAtCompileTime, SizeAtCompileTime */
ColsAtCompileTime = internal::traits<Derived>::ColsAtCompileTime,
/**< The number of columns at compile-time. This is just a copy of the value provided
* by the \a Derived type. If a value is not known at compile-time,
* it is set to the \a Dynamic constant.
* \sa MatrixBase::rows(), MatrixBase::cols(), RowsAtCompileTime, SizeAtCompileTime */
SizeAtCompileTime = (internal::size_at_compile_time<internal::traits<Derived>::RowsAtCompileTime,
internal::traits<Derived>::ColsAtCompileTime>::ret),
/**< This is equal to the number of coefficients, i.e. the number of
* rows times the number of columns, or to \a Dynamic if this is not
* known at compile-time. \sa RowsAtCompileTime, ColsAtCompileTime */
MaxRowsAtCompileTime = internal::traits<Derived>::MaxRowsAtCompileTime,
/**< This value is equal to the maximum possible number of rows that this expression
* might have. If this expression might have an arbitrarily high number of rows,
* this value is set to \a Dynamic.
*
* This value is useful to know when evaluating an expression, in order to determine
* whether it is possible to avoid doing a dynamic memory allocation.
*
* \sa RowsAtCompileTime, MaxColsAtCompileTime, MaxSizeAtCompileTime
*/
MaxColsAtCompileTime = internal::traits<Derived>::MaxColsAtCompileTime,
/**< This value is equal to the maximum possible number of columns that this expression
* might have. If this expression might have an arbitrarily high number of columns,
* this value is set to \a Dynamic.
*
* This value is useful to know when evaluating an expression, in order to determine
* whether it is possible to avoid doing a dynamic memory allocation.
*
* \sa ColsAtCompileTime, MaxRowsAtCompileTime, MaxSizeAtCompileTime
*/
MaxSizeAtCompileTime = (internal::size_at_compile_time<internal::traits<Derived>::MaxRowsAtCompileTime,
internal::traits<Derived>::MaxColsAtCompileTime>::ret),
/**< This value is equal to the maximum possible number of coefficients that this expression
* might have. If this expression might have an arbitrarily high number of coefficients,
* this value is set to \a Dynamic.
*
* This value is useful to know when evaluating an expression, in order to determine
* whether it is possible to avoid doing a dynamic memory allocation.
*
* \sa SizeAtCompileTime, MaxRowsAtCompileTime, MaxColsAtCompileTime
*/
IsVectorAtCompileTime = internal::traits<Derived>::MaxRowsAtCompileTime == 1
|| internal::traits<Derived>::MaxColsAtCompileTime == 1,
/**< This is set to true if either the number of rows or the number of
* columns is known at compile-time to be equal to 1. Indeed, in that case,
* we are dealing with a column-vector (if there is only one column) or with
* a row-vector (if there is only one row). */
Flags = internal::traits<Derived>::Flags,
/**< This stores expression \ref flags flags which may or may not be inherited by new expressions
* constructed from this one. See the \ref flags "list of flags".
*/
IsRowMajor = int(Flags) & RowMajorBit, /**< True if this expression has row-major storage order. */
InnerSizeAtCompileTime = int(IsVectorAtCompileTime) ? int(SizeAtCompileTime)
: int(IsRowMajor) ? int(ColsAtCompileTime) : int(RowsAtCompileTime),
InnerStrideAtCompileTime = internal::inner_stride_at_compile_time<Derived>::ret,
OuterStrideAtCompileTime = internal::outer_stride_at_compile_time<Derived>::ret
};
typedef typename internal::find_best_packet<Scalar,SizeAtCompileTime>::type PacketScalar;
enum { IsPlainObjectBase = 0 };
/** The plain matrix type corresponding to this expression.
* \sa PlainObject */
typedef Matrix<typename internal::traits<Derived>::Scalar,
internal::traits<Derived>::RowsAtCompileTime,
internal::traits<Derived>::ColsAtCompileTime,
AutoAlign | (internal::traits<Derived>::Flags&RowMajorBit ? RowMajor : ColMajor),
internal::traits<Derived>::MaxRowsAtCompileTime,
internal::traits<Derived>::MaxColsAtCompileTime
> PlainMatrix;
/** The plain array type corresponding to this expression.
* \sa PlainObject */
typedef Array<typename internal::traits<Derived>::Scalar,
internal::traits<Derived>::RowsAtCompileTime,
internal::traits<Derived>::ColsAtCompileTime,
AutoAlign | (internal::traits<Derived>::Flags&RowMajorBit ? RowMajor : ColMajor),
internal::traits<Derived>::MaxRowsAtCompileTime,
internal::traits<Derived>::MaxColsAtCompileTime
> PlainArray;
/** \brief The plain matrix or array type corresponding to this expression.
*
* This is not necessarily exactly the return type of eval(). In the case of plain matrices,
* the return type of eval() is a const reference to a matrix, not a matrix! It is however guaranteed
* that the return type of eval() is either PlainObject or const PlainObject&.
*/
typedef typename internal::conditional<internal::is_same<typename internal::traits<Derived>::XprKind,MatrixXpr >::value,
PlainMatrix, PlainArray>::type PlainObject;
/** \returns the number of nonzero coefficients which is in practice the number
* of stored coefficients. */
EIGEN_DEVICE_FUNC
inline Index nonZeros() const { return size(); }
/** \returns the outer size.
*
* \note For a vector, this returns just 1. For a matrix (non-vector), this is the major dimension
* with respect to the \ref TopicStorageOrders "storage order", i.e., the number of columns for a
* column-major matrix, and the number of rows for a row-major matrix. */
EIGEN_DEVICE_FUNC
Index outerSize() const
{
return IsVectorAtCompileTime ? 1
: int(IsRowMajor) ? this->rows() : this->cols();
}
/** \returns the inner size.
*
* \note For a vector, this is just the size. For a matrix (non-vector), this is the minor dimension
* with respect to the \ref TopicStorageOrders "storage order", i.e., the number of rows for a
* column-major matrix, and the number of columns for a row-major matrix. */
EIGEN_DEVICE_FUNC
Index innerSize() const
{
return IsVectorAtCompileTime ? this->size()
: int(IsRowMajor) ? this->cols() : this->rows();
}
/** Only plain matrices/arrays, not expressions, may be resized; therefore the only useful resize methods are
* Matrix::resize() and Array::resize(). The present method only asserts that the new size equals the old size, and does
* nothing else.
*/
EIGEN_DEVICE_FUNC
void resize(Index newSize)
{
EIGEN_ONLY_USED_FOR_DEBUG(newSize);
eigen_assert(newSize == this->size()
&& "DenseBase::resize() does not actually allow to resize.");
}
/** Only plain matrices/arrays, not expressions, may be resized; therefore the only useful resize methods are
* Matrix::resize() and Array::resize(). The present method only asserts that the new size equals the old size, and does
* nothing else.
*/
EIGEN_DEVICE_FUNC
void resize(Index rows, Index cols)
{
EIGEN_ONLY_USED_FOR_DEBUG(rows);
EIGEN_ONLY_USED_FOR_DEBUG(cols);
eigen_assert(rows == this->rows() && cols == this->cols()
&& "DenseBase::resize() does not actually allow to resize.");
}
#ifndef EIGEN_PARSED_BY_DOXYGEN
/** \internal Represents a matrix with all coefficients equal to one another*/
typedef CwiseNullaryOp<internal::scalar_constant_op<Scalar>,PlainObject> ConstantReturnType;
/** \internal \deprecated Represents a vector with linearly spaced coefficients that allows sequential access only. */
typedef CwiseNullaryOp<internal::linspaced_op<Scalar,PacketScalar>,PlainObject> SequentialLinSpacedReturnType;
/** \internal Represents a vector with linearly spaced coefficients that allows random access. */
typedef CwiseNullaryOp<internal::linspaced_op<Scalar,PacketScalar>,PlainObject> RandomAccessLinSpacedReturnType;
/** \internal the return type of MatrixBase::eigenvalues() */
typedef Matrix<typename NumTraits<typename internal::traits<Derived>::Scalar>::Real, internal::traits<Derived>::ColsAtCompileTime, 1> EigenvaluesReturnType;
#endif // not EIGEN_PARSED_BY_DOXYGEN
/** Copies \a other into *this. \returns a reference to *this. */
template<typename OtherDerived>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
Derived& operator=(const DenseBase<OtherDerived>& other);
/** Special case of the template operator=, in order to prevent the compiler
* from generating a default operator= (issue hit with g++ 4.1)
*/
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
Derived& operator=(const DenseBase& other);
template<typename OtherDerived>
EIGEN_DEVICE_FUNC
Derived& operator=(const EigenBase<OtherDerived> &other);
template<typename OtherDerived>
EIGEN_DEVICE_FUNC
Derived& operator+=(const EigenBase<OtherDerived> &other);
template<typename OtherDerived>
EIGEN_DEVICE_FUNC
Derived& operator-=(const EigenBase<OtherDerived> &other);
template<typename OtherDerived>
EIGEN_DEVICE_FUNC
Derived& operator=(const ReturnByValue<OtherDerived>& func);
/** \internal
* Copies \a other into *this without evaluating other. \returns a reference to *this.
* \deprecated */
template<typename OtherDerived>
EIGEN_DEVICE_FUNC
Derived& lazyAssign(const DenseBase<OtherDerived>& other);
EIGEN_DEVICE_FUNC
CommaInitializer<Derived> operator<< (const Scalar& s);
/** \deprecated it now returns \c *this */
template<unsigned int Added,unsigned int Removed>
EIGEN_DEPRECATED
const Derived& flagged() const
{ return derived(); }
template<typename OtherDerived>
EIGEN_DEVICE_FUNC
CommaInitializer<Derived> operator<< (const DenseBase<OtherDerived>& other);
typedef Transpose<Derived> TransposeReturnType;
EIGEN_DEVICE_FUNC
TransposeReturnType transpose();
typedef typename internal::add_const<Transpose<const Derived> >::type ConstTransposeReturnType;
EIGEN_DEVICE_FUNC
ConstTransposeReturnType transpose() const;
EIGEN_DEVICE_FUNC
void transposeInPlace();
EIGEN_DEVICE_FUNC static const ConstantReturnType
Constant(Index rows, Index cols, const Scalar& value);
EIGEN_DEVICE_FUNC static const ConstantReturnType
Constant(Index size, const Scalar& value);
EIGEN_DEVICE_FUNC static const ConstantReturnType
Constant(const Scalar& value);
EIGEN_DEVICE_FUNC static const SequentialLinSpacedReturnType
LinSpaced(Sequential_t, Index size, const Scalar& low, const Scalar& high);
EIGEN_DEVICE_FUNC static const RandomAccessLinSpacedReturnType
LinSpaced(Index size, const Scalar& low, const Scalar& high);
EIGEN_DEVICE_FUNC static const SequentialLinSpacedReturnType
LinSpaced(Sequential_t, const Scalar& low, const Scalar& high);
EIGEN_DEVICE_FUNC static const RandomAccessLinSpacedReturnType
LinSpaced(const Scalar& low, const Scalar& high);
template<typename CustomNullaryOp> EIGEN_DEVICE_FUNC
static const CwiseNullaryOp<CustomNullaryOp, PlainObject>
NullaryExpr(Index rows, Index cols, const CustomNullaryOp& func);
template<typename CustomNullaryOp> EIGEN_DEVICE_FUNC
static const CwiseNullaryOp<CustomNullaryOp, PlainObject>
NullaryExpr(Index size, const CustomNullaryOp& func);
template<typename CustomNullaryOp> EIGEN_DEVICE_FUNC
static const CwiseNullaryOp<CustomNullaryOp, PlainObject>
NullaryExpr(const CustomNullaryOp& func);
EIGEN_DEVICE_FUNC static const ConstantReturnType Zero(Index rows, Index cols);
EIGEN_DEVICE_FUNC static const ConstantReturnType Zero(Index size);
EIGEN_DEVICE_FUNC static const ConstantReturnType Zero();
EIGEN_DEVICE_FUNC static const ConstantReturnType Ones(Index rows, Index cols);
EIGEN_DEVICE_FUNC static const ConstantReturnType Ones(Index size);
EIGEN_DEVICE_FUNC static const ConstantReturnType Ones();
EIGEN_DEVICE_FUNC void fill(const Scalar& value);
EIGEN_DEVICE_FUNC Derived& setConstant(const Scalar& value);
EIGEN_DEVICE_FUNC Derived& setLinSpaced(Index size, const Scalar& low, const Scalar& high);
EIGEN_DEVICE_FUNC Derived& setLinSpaced(const Scalar& low, const Scalar& high);
EIGEN_DEVICE_FUNC Derived& setZero();
EIGEN_DEVICE_FUNC Derived& setOnes();
EIGEN_DEVICE_FUNC Derived& setRandom();
template<typename OtherDerived> EIGEN_DEVICE_FUNC
bool isApprox(const DenseBase<OtherDerived>& other,
const RealScalar& prec = NumTraits<Scalar>::dummy_precision()) const;
EIGEN_DEVICE_FUNC
bool isMuchSmallerThan(const RealScalar& other,
const RealScalar& prec = NumTraits<Scalar>::dummy_precision()) const;
template<typename OtherDerived> EIGEN_DEVICE_FUNC
bool isMuchSmallerThan(const DenseBase<OtherDerived>& other,
const RealScalar& prec = NumTraits<Scalar>::dummy_precision()) const;
EIGEN_DEVICE_FUNC bool isApproxToConstant(const Scalar& value, const RealScalar& prec = NumTraits<Scalar>::dummy_precision()) const;
EIGEN_DEVICE_FUNC bool isConstant(const Scalar& value, const RealScalar& prec = NumTraits<Scalar>::dummy_precision()) const;
EIGEN_DEVICE_FUNC bool isZero(const RealScalar& prec = NumTraits<Scalar>::dummy_precision()) const;
EIGEN_DEVICE_FUNC bool isOnes(const RealScalar& prec = NumTraits<Scalar>::dummy_precision()) const;
inline bool hasNaN() const;
inline bool allFinite() const;
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
Derived& operator*=(const Scalar& other);
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
Derived& operator/=(const Scalar& other);
typedef typename internal::add_const_on_value_type<typename internal::eval<Derived>::type>::type EvalReturnType;
/** \returns the matrix or vector obtained by evaluating this expression.
