investigating simplified painting invention

Full log:
LOADED DATASET INDEX: 0
Generating candidate predicates...
Done: created 9 candidates:
((0:obj).dirtiness<=0.478) 2.0
NOT-((0:obj).dirtiness<=0.478) 2.0
((0:obj).wetness<=0.5) 2.0
NOT-((0:obj).wetness<=0.5) 2.0
NOT-((0:obj).held<=0.5) 2.0
NOT-((0:robot).gripper_rot<=0.5) 2.0
NOT-((0:robot).fingers<=0.5) 2.0
((0:robot).gripper_rot<=0.25) 3.0
((0:obj).pose_y<=-0.333) 3.0
Applying predicates to data...
Done.
Selecting a subset...
Evaluating predicates: {NOT-((0:obj).wetness<=0.5), NOT-((0:obj).dirtiness<=0.478), NOT-((0:robot).fingers<=0.5), ((0:obj).dirtiness<=0.478), ((0:obj).wetness<=0.5), NOT-((0:obj).held<=0.5), NOT-((0:robot).gripper_rot<=0.5), ((0:robot).gripper_rot<=0.25), ((0:obj).pose_y<=-0.333)}, with total cost 20.0
	Total score: 21.09 computed in 29.200 seconds

starting search
Evaluating predicates: frozenset(), with total cost 0
	Total score: 11210.38 computed in 0.141 seconds

Starting hill climbing at state frozenset() with heuristic 11210.38
Searching for an improvement at depth 0
Evaluating predicates: frozenset({((0:obj).dirtiness<=0.478)}), with total cost 2.0
	Total score: 10512.45 computed in 0.150 seconds
Evaluating predicates: frozenset({((0:obj).pose_y<=-0.333)}), with total cost 3.0
	Total score: 3103.47 computed in 0.125 seconds
Evaluating predicates: frozenset({((0:obj).wetness<=0.5)}), with total cost 2.0
	Total score: 10312.49 computed in 0.174 seconds
Evaluating predicates: frozenset({((0:robot).gripper_rot<=0.25)}), with total cost 3.0
	Total score: 10313.44 computed in 0.147 seconds
Evaluating predicates: frozenset({NOT-((0:obj).dirtiness<=0.478)}), with total cost 2.0
	Total score: 13612.42 computed in 0.150 seconds
Evaluating predicates: frozenset({NOT-((0:obj).held<=0.5)}), with total cost 2.0
	Total score: 1642.47 computed in 0.144 seconds
Evaluating predicates: frozenset({NOT-((0:obj).wetness<=0.5)}), with total cost 2.0
	Total score: 13312.45 computed in 0.165 seconds
Evaluating predicates: frozenset({NOT-((0:robot).fingers<=0.5)}), with total cost 2.0
	Total score: 14012.43 computed in 0.164 seconds
Evaluating predicates: frozenset({NOT-((0:robot).gripper_rot<=0.5)}), with total cost 2.0
	Total score: 10612.44 computed in 0.232 seconds
Found an improvement at depth 0

Hill climbing reached new state frozenset({NOT-((0:obj).held<=0.5)}) with heuristic 1642.47
Searching for an improvement at depth 0
Evaluating predicates: frozenset({NOT-((0:obj).held<=0.5), ((0:obj).dirtiness<=0.478)}), with total cost 4.0
	Total score: 944.55 computed in 0.174 seconds
Evaluating predicates: frozenset({NOT-((0:obj).held<=0.5), ((0:obj).pose_y<=-0.333)}), with total cost 5.0
	Total score: 1645.56 computed in 0.140 seconds
Evaluating predicates: frozenset({NOT-((0:obj).held<=0.5), ((0:obj).wetness<=0.5)}), with total cost 4.0
	Total score: 744.6 computed in 0.211 seconds
Evaluating predicates: frozenset({NOT-((0:obj).held<=0.5), ((0:robot).gripper_rot<=0.25)}), with total cost 5.0
	Total score: 1615.54 computed in 0.183 seconds
Evaluating predicates: frozenset({NOT-((0:obj).held<=0.5), NOT-((0:obj).dirtiness<=0.478)}), with total cost 4.0
	Total score: 2844.52 computed in 0.172 seconds
Evaluating predicates: frozenset({NOT-((0:obj).held<=0.5), NOT-((0:obj).wetness<=0.5)}), with total cost 4.0
	Total score: 3144.56 computed in 0.203 seconds
Evaluating predicates: frozenset({NOT-((0:obj).held<=0.5), NOT-((0:robot).fingers<=0.5)}), with total cost 4.0
	Total score: 1644.52 computed in 0.147 seconds
Evaluating predicates: frozenset({NOT-((0:obj).held<=0.5), NOT-((0:robot).gripper_rot<=0.5)}), with total cost 4.0
	Total score: 1634.54 computed in 0.283 seconds
Found an improvement at depth 0

Hill climbing reached new state frozenset({NOT-((0:obj).held<=0.5), ((0:obj).wetness<=0.5)}) with heuristic 744.6
Searching for an improvement at depth 0
Evaluating predicates: frozenset({NOT-((0:obj).held<=0.5), ((0:obj).wetness<=0.5), ((0:obj).dirtiness<=0.478)}), with total cost 6.0
	Total score: 46.72 computed in 0.245 seconds
Evaluating predicates: frozenset({NOT-((0:obj).held<=0.5), ((0:obj).wetness<=0.5), ((0:obj).pose_y<=-0.333)}), with total cost 7.0
	Total score: 747.71 computed in 0.190 seconds
Evaluating predicates: frozenset({NOT-((0:obj).held<=0.5), ((0:obj).wetness<=0.5), ((0:robot).gripper_rot<=0.25)}), with total cost 7.0
	Total score: 717.67 computed in 0.256 seconds
Evaluating predicates: frozenset({NOT-((0:obj).held<=0.5), ((0:obj).wetness<=0.5), NOT-((0:obj).dirtiness<=0.478)}), with total cost 6.0
	Total score: 1946.67 computed in 0.247 seconds
Evaluating predicates: frozenset({NOT-((0:obj).held<=0.5), ((0:obj).wetness<=0.5), NOT-((0:obj).wetness<=0.5)}), with total cost 6.0
	Total score: 746.63 computed in 0.216 seconds
Evaluating predicates: frozenset({NOT-((0:robot).fingers<=0.5), NOT-((0:obj).held<=0.5), ((0:obj).wetness<=0.5)}), with total cost 6.0
	Total score: 746.65 computed in 0.213 seconds
Evaluating predicates: frozenset({NOT-((0:obj).held<=0.5), NOT-((0:robot).gripper_rot<=0.5), ((0:obj).wetness<=0.5)}), with total cost 6.0
	Total score: 736.67 computed in 0.366 seconds
Found an improvement at depth 0

