The filament-group golden harness landed with H2C/A2L support (#14685). Its
"FilamentGroup golden regression" / stress_66 case fails intermittently on
Windows x64, on main and on unrelated PRs alike. The test depends on how fast the
runner is.
The k-medoids clustering these goldens exercise is an anytime search bounded by a
3 second wall clock. Every restart is seeded from its own index, so nothing about
it is random. What varies is how many restarts fit in the budget, and the best
cost is a minimum over completed restarts, so a slower runner is never better.
Grading a score produced that way measures the machine as much as the code.
Add a ClusteringBudget struct and let the tests set it. The defaults are the
current 3 seconds and 30 restarts, so slicing behavior is unchanged. A
non-positive timeout removes the wall clock and bounds the search by restart
count alone.
The goldens are then graded under a fixed budget of four restarts, where every
one of them reaches the BambuStudio reference within 3%, so the score becomes a
property of the code. This retires the machine-specific 125103 lock on stress_66.
The default wall-clock path keeps its own test, asserting the grouping is valid
and the search does not run away. It makes no score assertion, because under a
wall clock that number is not a property of the code.
The golden test also checks the run fits in ten times the default wall clock.
Slicing quality depends on how many restarts fit in the budget, so a search an
order of magnitude slower would degrade real groupings while a fixed-budget score
gate stayed green.
The 3% tolerance stays as the parity allowance against the goldens. It also
covers a small spread across standard libraries: the k-medoids search seeds each
restart with std::shuffle, whose algorithm the C++ standard leaves unspecified,
so libstdc++, libc++ and the MSVC STL permute the same seed differently, start
from different medoids, and settle on slightly different groupings, about 3e-4
apart and only on the goldens heavy enough to reach the k-medoids search.
1. Try to merge filaments before grouping
2. Set max match num for machine filamnet in match mode
jira:STUDIO-10392
Signed-off-by: xun.zhang <xun.zhang@bambulab.com>
Change-Id: I2451d838e07ee02f493fda4dc702f3d13b2ad37b
(cherry picked from commit d15fc37ff2fa048e38a0b132bb3ae525ff666fab)
1. Add filament_is_support field. Format the filament type
2. Optimize machine filament info logic
jira:STUDIO-10326
Signed-off-by: xun.zhang <xun.zhang@bambulab.com>
Change-Id: Ia8bfc37095339e73c98209b4e3f1e0804e511e88
(cherry picked from commit 001144400b841629439a890d46fa40a7296689ba)
1.Caused by too big tolerance
jira:STUDIO-10236
Signed-off-by: xun.zhang <xun.zhang@bambulab.com>
Change-Id: I0ba182991bc70ca2d3a34a85b87fa7539c5e50d9
(cherry picked from commit d2ae5ea32c55c0023b27fac73a7479c2bd9a7e1c)
1.Add filament type into consideration when selecting best map for
ams in filament saving mode
jira:NONE
Signed-off-by: xun.zhang <xun.zhang@bambulab.com>
Change-Id: I7d4a4ff66da479ab560eaeea614e5bbf0f930d3f
(cherry picked from commit 4b6f82d042cadbd65e66edbb1c8287791da5caa3)
1.When there are identical materials, try to make the quantity of
materials for each nozzle as similar as possible after grouping.
2.Fix an encoding error
jira:NONE
Signed-off-by: xun.zhang <xun.zhang@bambulab.com>
Change-Id: Iad77d7a995b9c25d004026f409c7e2ecbb8269db
(cherry picked from commit 13d7cd06252678b6b084d17438e99ff808a4191d)
(cherry picked from commit 3cd587d09e066d6c6a27d466b7f663dfd5d674f3)
1.Should use idx in used filaments in filament group algorithm
jira:NONE
Signed-off-by: xun.zhang <xun.zhang@bambulab.com>
Change-Id: I5acc849827d84db090e61a45e80a3df2866b2724
(cherry picked from commit 84c55c10edfda91df16843f317ebc7912205b202)
(cherry picked from commit daaca0368626e68ba5bdb0ce6a90111292454119)
1. Do not consider empty filament when selecting group for ams
2. Function "collect_filaments_in_groups" is frequently called,
optimize memory allocation to speed up.
jira: NONE
Signed-off-by: xun.zhang <xun.zhang@bambulab.com>
Change-Id: Iad8c9a257bc2dd832c77e650f8c052fb9d8379a0
(cherry picked from commit 21379e13366fd70f0042e85dcf8ee220185c782d)
1.Caused by uninitialized filament map in mapping for AMS
jira:NONE
Signed-off-by: xun.zhang <xun.zhang@bambulab.com>
Change-Id: I71ce6001fc6f2e72eb9303fcaba0bac16ad70dc9
(cherry picked from commit 48023e4c154c118c9396b6065b7e2476970fd441)
1.Only consider groups with a distance within the threshold
jira:NONE
Signed-off-by: xun.zhang <xun.zhang@bambulab.com>
Change-Id: I91526a796a0f7f1ed3e77c41076c1f85620dd944
(cherry picked from commit 1379b838466f9b0a188fc916c31916626b933dc4)
1.Use max flow network to handle limit
2.Support setting master extruder id
3.Fix the issue in the KMedoids algorithm where data is overwritten
after each retry.
jira:NONE
Signed-off-by: xun.zhang <xun.zhang@bambulab.com>
Change-Id: Idd2bedf39f61e7a65eb4199852f60b8fbebe0a7d
(cherry picked from commit 3cfb49a1b9dc2c76066ec441f1028f99a4bf99c4)
1.Set the default size of ams filament to 2
jira:NONE
Signed-off-by: xun.zhang <xun.zhang@bambulab.com>
Change-Id: Ie985ecfa44cb9fcaf21406303e32bb56e731f351
(cherry picked from commit b4d85663325eb9be1be48e1eee3d3128e31650db)
1.If the group result differs little in flush,we will choose the one
that best fits the ams filaments
jira:NONE
Signed-off-by: xun.zhang <xun.zhang@bambulab.com>
Change-Id: Icd147b406e3494c841ef13564ad1b1231ad798fd
(cherry picked from commit 0b95bdd9d950918ea6979da6b4d62b2d2cd25b99)
1.When capacity is greater than the num of filaments, always choose the
map result that can be accommodated
2.In BestFit strategy,always try to fill up the existing capacity
3.In BestCost strategy, just try the group with fewest flush
jira:NEW
Signed-off-by: xun.zhang <xun.zhang@bambulab.com>
Change-Id: Ifd6d64b77774039e57ffff26cf2243a4d3f89054
(cherry picked from commit cddf8cae27f4320f830b463e92703d3a6cf344e7)
1.When n<10, calc all case cost
2.When n>10, first k-medoids algorithm first
3.Enable setting group size
jira:NEW
Signed-off-by: xun.zhang <xun.zhang@bambulab.com>
Change-Id: I625f47e0235c70e440c6d489b052a156fbffca3f
(cherry picked from commit 9ec276d3d7114fff7a33213c3b47ce88df85f2ee)