*
* Notice that in the case of a plain matrix or vector (not an expression) this function just returns
* a const reference, in order to avoid a useless copy.
*
* \warning Be carefull with eval() and the auto C++ keyword, as detailed in this \link TopicPitfalls_auto_keyword page \endlink.
*/
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE EvalReturnType eval() const
{
// Even though MSVC does not honor strong inlining when the return type
// is a dynamic matrix, we desperately need strong inlining for fixed
// size types on MSVC.
return typename internal::eval<Derived>::type(derived());
}
/** swaps *this with the expression \a other.
*
*/
template<typename OtherDerived>
EIGEN_DEVICE_FUNC
void swap(const DenseBase<OtherDerived>& other)
{
EIGEN_STATIC_ASSERT(!OtherDerived::IsPlainObjectBase,THIS_EXPRESSION_IS_NOT_A_LVALUE__IT_IS_READ_ONLY);
eigen_assert(rows()==other.rows() && cols()==other.cols());
call_assignment(derived(), other.const_cast_derived(), internal::swap_assign_op<Scalar>());
}
/** swaps *this with the matrix or array \a other.
*
*/
template<typename OtherDerived>
EIGEN_DEVICE_FUNC
void swap(PlainObjectBase<OtherDerived>& other)
{
eigen_assert(rows()==other.rows() && cols()==other.cols());
call_assignment(derived(), other.derived(), internal::swap_assign_op<Scalar>());
}
EIGEN_DEVICE_FUNC inline const NestByValue<Derived> nestByValue() const;
EIGEN_DEVICE_FUNC inline const ForceAlignedAccess<Derived> forceAlignedAccess() const;
EIGEN_DEVICE_FUNC inline ForceAlignedAccess<Derived> forceAlignedAccess();
template<bool Enable> EIGEN_DEVICE_FUNC
inline const typename internal::conditional<Enable,ForceAlignedAccess<Derived>,Derived&>::type forceAlignedAccessIf() const;
template<bool Enable> EIGEN_DEVICE_FUNC
inline typename internal::conditional<Enable,ForceAlignedAccess<Derived>,Derived&>::type forceAlignedAccessIf();
EIGEN_DEVICE_FUNC Scalar sum() const;
EIGEN_DEVICE_FUNC Scalar mean() const;
EIGEN_DEVICE_FUNC Scalar trace() const;
EIGEN_DEVICE_FUNC Scalar prod() const;
EIGEN_DEVICE_FUNC typename internal::traits<Derived>::Scalar minCoeff() const;
EIGEN_DEVICE_FUNC typename internal::traits<Derived>::Scalar maxCoeff() const;
template<typename IndexType> EIGEN_DEVICE_FUNC
typename internal::traits<Derived>::Scalar minCoeff(IndexType* row, IndexType* col) const;
template<typename IndexType> EIGEN_DEVICE_FUNC
typename internal::traits<Derived>::Scalar maxCoeff(IndexType* row, IndexType* col) const;
template<typename IndexType> EIGEN_DEVICE_FUNC
typename internal::traits<Derived>::Scalar minCoeff(IndexType* index) const;
template<typename IndexType> EIGEN_DEVICE_FUNC
typename internal::traits<Derived>::Scalar maxCoeff(IndexType* index) const;
template<typename BinaryOp>
EIGEN_DEVICE_FUNC
Scalar redux(const BinaryOp& func) const;
template<typename Visitor>
EIGEN_DEVICE_FUNC
void visit(Visitor& func) const;
/** \returns a WithFormat proxy object allowing to print a matrix the with given
* format \a fmt.
*
* See class IOFormat for some examples.
*
* \sa class IOFormat, class WithFormat
*/
inline const WithFormat<Derived> format(const IOFormat& fmt) const
{
return WithFormat<Derived>(derived(), fmt);
}
/** \returns the unique coefficient of a 1x1 expression */
EIGEN_DEVICE_FUNC
CoeffReturnType value() const
{
EIGEN_STATIC_ASSERT_SIZE_1x1(Derived)
eigen_assert(this->rows() == 1 && this->cols() == 1);
return derived().coeff(0,0);
}
EIGEN_DEVICE_FUNC bool all() const;
EIGEN_DEVICE_FUNC bool any() const;
EIGEN_DEVICE_FUNC Index count() const;
typedef VectorwiseOp<Derived, Horizontal> RowwiseReturnType;
typedef const VectorwiseOp<const Derived, Horizontal> ConstRowwiseReturnType;
typedef VectorwiseOp<Derived, Vertical> ColwiseReturnType;
typedef const VectorwiseOp<const Derived, Vertical> ConstColwiseReturnType;
/** \returns a VectorwiseOp wrapper of *this providing additional partial reduction operations
*
* Example: \include MatrixBase_rowwise.cpp
* Output: \verbinclude MatrixBase_rowwise.out
*
* \sa colwise(), class VectorwiseOp, \ref TutorialReductionsVisitorsBroadcasting
*/
//Code moved here due to a CUDA compiler bug
EIGEN_DEVICE_FUNC inline ConstRowwiseReturnType rowwise() const {
return ConstRowwiseReturnType(derived());
}
EIGEN_DEVICE_FUNC RowwiseReturnType rowwise();
/** \returns a VectorwiseOp wrapper of *this providing additional partial reduction operations
*
* Example: \include MatrixBase_colwise.cpp
* Output: \verbinclude MatrixBase_colwise.out
*
* \sa rowwise(), class VectorwiseOp, \ref TutorialReductionsVisitorsBroadcasting
*/
EIGEN_DEVICE_FUNC inline ConstColwiseReturnType colwise() const {
return ConstColwiseReturnType(derived());
}
EIGEN_DEVICE_FUNC ColwiseReturnType colwise();
typedef CwiseNullaryOp<internal::scalar_random_op<Scalar>,PlainObject> RandomReturnType;
static const RandomReturnType Random(Index rows, Index cols);
static const RandomReturnType Random(Index size);
static const RandomReturnType Random();
template<typename ThenDerived,typename ElseDerived>
const Select<Derived,ThenDerived,ElseDerived>
select(const DenseBase<ThenDerived>& thenMatrix,
const DenseBase<ElseDerived>& elseMatrix) const;
template<typename ThenDerived>
inline const Select<Derived,ThenDerived, typename ThenDerived::ConstantReturnType>
select(const DenseBase<ThenDerived>& thenMatrix, const typename ThenDerived::Scalar& elseScalar) const;
template<typename ElseDerived>
inline const Select<Derived, typename ElseDerived::ConstantReturnType, ElseDerived >
select(const typename ElseDerived::Scalar& thenScalar, const DenseBase<ElseDerived>& elseMatrix) const;
template<int p> RealScalar lpNorm() const;
template<int RowFactor, int ColFactor>
EIGEN_DEVICE_FUNC
const Replicate<Derived,RowFactor,ColFactor> replicate() const;
/**
* \return an expression of the replication of \c *this
*
* Example: \include MatrixBase_replicate_int_int.cpp
* Output: \verbinclude MatrixBase_replicate_int_int.out
*
* \sa VectorwiseOp::replicate(), DenseBase::replicate<int,int>(), class Replicate
*/
//Code moved here due to a CUDA compiler bug
EIGEN_DEVICE_FUNC
const Replicate<Derived, Dynamic, Dynamic> replicate(Index rowFactor, Index colFactor) const
{
return Replicate<Derived, Dynamic, Dynamic>(derived(), rowFactor, colFactor);
}
typedef Reverse<Derived, BothDirections> ReverseReturnType;
typedef const Reverse<const Derived, BothDirections> ConstReverseReturnType;
EIGEN_DEVICE_FUNC ReverseReturnType reverse();
/** This is the const version of reverse(). */
//Code moved here due to a CUDA compiler bug
EIGEN_DEVICE_FUNC ConstReverseReturnType reverse() const
{
return ConstReverseReturnType(derived());
}
EIGEN_DEVICE_FUNC void reverseInPlace();
#define EIGEN_CURRENT_STORAGE_BASE_CLASS Eigen::DenseBase
#define EIGEN_DOC_BLOCK_ADDONS_NOT_INNER_PANEL
#define EIGEN_DOC_BLOCK_ADDONS_INNER_PANEL_IF(COND)
# include "../plugins/BlockMethods.h"
# ifdef EIGEN_DENSEBASE_PLUGIN
# include EIGEN_DENSEBASE_PLUGIN
# endif
#undef EIGEN_CURRENT_STORAGE_BASE_CLASS
#undef EIGEN_DOC_BLOCK_ADDONS_NOT_INNER_PANEL
#undef EIGEN_DOC_BLOCK_ADDONS_INNER_PANEL_IF
// disable the use of evalTo for dense objects with a nice compilation error
template<typename Dest>
EIGEN_DEVICE_FUNC
inline void evalTo(Dest& ) const
{
EIGEN_STATIC_ASSERT((internal::is_same<Dest,void>::value),THE_EVAL_EVALTO_FUNCTION_SHOULD_NEVER_BE_CALLED_FOR_DENSE_OBJECTS);
}
protected:
/** Default constructor. Do nothing. */
EIGEN_DEVICE_FUNC DenseBase()
{
/* Just checks for self-consistency of the flags.
* Only do it when debugging Eigen, as this borders on paranoiac and could slow compilation down
*/
#ifdef EIGEN_INTERNAL_DEBUGGING
EIGEN_STATIC_ASSERT((EIGEN_IMPLIES(MaxRowsAtCompileTime==1 && MaxColsAtCompileTime!=1, int(IsRowMajor))
&& EIGEN_IMPLIES(MaxColsAtCompileTime==1 && MaxRowsAtCompileTime!=1, int(!IsRowMajor))),
INVALID_STORAGE_ORDER_FOR_THIS_VECTOR_EXPRESSION)
#endif
}
private:
EIGEN_DEVICE_FUNC explicit DenseBase(int);
EIGEN_DEVICE_FUNC DenseBase(int,int);
template<typename OtherDerived> EIGEN_DEVICE_FUNC explicit DenseBase(const DenseBase<OtherDerived>&);
};
} // end namespace Eigen
#endif // EIGEN_DENSEBASE_H

View File

@@ -0,0 +1,681 @@
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2006-2010 Benoit Jacob <jacob.benoit.1@gmail.com>
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#ifndef EIGEN_DENSECOEFFSBASE_H
#define EIGEN_DENSECOEFFSBASE_H
namespace Eigen {
namespace internal {
template<typename T> struct add_const_on_value_type_if_arithmetic
{
typedef typename conditional<is_arithmetic<T>::value, T, typename add_const_on_value_type<T>::type>::type type;
};
}
/** \brief Base class providing read-only coefficient access to matrices and arrays.
* \ingroup Core_Module
* \tparam Derived Type of the derived class
* \tparam #ReadOnlyAccessors Constant indicating read-only access
*
* This class defines the \c operator() \c const function and friends, which can be used to read specific
* entries of a matrix or array.
*
* \sa DenseCoeffsBase<Derived, WriteAccessors>, DenseCoeffsBase<Derived, DirectAccessors>,
* \ref TopicClassHierarchy
*/
template<typename Derived>
class DenseCoeffsBase<Derived,ReadOnlyAccessors> : public EigenBase<Derived>
{
public:
typedef typename internal::traits<Derived>::StorageKind StorageKind;
typedef typename internal::traits<Derived>::Scalar Scalar;
typedef typename internal::packet_traits<Scalar>::type PacketScalar;
// Explanation for this CoeffReturnType typedef.
// - This is the return type of the coeff() method.
// - The LvalueBit means exactly that we can offer a coeffRef() method, which means exactly that we can get references
// to coeffs, which means exactly that we can have coeff() return a const reference (as opposed to returning a value).
// - The is_artihmetic check is required since "const int", "const double", etc. will cause warnings on some systems
// while the declaration of "const T", where T is a non arithmetic type does not. Always returning "const Scalar&" is
// not possible, since the underlying expressions might not offer a valid address the reference could be referring to.
typedef typename internal::conditional<bool(internal::traits<Derived>::Flags&LvalueBit),
const Scalar&,
typename internal::conditional<internal::is_arithmetic<Scalar>::value, Scalar, const Scalar>::type
>::type CoeffReturnType;
typedef typename internal::add_const_on_value_type_if_arithmetic<
typename internal::packet_traits<Scalar>::type
>::type PacketReturnType;
typedef EigenBase<Derived> Base;
using Base::rows;
using Base::cols;
using Base::size;
using Base::derived;
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Index rowIndexByOuterInner(Index outer, Index inner) const
{
return int(Derived::RowsAtCompileTime) == 1 ? 0
: int(Derived::ColsAtCompileTime) == 1 ? inner
: int(Derived::Flags)&RowMajorBit ? outer
: inner;
}
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Index colIndexByOuterInner(Index outer, Index inner) const
{
return int(Derived::ColsAtCompileTime) == 1 ? 0
: int(Derived::RowsAtCompileTime) == 1 ? inner
: int(Derived::Flags)&RowMajorBit ? inner
: outer;
}
/** Short version: don't use this function, use
* \link operator()(Index,Index) const \endlink instead.