Hill climbing reached new state frozenset({NOT-((0:obj).held<=0.5), ((0:obj).wetness<=0.5), ((0:obj).dirtiness<=0.478)}) with heuristic 46.72
Searching for an improvement at depth 0
Evaluating predicates: frozenset({NOT-((0:obj).held<=0.5), ((0:obj).wetness<=0.5), ((0:obj).pose_y<=-0.333), ((0:obj).dirtiness<=0.478)}), with total cost 9.0
	Total score: 49.84 computed in 0.223 seconds
Evaluating predicates: frozenset({NOT-((0:obj).held<=0.5), ((0:obj).wetness<=0.5), ((0:robot).gripper_rot<=0.25), ((0:obj).dirtiness<=0.478)}), with total cost 9.0
	Total score: 19.79 computed in 0.311 seconds
Evaluating predicates: frozenset({NOT-((0:obj).held<=0.5), ((0:obj).wetness<=0.5), NOT-((0:obj).dirtiness<=0.478), ((0:obj).dirtiness<=0.478)}), with total cost 8.0
	Total score: 48.74 computed in 0.240 seconds
Evaluating predicates: frozenset({NOT-((0:obj).held<=0.5), ((0:obj).wetness<=0.5), NOT-((0:obj).wetness<=0.5), ((0:obj).dirtiness<=0.478)}), with total cost 8.0
	Total score: 48.76 computed in 0.246 seconds
Evaluating predicates: frozenset({NOT-((0:robot).fingers<=0.5), NOT-((0:obj).held<=0.5), ((0:obj).wetness<=0.5), ((0:obj).dirtiness<=0.478)}), with total cost 8.0
	Total score: 48.77 computed in 0.255 seconds
Evaluating predicates: frozenset({NOT-((0:obj).held<=0.5), NOT-((0:robot).gripper_rot<=0.5), ((0:obj).wetness<=0.5), ((0:obj).dirtiness<=0.478)}), with total cost 8.0
	Total score: 38.79 computed in 0.296 seconds
Found an improvement at depth 0

Hill climbing reached new state frozenset({NOT-((0:obj).held<=0.5), ((0:obj).wetness<=0.5), ((0:robot).gripper_rot<=0.25), ((0:obj).dirtiness<=0.478)}) with heuristic 19.79
Searching for an improvement at depth 0
Evaluating predicates: frozenset({((0:obj).wetness<=0.5), NOT-((0:obj).held<=0.5), ((0:robot).gripper_rot<=0.25), ((0:obj).pose_y<=-0.333), ((0:obj).dirtiness<=0.478)}), with total cost 12.0
	Total score: 22.92 computed in 0.419 seconds
Evaluating predicates: frozenset({((0:obj).wetness<=0.5), NOT-((0:obj).dirtiness<=0.478), NOT-((0:obj).held<=0.5), ((0:robot).gripper_rot<=0.25), ((0:obj).dirtiness<=0.478)}), with total cost 11.0
	Total score: 21.81 computed in 0.319 seconds
Evaluating predicates: frozenset({((0:obj).wetness<=0.5), NOT-((0:obj).wetness<=0.5), NOT-((0:obj).held<=0.5), ((0:robot).gripper_rot<=0.25), ((0:obj).dirtiness<=0.478)}), with total cost 11.0
	Total score: 21.83 computed in 0.329 seconds
Evaluating predicates: frozenset({((0:obj).wetness<=0.5), NOT-((0:obj).held<=0.5), NOT-((0:robot).fingers<=0.5), ((0:robot).gripper_rot<=0.25), ((0:obj).dirtiness<=0.478)}), with total cost 11.0
	Total score: 21.86 computed in 0.319 seconds
Evaluating predicates: frozenset({((0:obj).wetness<=0.5), NOT-((0:obj).held<=0.5), NOT-((0:robot).gripper_rot<=0.5), ((0:robot).gripper_rot<=0.25), ((0:obj).dirtiness<=0.478)}), with total cost 11.0
	Total score: 11.83 computed in 0.345 seconds
Found an improvement at depth 0

Hill climbing reached new state frozenset({((0:obj).wetness<=0.5), NOT-((0:obj).held<=0.5), NOT-((0:robot).gripper_rot<=0.5), ((0:robot).gripper_rot<=0.25), ((0:obj).dirtiness<=0.478)}) with heuristic 11.83
Searching for an improvement at depth 0
Evaluating predicates: frozenset({((0:obj).wetness<=0.5), NOT-((0:obj).held<=0.5), NOT-((0:robot).gripper_rot<=0.5), ((0:robot).gripper_rot<=0.25), ((0:obj).pose_y<=-0.333), ((0:obj).dirtiness<=0.478)}), with total cost 14.0
	Total score: 14.96 computed in 0.308 seconds
Evaluating predicates: frozenset({((0:obj).wetness<=0.5), NOT-((0:obj).dirtiness<=0.478), NOT-((0:obj).held<=0.5), NOT-((0:robot).gripper_rot<=0.5), ((0:robot).gripper_rot<=0.25), ((0:obj).dirtiness<=0.478)}), with total cost 13.0
	Total score: 13.85 computed in 0.308 seconds
Evaluating predicates: frozenset({((0:obj).wetness<=0.5), NOT-((0:obj).wetness<=0.5), NOT-((0:obj).held<=0.5), NOT-((0:robot).gripper_rot<=0.5), ((0:robot).gripper_rot<=0.25), ((0:obj).dirtiness<=0.478)}), with total cost 13.0
	Total score: 13.87 computed in 0.523 seconds
Evaluating predicates: frozenset({((0:obj).wetness<=0.5), NOT-((0:robot).fingers<=0.5), NOT-((0:obj).held<=0.5), NOT-((0:robot).gripper_rot<=0.5), ((0:robot).gripper_rot<=0.25), ((0:obj).dirtiness<=0.478)}), with total cost 13.0
	Total score: 13.9 computed in 0.276 seconds
No improvement found at depth 0

Terminating hill climbing, could not improve score

Selected 5 predicates out of 9 candidates:
	 ((0:obj).wetness<=0.5)
	 NOT-((0:obj).held<=0.5)
	 NOT-((0:robot).gripper_rot<=0.5)
	 ((0:robot).gripper_rot<=0.25)
	 ((0:obj).dirtiness<=0.478)
Evaluating predicates: frozenset({((0:obj).wetness<=0.5), NOT-((0:obj).held<=0.5), NOT-((0:robot).gripper_rot<=0.5), ((0:robot).gripper_rot<=0.25), ((0:obj).dirtiness<=0.478)}), with total cost 11.0
	Total score: 11.83 computed in 0.309 seconds
Done.

Full log after disabling grammar_search_size_weight and pred_complexity_weight:
LOADED DATASET INDEX: 0
Generating candidate predicates...
Done: created 9 candidates:
((0:obj).dirtiness<=0.478) 2.0
NOT-((0:obj).dirtiness<=0.478) 2.0
((0:obj).wetness<=0.5) 2.0
NOT-((0:obj).wetness<=0.5) 2.0
NOT-((0:obj).held<=0.5) 2.0
NOT-((0:robot).gripper_rot<=0.5) 2.0
NOT-((0:robot).fingers<=0.5) 2.0
((0:robot).gripper_rot<=0.25) 3.0
((0:obj).pose_y<=-0.333) 3.0
Applying predicates to data...
Done.
Selecting a subset...
Evaluating predicates: {NOT-((0:obj).wetness<=0.5), NOT-((0:obj).dirtiness<=0.478), NOT-((0:robot).fingers<=0.5), ((0:obj).dirtiness<=0.478), ((0:obj).wetness<=0.5), NOT-((0:obj).held<=0.5), NOT-((0:robot).gripper_rot<=0.5), ((0:robot).gripper_rot<=0.25), ((0:obj).pose_y<=-0.333)}, with total cost 20.0
	Total score: 0.0 computed in 29.237 seconds

starting search
Evaluating predicates: frozenset(), with total cost 0
	Total score: 11210.0 computed in 0.125 seconds