*
* Long version: this function is similar to
* \link operator()(Index,Index) const \endlink, but without the assertion.
* Use this for limiting the performance cost of debugging code when doing
* repeated coefficient access. Only use this when it is guaranteed that the
* parameters \a row and \a col are in range.
*
* If EIGEN_INTERNAL_DEBUGGING is defined, an assertion will be made, making this
* function equivalent to \link operator()(Index,Index) const \endlink.
*
* \sa operator()(Index,Index) const, coeffRef(Index,Index), coeff(Index) const
*/
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE CoeffReturnType coeff(Index row, Index col) const
{
eigen_internal_assert(row >= 0 && row < rows()
&& col >= 0 && col < cols());
return internal::evaluator<Derived>(derived()).coeff(row,col);
}
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE CoeffReturnType coeffByOuterInner(Index outer, Index inner) const
{
return coeff(rowIndexByOuterInner(outer, inner),
colIndexByOuterInner(outer, inner));
}
/** \returns the coefficient at given the given row and column.
*
* \sa operator()(Index,Index), operator[](Index)
*/
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE CoeffReturnType operator()(Index row, Index col) const
{
eigen_assert(row >= 0 && row < rows()
&& col >= 0 && col < cols());
return coeff(row, col);
}
/** Short version: don't use this function, use
* \link operator[](Index) const \endlink instead.
*
* Long version: this function is similar to
* \link operator[](Index) const \endlink, but without the assertion.
* Use this for limiting the performance cost of debugging code when doing
* repeated coefficient access. Only use this when it is guaranteed that the
* parameter \a index is in range.
*
* If EIGEN_INTERNAL_DEBUGGING is defined, an assertion will be made, making this
* function equivalent to \link operator[](Index) const \endlink.
*
* \sa operator[](Index) const, coeffRef(Index), coeff(Index,Index) const
*/
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE CoeffReturnType
coeff(Index index) const
{
EIGEN_STATIC_ASSERT(internal::evaluator<Derived>::Flags & LinearAccessBit,
THIS_COEFFICIENT_ACCESSOR_TAKING_ONE_ACCESS_IS_ONLY_FOR_EXPRESSIONS_ALLOWING_LINEAR_ACCESS)
eigen_internal_assert(index >= 0 && index < size());
return internal::evaluator<Derived>(derived()).coeff(index);
}
/** \returns the coefficient at given index.
*
* This method is allowed only for vector expressions, and for matrix expressions having the LinearAccessBit.
*
* \sa operator[](Index), operator()(Index,Index) const, x() const, y() const,
* z() const, w() const
*/
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE CoeffReturnType
operator[](Index index) const
{
EIGEN_STATIC_ASSERT(Derived::IsVectorAtCompileTime,
THE_BRACKET_OPERATOR_IS_ONLY_FOR_VECTORS__USE_THE_PARENTHESIS_OPERATOR_INSTEAD)
eigen_assert(index >= 0 && index < size());
return coeff(index);
}
/** \returns the coefficient at given index.
*
* This is synonymous to operator[](Index) const.
*
* This method is allowed only for vector expressions, and for matrix expressions having the LinearAccessBit.
*
* \sa operator[](Index), operator()(Index,Index) const, x() const, y() const,
* z() const, w() const
*/
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE CoeffReturnType
operator()(Index index) const
{
eigen_assert(index >= 0 && index < size());
return coeff(index);
}
/** equivalent to operator[](0). */
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE CoeffReturnType
x() const { return (*this)[0]; }
/** equivalent to operator[](1). */
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE CoeffReturnType
y() const
{
EIGEN_STATIC_ASSERT(Derived::SizeAtCompileTime==-1 || Derived::SizeAtCompileTime>=2, OUT_OF_RANGE_ACCESS);
return (*this)[1];
}
/** equivalent to operator[](2). */
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE CoeffReturnType
z() const
{
EIGEN_STATIC_ASSERT(Derived::SizeAtCompileTime==-1 || Derived::SizeAtCompileTime>=3, OUT_OF_RANGE_ACCESS);
return (*this)[2];
}
/** equivalent to operator[](3). */
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE CoeffReturnType
w() const
{
EIGEN_STATIC_ASSERT(Derived::SizeAtCompileTime==-1 || Derived::SizeAtCompileTime>=4, OUT_OF_RANGE_ACCESS);
return (*this)[3];
}
/** \internal
* \returns the packet of coefficients starting at the given row and column. It is your responsibility
* to ensure that a packet really starts there. This method is only available on expressions having the
* PacketAccessBit.
*
* The \a LoadMode parameter may have the value \a #Aligned or \a #Unaligned. Its effect is to select
* the appropriate vectorization instruction. Aligned access is faster, but is only possible for packets
* starting at an address which is a multiple of the packet size.
*/
template<int LoadMode>
EIGEN_STRONG_INLINE PacketReturnType packet(Index row, Index col) const
{
typedef typename internal::packet_traits<Scalar>::type DefaultPacketType;
eigen_internal_assert(row >= 0 && row < rows() && col >= 0 && col < cols());
return internal::evaluator<Derived>(derived()).template packet<LoadMode,DefaultPacketType>(row,col);
}
/** \internal */
template<int LoadMode>
EIGEN_STRONG_INLINE PacketReturnType packetByOuterInner(Index outer, Index inner) const
{
return packet<LoadMode>(rowIndexByOuterInner(outer, inner),
colIndexByOuterInner(outer, inner));
}
/** \internal
* \returns the packet of coefficients starting at the given index. It is your responsibility
* to ensure that a packet really starts there. This method is only available on expressions having the
* PacketAccessBit and the LinearAccessBit.
*
* The \a LoadMode parameter may have the value \a #Aligned or \a #Unaligned. Its effect is to select
* the appropriate vectorization instruction. Aligned access is faster, but is only possible for packets
* starting at an address which is a multiple of the packet size.
*/
template<int LoadMode>
EIGEN_STRONG_INLINE PacketReturnType packet(Index index) const
{
EIGEN_STATIC_ASSERT(internal::evaluator<Derived>::Flags & LinearAccessBit,
THIS_COEFFICIENT_ACCESSOR_TAKING_ONE_ACCESS_IS_ONLY_FOR_EXPRESSIONS_ALLOWING_LINEAR_ACCESS)
typedef typename internal::packet_traits<Scalar>::type DefaultPacketType;
eigen_internal_assert(index >= 0 && index < size());
return internal::evaluator<Derived>(derived()).template packet<LoadMode,DefaultPacketType>(index);
}
protected:
// explanation: DenseBase is doing "using ..." on the methods from DenseCoeffsBase.
// But some methods are only available in the DirectAccess case.
// So we add dummy methods here with these names, so that "using... " doesn't fail.
// It's not private so that the child class DenseBase can access them, and it's not public
// either since it's an implementation detail, so has to be protected.
void coeffRef();
void coeffRefByOuterInner();
void writePacket();
void writePacketByOuterInner();
void copyCoeff();
void copyCoeffByOuterInner();
void copyPacket();
void copyPacketByOuterInner();
void stride();
void innerStride();
void outerStride();
void rowStride();
void colStride();
};
/** \brief Base class providing read/write coefficient access to matrices and arrays.
* \ingroup Core_Module
* \tparam Derived Type of the derived class
* \tparam #WriteAccessors Constant indicating read/write access
*
* This class defines the non-const \c operator() function and friends, which can be used to write specific
* entries of a matrix or array. This class inherits DenseCoeffsBase<Derived, ReadOnlyAccessors> which
* defines the const variant for reading specific entries.
*
* \sa DenseCoeffsBase<Derived, DirectAccessors>, \ref TopicClassHierarchy
*/
template<typename Derived>
class DenseCoeffsBase<Derived, WriteAccessors> : public DenseCoeffsBase<Derived, ReadOnlyAccessors>
{
public:
typedef DenseCoeffsBase<Derived, ReadOnlyAccessors> Base;
typedef typename internal::traits<Derived>::StorageKind StorageKind;
typedef typename internal::traits<Derived>::Scalar Scalar;
typedef typename internal::packet_traits<Scalar>::type PacketScalar;
typedef typename NumTraits<Scalar>::Real RealScalar;
using Base::coeff;
using Base::rows;
using Base::cols;
using Base::size;
using Base::derived;
using Base::rowIndexByOuterInner;
using Base::colIndexByOuterInner;
using Base::operator[];
using Base::operator();
using Base::x;
using Base::y;
using Base::z;
using Base::w;
/** Short version: don't use this function, use
* \link operator()(Index,Index) \endlink instead.
*
* Long version: this function is similar to
* \link operator()(Index,Index) \endlink, but without the assertion.
* Use this for limiting the performance cost of debugging code when doing
* repeated coefficient access. Only use this when it is guaranteed that the
* parameters \a row and \a col are in range.
*
* If EIGEN_INTERNAL_DEBUGGING is defined, an assertion will be made, making this
* function equivalent to \link operator()(Index,Index) \endlink.
*
* \sa operator()(Index,Index), coeff(Index, Index) const, coeffRef(Index)
*/
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Scalar& coeffRef(Index row, Index col)
{
eigen_internal_assert(row >= 0 && row < rows()
&& col >= 0 && col < cols());
return internal::evaluator<Derived>(derived()).coeffRef(row,col);
}
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Scalar&
coeffRefByOuterInner(Index outer, Index inner)
{
return coeffRef(rowIndexByOuterInner(outer, inner),
colIndexByOuterInner(outer, inner));
}
/** \returns a reference to the coefficient at given the given row and column.
*
* \sa operator[](Index)
*/
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Scalar&
operator()(Index row, Index col)
{
eigen_assert(row >= 0 && row < rows()
&& col >= 0 && col < cols());
return coeffRef(row, col);
}
/** Short version: don't use this function, use
* \link operator[](Index) \endlink instead.
*
* Long version: this function is similar to
* \link operator[](Index) \endlink, but without the assertion.
* Use this for limiting the performance cost of debugging code when doing
* repeated coefficient access. Only use this when it is guaranteed that the
* parameters \a row and \a col are in range.
*
* If EIGEN_INTERNAL_DEBUGGING is defined, an assertion will be made, making this
* function equivalent to \link operator[](Index) \endlink.
*
* \sa operator[](Index), coeff(Index) const, coeffRef(Index,Index)
*/
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Scalar&
coeffRef(Index index)
{
EIGEN_STATIC_ASSERT(internal::evaluator<Derived>::Flags & LinearAccessBit,
THIS_COEFFICIENT_ACCESSOR_TAKING_ONE_ACCESS_IS_ONLY_FOR_EXPRESSIONS_ALLOWING_LINEAR_ACCESS)
eigen_internal_assert(index >= 0 && index < size());
return internal::evaluator<Derived>(derived()).coeffRef(index);
}
/** \returns a reference to the coefficient at given index.
*
* This method is allowed only for vector expressions, and for matrix expressions having the LinearAccessBit.
*
* \sa operator[](Index) const, operator()(Index,Index), x(), y(), z(), w()
*/
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Scalar&
operator[](Index index)
{
EIGEN_STATIC_ASSERT(Derived::IsVectorAtCompileTime,
THE_BRACKET_OPERATOR_IS_ONLY_FOR_VECTORS__USE_THE_PARENTHESIS_OPERATOR_INSTEAD)
eigen_assert(index >= 0 && index < size());
return coeffRef(index);
}
/** \returns a reference to the coefficient at given index.
*
* This is synonymous to operator[](Index).
*
* This method is allowed only for vector expressions, and for matrix expressions having the LinearAccessBit.
*
* \sa operator[](Index) const, operator()(Index,Index), x(), y(), z(), w()
*/
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Scalar&
operator()(Index index)
{
eigen_assert(index >= 0 && index < size());
return coeffRef(index);
}
/** equivalent to operator[](0). */
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Scalar&
x() { return (*this)[0]; }
/** equivalent to operator[](1). */
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Scalar&
y()
{
EIGEN_STATIC_ASSERT(Derived::SizeAtCompileTime==-1 || Derived::SizeAtCompileTime>=2, OUT_OF_RANGE_ACCESS);
return (*this)[1];
}
/** equivalent to operator[](2). */
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Scalar&
z()
{
EIGEN_STATIC_ASSERT(Derived::SizeAtCompileTime==-1 || Derived::SizeAtCompileTime>=3, OUT_OF_RANGE_ACCESS);
return (*this)[2];
}
/** equivalent to operator[](3). */
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Scalar&
w()
{
EIGEN_STATIC_ASSERT(Derived::SizeAtCompileTime==-1 || Derived::SizeAtCompileTime>=4, OUT_OF_RANGE_ACCESS);
return (*this)[3];
}
};
/** \brief Base class providing direct read-only coefficient access to matrices and arrays.
* \ingroup Core_Module
* \tparam Derived Type of the derived class
* \tparam #DirectAccessors Constant indicating direct access
*
* This class defines functions to work with strides which can be used to access entries directly. This class
* inherits DenseCoeffsBase<Derived, ReadOnlyAccessors> which defines functions to access entries read-only using
* \c operator() .
*
* \sa \blank \ref TopicClassHierarchy
*/
template<typename Derived>
class DenseCoeffsBase<Derived, DirectAccessors> : public DenseCoeffsBase<Derived, ReadOnlyAccessors>
{
public:
typedef DenseCoeffsBase<Derived, ReadOnlyAccessors> Base;
typedef typename internal::traits<Derived>::Scalar Scalar;
typedef typename NumTraits<Scalar>::Real RealScalar;
using Base::rows;
using Base::cols;
using Base::size;
using Base::derived;
/** \returns the pointer increment between two consecutive elements within a slice in the inner direction.