Starting hill climbing at state frozenset() with heuristic 11210.0
Searching for an improvement at depth 0
Evaluating predicates: frozenset({((0:obj).dirtiness<=0.478)}), with total cost 2.0
	Total score: 10510.0 computed in 0.144 seconds
Evaluating predicates: frozenset({((0:obj).pose_y<=-0.333)}), with total cost 3.0
	Total score: 3100.0 computed in 0.137 seconds
Evaluating predicates: frozenset({((0:obj).wetness<=0.5)}), with total cost 2.0
	Total score: 10310.0 computed in 0.185 seconds
Evaluating predicates: frozenset({((0:robot).gripper_rot<=0.25)}), with total cost 3.0
	Total score: 10310.0 computed in 0.215 seconds
Evaluating predicates: frozenset({NOT-((0:obj).dirtiness<=0.478)}), with total cost 2.0
	Total score: 13610.0 computed in 0.143 seconds
Evaluating predicates: frozenset({NOT-((0:obj).held<=0.5)}), with total cost 2.0
	Total score: 1640.0 computed in 0.186 seconds
Evaluating predicates: frozenset({NOT-((0:obj).wetness<=0.5)}), with total cost 2.0
	Total score: 13310.0 computed in 0.163 seconds
Evaluating predicates: frozenset({NOT-((0:robot).fingers<=0.5)}), with total cost 2.0
	Total score: 14010.0 computed in 0.154 seconds
Evaluating predicates: frozenset({NOT-((0:robot).gripper_rot<=0.5)}), with total cost 2.0
	Total score: 10610.0 computed in 0.136 seconds
Found an improvement at depth 0

Hill climbing reached new state frozenset({NOT-((0:obj).held<=0.5)}) with heuristic 1640.0
Searching for an improvement at depth 0
Evaluating predicates: frozenset({NOT-((0:obj).held<=0.5), ((0:obj).dirtiness<=0.478)}), with total cost 4.0
	Total score: 940.0 computed in 0.168 seconds
Evaluating predicates: frozenset({NOT-((0:obj).held<=0.5), ((0:obj).pose_y<=-0.333)}), with total cost 5.0
	Total score: 1640.0 computed in 0.137 seconds
Evaluating predicates: frozenset({NOT-((0:obj).held<=0.5), ((0:obj).wetness<=0.5)}), with total cost 4.0
	Total score: 740.0 computed in 0.324 seconds
Evaluating predicates: frozenset({NOT-((0:obj).held<=0.5), ((0:robot).gripper_rot<=0.25)}), with total cost 5.0
	Total score: 1610.0 computed in 0.184 seconds
Evaluating predicates: frozenset({NOT-((0:obj).held<=0.5), NOT-((0:obj).dirtiness<=0.478)}), with total cost 4.0
	Total score: 2840.0 computed in 0.166 seconds
Evaluating predicates: frozenset({NOT-((0:obj).held<=0.5), NOT-((0:obj).wetness<=0.5)}), with total cost 4.0
	Total score: 3140.0 computed in 0.199 seconds
Evaluating predicates: frozenset({NOT-((0:obj).held<=0.5), NOT-((0:robot).fingers<=0.5)}), with total cost 4.0
	Total score: 1640.0 computed in 0.164 seconds
Evaluating predicates: frozenset({NOT-((0:obj).held<=0.5), NOT-((0:robot).gripper_rot<=0.5)}), with total cost 4.0
	Total score: 1630.0 computed in 0.168 seconds
Found an improvement at depth 0

Hill climbing reached new state frozenset({NOT-((0:obj).held<=0.5), ((0:obj).wetness<=0.5)}) with heuristic 740.0
Searching for an improvement at depth 0
Evaluating predicates: frozenset({NOT-((0:obj).held<=0.5), ((0:obj).wetness<=0.5), ((0:obj).dirtiness<=0.478)}), with total cost 6.0
	Total score: 40.0 computed in 0.256 seconds
Evaluating predicates: frozenset({NOT-((0:obj).held<=0.5), ((0:obj).wetness<=0.5), ((0:obj).pose_y<=-0.333)}), with total cost 7.0
	Total score: 740.0 computed in 0.184 seconds
Evaluating predicates: frozenset({NOT-((0:obj).held<=0.5), ((0:obj).wetness<=0.5), ((0:robot).gripper_rot<=0.25)}), with total cost 7.0
	Total score: 710.0 computed in 0.372 seconds
Evaluating predicates: frozenset({NOT-((0:obj).held<=0.5), ((0:obj).wetness<=0.5), NOT-((0:obj).dirtiness<=0.478)}), with total cost 6.0
	Total score: 1940.0 computed in 0.246 seconds
Evaluating predicates: frozenset({NOT-((0:obj).held<=0.5), ((0:obj).wetness<=0.5), NOT-((0:obj).wetness<=0.5)}), with total cost 6.0
	Total score: 740.0 computed in 0.216 seconds
Evaluating predicates: frozenset({NOT-((0:robot).fingers<=0.5), NOT-((0:obj).held<=0.5), ((0:obj).wetness<=0.5)}), with total cost 6.0
	Total score: 740.0 computed in 0.215 seconds
Evaluating predicates: frozenset({NOT-((0:obj).held<=0.5), NOT-((0:robot).gripper_rot<=0.5), ((0:obj).wetness<=0.5)}), with total cost 6.0
	Total score: 730.0 computed in 0.277 seconds
Found an improvement at depth 0

Hill climbing reached new state frozenset({NOT-((0:obj).held<=0.5), ((0:obj).wetness<=0.5), ((0:obj).dirtiness<=0.478)}) with heuristic 40.0
Searching for an improvement at depth 0
Evaluating predicates: frozenset({NOT-((0:obj).held<=0.5), ((0:obj).wetness<=0.5), ((0:obj).pose_y<=-0.333), ((0:obj).dirtiness<=0.478)}), with total cost 9.0
	Total score: 40.0 computed in 0.240 seconds
Evaluating predicates: frozenset({NOT-((0:obj).held<=0.5), ((0:obj).wetness<=0.5), ((0:robot).gripper_rot<=0.25), ((0:obj).dirtiness<=0.478)}), with total cost 9.0
	Total score: 10.0 computed in 0.451 seconds
Evaluating predicates: frozenset({NOT-((0:obj).held<=0.5), ((0:obj).wetness<=0.5), NOT-((0:obj).dirtiness<=0.478), ((0:obj).dirtiness<=0.478)}), with total cost 8.0
	Total score: 40.0 computed in 0.251 seconds
Evaluating predicates: frozenset({NOT-((0:obj).held<=0.5), ((0:obj).wetness<=0.5), NOT-((0:obj).wetness<=0.5), ((0:obj).dirtiness<=0.478)}), with total cost 8.0
	Total score: 40.0 computed in 0.249 seconds
Evaluating predicates: frozenset({NOT-((0:robot).fingers<=0.5), NOT-((0:obj).held<=0.5), ((0:obj).wetness<=0.5), ((0:obj).dirtiness<=0.478)}), with total cost 8.0
	Total score: 40.0 computed in 0.257 seconds
Evaluating predicates: frozenset({NOT-((0:obj).held<=0.5), NOT-((0:robot).gripper_rot<=0.5), ((0:obj).wetness<=0.5), ((0:obj).dirtiness<=0.478)}), with total cost 8.0
	Total score: 30.0 computed in 0.287 seconds
Found an improvement at depth 0