*
* \sa outerStride(), rowStride(), colStride()
*/
EIGEN_DEVICE_FUNC
inline Index innerStride() const
{
return derived().innerStride();
}
/** \returns the pointer increment between two consecutive inner slices (for example, between two consecutive columns
* in a column-major matrix).
*
* \sa innerStride(), rowStride(), colStride()
*/
EIGEN_DEVICE_FUNC
inline Index outerStride() const
{
return derived().outerStride();
}
// FIXME shall we remove it ?
inline Index stride() const
{
return Derived::IsVectorAtCompileTime ? innerStride() : outerStride();
}
/** \returns the pointer increment between two consecutive rows.
*
* \sa innerStride(), outerStride(), colStride()
*/
EIGEN_DEVICE_FUNC
inline Index rowStride() const
{
return Derived::IsRowMajor ? outerStride() : innerStride();
}
/** \returns the pointer increment between two consecutive columns.
*
* \sa innerStride(), outerStride(), rowStride()
*/
EIGEN_DEVICE_FUNC
inline Index colStride() const
{
return Derived::IsRowMajor ? innerStride() : outerStride();
}
};
/** \brief Base class providing direct read/write coefficient access to matrices and arrays.
* \ingroup Core_Module
* \tparam Derived Type of the derived class
* \tparam #DirectWriteAccessors Constant indicating direct access
*
* This class defines functions to work with strides which can be used to access entries directly. This class
* inherits DenseCoeffsBase<Derived, WriteAccessors> which defines functions to access entries read/write using
* \c operator().
*
* \sa \blank \ref TopicClassHierarchy
*/
template<typename Derived>
class DenseCoeffsBase<Derived, DirectWriteAccessors>
: public DenseCoeffsBase<Derived, WriteAccessors>
{
public:
typedef DenseCoeffsBase<Derived, WriteAccessors> Base;
typedef typename internal::traits<Derived>::Scalar Scalar;
typedef typename NumTraits<Scalar>::Real RealScalar;
using Base::rows;
using Base::cols;
using Base::size;
using Base::derived;
/** \returns the pointer increment between two consecutive elements within a slice in the inner direction.
*
* \sa outerStride(), rowStride(), colStride()
*/
EIGEN_DEVICE_FUNC
inline Index innerStride() const
{
return derived().innerStride();
}
/** \returns the pointer increment between two consecutive inner slices (for example, between two consecutive columns
* in a column-major matrix).
*
* \sa innerStride(), rowStride(), colStride()
*/
EIGEN_DEVICE_FUNC
inline Index outerStride() const
{
return derived().outerStride();
}
// FIXME shall we remove it ?
inline Index stride() const
{
return Derived::IsVectorAtCompileTime ? innerStride() : outerStride();
}
/** \returns the pointer increment between two consecutive rows.
*
* \sa innerStride(), outerStride(), colStride()
*/
EIGEN_DEVICE_FUNC
inline Index rowStride() const
{
return Derived::IsRowMajor ? outerStride() : innerStride();
}
/** \returns the pointer increment between two consecutive columns.
*
* \sa innerStride(), outerStride(), rowStride()
*/
EIGEN_DEVICE_FUNC
inline Index colStride() const
{
return Derived::IsRowMajor ? innerStride() : outerStride();
}
};
namespace internal {
template<int Alignment, typename Derived, bool JustReturnZero>
struct first_aligned_impl
{
static inline Index run(const Derived&)
{ return 0; }
};
template<int Alignment, typename Derived>
struct first_aligned_impl<Alignment, Derived, false>
{
static inline Index run(const Derived& m)
{
return internal::first_aligned<Alignment>(m.data(), m.size());
}
};
/** \internal \returns the index of the first element of the array stored by \a m that is properly aligned with respect to \a Alignment for vectorization.
*
* \tparam Alignment requested alignment in Bytes.
*
* There is also the variant first_aligned(const Scalar*, Integer) defined in Memory.h. See it for more
* documentation.
*/
template<int Alignment, typename Derived>
static inline Index first_aligned(const DenseBase<Derived>& m)
{
enum { ReturnZero = (int(evaluator<Derived>::Alignment) >= Alignment) || !(Derived::Flags & DirectAccessBit) };
return first_aligned_impl<Alignment, Derived, ReturnZero>::run(m.derived());
}
template<typename Derived>
static inline Index first_default_aligned(const DenseBase<Derived>& m)
{
typedef typename Derived::Scalar Scalar;
typedef typename packet_traits<Scalar>::type DefaultPacketType;
return internal::first_aligned<int(unpacket_traits<DefaultPacketType>::alignment),Derived>(m);
}
template<typename Derived, bool HasDirectAccess = has_direct_access<Derived>::ret>
struct inner_stride_at_compile_time
{
enum { ret = traits<Derived>::InnerStrideAtCompileTime };
};
template<typename Derived>
struct inner_stride_at_compile_time<Derived, false>
{
enum { ret = 0 };
};
template<typename Derived, bool HasDirectAccess = has_direct_access<Derived>::ret>
struct outer_stride_at_compile_time
{
enum { ret = traits<Derived>::OuterStrideAtCompileTime };
};
template<typename Derived>
struct outer_stride_at_compile_time<Derived, false>
{
enum { ret = 0 };
};
} // end namespace internal
} // end namespace Eigen
#endif // EIGEN_DENSECOEFFSBASE_H

View File

@@ -0,0 +1,570 @@
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
// Copyright (C) 2006-2009 Benoit Jacob <jacob.benoit.1@gmail.com>
// Copyright (C) 2010-2013 Hauke Heibel <hauke.heibel@gmail.com>
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#ifndef EIGEN_MATRIXSTORAGE_H
#define EIGEN_MATRIXSTORAGE_H
#ifdef EIGEN_DENSE_STORAGE_CTOR_PLUGIN
#define EIGEN_INTERNAL_DENSE_STORAGE_CTOR_PLUGIN(X) X; EIGEN_DENSE_STORAGE_CTOR_PLUGIN;
#else
#define EIGEN_INTERNAL_DENSE_STORAGE_CTOR_PLUGIN(X)
#endif
namespace Eigen {
namespace internal {
struct constructor_without_unaligned_array_assert {};
template<typename T, int Size>
EIGEN_DEVICE_FUNC
void check_static_allocation_size()
{
// if EIGEN_STACK_ALLOCATION_LIMIT is defined to 0, then no limit
#if EIGEN_STACK_ALLOCATION_LIMIT
EIGEN_STATIC_ASSERT(Size * sizeof(T) <= EIGEN_STACK_ALLOCATION_LIMIT, OBJECT_ALLOCATED_ON_STACK_IS_TOO_BIG);
#endif
}
/** \internal
* Static array. If the MatrixOrArrayOptions require auto-alignment, the array will be automatically aligned:
* to 16 bytes boundary if the total size is a multiple of 16 bytes.
*/
template <typename T, int Size, int MatrixOrArrayOptions,
int Alignment = (MatrixOrArrayOptions&DontAlign) ? 0
: compute_default_alignment<T,Size>::value >
struct plain_array
{
T array[Size];
EIGEN_DEVICE_FUNC
plain_array()
{
check_static_allocation_size<T,Size>();
}
EIGEN_DEVICE_FUNC
plain_array(constructor_without_unaligned_array_assert)
{
check_static_allocation_size<T,Size>();
}
};
#if defined(EIGEN_DISABLE_UNALIGNED_ARRAY_ASSERT)
#define EIGEN_MAKE_UNALIGNED_ARRAY_ASSERT(sizemask)
#elif EIGEN_GNUC_AT_LEAST(4,7)
// GCC 4.7 is too aggressive in its optimizations and remove the alignement test based on the fact the array is declared to be aligned.
// See this bug report: http://gcc.gnu.org/bugzilla/show_bug.cgi?id=53900
// Hiding the origin of the array pointer behind a function argument seems to do the trick even if the function is inlined:
template<typename PtrType>
EIGEN_ALWAYS_INLINE PtrType eigen_unaligned_array_assert_workaround_gcc47(PtrType array) { return array; }
#define EIGEN_MAKE_UNALIGNED_ARRAY_ASSERT(sizemask) \
eigen_assert((internal::UIntPtr(eigen_unaligned_array_assert_workaround_gcc47(array)) & (sizemask)) == 0 \
&& "this assertion is explained here: " \
"http://eigen.tuxfamily.org/dox-devel/group__TopicUnalignedArrayAssert.html" \
" **** READ THIS WEB PAGE !!! ****");
#else
#define EIGEN_MAKE_UNALIGNED_ARRAY_ASSERT(sizemask) \
eigen_assert((internal::UIntPtr(array) & (sizemask)) == 0 \
&& "this assertion is explained here: " \
"http://eigen.tuxfamily.org/dox-devel/group__TopicUnalignedArrayAssert.html" \
" **** READ THIS WEB PAGE !!! ****");
#endif
template <typename T, int Size, int MatrixOrArrayOptions>
struct plain_array<T, Size, MatrixOrArrayOptions, 8>
{
EIGEN_ALIGN_TO_BOUNDARY(8) T array[Size];
EIGEN_DEVICE_FUNC
plain_array()
{
EIGEN_MAKE_UNALIGNED_ARRAY_ASSERT(7);
check_static_allocation_size<T,Size>();
}
EIGEN_DEVICE_FUNC
plain_array(constructor_without_unaligned_array_assert)
{
check_static_allocation_size<T,Size>();
}
};
template <typename T, int Size, int MatrixOrArrayOptions>
struct plain_array<T, Size, MatrixOrArrayOptions, 16>
{
EIGEN_ALIGN_TO_BOUNDARY(16) T array[Size];
EIGEN_DEVICE_FUNC
plain_array()
{
EIGEN_MAKE_UNALIGNED_ARRAY_ASSERT(15);
check_static_allocation_size<T,Size>();
}
EIGEN_DEVICE_FUNC
plain_array(constructor_without_unaligned_array_assert)
{
check_static_allocation_size<T,Size>();
}
};
template <typename T, int Size, int MatrixOrArrayOptions>
struct plain_array<T, Size, MatrixOrArrayOptions, 32>
{
EIGEN_ALIGN_TO_BOUNDARY(32) T array[Size];
EIGEN_DEVICE_FUNC
plain_array()
{
EIGEN_MAKE_UNALIGNED_ARRAY_ASSERT(31);
check_static_allocation_size<T,Size>();
}
EIGEN_DEVICE_FUNC
plain_array(constructor_without_unaligned_array_assert)
{
check_static_allocation_size<T,Size>();
}
};
template <typename T, int Size, int MatrixOrArrayOptions>
struct plain_array<T, Size, MatrixOrArrayOptions, 64>
{
EIGEN_ALIGN_TO_BOUNDARY(64) T array[Size];
EIGEN_DEVICE_FUNC
plain_array()
{
EIGEN_MAKE_UNALIGNED_ARRAY_ASSERT(63);
check_static_allocation_size<T,Size>();
}
EIGEN_DEVICE_FUNC
plain_array(constructor_without_unaligned_array_assert)
{
check_static_allocation_size<T,Size>();
}
};
template <typename T, int MatrixOrArrayOptions, int Alignment>
struct plain_array<T, 0, MatrixOrArrayOptions, Alignment>
{
T array[1];
EIGEN_DEVICE_FUNC plain_array() {}
EIGEN_DEVICE_FUNC plain_array(constructor_without_unaligned_array_assert) {}
};
} // end namespace internal
/** \internal
*
* \class DenseStorage
* \ingroup Core_Module
*
* \brief Stores the data of a matrix
*
* This class stores the data of fixed-size, dynamic-size or mixed matrices
* in a way as compact as possible.
*
* \sa Matrix
*/
template<typename T, int Size, int _Rows, int _Cols, int _Options> class DenseStorage;
// purely fixed-size matrix
template<typename T, int Size, int _Rows, int _Cols, int _Options> class DenseStorage
{
internal::plain_array<T,Size,_Options> m_data;
public:
EIGEN_DEVICE_FUNC DenseStorage() {
EIGEN_INTERNAL_DENSE_STORAGE_CTOR_PLUGIN(Index size = Size)
}
EIGEN_DEVICE_FUNC
explicit DenseStorage(internal::constructor_without_unaligned_array_assert)
: m_data(internal::constructor_without_unaligned_array_assert()) {}
EIGEN_DEVICE_FUNC
DenseStorage(const DenseStorage& other) : m_data(other.m_data) {
EIGEN_INTERNAL_DENSE_STORAGE_CTOR_PLUGIN(Index size = Size)
}
EIGEN_DEVICE_FUNC
DenseStorage& operator=(const DenseStorage& other)
{
if (this != &other) m_data = other.m_data;
return *this;
}
EIGEN_DEVICE_FUNC DenseStorage(Index size, Index rows, Index cols) {
EIGEN_INTERNAL_DENSE_STORAGE_CTOR_PLUGIN({})
eigen_internal_assert(size==rows*cols && rows==_Rows && cols==_Cols);
EIGEN_UNUSED_VARIABLE(size);
EIGEN_UNUSED_VARIABLE(rows);
EIGEN_UNUSED_VARIABLE(cols);
}
EIGEN_DEVICE_FUNC void swap(DenseStorage& other) { std::swap(m_data,other.m_data); }
EIGEN_DEVICE_FUNC static Index rows(void) {return _Rows;}
EIGEN_DEVICE_FUNC static Index cols(void) {return _Cols;}
EIGEN_DEVICE_FUNC void conservativeResize(Index,Index,Index) {}
EIGEN_DEVICE_FUNC void resize(Index,Index,Index) {}
EIGEN_DEVICE_FUNC const T *data() const { return m_data.array; }
EIGEN_DEVICE_FUNC T *data() { return m_data.array; }
};
// null matrix
template<typename T, int _Rows, int _Cols, int _Options> class DenseStorage<T, 0, _Rows, _Cols, _Options>
{
public:
EIGEN_DEVICE_FUNC DenseStorage() {}
EIGEN_DEVICE_FUNC explicit DenseStorage(internal::constructor_without_unaligned_array_assert) {}
EIGEN_DEVICE_FUNC DenseStorage(const DenseStorage&) {}
EIGEN_DEVICE_FUNC DenseStorage& operator=(const DenseStorage&) { return *this; }
EIGEN_DEVICE_FUNC DenseStorage(Index,Index,Index) {}
EIGEN_DEVICE_FUNC void swap(DenseStorage& ) {}
EIGEN_DEVICE_FUNC static Index rows(void) {return _Rows;}
EIGEN_DEVICE_FUNC static Index cols(void) {return _Cols;}
EIGEN_DEVICE_FUNC void conservativeResize(Index,Index,Index) {}
EIGEN_DEVICE_FUNC void resize(Index,Index,Index) {}
EIGEN_DEVICE_FUNC const T *data() const { return 0; }
EIGEN_DEVICE_FUNC T *data() { return 0; }
};
// more specializations for null matrices; these are necessary to resolve ambiguities
template<typename T, int _Options> class DenseStorage<T, 0, Dynamic, Dynamic, _Options>
: public DenseStorage<T, 0, 0, 0, _Options> { };
template<typename T, int _Rows, int _Options> class DenseStorage<T, 0, _Rows, Dynamic, _Options>
: public DenseStorage<T, 0, 0, 0, _Options> { };
template<typename T, int _Cols, int _Options> class DenseStorage<T, 0, Dynamic, _Cols, _Options>
: public DenseStorage<T, 0, 0, 0, _Options> { };
// dynamic-size matrix with fixed-size storage
template<typename T, int Size, int _Options> class DenseStorage<T, Size, Dynamic, Dynamic, _Options>
{
internal::plain_array<T,Size,_Options> m_data;
Index m_rows;
Index m_cols;
public:
EIGEN_DEVICE_FUNC DenseStorage() : m_rows(0), m_cols(0) {}
EIGEN_DEVICE_FUNC explicit DenseStorage(internal::constructor_without_unaligned_array_assert)
: m_data(internal::constructor_without_unaligned_array_assert()), m_rows(0), m_cols(0) {}
EIGEN_DEVICE_FUNC DenseStorage(const DenseStorage& other) : m_data(other.m_data), m_rows(other.m_rows), m_cols(other.m_cols) {}
EIGEN_DEVICE_FUNC DenseStorage& operator=(const DenseStorage& other)
{
if (this != &other)
{
m_data = other.m_data;
m_rows = other.m_rows;
m_cols = other.m_cols;
}
return *this;
}
EIGEN_DEVICE_FUNC DenseStorage(Index, Index rows, Index cols) : m_rows(rows), m_cols(cols) {}
EIGEN_DEVICE_FUNC void swap(DenseStorage& other)
{ std::swap(m_data,other.m_data); std::swap(m_rows,other.m_rows); std::swap(m_cols,other.m_cols); }
EIGEN_DEVICE_FUNC Index rows() const {return m_rows;}
EIGEN_DEVICE_FUNC Index cols() const {return m_cols;}
EIGEN_DEVICE_FUNC void conservativeResize(Index, Index rows, Index cols) { m_rows = rows; m_cols = cols; }
EIGEN_DEVICE_FUNC void resize(Index, Index rows, Index cols) { m_rows = rows; m_cols = cols; }
EIGEN_DEVICE_FUNC const T *data() const { return m_data.array; }
EIGEN_DEVICE_FUNC T *data() { return m_data.array; }
};
// dynamic-size matrix with fixed-size storage and fixed width
template<typename T, int Size, int _Cols, int _Options> class DenseStorage<T, Size, Dynamic, _Cols, _Options>
{
internal::plain_array<T,Size,_Options> m_data;
Index m_rows;
public:
EIGEN_DEVICE_FUNC DenseStorage() : m_rows(0) {}
EIGEN_DEVICE_FUNC explicit DenseStorage(internal::constructor_without_unaligned_array_assert)
: m_data(internal::constructor_without_unaligned_array_assert()), m_rows(0) {}
EIGEN_DEVICE_FUNC DenseStorage(const DenseStorage& other) : m_data(other.m_data), m_rows(other.m_rows) {}
EIGEN_DEVICE_FUNC DenseStorage& operator=(const DenseStorage& other)
{
if (this != &other)
{
m_data = other.m_data;
m_rows = other.m_rows;
}
return *this;
}
EIGEN_DEVICE_FUNC DenseStorage(Index, Index rows, Index) : m_rows(rows) {}
EIGEN_DEVICE_FUNC void swap(DenseStorage& other) { std::swap(m_data,other.m_data); std::swap(m_rows,other.m_rows); }
EIGEN_DEVICE_FUNC Index rows(void) const {return m_rows;}
EIGEN_DEVICE_FUNC Index cols(void) const {return _Cols;}
EIGEN_DEVICE_FUNC void conservativeResize(Index, Index rows, Index) { m_rows = rows; }
EIGEN_DEVICE_FUNC void resize(Index, Index rows, Index) { m_rows = rows; }
EIGEN_DEVICE_FUNC const T *data() const { return m_data.array; }
EIGEN_DEVICE_FUNC T *data() { return m_data.array; }
};
// dynamic-size matrix with fixed-size storage and fixed height
template<typename T, int Size, int _Rows, int _Options> class DenseStorage<T, Size, _Rows, Dynamic, _Options>
{
internal::plain_array<T,Size,_Options> m_data;
Index m_cols;
public:
EIGEN_DEVICE_FUNC DenseStorage() : m_cols(0) {}
EIGEN_DEVICE_FUNC explicit DenseStorage(internal::constructor_without_unaligned_array_assert)
: m_data(internal::constructor_without_unaligned_array_assert()), m_cols(0) {}
EIGEN_DEVICE_FUNC DenseStorage(const DenseStorage& other) : m_data(other.m_data), m_cols(other.m_cols) {}
EIGEN_DEVICE_FUNC DenseStorage& operator=(const DenseStorage& other)
{
if (this != &other)
{
m_data = other.m_data;
m_cols = other.m_cols;
}
return *this;
}
EIGEN_DEVICE_FUNC DenseStorage(Index, Index, Index cols) : m_cols(cols) {}
EIGEN_DEVICE_FUNC void swap(DenseStorage& other) { std::swap(m_data,other.m_data); std::swap(m_cols,other.m_cols); }
EIGEN_DEVICE_FUNC Index rows(void) const {return _Rows;}
EIGEN_DEVICE_FUNC Index cols(void) const {return m_cols;}
void conservativeResize(Index, Index, Index cols) { m_cols = cols; }
void resize(Index, Index, Index cols) { m_cols = cols; }
EIGEN_DEVICE_FUNC const T *data() const { return m_data.array; }
EIGEN_DEVICE_FUNC T *data() { return m_data.array; }
};
// purely dynamic matrix.
template<typename T, int _Options> class DenseStorage<T, Dynamic, Dynamic, Dynamic, _Options>
{
T *m_data;
Index m_rows;
Index m_cols;
public:
EIGEN_DEVICE_FUNC DenseStorage() : m_data(0), m_rows(0), m_cols(0) {}
EIGEN_DEVICE_FUNC explicit DenseStorage(internal::constructor_without_unaligned_array_assert)
: m_data(0), m_rows(0), m_cols(0) {}
EIGEN_DEVICE_FUNC DenseStorage(Index size, Index rows, Index cols)
: m_data(internal::conditional_aligned_new_auto<T,(_Options&DontAlign)==0>(size)), m_rows(rows), m_cols(cols)
{
EIGEN_INTERNAL_DENSE_STORAGE_CTOR_PLUGIN({})
eigen_internal_assert(size==rows*cols && rows>=0 && cols >=0);
}
EIGEN_DEVICE_FUNC DenseStorage(const DenseStorage& other)
: m_data(internal::conditional_aligned_new_auto<T,(_Options&DontAlign)==0>(other.m_rows*other.m_cols))
, m_rows(other.m_rows)
, m_cols(other.m_cols)
{
EIGEN_INTERNAL_DENSE_STORAGE_CTOR_PLUGIN(Index size = m_rows*m_cols)
internal::smart_copy(other.m_data, other.m_data+other.m_rows*other.m_cols, m_data);
}
EIGEN_DEVICE_FUNC DenseStorage& operator=(const DenseStorage& other)
{
if (this != &other)
{
DenseStorage tmp(other);
this->swap(tmp);
}
return *this;
}
#if EIGEN_HAS_RVALUE_REFERENCES
EIGEN_DEVICE_FUNC
DenseStorage(DenseStorage&& other) EIGEN_NOEXCEPT
: m_data(std::move(other.m_data))
, m_rows(std::move(other.m_rows))
, m_cols(std::move(other.m_cols))
{
other.m_data = nullptr;
other.m_rows = 0;
other.m_cols = 0;
}
EIGEN_DEVICE_FUNC
DenseStorage& operator=(DenseStorage&& other) EIGEN_NOEXCEPT
{
using std::swap;
swap(m_data, other.m_data);
swap(m_rows, other.m_rows);
swap(m_cols, other.m_cols);
return *this;
}
#endif
EIGEN_DEVICE_FUNC ~DenseStorage() { internal::conditional_aligned_delete_auto<T,(_Options&DontAlign)==0>(m_data, m_rows*m_cols); }
EIGEN_DEVICE_FUNC void swap(DenseStorage& other)
{ std::swap(m_data,other.m_data); std::swap(m_rows,other.m_rows); std::swap(m_cols,other.m_cols); }
EIGEN_DEVICE_FUNC Index rows(void) const {return m_rows;}
EIGEN_DEVICE_FUNC Index cols(void) const {return m_cols;}
void conservativeResize(Index size, Index rows, Index cols)
{
m_data = internal::conditional_aligned_realloc_new_auto<T,(_Options&DontAlign)==0>(m_data, size, m_rows*m_cols);
m_rows = rows;
m_cols = cols;
}
EIGEN_DEVICE_FUNC void resize(Index size, Index rows, Index cols)
{
if(size != m_rows*m_cols)
{
internal::conditional_aligned_delete_auto<T,(_Options&DontAlign)==0>(m_data, m_rows*m_cols);
if (size)
m_data = internal::conditional_aligned_new_auto<T,(_Options&DontAlign)==0>(size);
else
m_data = 0;
EIGEN_INTERNAL_DENSE_STORAGE_CTOR_PLUGIN({})
}
m_rows = rows;
m_cols = cols;
}
EIGEN_DEVICE_FUNC const T *data() const { return m_data; }
EIGEN_DEVICE_FUNC T *data() { return m_data; }
};
// matrix with dynamic width and fixed height (so that matrix has dynamic size).
template<typename T, int _Rows, int _Options> class DenseStorage<T, Dynamic, _Rows, Dynamic, _Options>
{
T *m_data;
Index m_cols;
public:
EIGEN_DEVICE_FUNC DenseStorage() : m_data(0), m_cols(0) {}
explicit DenseStorage(internal::constructor_without_unaligned_array_assert) : m_data(0), m_cols(0) {}
EIGEN_DEVICE_FUNC DenseStorage(Index size, Index rows, Index cols) : m_data(internal::conditional_aligned_new_auto<T,(_Options&DontAlign)==0>(size)), m_cols(cols)
{
EIGEN_INTERNAL_DENSE_STORAGE_CTOR_PLUGIN({})
eigen_internal_assert(size==rows*cols && rows==_Rows && cols >=0);
EIGEN_UNUSED_VARIABLE(rows);
}
EIGEN_DEVICE_FUNC DenseStorage(const DenseStorage& other)
: m_data(internal::conditional_aligned_new_auto<T,(_Options&DontAlign)==0>(_Rows*other.m_cols))
, m_cols(other.m_cols)
{
EIGEN_INTERNAL_DENSE_STORAGE_CTOR_PLUGIN(Index size = m_cols*_Rows)
internal::smart_copy(other.m_data, other.m_data+_Rows*m_cols, m_data);
}
EIGEN_DEVICE_FUNC DenseStorage& operator=(const DenseStorage& other)
{
if (this != &other)
{
DenseStorage tmp(other);
this->swap(tmp);
}
return *this;
}
#if EIGEN_HAS_RVALUE_REFERENCES
EIGEN_DEVICE_FUNC
DenseStorage(DenseStorage&& other) EIGEN_NOEXCEPT
: m_data(std::move(other.m_data))
, m_cols(std::move(other.m_cols))
{
other.m_data = nullptr;
other.m_cols = 0;
}
EIGEN_DEVICE_FUNC
DenseStorage& operator=(DenseStorage&& other) EIGEN_NOEXCEPT
{
using std::swap;
swap(m_data, other.m_data);
swap(m_cols, other.m_cols);
return *this;
}
#endif
EIGEN_DEVICE_FUNC ~DenseStorage() { internal::conditional_aligned_delete_auto<T,(_Options&DontAlign)==0>(m_data, _Rows*m_cols); }
EIGEN_DEVICE_FUNC void swap(DenseStorage& other) { std::swap(m_data,other.m_data); std::swap(m_cols,other.m_cols); }
EIGEN_DEVICE_FUNC static Index rows(void) {return _Rows;}
EIGEN_DEVICE_FUNC Index cols(void) const {return m_cols;}
EIGEN_DEVICE_FUNC void conservativeResize(Index size, Index, Index cols)
{
m_data = internal::conditional_aligned_realloc_new_auto<T,(_Options&DontAlign)==0>(m_data, size, _Rows*m_cols);
m_cols = cols;
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void resize(Index size, Index, Index cols)
{
if(size != _Rows*m_cols)
{
internal::conditional_aligned_delete_auto<T,(_Options&DontAlign)==0>(m_data, _Rows*m_cols);
if (size)
m_data = internal::conditional_aligned_new_auto<T,(_Options&DontAlign)==0>(size);
else
m_data = 0;
EIGEN_INTERNAL_DENSE_STORAGE_CTOR_PLUGIN({})
}
m_cols = cols;
}
EIGEN_DEVICE_FUNC const T *data() const { return m_data; }
EIGEN_DEVICE_FUNC T *data() { return m_data; }
};
// matrix with dynamic height and fixed width (so that matrix has dynamic size).