Hill climbing reached new state frozenset({NOT-((0:obj).held<=0.5), ((0:obj).wetness<=0.5), ((0:robot).gripper_rot<=0.25), ((0:obj).dirtiness<=0.478)}) with heuristic 10.0
Searching for an improvement at depth 0
Evaluating predicates: frozenset({((0:obj).wetness<=0.5), NOT-((0:obj).held<=0.5), ((0:robot).gripper_rot<=0.25), ((0:obj).pose_y<=-0.333), ((0:obj).dirtiness<=0.478)}), with total cost 12.0
	Total score: 10.0 computed in 0.261 seconds
Evaluating predicates: frozenset({((0:obj).wetness<=0.5), NOT-((0:obj).dirtiness<=0.478), NOT-((0:obj).held<=0.5), ((0:robot).gripper_rot<=0.25), ((0:obj).dirtiness<=0.478)}), with total cost 11.0
	Total score: 10.0 computed in 0.290 seconds
Evaluating predicates: frozenset({((0:obj).wetness<=0.5), NOT-((0:obj).wetness<=0.5), NOT-((0:obj).held<=0.5), ((0:robot).gripper_rot<=0.25), ((0:obj).dirtiness<=0.478)}), with total cost 11.0
	Total score: 10.0 computed in 0.455 seconds
Evaluating predicates: frozenset({((0:obj).wetness<=0.5), NOT-((0:obj).held<=0.5), NOT-((0:robot).fingers<=0.5), ((0:robot).gripper_rot<=0.25), ((0:obj).dirtiness<=0.478)}), with total cost 11.0
	Total score: 10.0 computed in 0.295 seconds
Evaluating predicates: frozenset({((0:obj).wetness<=0.5), NOT-((0:obj).held<=0.5), NOT-((0:robot).gripper_rot<=0.5), ((0:robot).gripper_rot<=0.25), ((0:obj).dirtiness<=0.478)}), with total cost 11.0
	Total score: 0.0 computed in 0.301 seconds
Found an improvement at depth 0

Hill climbing reached new state frozenset({((0:obj).wetness<=0.5), NOT-((0:obj).held<=0.5), NOT-((0:robot).gripper_rot<=0.5), ((0:robot).gripper_rot<=0.25), ((0:obj).dirtiness<=0.478)}) with heuristic 0.0
Searching for an improvement at depth 0
Evaluating predicates: frozenset({((0:obj).wetness<=0.5), NOT-((0:obj).held<=0.5), NOT-((0:robot).gripper_rot<=0.5), ((0:robot).gripper_rot<=0.25), ((0:obj).pose_y<=-0.333), ((0:obj).dirtiness<=0.478)}), with total cost 14.0
	Total score: 0.0 computed in 0.283 seconds
Evaluating predicates: frozenset({((0:obj).wetness<=0.5), NOT-((0:obj).dirtiness<=0.478), NOT-((0:obj).held<=0.5), NOT-((0:robot).gripper_rot<=0.5), ((0:robot).gripper_rot<=0.25), ((0:obj).dirtiness<=0.478)}), with total cost 13.0
	Total score: 0.0 computed in 0.287 seconds
Evaluating predicates: frozenset({((0:obj).wetness<=0.5), NOT-((0:obj).wetness<=0.5), NOT-((0:obj).held<=0.5), NOT-((0:robot).gripper_rot<=0.5), ((0:robot).gripper_rot<=0.25), ((0:obj).dirtiness<=0.478)}), with total cost 13.0
	Total score: 0.0 computed in 0.307 seconds
Evaluating predicates: frozenset({((0:obj).wetness<=0.5), NOT-((0:robot).fingers<=0.5), NOT-((0:obj).held<=0.5), NOT-((0:robot).gripper_rot<=0.5), ((0:robot).gripper_rot<=0.25), ((0:obj).dirtiness<=0.478)}), with total cost 13.0
	Total score: 0.0 computed in 0.261 seconds
No improvement found at depth 0

Terminating hill climbing, could not improve score

Selected 5 predicates out of 9 candidates:
	 ((0:obj).wetness<=0.5)
	 NOT-((0:obj).held<=0.5)
	 NOT-((0:robot).gripper_rot<=0.5)
	 ((0:robot).gripper_rot<=0.25)
	 ((0:obj).dirtiness<=0.478)
Evaluating predicates: frozenset({((0:obj).wetness<=0.5), NOT-((0:obj).held<=0.5), NOT-((0:robot).gripper_rot<=0.5), ((0:robot).gripper_rot<=0.25), ((0:obj).dirtiness<=0.478)}), with total cost 11.0
	Total score: 0.0 computed in 0.300 seconds
Done.

below is an encouraging log with the following changes:

train on 2 objects instead of 1
hadd_count_depth0 instead of exact_count
grammar_search_hill_climbing_depth 1 instead of 0
debug grammar thresholds manually updated

python src/main.py --env painting --approach grammar_search_invention --seed 0 --excluded_predicates all --grammar_search_grammar_includes_givens 0 --grammar_search_grammar_includes_foralls 0 --grammar_search_score_function hadd_count_depth0 --grammar_search_hill_climbing_depth 1
pybullet build time: Oct 12 2021 17:17:06
INFO:root:Importing iGibson (igibson module)
INFO:root:Assets path: /Users/tom/phd/iGibson/igibson/data/assets
INFO:root:Gibson Dataset path: /Users/tom/phd/iGibson/igibson/data/g_dataset
INFO:root:iG Dataset path: /Users/tom/phd/iGibson/igibson/data/ig_dataset
INFO:root:3D-FRONT Dataset path: /Users/tom/phd/iGibson/igibson/data/threedfront_dataset
INFO:root:CubiCasa5K Dataset path: /Users/tom/phd/iGibson/igibson/data/cubicasa_dataset
INFO:root:iGibson Key path: /Users/tom/phd/iGibson/igibson/data/igibson.key
INFO:root:Example path: /Users/tom/phd/iGibson/igibson/examples
INFO:root:Example config path: /Users/tom/phd/iGibson/igibson/examples/configs
Seed: 0
Env: painting
Approach: grammar_search_invention
Timeout: 10