template<typename T, int _Cols, int _Options> class DenseStorage<T, Dynamic, Dynamic, _Cols, _Options>
{
T *m_data;
Index m_rows;
public:
EIGEN_DEVICE_FUNC DenseStorage() : m_data(0), m_rows(0) {}
explicit DenseStorage(internal::constructor_without_unaligned_array_assert) : m_data(0), m_rows(0) {}
EIGEN_DEVICE_FUNC DenseStorage(Index size, Index rows, Index cols) : m_data(internal::conditional_aligned_new_auto<T,(_Options&DontAlign)==0>(size)), m_rows(rows)
{
EIGEN_INTERNAL_DENSE_STORAGE_CTOR_PLUGIN({})
eigen_internal_assert(size==rows*cols && rows>=0 && cols == _Cols);
EIGEN_UNUSED_VARIABLE(cols);
}
EIGEN_DEVICE_FUNC DenseStorage(const DenseStorage& other)
: m_data(internal::conditional_aligned_new_auto<T,(_Options&DontAlign)==0>(other.m_rows*_Cols))
, m_rows(other.m_rows)
{
EIGEN_INTERNAL_DENSE_STORAGE_CTOR_PLUGIN(Index size = m_rows*_Cols)
internal::smart_copy(other.m_data, other.m_data+other.m_rows*_Cols, m_data);
}
EIGEN_DEVICE_FUNC DenseStorage& operator=(const DenseStorage& other)
{
if (this != &other)
{
DenseStorage tmp(other);
this->swap(tmp);
}
return *this;
}
#if EIGEN_HAS_RVALUE_REFERENCES
EIGEN_DEVICE_FUNC
DenseStorage(DenseStorage&& other) EIGEN_NOEXCEPT
: m_data(std::move(other.m_data))
, m_rows(std::move(other.m_rows))
{
other.m_data = nullptr;
other.m_rows = 0;
}
EIGEN_DEVICE_FUNC
DenseStorage& operator=(DenseStorage&& other) EIGEN_NOEXCEPT
{
using std::swap;
swap(m_data, other.m_data);
swap(m_rows, other.m_rows);
return *this;
}
#endif
EIGEN_DEVICE_FUNC ~DenseStorage() { internal::conditional_aligned_delete_auto<T,(_Options&DontAlign)==0>(m_data, _Cols*m_rows); }
EIGEN_DEVICE_FUNC void swap(DenseStorage& other) { std::swap(m_data,other.m_data); std::swap(m_rows,other.m_rows); }
EIGEN_DEVICE_FUNC Index rows(void) const {return m_rows;}
EIGEN_DEVICE_FUNC static Index cols(void) {return _Cols;}
void conservativeResize(Index size, Index rows, Index)
{
m_data = internal::conditional_aligned_realloc_new_auto<T,(_Options&DontAlign)==0>(m_data, size, m_rows*_Cols);
m_rows = rows;
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void resize(Index size, Index rows, Index)
{
if(size != m_rows*_Cols)
{
internal::conditional_aligned_delete_auto<T,(_Options&DontAlign)==0>(m_data, _Cols*m_rows);
if (size)
m_data = internal::conditional_aligned_new_auto<T,(_Options&DontAlign)==0>(size);
else
m_data = 0;
EIGEN_INTERNAL_DENSE_STORAGE_CTOR_PLUGIN({})
}
m_rows = rows;
}
EIGEN_DEVICE_FUNC const T *data() const { return m_data; }
EIGEN_DEVICE_FUNC T *data() { return m_data; }
};
} // end namespace Eigen
#endif // EIGEN_MATRIX_H

View File

@@ -0,0 +1,260 @@
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2007-2009 Benoit Jacob <jacob.benoit.1@gmail.com>
// Copyright (C) 2009-2010 Gael Guennebaud <gael.guennebaud@inria.fr>
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#ifndef EIGEN_DIAGONAL_H
#define EIGEN_DIAGONAL_H
namespace Eigen {
/** \class Diagonal
* \ingroup Core_Module
*
* \brief Expression of a diagonal/subdiagonal/superdiagonal in a matrix
*
* \param MatrixType the type of the object in which we are taking a sub/main/super diagonal
* \param DiagIndex the index of the sub/super diagonal. The default is 0 and it means the main diagonal.
* A positive value means a superdiagonal, a negative value means a subdiagonal.
* You can also use DynamicIndex so the index can be set at runtime.
*
* The matrix is not required to be square.
*
* This class represents an expression of the main diagonal, or any sub/super diagonal
* of a square matrix. It is the return type of MatrixBase::diagonal() and MatrixBase::diagonal(Index) and most of the
* time this is the only way it is used.
*
* \sa MatrixBase::diagonal(), MatrixBase::diagonal(Index)
*/
namespace internal {
template<typename MatrixType, int DiagIndex>
struct traits<Diagonal<MatrixType,DiagIndex> >
: traits<MatrixType>
{
typedef typename ref_selector<MatrixType>::type MatrixTypeNested;
typedef typename remove_reference<MatrixTypeNested>::type _MatrixTypeNested;
typedef typename MatrixType::StorageKind StorageKind;
enum {
RowsAtCompileTime = (int(DiagIndex) == DynamicIndex || int(MatrixType::SizeAtCompileTime) == Dynamic) ? Dynamic
: (EIGEN_PLAIN_ENUM_MIN(MatrixType::RowsAtCompileTime - EIGEN_PLAIN_ENUM_MAX(-DiagIndex, 0),
MatrixType::ColsAtCompileTime - EIGEN_PLAIN_ENUM_MAX( DiagIndex, 0))),
ColsAtCompileTime = 1,
MaxRowsAtCompileTime = int(MatrixType::MaxSizeAtCompileTime) == Dynamic ? Dynamic
: DiagIndex == DynamicIndex ? EIGEN_SIZE_MIN_PREFER_FIXED(MatrixType::MaxRowsAtCompileTime,
MatrixType::MaxColsAtCompileTime)
: (EIGEN_PLAIN_ENUM_MIN(MatrixType::MaxRowsAtCompileTime - EIGEN_PLAIN_ENUM_MAX(-DiagIndex, 0),
MatrixType::MaxColsAtCompileTime - EIGEN_PLAIN_ENUM_MAX( DiagIndex, 0))),
MaxColsAtCompileTime = 1,
MaskLvalueBit = is_lvalue<MatrixType>::value ? LvalueBit : 0,
Flags = (unsigned int)_MatrixTypeNested::Flags & (RowMajorBit | MaskLvalueBit | DirectAccessBit) & ~RowMajorBit, // FIXME DirectAccessBit should not be handled by expressions
MatrixTypeOuterStride = outer_stride_at_compile_time<MatrixType>::ret,
InnerStrideAtCompileTime = MatrixTypeOuterStride == Dynamic ? Dynamic : MatrixTypeOuterStride+1,
OuterStrideAtCompileTime = 0
};
};
}
template<typename MatrixType, int _DiagIndex> class Diagonal
: public internal::dense_xpr_base< Diagonal<MatrixType,_DiagIndex> >::type
{
public:
enum { DiagIndex = _DiagIndex };
typedef typename internal::dense_xpr_base<Diagonal>::type Base;
EIGEN_DENSE_PUBLIC_INTERFACE(Diagonal)
EIGEN_DEVICE_FUNC
explicit inline Diagonal(MatrixType& matrix, Index a_index = DiagIndex) : m_matrix(matrix), m_index(a_index)
{
eigen_assert( a_index <= m_matrix.cols() && -a_index <= m_matrix.rows() );
}
EIGEN_INHERIT_ASSIGNMENT_OPERATORS(Diagonal)
EIGEN_DEVICE_FUNC
inline Index rows() const
{
return m_index.value()<0 ? numext::mini<Index>(m_matrix.cols(),m_matrix.rows()+m_index.value())
: numext::mini<Index>(m_matrix.rows(),m_matrix.cols()-m_index.value());
}
EIGEN_DEVICE_FUNC
inline Index cols() const { return 1; }
EIGEN_DEVICE_FUNC
inline Index innerStride() const
{
return m_matrix.outerStride() + 1;
}
EIGEN_DEVICE_FUNC
inline Index outerStride() const
{
return 0;
}
typedef typename internal::conditional<
internal::is_lvalue<MatrixType>::value,
Scalar,
const Scalar
>::type ScalarWithConstIfNotLvalue;
EIGEN_DEVICE_FUNC
inline ScalarWithConstIfNotLvalue* data() { return &(m_matrix.coeffRef(rowOffset(), colOffset())); }
EIGEN_DEVICE_FUNC
inline const Scalar* data() const { return &(m_matrix.coeffRef(rowOffset(), colOffset())); }
EIGEN_DEVICE_FUNC
inline Scalar& coeffRef(Index row, Index)
{
EIGEN_STATIC_ASSERT_LVALUE(MatrixType)
return m_matrix.coeffRef(row+rowOffset(), row+colOffset());
}
EIGEN_DEVICE_FUNC
inline const Scalar& coeffRef(Index row, Index) const
{
return m_matrix.coeffRef(row+rowOffset(), row+colOffset());
}
EIGEN_DEVICE_FUNC
inline CoeffReturnType coeff(Index row, Index) const
{
return m_matrix.coeff(row+rowOffset(), row+colOffset());
}
EIGEN_DEVICE_FUNC
inline Scalar& coeffRef(Index idx)
{
EIGEN_STATIC_ASSERT_LVALUE(MatrixType)
return m_matrix.coeffRef(idx+rowOffset(), idx+colOffset());
}
EIGEN_DEVICE_FUNC
inline const Scalar& coeffRef(Index idx) const
{
return m_matrix.coeffRef(idx+rowOffset(), idx+colOffset());
}
EIGEN_DEVICE_FUNC
inline CoeffReturnType coeff(Index idx) const
{
return m_matrix.coeff(idx+rowOffset(), idx+colOffset());
}
EIGEN_DEVICE_FUNC
inline const typename internal::remove_all<typename MatrixType::Nested>::type&
nestedExpression() const
{
return m_matrix;
}
EIGEN_DEVICE_FUNC
inline Index index() const
{
return m_index.value();
}
protected:
typename internal::ref_selector<MatrixType>::non_const_type m_matrix;
const internal::variable_if_dynamicindex<Index, DiagIndex> m_index;
private:
// some compilers may fail to optimize std::max etc in case of compile-time constants...
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Index absDiagIndex() const { return m_index.value()>0 ? m_index.value() : -m_index.value(); }
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Index rowOffset() const { return m_index.value()>0 ? 0 : -m_index.value(); }
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Index colOffset() const { return m_index.value()>0 ? m_index.value() : 0; }
// trigger a compile-time error if someone try to call packet
template<int LoadMode> typename MatrixType::PacketReturnType packet(Index) const;
template<int LoadMode> typename MatrixType::PacketReturnType packet(Index,Index) const;
};
/** \returns an expression of the main diagonal of the matrix \c *this
*
* \c *this is not required to be square.
*
* Example: \include MatrixBase_diagonal.cpp
* Output: \verbinclude MatrixBase_diagonal.out
*
* \sa class Diagonal */
template<typename Derived>
inline typename MatrixBase<Derived>::DiagonalReturnType
MatrixBase<Derived>::diagonal()
{
return DiagonalReturnType(derived());
}
/** This is the const version of diagonal(). */
template<typename Derived>
inline typename MatrixBase<Derived>::ConstDiagonalReturnType
MatrixBase<Derived>::diagonal() const
{
return ConstDiagonalReturnType(derived());
}
/** \returns an expression of the \a DiagIndex-th sub or super diagonal of the matrix \c *this
*
* \c *this is not required to be square.
*
* The template parameter \a DiagIndex represent a super diagonal if \a DiagIndex > 0
* and a sub diagonal otherwise. \a DiagIndex == 0 is equivalent to the main diagonal.
*
* Example: \include MatrixBase_diagonal_int.cpp
* Output: \verbinclude MatrixBase_diagonal_int.out
*
* \sa MatrixBase::diagonal(), class Diagonal */
template<typename Derived>
inline typename MatrixBase<Derived>::DiagonalDynamicIndexReturnType
MatrixBase<Derived>::diagonal(Index index)
{
return DiagonalDynamicIndexReturnType(derived(), index);
}
/** This is the const version of diagonal(Index). */
template<typename Derived>
inline typename MatrixBase<Derived>::ConstDiagonalDynamicIndexReturnType
MatrixBase<Derived>::diagonal(Index index) const
{
return ConstDiagonalDynamicIndexReturnType(derived(), index);
}
/** \returns an expression of the \a DiagIndex-th sub or super diagonal of the matrix \c *this
*
* \c *this is not required to be square.
*
* The template parameter \a DiagIndex represent a super diagonal if \a DiagIndex > 0
* and a sub diagonal otherwise. \a DiagIndex == 0 is equivalent to the main diagonal.
*
* Example: \include MatrixBase_diagonal_template_int.cpp
* Output: \verbinclude MatrixBase_diagonal_template_int.out
*
* \sa MatrixBase::diagonal(), class Diagonal */
template<typename Derived>
template<int Index_>
inline typename MatrixBase<Derived>::template DiagonalIndexReturnType<Index_>::Type
MatrixBase<Derived>::diagonal()
{
return typename DiagonalIndexReturnType<Index_>::Type(derived());
}
/** This is the const version of diagonal<int>(). */
template<typename Derived>
template<int Index_>
inline typename MatrixBase<Derived>::template ConstDiagonalIndexReturnType<Index_>::Type
MatrixBase<Derived>::diagonal() const
{
return typename ConstDiagonalIndexReturnType<Index_>::Type(derived());
}
} // end namespace Eigen
#endif // EIGEN_DIAGONAL_H

View File

@@ -0,0 +1,343 @@
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2009 Gael Guennebaud <gael.guennebaud@inria.fr>
// Copyright (C) 2007-2009 Benoit Jacob <jacob.benoit.1@gmail.com>
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#ifndef EIGEN_DIAGONALMATRIX_H
#define EIGEN_DIAGONALMATRIX_H
namespace Eigen {
#ifndef EIGEN_PARSED_BY_DOXYGEN
template<typename Derived>
class DiagonalBase : public EigenBase<Derived>
{
public:
typedef typename internal::traits<Derived>::DiagonalVectorType DiagonalVectorType;
typedef typename DiagonalVectorType::Scalar Scalar;
typedef typename DiagonalVectorType::RealScalar RealScalar;
typedef typename internal::traits<Derived>::StorageKind StorageKind;
typedef typename internal::traits<Derived>::StorageIndex StorageIndex;
enum {
RowsAtCompileTime = DiagonalVectorType::SizeAtCompileTime,
ColsAtCompileTime = DiagonalVectorType::SizeAtCompileTime,
MaxRowsAtCompileTime = DiagonalVectorType::MaxSizeAtCompileTime,
MaxColsAtCompileTime = DiagonalVectorType::MaxSizeAtCompileTime,
IsVectorAtCompileTime = 0,
Flags = NoPreferredStorageOrderBit
};
typedef Matrix<Scalar, RowsAtCompileTime, ColsAtCompileTime, 0, MaxRowsAtCompileTime, MaxColsAtCompileTime> DenseMatrixType;
typedef DenseMatrixType DenseType;
typedef DiagonalMatrix<Scalar,DiagonalVectorType::SizeAtCompileTime,DiagonalVectorType::MaxSizeAtCompileTime> PlainObject;
EIGEN_DEVICE_FUNC
inline const Derived& derived() const { return *static_cast<const Derived*>(this); }
EIGEN_DEVICE_FUNC
inline Derived& derived() { return *static_cast<Derived*>(this); }
EIGEN_DEVICE_FUNC
DenseMatrixType toDenseMatrix() const { return derived(); }
EIGEN_DEVICE_FUNC
inline const DiagonalVectorType& diagonal() const { return derived().diagonal(); }
EIGEN_DEVICE_FUNC
inline DiagonalVectorType& diagonal() { return derived().diagonal(); }
EIGEN_DEVICE_FUNC
inline Index rows() const { return diagonal().size(); }
EIGEN_DEVICE_FUNC
inline Index cols() const { return diagonal().size(); }
template<typename MatrixDerived>
EIGEN_DEVICE_FUNC
const Product<Derived,MatrixDerived,LazyProduct>
operator*(const MatrixBase<MatrixDerived> &matrix) const
{
return Product<Derived, MatrixDerived, LazyProduct>(derived(),matrix.derived());
}
typedef DiagonalWrapper<const CwiseUnaryOp<internal::scalar_inverse_op<Scalar>, const DiagonalVectorType> > InverseReturnType;
EIGEN_DEVICE_FUNC
inline const InverseReturnType
inverse() const
{
return InverseReturnType(diagonal().cwiseInverse());
}
EIGEN_DEVICE_FUNC
inline const DiagonalWrapper<const EIGEN_EXPR_BINARYOP_SCALAR_RETURN_TYPE(DiagonalVectorType,Scalar,product) >
operator*(const Scalar& scalar) const
{
return DiagonalWrapper<const EIGEN_EXPR_BINARYOP_SCALAR_RETURN_TYPE(DiagonalVectorType,Scalar,product) >(diagonal() * scalar);
}
EIGEN_DEVICE_FUNC
friend inline const DiagonalWrapper<const EIGEN_SCALAR_BINARYOP_EXPR_RETURN_TYPE(Scalar,DiagonalVectorType,product) >
operator*(const Scalar& scalar, const DiagonalBase& other)
{
return DiagonalWrapper<const EIGEN_SCALAR_BINARYOP_EXPR_RETURN_TYPE(Scalar,DiagonalVectorType,product) >(scalar * other.diagonal());
}
};
#endif
/** \class DiagonalMatrix
* \ingroup Core_Module
*
* \brief Represents a diagonal matrix with its storage
*
* \param _Scalar the type of coefficients
* \param SizeAtCompileTime the dimension of the matrix, or Dynamic
* \param MaxSizeAtCompileTime the dimension of the matrix, or Dynamic. This parameter is optional and defaults
* to SizeAtCompileTime. Most of the time, you do not need to specify it.
*
* \sa class DiagonalWrapper
*/
namespace internal {
template<typename _Scalar, int SizeAtCompileTime, int MaxSizeAtCompileTime>
struct traits<DiagonalMatrix<_Scalar,SizeAtCompileTime,MaxSizeAtCompileTime> >
: traits<Matrix<_Scalar,SizeAtCompileTime,SizeAtCompileTime,0,MaxSizeAtCompileTime,MaxSizeAtCompileTime> >
{
typedef Matrix<_Scalar,SizeAtCompileTime,1,0,MaxSizeAtCompileTime,1> DiagonalVectorType;
typedef DiagonalShape StorageKind;
enum {
Flags = LvalueBit | NoPreferredStorageOrderBit
};
};
}
template<typename _Scalar, int SizeAtCompileTime, int MaxSizeAtCompileTime>
class DiagonalMatrix
: public DiagonalBase<DiagonalMatrix<_Scalar,SizeAtCompileTime,MaxSizeAtCompileTime> >
{
public:
#ifndef EIGEN_PARSED_BY_DOXYGEN
typedef typename internal::traits<DiagonalMatrix>::DiagonalVectorType DiagonalVectorType;
typedef const DiagonalMatrix& Nested;
typedef _Scalar Scalar;
typedef typename internal::traits<DiagonalMatrix>::StorageKind StorageKind;
typedef typename internal::traits<DiagonalMatrix>::StorageIndex StorageIndex;
#endif
protected:
DiagonalVectorType m_diagonal;
public:
/** const version of diagonal(). */
EIGEN_DEVICE_FUNC
inline const DiagonalVectorType& diagonal() const { return m_diagonal; }
/** \returns a reference to the stored vector of diagonal coefficients. */
EIGEN_DEVICE_FUNC
inline DiagonalVectorType& diagonal() { return m_diagonal; }
/** Default constructor without initialization */
EIGEN_DEVICE_FUNC
inline DiagonalMatrix() {}
/** Constructs a diagonal matrix with given dimension */
EIGEN_DEVICE_FUNC
explicit inline DiagonalMatrix(Index dim) : m_diagonal(dim) {}
/** 2D constructor. */
EIGEN_DEVICE_FUNC
inline DiagonalMatrix(const Scalar& x, const Scalar& y) : m_diagonal(x,y) {}
/** 3D constructor. */
EIGEN_DEVICE_FUNC
inline DiagonalMatrix(const Scalar& x, const Scalar& y, const Scalar& z) : m_diagonal(x,y,z) {}
/** Copy constructor. */
template<typename OtherDerived>
EIGEN_DEVICE_FUNC
inline DiagonalMatrix(const DiagonalBase<OtherDerived>& other) : m_diagonal(other.diagonal()) {}
#ifndef EIGEN_PARSED_BY_DOXYGEN
/** copy constructor. prevent a default copy constructor from hiding the other templated constructor */
inline DiagonalMatrix(const DiagonalMatrix& other) : m_diagonal(other.diagonal()) {}
#endif
/** generic constructor from expression of the diagonal coefficients */
template<typename OtherDerived>
EIGEN_DEVICE_FUNC
explicit inline DiagonalMatrix(const MatrixBase<OtherDerived>& other) : m_diagonal(other)
{}
/** Copy operator. */
template<typename OtherDerived>
EIGEN_DEVICE_FUNC
DiagonalMatrix& operator=(const DiagonalBase<OtherDerived>& other)
{
m_diagonal = other.diagonal();
return *this;
}
#ifndef EIGEN_PARSED_BY_DOXYGEN
/** This is a special case of the templated operator=. Its purpose is to
* prevent a default operator= from hiding the templated operator=.
*/
EIGEN_DEVICE_FUNC
DiagonalMatrix& operator=(const DiagonalMatrix& other)
{
m_diagonal = other.diagonal();
return *this;
}
#endif
/** Resizes to given size. */
EIGEN_DEVICE_FUNC
inline void resize(Index size) { m_diagonal.resize(size); }
/** Sets all coefficients to zero. */
EIGEN_DEVICE_FUNC
inline void setZero() { m_diagonal.setZero(); }
/** Resizes and sets all coefficients to zero. */
EIGEN_DEVICE_FUNC
inline void setZero(Index size) { m_diagonal.setZero(size); }
/** Sets this matrix to be the identity matrix of the current size. */
EIGEN_DEVICE_FUNC
inline void setIdentity() { m_diagonal.setOnes(); }
/** Sets this matrix to be the identity matrix of the given size. */
EIGEN_DEVICE_FUNC
inline void setIdentity(Index size) { m_diagonal.setOnes(size); }
};
/** \class DiagonalWrapper
* \ingroup Core_Module
*
* \brief Expression of a diagonal matrix
*
* \param _DiagonalVectorType the type of the vector of diagonal coefficients
*
* This class is an expression of a diagonal matrix, but not storing its own vector of diagonal coefficients,
* instead wrapping an existing vector expression. It is the return type of MatrixBase::asDiagonal()
* and most of the time this is the only way that it is used.
*
* \sa class DiagonalMatrix, class DiagonalBase, MatrixBase::asDiagonal()
*/
namespace internal {
template<typename _DiagonalVectorType>
struct traits<DiagonalWrapper<_DiagonalVectorType> >
{
typedef _DiagonalVectorType DiagonalVectorType;
typedef typename DiagonalVectorType::Scalar Scalar;
typedef typename DiagonalVectorType::StorageIndex StorageIndex;
typedef DiagonalShape StorageKind;
typedef typename traits<DiagonalVectorType>::XprKind XprKind;
enum {
RowsAtCompileTime = DiagonalVectorType::SizeAtCompileTime,
ColsAtCompileTime = DiagonalVectorType::SizeAtCompileTime,
MaxRowsAtCompileTime = DiagonalVectorType::MaxSizeAtCompileTime,
MaxColsAtCompileTime = DiagonalVectorType::MaxSizeAtCompileTime,
Flags = (traits<DiagonalVectorType>::Flags & LvalueBit) | NoPreferredStorageOrderBit
};
};
}
template<typename _DiagonalVectorType>
class DiagonalWrapper
: public DiagonalBase<DiagonalWrapper<_DiagonalVectorType> >, internal::no_assignment_operator
{
public:
#ifndef EIGEN_PARSED_BY_DOXYGEN
typedef _DiagonalVectorType DiagonalVectorType;
typedef DiagonalWrapper Nested;
#endif
/** Constructor from expression of diagonal coefficients to wrap. */
EIGEN_DEVICE_FUNC
explicit inline DiagonalWrapper(DiagonalVectorType& a_diagonal) : m_diagonal(a_diagonal) {}
/** \returns a const reference to the wrapped expression of diagonal coefficients. */
EIGEN_DEVICE_FUNC
const DiagonalVectorType& diagonal() const { return m_diagonal; }
protected:
typename DiagonalVectorType::Nested m_diagonal;
};
/** \returns a pseudo-expression of a diagonal matrix with *this as vector of diagonal coefficients
*
* \only_for_vectors
*
* Example: \include MatrixBase_asDiagonal.cpp
* Output: \verbinclude MatrixBase_asDiagonal.out
*
* \sa class DiagonalWrapper, class DiagonalMatrix, diagonal(), isDiagonal()
**/
template<typename Derived>
inline const DiagonalWrapper<const Derived>
MatrixBase<Derived>::asDiagonal() const
{
return DiagonalWrapper<const Derived>(derived());
}
/** \returns true if *this is approximately equal to a diagonal matrix,
* within the precision given by \a prec.