Full config:
namespace(num_train_tasks=15, num_test_tasks=50, max_num_steps_check_policy=100, cover_num_blocks=2, cover_num_targets=2, cover_block_widths=[0.1, 0.07], cover_target_widths=[0.05, 0.03], cover_multistep_action_limits=[-inf, inf], cover_multistep_use_learned_equivalents=True, cluttered_table_num_cans_train=5, cluttered_table_num_cans_test=10, cluttered_table_can_radius=0.01, cluttered_table_collision_angle_thresh=0.7853981633974483, repeated_nextto_num_dots=25, painting_train_families=['box_and_shelf'], behavior_config_file='examples/configs/njk_re-shelving_library_books_full_obs.yaml', behavior_mode='headless', behavior_action_timestep=0.1, behavior_physics_timestep=0.008333333333333333, random_options_max_tries=100, option_model_name='default', max_num_steps_option_rollout=1000, max_skeletons_optimized=8, max_samples_per_step=10, results_dir='results', approach_dir='saved_approaches', data_dir='saved_datasets', video_dir='videos', video_fps=2, offline_data_planning_timeout=3, teacher_dataset_label_ratio=1.0, min_data_for_nsrt=3, learn_side_predicates=False, normalization_scale_clip=1, learning_rate=0.001, mlp_regressor_max_itr=10000, mlp_regressor_hid_sizes=[32, 32], mlp_regressor_clip_gradients=False, mlp_regressor_gradient_clip_value=5, mlp_classifier_hid_sizes=[32, 32], mlp_classifier_balance_data=True, neural_gaus_regressor_hid_sizes=[32, 32], neural_gaus_regressor_max_itr=10000, neural_gaus_regressor_sample_clip=1, mlp_classifier_n_iter_no_change=5000, option_learner='no_learning', sampler_learner='neural', max_rejection_sampling_tries=100, sampler_mlp_classifier_max_itr=10000, iterative_invention_accept_score=0.999, predicate_mlp_classifier_max_itr=1000, interactive_known_predicates='GripperOpen,Holding,Clear,NextToTable,NextToDoor,NextToDial,InRegion,Borders,Connects,IsBoringRoom,IsPlayroom,IsBoringRoomDoor,IsPlayroomDoor,DoorOpen,DoorClosed,LightOn,LightOff,On,OnTable', interactive_num_episodes=0, interactive_max_num_steps=21, interactive_relearn_every=1, interactive_num_babbles=10, interactive_max_num_atoms_babbled=1, interactive_num_tasks_babbled=5, interactive_atom_type_babbled='ground', grammar_search_grammar_includes_givens=0, grammar_search_grammar_includes_foralls=0, grammar_search_true_pos_weight=10, grammar_search_false_pos_weight=1, grammar_search_bf_weight=1, grammar_search_size_weight=0.0, grammar_search_pred_complexity_weight=0.0, grammar_search_max_predicates=50, grammar_search_predicate_cost_upper_bound=6, grammar_search_score_function='hadd_count_depth0', grammar_search_heuristic_based_weight=10.0, grammar_search_heuristic_based_max_demos=15, grammar_search_heuristic_based_max_nondemos=50, grammar_search_lookahead_based_temperature=10.0, grammar_search_task_planning_timeout=1.0, grammar_search_hill_climbing_depth=1, grammar_search_parallelize_hill_climbing=False, grammar_search_off_demo_count_penalty=1.0, grammar_search_on_demo_count_penalty=10.0, grammar_search_suspicious_penalty=10.0, get_arg_specific_settings=<staticmethod object at 0x1019a74c0>, task_planning_heuristic='hadd', sesame_propagate_failures='immediately', offline_data_method='demo+replay', offline_data_num_replays=500, env='painting', approach='grammar_search_invention', excluded_predicates='all', seed=0, timeout=10, make_videos=False, load_approach=False, remake_data=False, experiment_id='')
Git commit hash: 39f579aac6003b72aeb72e7d082d9e8ca36b6f78
All non-goal predicates excluded: {'OnTable', 'IsWet', 'Holding', 'HoldingTop', 'HoldingSide', 'IsDirty', 'IsDry', 'GripperOpen', 'IsClean'}

LOADED DATASET INDEX: 0
Generating candidate predicates...
Done: created 9 candidates:
((0:obj).dirtiness<=0.489) 2.0
NOT-((0:obj).dirtiness<=0.489) 2.0
((0:obj).wetness<=0.5) 2.0
NOT-((0:obj).wetness<=0.5) 2.0
NOT-((0:obj).held<=0.5) 2.0
NOT-((0:robot).gripper_rot<=0.5) 2.0
NOT-((0:robot).fingers<=0.5) 2.0
((0:robot).gripper_rot<=0.25) 3.0
((0:obj).pose_y<=-0.291) 3.0
Applying predicates to data...
Done.
Selecting a subset...
Evaluating predicates: {NOT-((0:obj).wetness<=0.5), ((0:obj).pose_y<=-0.291), NOT-((0:obj).dirtiness<=0.489), NOT-((0:robot).fingers<=0.5), ((0:obj).wetness<=0.5), NOT-((0:obj).held<=0.5), NOT-((0:robot).gripper_rot<=0.5), ((0:robot).gripper_rot<=0.25), ((0:obj).dirtiness<=0.489)}, with total cost 20.0
	Total score: 360.0 computed in 0.347 seconds

starting search
Evaluating predicates: frozenset(), with total cost 0
	Total score: 29440.0 computed in 0.097 seconds

Starting hill climbing at state frozenset() with heuristic 29440.0
Searching for an improvement at depth 0
Evaluating predicates: frozenset({((0:obj).dirtiness<=0.489)}), with total cost 2.0
	Total score: 25040.0 computed in 0.157 seconds
Evaluating predicates: frozenset({((0:obj).pose_y<=-0.291)}), with total cost 3.0
	Total score: 8000.0 computed in 0.109 seconds
Evaluating predicates: frozenset({((0:obj).wetness<=0.5)}), with total cost 2.0
	Total score: 30740.0 computed in 0.168 seconds
Evaluating predicates: frozenset({((0:robot).gripper_rot<=0.25)}), with total cost 3.0
	Total score: 25750.0 computed in 0.214 seconds
Evaluating predicates: frozenset({NOT-((0:obj).dirtiness<=0.489)}), with total cost 2.0
	Total score: 34240.0 computed in 0.119 seconds
Evaluating predicates: frozenset({NOT-((0:obj).held<=0.5)}), with total cost 2.0
	Total score: 12960.0 computed in 0.110 seconds
Evaluating predicates: frozenset({NOT-((0:obj).wetness<=0.5)}), with total cost 2.0
	Total score: 36140.0 computed in 0.137 seconds
Evaluating predicates: frozenset({NOT-((0:robot).fingers<=0.5)}), with total cost 2.0
	Total score: 35940.0 computed in 0.140 seconds
Evaluating predicates: frozenset({NOT-((0:robot).gripper_rot<=0.5)}), with total cost 2.0
	Total score: 25040.0 computed in 0.234 seconds
Found an improvement at depth 0

Hill climbing reached new state frozenset({((0:obj).pose_y<=-0.291)}) with heuristic 8000.0
Searching for an improvement at depth 0
Evaluating predicates: frozenset({((0:obj).pose_y<=-0.291), ((0:obj).dirtiness<=0.489)}), with total cost 5.0
	Total score: 6500.0 computed in 0.152 seconds
Evaluating predicates: frozenset({((0:obj).wetness<=0.5), ((0:obj).pose_y<=-0.291)}), with total cost 5.0
	Total score: 9300.0 computed in 0.170 seconds
Evaluating predicates: frozenset({((0:robot).gripper_rot<=0.25), ((0:obj).pose_y<=-0.291)}), with total cost 6.0
	Total score: 12410.0 computed in 0.135 seconds
Evaluating predicates: frozenset({NOT-((0:obj).dirtiness<=0.489), ((0:obj).pose_y<=-0.291)}), with total cost 5.0
	Total score: 12800.0 computed in 0.132 seconds
Evaluating predicates: frozenset({NOT-((0:obj).held<=0.5), ((0:obj).pose_y<=-0.291)}), with total cost 5.0
	Total score: 12960.0 computed in 0.133 seconds
Evaluating predicates: frozenset({NOT-((0:obj).wetness<=0.5), ((0:obj).pose_y<=-0.291)}), with total cost 5.0
	Total score: 14700.0 computed in 0.261 seconds
Evaluating predicates: frozenset({NOT-((0:robot).fingers<=0.5), ((0:obj).pose_y<=-0.291)}), with total cost 5.0
	Total score: 14500.0 computed in 0.136 seconds
Evaluating predicates: frozenset({NOT-((0:robot).gripper_rot<=0.5), ((0:obj).pose_y<=-0.291)}), with total cost 5.0
	Total score: 11610.0 computed in 0.133 seconds
Found an improvement at depth 0