*
* Example: \include MatrixBase_isDiagonal.cpp
* Output: \verbinclude MatrixBase_isDiagonal.out
*
* \sa asDiagonal()
*/
template<typename Derived>
bool MatrixBase<Derived>::isDiagonal(const RealScalar& prec) const
{
if(cols() != rows()) return false;
RealScalar maxAbsOnDiagonal = static_cast<RealScalar>(-1);
for(Index j = 0; j < cols(); ++j)
{
RealScalar absOnDiagonal = numext::abs(coeff(j,j));
if(absOnDiagonal > maxAbsOnDiagonal) maxAbsOnDiagonal = absOnDiagonal;
}
for(Index j = 0; j < cols(); ++j)
for(Index i = 0; i < j; ++i)
{
if(!internal::isMuchSmallerThan(coeff(i, j), maxAbsOnDiagonal, prec)) return false;
if(!internal::isMuchSmallerThan(coeff(j, i), maxAbsOnDiagonal, prec)) return false;
}
return true;
}
namespace internal {
template<> struct storage_kind_to_shape<DiagonalShape> { typedef DiagonalShape Shape; };
struct Diagonal2Dense {};
template<> struct AssignmentKind<DenseShape,DiagonalShape> { typedef Diagonal2Dense Kind; };
// Diagonal matrix to Dense assignment
template< typename DstXprType, typename SrcXprType, typename Functor>
struct Assignment<DstXprType, SrcXprType, Functor, Diagonal2Dense>
{
static void run(DstXprType &dst, const SrcXprType &src, const internal::assign_op<typename DstXprType::Scalar,typename SrcXprType::Scalar> &/*func*/)
{
Index dstRows = src.rows();
Index dstCols = src.cols();
if((dst.rows()!=dstRows) || (dst.cols()!=dstCols))
dst.resize(dstRows, dstCols);
dst.setZero();
dst.diagonal() = src.diagonal();
}
static void run(DstXprType &dst, const SrcXprType &src, const internal::add_assign_op<typename DstXprType::Scalar,typename SrcXprType::Scalar> &/*func*/)
{ dst.diagonal() += src.diagonal(); }
static void run(DstXprType &dst, const SrcXprType &src, const internal::sub_assign_op<typename DstXprType::Scalar,typename SrcXprType::Scalar> &/*func*/)
{ dst.diagonal() -= src.diagonal(); }
};
} // namespace internal
} // end namespace Eigen
#endif // EIGEN_DIAGONALMATRIX_H

View File

@@ -0,0 +1,28 @@
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
// Copyright (C) 2007-2009 Benoit Jacob <jacob.benoit.1@gmail.com>
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#ifndef EIGEN_DIAGONALPRODUCT_H
#define EIGEN_DIAGONALPRODUCT_H
namespace Eigen {
/** \returns the diagonal matrix product of \c *this by the diagonal matrix \a diagonal.
*/
template<typename Derived>
template<typename DiagonalDerived>
inline const Product<Derived, DiagonalDerived, LazyProduct>
MatrixBase<Derived>::operator*(const DiagonalBase<DiagonalDerived> &a_diagonal) const
{
return Product<Derived, DiagonalDerived, LazyProduct>(derived(),a_diagonal.derived());
}
} // end namespace Eigen
#endif // EIGEN_DIAGONALPRODUCT_H

View File

@@ -0,0 +1,318 @@
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2006-2008, 2010 Benoit Jacob <jacob.benoit.1@gmail.com>
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#ifndef EIGEN_DOT_H
#define EIGEN_DOT_H
namespace Eigen {
namespace internal {
// helper function for dot(). The problem is that if we put that in the body of dot(), then upon calling dot
// with mismatched types, the compiler emits errors about failing to instantiate cwiseProduct BEFORE
// looking at the static assertions. Thus this is a trick to get better compile errors.
template<typename T, typename U,
// the NeedToTranspose condition here is taken straight from Assign.h
bool NeedToTranspose = T::IsVectorAtCompileTime
&& U::IsVectorAtCompileTime
&& ((int(T::RowsAtCompileTime) == 1 && int(U::ColsAtCompileTime) == 1)
| // FIXME | instead of || to please GCC 4.4.0 stupid warning "suggest parentheses around &&".
// revert to || as soon as not needed anymore.
(int(T::ColsAtCompileTime) == 1 && int(U::RowsAtCompileTime) == 1))
>
struct dot_nocheck
{
typedef scalar_conj_product_op<typename traits<T>::Scalar,typename traits<U>::Scalar> conj_prod;
typedef typename conj_prod::result_type ResScalar;
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE
static ResScalar run(const MatrixBase<T>& a, const MatrixBase<U>& b)
{
return a.template binaryExpr<conj_prod>(b).sum();
}
};
template<typename T, typename U>
struct dot_nocheck<T, U, true>
{
typedef scalar_conj_product_op<typename traits<T>::Scalar,typename traits<U>::Scalar> conj_prod;
typedef typename conj_prod::result_type ResScalar;
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE
static ResScalar run(const MatrixBase<T>& a, const MatrixBase<U>& b)
{
return a.transpose().template binaryExpr<conj_prod>(b).sum();
}
};
} // end namespace internal
/** \fn MatrixBase::dot
* \returns the dot product of *this with other.
*
* \only_for_vectors
*
* \note If the scalar type is complex numbers, then this function returns the hermitian
* (sesquilinear) dot product, conjugate-linear in the first variable and linear in the
* second variable.
*
* \sa squaredNorm(), norm()
*/
template<typename Derived>
template<typename OtherDerived>
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE
typename ScalarBinaryOpTraits<typename internal::traits<Derived>::Scalar,typename internal::traits<OtherDerived>::Scalar>::ReturnType
MatrixBase<Derived>::dot(const MatrixBase<OtherDerived>& other) const
{
EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
EIGEN_STATIC_ASSERT_VECTOR_ONLY(OtherDerived)
EIGEN_STATIC_ASSERT_SAME_VECTOR_SIZE(Derived,OtherDerived)
#if !(defined(EIGEN_NO_STATIC_ASSERT) && defined(EIGEN_NO_DEBUG))
typedef internal::scalar_conj_product_op<Scalar,typename OtherDerived::Scalar> func;
EIGEN_CHECK_BINARY_COMPATIBILIY(func,Scalar,typename OtherDerived::Scalar);
#endif
eigen_assert(size() == other.size());
return internal::dot_nocheck<Derived,OtherDerived>::run(*this, other);
}
//---------- implementation of L2 norm and related functions ----------
/** \returns, for vectors, the squared \em l2 norm of \c *this, and for matrices the Frobenius norm.
* In both cases, it consists in the sum of the square of all the matrix entries.
* For vectors, this is also equals to the dot product of \c *this with itself.
*
* \sa dot(), norm(), lpNorm()
*/
template<typename Derived>
EIGEN_STRONG_INLINE typename NumTraits<typename internal::traits<Derived>::Scalar>::Real MatrixBase<Derived>::squaredNorm() const
{
return numext::real((*this).cwiseAbs2().sum());
}
/** \returns, for vectors, the \em l2 norm of \c *this, and for matrices the Frobenius norm.
* In both cases, it consists in the square root of the sum of the square of all the matrix entries.
* For vectors, this is also equals to the square root of the dot product of \c *this with itself.
*
* \sa lpNorm(), dot(), squaredNorm()
*/
template<typename Derived>
EIGEN_STRONG_INLINE typename NumTraits<typename internal::traits<Derived>::Scalar>::Real MatrixBase<Derived>::norm() const
{
return numext::sqrt(squaredNorm());
}
/** \returns an expression of the quotient of \c *this by its own norm.
*
* \warning If the input vector is too small (i.e., this->norm()==0),
* then this function returns a copy of the input.
*
* \only_for_vectors
*
* \sa norm(), normalize()
*/
template<typename Derived>
EIGEN_STRONG_INLINE const typename MatrixBase<Derived>::PlainObject
MatrixBase<Derived>::normalized() const
{
typedef typename internal::nested_eval<Derived,2>::type _Nested;
_Nested n(derived());
RealScalar z = n.squaredNorm();
// NOTE: after extensive benchmarking, this conditional does not impact performance, at least on recent x86 CPU
if(z>RealScalar(0))
return n / numext::sqrt(z);
else
return n;
}
/** Normalizes the vector, i.e. divides it by its own norm.
*
* \only_for_vectors
*
* \warning If the input vector is too small (i.e., this->norm()==0), then \c *this is left unchanged.
*
* \sa norm(), normalized()
*/
template<typename Derived>
EIGEN_STRONG_INLINE void MatrixBase<Derived>::normalize()
{
RealScalar z = squaredNorm();
// NOTE: after extensive benchmarking, this conditional does not impact performance, at least on recent x86 CPU
if(z>RealScalar(0))
derived() /= numext::sqrt(z);
}
/** \returns an expression of the quotient of \c *this by its own norm while avoiding underflow and overflow.
*
* \only_for_vectors
*
* This method is analogue to the normalized() method, but it reduces the risk of
* underflow and overflow when computing the norm.
*
* \warning If the input vector is too small (i.e., this->norm()==0),
* then this function returns a copy of the input.
*
* \sa stableNorm(), stableNormalize(), normalized()
*/
template<typename Derived>
EIGEN_STRONG_INLINE const typename MatrixBase<Derived>::PlainObject
MatrixBase<Derived>::stableNormalized() const
{
typedef typename internal::nested_eval<Derived,3>::type _Nested;
_Nested n(derived());
RealScalar w = n.cwiseAbs().maxCoeff();
RealScalar z = (n/w).squaredNorm();
if(z>RealScalar(0))
return n / (numext::sqrt(z)*w);
else
return n;
}
/** Normalizes the vector while avoid underflow and overflow
*
* \only_for_vectors
*
* This method is analogue to the normalize() method, but it reduces the risk of
* underflow and overflow when computing the norm.
*
* \warning If the input vector is too small (i.e., this->norm()==0), then \c *this is left unchanged.
*
* \sa stableNorm(), stableNormalized(), normalize()
*/
template<typename Derived>
EIGEN_STRONG_INLINE void MatrixBase<Derived>::stableNormalize()
{
RealScalar w = cwiseAbs().maxCoeff();
RealScalar z = (derived()/w).squaredNorm();
if(z>RealScalar(0))
derived() /= numext::sqrt(z)*w;
}
//---------- implementation of other norms ----------
namespace internal {
template<typename Derived, int p>
struct lpNorm_selector
{
typedef typename NumTraits<typename traits<Derived>::Scalar>::Real RealScalar;
EIGEN_DEVICE_FUNC
static inline RealScalar run(const MatrixBase<Derived>& m)
{
EIGEN_USING_STD_MATH(pow)
return pow(m.cwiseAbs().array().pow(p).sum(), RealScalar(1)/p);
}
};
template<typename Derived>
struct lpNorm_selector<Derived, 1>
{
EIGEN_DEVICE_FUNC
static inline typename NumTraits<typename traits<Derived>::Scalar>::Real run(const MatrixBase<Derived>& m)
{
return m.cwiseAbs().sum();
}
};
template<typename Derived>
struct lpNorm_selector<Derived, 2>
{
EIGEN_DEVICE_FUNC
static inline typename NumTraits<typename traits<Derived>::Scalar>::Real run(const MatrixBase<Derived>& m)
{
return m.norm();
}
};
template<typename Derived>
struct lpNorm_selector<Derived, Infinity>
{
typedef typename NumTraits<typename traits<Derived>::Scalar>::Real RealScalar;
EIGEN_DEVICE_FUNC
static inline RealScalar run(const MatrixBase<Derived>& m)
{
if(Derived::SizeAtCompileTime==0 || (Derived::SizeAtCompileTime==Dynamic && m.size()==0))
return RealScalar(0);
return m.cwiseAbs().maxCoeff();
}
};
} // end namespace internal
/** \returns the \b coefficient-wise \f$ \ell^p \f$ norm of \c *this, that is, returns the p-th root of the sum of the p-th powers of the absolute values
* of the coefficients of \c *this. If \a p is the special value \a Eigen::Infinity, this function returns the \f$ \ell^\infty \f$
* norm, that is the maximum of the absolute values of the coefficients of \c *this.
*
* In all cases, if \c *this is empty, then the value 0 is returned.
*
* \note For matrices, this function does not compute the <a href="https://en.wikipedia.org/wiki/Operator_norm">operator-norm</a>. That is, if \c *this is a matrix, then its coefficients are interpreted as a 1D vector. Nonetheless, you can easily compute the 1-norm and \f$\infty\f$-norm matrix operator norms using \link TutorialReductionsVisitorsBroadcastingReductionsNorm partial reductions \endlink.
*
* \sa norm()
*/
template<typename Derived>
template<int p>
#ifndef EIGEN_PARSED_BY_DOXYGEN
inline typename NumTraits<typename internal::traits<Derived>::Scalar>::Real
#else
MatrixBase<Derived>::RealScalar
#endif
MatrixBase<Derived>::lpNorm() const
{
return internal::lpNorm_selector<Derived, p>::run(*this);
}
//---------- implementation of isOrthogonal / isUnitary ----------
/** \returns true if *this is approximately orthogonal to \a other,
* within the precision given by \a prec.
*
* Example: \include MatrixBase_isOrthogonal.cpp
* Output: \verbinclude MatrixBase_isOrthogonal.out
*/
template<typename Derived>
template<typename OtherDerived>
bool MatrixBase<Derived>::isOrthogonal
(const MatrixBase<OtherDerived>& other, const RealScalar& prec) const
{
typename internal::nested_eval<Derived,2>::type nested(derived());
typename internal::nested_eval<OtherDerived,2>::type otherNested(other.derived());
return numext::abs2(nested.dot(otherNested)) <= prec * prec * nested.squaredNorm() * otherNested.squaredNorm();
}
/** \returns true if *this is approximately an unitary matrix,
* within the precision given by \a prec. In the case where the \a Scalar
* type is real numbers, a unitary matrix is an orthogonal matrix, whence the name.
*
* \note This can be used to check whether a family of vectors forms an orthonormal basis.
* Indeed, \c m.isUnitary() returns true if and only if the columns (equivalently, the rows) of m form an
* orthonormal basis.
*
* Example: \include MatrixBase_isUnitary.cpp
* Output: \verbinclude MatrixBase_isUnitary.out
*/
template<typename Derived>
bool MatrixBase<Derived>::isUnitary(const RealScalar& prec) const
{
typename internal::nested_eval<Derived,1>::type self(derived());
for(Index i = 0; i < cols(); ++i)
{
if(!internal::isApprox(self.col(i).squaredNorm(), static_cast<RealScalar>(1), prec))
return false;
for(Index j = 0; j < i; ++j)
if(!internal::isMuchSmallerThan(self.col(i).dot(self.col(j)), static_cast<Scalar>(1), prec))
return false;
}
return true;
}
} // end namespace Eigen
#endif // EIGEN_DOT_H

Some files were not shown because too many files have changed in this diff Show More