Hill climbing reached new state frozenset({((0:obj).pose_y<=-0.291), ((0:obj).dirtiness<=0.489)}) with heuristic 6500.0
Searching for an improvement at depth 0
Evaluating predicates: frozenset({((0:obj).wetness<=0.5), ((0:obj).pose_y<=-0.291), ((0:obj).dirtiness<=0.489)}), with total cost 7.0
	Total score: 7140.0 computed in 0.210 seconds
Evaluating predicates: frozenset({((0:robot).gripper_rot<=0.25), ((0:obj).pose_y<=-0.291), ((0:obj).dirtiness<=0.489)}), with total cost 8.0
	Total score: 10610.0 computed in 0.198 seconds
Evaluating predicates: frozenset({NOT-((0:obj).dirtiness<=0.489), ((0:obj).pose_y<=-0.291), ((0:obj).dirtiness<=0.489)}), with total cost 7.0
	Total score: 6500.0 computed in 0.155 seconds
Evaluating predicates: frozenset({NOT-((0:obj).held<=0.5), ((0:obj).pose_y<=-0.291), ((0:obj).dirtiness<=0.489)}), with total cost 7.0
	Total score: 12640.0 computed in 0.315 seconds
Evaluating predicates: frozenset({NOT-((0:obj).wetness<=0.5), ((0:obj).pose_y<=-0.291), ((0:obj).dirtiness<=0.489)}), with total cost 7.0
	Total score: 13600.0 computed in 0.203 seconds
Evaluating predicates: frozenset({NOT-((0:robot).fingers<=0.5), ((0:obj).pose_y<=-0.291), ((0:obj).dirtiness<=0.489)}), with total cost 7.0
	Total score: 10600.0 computed in 0.228 seconds
Evaluating predicates: frozenset({NOT-((0:robot).gripper_rot<=0.5), ((0:obj).pose_y<=-0.291), ((0:obj).dirtiness<=0.489)}), with total cost 7.0
	Total score: 9310.0 computed in 0.187 seconds
No improvement found at depth 0
Searching for an improvement at depth 1
Evaluating predicates: frozenset({((0:obj).wetness<=0.5), ((0:robot).gripper_rot<=0.25), ((0:obj).pose_y<=-0.291), ((0:obj).dirtiness<=0.489)}), with total cost 10.0
	Total score: 10650.0 computed in 0.259 seconds
Evaluating predicates: frozenset({((0:obj).wetness<=0.5), NOT-((0:obj).dirtiness<=0.489), ((0:obj).pose_y<=-0.291), ((0:obj).dirtiness<=0.489)}), with total cost 9.0
	Total score: 7140.0 computed in 0.213 seconds
Evaluating predicates: frozenset({NOT-((0:obj).held<=0.5), ((0:obj).wetness<=0.5), ((0:obj).pose_y<=-0.291), ((0:obj).dirtiness<=0.489)}), with total cost 9.0
	Total score: 12760.0 computed in 0.243 seconds
Evaluating predicates: frozenset({((0:obj).wetness<=0.5), NOT-((0:obj).wetness<=0.5), ((0:obj).pose_y<=-0.291), ((0:obj).dirtiness<=0.489)}), with total cost 9.0
	Total score: 7140.0 computed in 0.480 seconds
Evaluating predicates: frozenset({NOT-((0:robot).fingers<=0.5), ((0:obj).wetness<=0.5), ((0:obj).pose_y<=-0.291), ((0:obj).dirtiness<=0.489)}), with total cost 9.0
	Total score: 10540.0 computed in 0.288 seconds
Evaluating predicates: frozenset({NOT-((0:robot).gripper_rot<=0.5), ((0:obj).wetness<=0.5), ((0:obj).pose_y<=-0.291), ((0:obj).dirtiness<=0.489)}), with total cost 9.0
	Total score: 10460.0 computed in 0.314 seconds
Evaluating predicates: frozenset({NOT-((0:obj).dirtiness<=0.489), ((0:robot).gripper_rot<=0.25), ((0:obj).pose_y<=-0.291), ((0:obj).dirtiness<=0.489)}), with total cost 10.0
	Total score: 10610.0 computed in 0.195 seconds
Evaluating predicates: frozenset({NOT-((0:obj).held<=0.5), ((0:robot).gripper_rot<=0.25), ((0:obj).pose_y<=-0.291), ((0:obj).dirtiness<=0.489)}), with total cost 10.0
	Total score: 16120.0 computed in 0.232 seconds
Evaluating predicates: frozenset({NOT-((0:obj).wetness<=0.5), ((0:robot).gripper_rot<=0.25), ((0:obj).pose_y<=-0.291), ((0:obj).dirtiness<=0.489)}), with total cost 10.0
	Total score: 16310.0 computed in 0.228 seconds
Evaluating predicates: frozenset({NOT-((0:robot).fingers<=0.5), ((0:robot).gripper_rot<=0.25), ((0:obj).pose_y<=-0.291), ((0:obj).dirtiness<=0.489)}), with total cost 10.0
	Total score: 10810.0 computed in 0.189 seconds
Evaluating predicates: frozenset({NOT-((0:robot).gripper_rot<=0.5), ((0:robot).gripper_rot<=0.25), ((0:obj).pose_y<=-0.291), ((0:obj).dirtiness<=0.489)}), with total cost 10.0
	Total score: 13420.0 computed in 0.199 seconds
Evaluating predicates: frozenset({NOT-((0:obj).held<=0.5), NOT-((0:obj).dirtiness<=0.489), ((0:obj).pose_y<=-0.291), ((0:obj).dirtiness<=0.489)}), with total cost 9.0
	Total score: 12640.0 computed in 0.371 seconds
Evaluating predicates: frozenset({NOT-((0:obj).dirtiness<=0.489), NOT-((0:obj).wetness<=0.5), ((0:obj).pose_y<=-0.291), ((0:obj).dirtiness<=0.489)}), with total cost 9.0
	Total score: 13600.0 computed in 0.201 seconds
Evaluating predicates: frozenset({NOT-((0:robot).fingers<=0.5), NOT-((0:obj).dirtiness<=0.489), ((0:obj).pose_y<=-0.291), ((0:obj).dirtiness<=0.489)}), with total cost 9.0
	Total score: 10600.0 computed in 0.212 seconds
Evaluating predicates: frozenset({NOT-((0:robot).gripper_rot<=0.5), NOT-((0:obj).dirtiness<=0.489), ((0:obj).pose_y<=-0.291), ((0:obj).dirtiness<=0.489)}), with total cost 9.0
	Total score: 9310.0 computed in 0.218 seconds
Evaluating predicates: frozenset({NOT-((0:obj).held<=0.5), NOT-((0:obj).wetness<=0.5), ((0:obj).pose_y<=-0.291), ((0:obj).dirtiness<=0.489)}), with total cost 9.0
	Total score: 14340.0 computed in 0.245 seconds
Evaluating predicates: frozenset({NOT-((0:obj).held<=0.5), NOT-((0:robot).fingers<=0.5), ((0:obj).pose_y<=-0.291), ((0:obj).dirtiness<=0.489)}), with total cost 9.0
	Total score: 2060.0 computed in 0.164 seconds
Evaluating predicates: frozenset({NOT-((0:obj).held<=0.5), NOT-((0:robot).gripper_rot<=0.5), ((0:obj).pose_y<=-0.291), ((0:obj).dirtiness<=0.489)}), with total cost 9.0
	Total score: 14620.0 computed in 0.224 seconds
Evaluating predicates: frozenset({NOT-((0:robot).fingers<=0.5), NOT-((0:obj).wetness<=0.5), ((0:obj).pose_y<=-0.291), ((0:obj).dirtiness<=0.489)}), with total cost 9.0
	Total score: 16800.0 computed in 0.251 seconds
Evaluating predicates: frozenset({NOT-((0:robot).gripper_rot<=0.5), NOT-((0:obj).wetness<=0.5), ((0:obj).pose_y<=-0.291), ((0:obj).dirtiness<=0.489)}), with total cost 9.0
	Total score: 14710.0 computed in 0.262 seconds
Evaluating predicates: frozenset({NOT-((0:robot).gripper_rot<=0.5), NOT-((0:robot).fingers<=0.5), ((0:obj).pose_y<=-0.291), ((0:obj).dirtiness<=0.489)}), with total cost 9.0
	Total score: 11210.0 computed in 0.414 seconds
Found an improvement at depth 1

Hill climbing reached new state frozenset({NOT-((0:obj).held<=0.5), NOT-((0:robot).fingers<=0.5), ((0:obj).pose_y<=-0.291), ((0:obj).dirtiness<=0.489)}) with heuristic 2060.0
Searching for an improvement at depth 0
Evaluating predicates: frozenset({((0:obj).wetness<=0.5), ((0:obj).pose_y<=-0.291), NOT-((0:obj).held<=0.5), NOT-((0:robot).fingers<=0.5), ((0:obj).dirtiness<=0.489)}), with total cost 11.0
	Total score: 710.0 computed in 0.244 seconds
Evaluating predicates: frozenset({((0:obj).pose_y<=-0.291), NOT-((0:obj).held<=0.5), NOT-((0:robot).fingers<=0.5), ((0:robot).gripper_rot<=0.25), ((0:obj).dirtiness<=0.489)}), with total cost 12.0
	Total score: 3330.0 computed in 0.202 seconds
Evaluating predicates: frozenset({NOT-((0:obj).dirtiness<=0.489), ((0:obj).pose_y<=-0.291), NOT-((0:obj).held<=0.5), NOT-((0:robot).fingers<=0.5), ((0:obj).dirtiness<=0.489)}), with total cost 11.0
	Total score: 2060.0 computed in 0.191 seconds
Evaluating predicates: frozenset({NOT-((0:obj).wetness<=0.5), ((0:obj).pose_y<=-0.291), NOT-((0:obj).held<=0.5), NOT-((0:robot).fingers<=0.5), ((0:obj).dirtiness<=0.489)}), with total cost 11.0
	Total score: 5760.0 computed in 0.233 seconds
Evaluating predicates: frozenset({NOT-((0:robot).gripper_rot<=0.5), ((0:obj).pose_y<=-0.291), NOT-((0:obj).held<=0.5), NOT-((0:robot).fingers<=0.5), ((0:obj).dirtiness<=0.489)}), with total cost 11.0
	Total score: 2530.0 computed in 0.192 seconds
Found an improvement at depth 0

Hill climbing reached new state frozenset({((0:obj).wetness<=0.5), ((0:obj).pose_y<=-0.291), NOT-((0:obj).held<=0.5), NOT-((0:robot).fingers<=0.5), ((0:obj).dirtiness<=0.489)}) with heuristic 710.0
Searching for an improvement at depth 0
Evaluating predicates: frozenset({((0:obj).wetness<=0.5), ((0:obj).pose_y<=-0.291), NOT-((0:obj).held<=0.5), NOT-((0:robot).fingers<=0.5), ((0:robot).gripper_rot<=0.25), ((0:obj).dirtiness<=0.489)}), with total cost 14.0
	Total score: 2180.0 computed in 0.253 seconds
Evaluating predicates: frozenset({((0:obj).wetness<=0.5), NOT-((0:obj).dirtiness<=0.489), ((0:obj).pose_y<=-0.291), NOT-((0:obj).held<=0.5), NOT-((0:robot).fingers<=0.5), ((0:obj).dirtiness<=0.489)}), with total cost 13.0
	Total score: 710.0 computed in 0.239 seconds
Evaluating predicates: frozenset({((0:obj).wetness<=0.5), NOT-((0:obj).wetness<=0.5), ((0:obj).pose_y<=-0.291), NOT-((0:obj).held<=0.5), NOT-((0:robot).fingers<=0.5), ((0:obj).dirtiness<=0.489)}), with total cost 13.0
	Total score: 710.0 computed in 0.317 seconds
Evaluating predicates: frozenset({NOT-((0:robot).gripper_rot<=0.5), ((0:obj).wetness<=0.5), ((0:obj).pose_y<=-0.291), NOT-((0:obj).held<=0.5), NOT-((0:robot).fingers<=0.5), ((0:obj).dirtiness<=0.489)}), with total cost 13.0
	Total score: 1170.0 computed in 0.261 seconds
No improvement found at depth 0
Searching for an improvement at depth 1
Evaluating predicates: frozenset({((0:obj).wetness<=0.5), NOT-((0:obj).dirtiness<=0.489), ((0:obj).pose_y<=-0.291), NOT-((0:obj).held<=0.5), NOT-((0:robot).fingers<=0.5), ((0:robot).gripper_rot<=0.25), ((0:obj).dirtiness<=0.489)}), with total cost 16.0
	Total score: 2180.0 computed in 0.246 seconds
Evaluating predicates: frozenset({((0:obj).wetness<=0.5), NOT-((0:obj).wetness<=0.5), ((0:obj).pose_y<=-0.291), NOT-((0:obj).held<=0.5), NOT-((0:robot).fingers<=0.5), ((0:robot).gripper_rot<=0.25), ((0:obj).dirtiness<=0.489)}), with total cost 16.0
	Total score: 2180.0 computed in 0.479 seconds
Evaluating predicates: frozenset({NOT-((0:robot).gripper_rot<=0.5), ((0:obj).wetness<=0.5), ((0:obj).pose_y<=-0.291), NOT-((0:obj).held<=0.5), NOT-((0:robot).fingers<=0.5), ((0:robot).gripper_rot<=0.25), ((0:obj).dirtiness<=0.489)}), with total cost 16.0
	Total score: 360.0 computed in 0.236 seconds
Evaluating predicates: frozenset({((0:obj).wetness<=0.5), NOT-((0:obj).dirtiness<=0.489), ((0:obj).pose_y<=-0.291), NOT-((0:obj).wetness<=0.5), NOT-((0:obj).held<=0.5), NOT-((0:robot).fingers<=0.5), ((0:obj).dirtiness<=0.489)}), with total cost 15.0
	Total score: 710.0 computed in 0.213 seconds
Evaluating predicates: frozenset({NOT-((0:robot).gripper_rot<=0.5), ((0:obj).wetness<=0.5), NOT-((0:obj).dirtiness<=0.489), ((0:obj).pose_y<=-0.291), NOT-((0:obj).held<=0.5), NOT-((0:robot).fingers<=0.5), ((0:obj).dirtiness<=0.489)}), with total cost 15.0
	Total score: 1170.0 computed in 0.239 seconds
Evaluating predicates: frozenset({NOT-((0:robot).gripper_rot<=0.5), ((0:obj).wetness<=0.5), NOT-((0:obj).wetness<=0.5), ((0:obj).pose_y<=-0.291), NOT-((0:obj).held<=0.5), NOT-((0:robot).fingers<=0.5), ((0:obj).dirtiness<=0.489)}), with total cost 15.0
	Total score: 1170.0 computed in 0.257 seconds
Found an improvement at depth 1

Hill climbing reached new state frozenset({NOT-((0:robot).gripper_rot<=0.5), ((0:obj).wetness<=0.5), ((0:obj).pose_y<=-0.291), NOT-((0:obj).held<=0.5), NOT-((0:robot).fingers<=0.5), ((0:robot).gripper_rot<=0.25), ((0:obj).dirtiness<=0.489)}) with heuristic 360.0
Searching for an improvement at depth 0
Evaluating predicates: frozenset({NOT-((0:robot).gripper_rot<=0.5), ((0:obj).wetness<=0.5), NOT-((0:obj).dirtiness<=0.489), ((0:obj).pose_y<=-0.291), NOT-((0:obj).held<=0.5), NOT-((0:robot).fingers<=0.5), ((0:robot).gripper_rot<=0.25), ((0:obj).dirtiness<=0.489)}), with total cost 18.0
	Total score: 360.0 computed in 0.241 seconds
Evaluating predicates: frozenset({NOT-((0:robot).gripper_rot<=0.5), ((0:obj).wetness<=0.5), NOT-((0:obj).wetness<=0.5), ((0:obj).pose_y<=-0.291), NOT-((0:obj).held<=0.5), NOT-((0:robot).fingers<=0.5), ((0:robot).gripper_rot<=0.25), ((0:obj).dirtiness<=0.489)}), with total cost 18.0
	Total score: 360.0 computed in 0.252 seconds
No improvement found at depth 0
Searching for an improvement at depth 1
Evaluating predicates: frozenset({NOT-((0:obj).wetness<=0.5), ((0:obj).pose_y<=-0.291), NOT-((0:obj).dirtiness<=0.489), NOT-((0:robot).fingers<=0.5), ((0:obj).wetness<=0.5), NOT-((0:obj).held<=0.5), NOT-((0:robot).gripper_rot<=0.5), ((0:robot).gripper_rot<=0.25), ((0:obj).dirtiness<=0.489)}), with total cost 20.0
	Total score: 360.0 computed in 0.261 seconds
No improvement found at depth 1

Terminating hill climbing, could not improve score

Selected 7 predicates out of 9 candidates:
	 NOT-((0:robot).gripper_rot<=0.5)
	 ((0:obj).wetness<=0.5)
	 ((0:obj).pose_y<=-0.291)
	 NOT-((0:obj).held<=0.5)
	 NOT-((0:robot).fingers<=0.5)
	 ((0:robot).gripper_rot<=0.25)
	 ((0:obj).dirtiness<=0.489)
Evaluating predicates: frozenset({NOT-((0:robot).gripper_rot<=0.5), ((0:obj).wetness<=0.5), ((0:obj).pose_y<=-0.291), NOT-((0:obj).held<=0.5), NOT-((0:robot).fingers<=0.5), ((0:robot).gripper_rot<=0.25), ((0:obj).dirtiness<=0.489)}), with total cost 16.0
	Total score: 360.0 computed in 0.243 seconds
Done.

Learning NSRTs on 396 trajectories...

Doing option learning...

Learned operators with option specs:
STRIPS-Op0:
    Parameters: [?x0:obj, ?x1:robot]
    Preconditions: [((0:obj).pose_y<=-0.291)(?x0:obj), NOT-((0:robot).fingers<=0.5)(?x1:robot)]
    Add Effects: [((0:robot).gripper_rot<=0.25)(?x1:robot), NOT-((0:obj).held<=0.5)(?x0:obj)]
    Delete Effects: [NOT-((0:robot).fingers<=0.5)(?x1:robot)]
    Side Predicates: []
    Option Spec: Pick(?x1:robot, ?x0:obj)
STRIPS-Op1:
    Parameters: [?x0:obj, ?x1:robot]
    Preconditions: [((0:obj).dirtiness<=0.489)(?x0:obj), ((0:obj).pose_y<=-0.291)(?x0:obj), NOT-((0:obj).held<=0.5)(?x0:obj)]
    Add Effects: [((0:obj).wetness<=0.5)(?x0:obj)]
    Delete Effects: []
    Side Predicates: []
    Option Spec: Dry(?x1:robot)
STRIPS-Op2:
    Parameters: [?x0:obj, ?x1:shelf, ?x2:robot]
    Preconditions: [((0:obj).dirtiness<=0.489)(?x0:obj), ((0:obj).pose_y<=-0.291)(?x0:obj), ((0:obj).wetness<=0.5)(?x0:obj), NOT-((0:obj).held<=0.5)(?x0:obj)]
    Add Effects: [IsShelfColor(?x0:obj, ?x1:shelf)]
    Delete Effects: []
    Side Predicates: []
    Option Spec: Paint(?x2:robot)
STRIPS-Op3:
    Parameters: [?x0:obj, ?x1:shelf, ?x2:robot]
    Preconditions: [((0:obj).dirtiness<=0.489)(?x0:obj), ((0:obj).pose_y<=-0.291)(?x0:obj), ((0:robot).gripper_rot<=0.25)(?x2:robot), NOT-((0:obj).held<=0.5)(?x0:obj)]
    Add Effects: [InShelf(?x0:obj, ?x1:shelf), NOT-((0:robot).fingers<=0.5)(?x2:robot)]
    Delete Effects: [((0:obj).pose_y<=-0.291)(?x0:obj), ((0:robot).gripper_rot<=0.25)(?x2:robot), NOT-((0:obj).held<=0.5)(?x0:obj)]
    Side Predicates: []
    Option Spec: Place(?x2:robot)
STRIPS-Op4:
    Parameters: [?x0:obj, ?x1:robot]
    Preconditions: [((0:obj).pose_y<=-0.291)(?x0:obj), ((0:obj).wetness<=0.5)(?x0:obj), NOT-((0:obj).held<=0.5)(?x0:obj)]
    Add Effects: [((0:obj).dirtiness<=0.489)(?x0:obj)]
    Delete Effects: [((0:obj).wetness<=0.5)(?x0:obj)]
    Side Predicates: []
    Option Spec: Wash(?x1:robot)
STRIPS-Op5:
    Parameters: [?x0:obj, ?x1:robot]
    Preconditions: [((0:obj).pose_y<=-0.291)(?x0:obj), NOT-((0:robot).fingers<=0.5)(?x1:robot)]
    Add Effects: [NOT-((0:obj).held<=0.5)(?x0:obj), NOT-((0:robot).gripper_rot<=0.5)(?x1:robot)]
    Delete Effects: [NOT-((0:robot).fingers<=0.5)(?x1:robot)]
    Side Predicates: []
    Option Spec: Pick(?x1:robot, ?x0:obj)
STRIPS-Op6:
    Parameters: [?x0:obj, ?x1:box, ?x2:robot]
    Preconditions: [((0:obj).dirtiness<=0.489)(?x0:obj), ((0:obj).pose_y<=-0.291)(?x0:obj), ((0:obj).wetness<=0.5)(?x0:obj), NOT-((0:obj).held<=0.5)(?x0:obj)]
    Add Effects: [IsBoxColor(?x0:obj, ?x1:box)]
    Delete Effects: []
    Side Predicates: []
    Option Spec: Paint(?x2:robot)
STRIPS-Op7:
    Parameters: [?x0:obj, ?x1:box, ?x2:robot]
    Preconditions: [((0:obj).pose_y<=-0.291)(?x0:obj), NOT-((0:obj).held<=0.5)(?x0:obj), NOT-((0:robot).gripper_rot<=0.5)(?x2:robot)]
    Add Effects: [InBox(?x0:obj, ?x1:box), NOT-((0:robot).fingers<=0.5)(?x2:robot)]
    Delete Effects: [((0:obj).pose_y<=-0.291)(?x0:obj), NOT-((0:obj).held<=0.5)(?x0:obj), NOT-((0:robot).gripper_rot<=0.5)(?x2:robot)]
    Side Predicates: []
    Option Spec: Place(?x2:robot)
STRIPS-Op8:
    Parameters: [?x0:obj, ?x1:box, ?x2:shelf, ?x3:robot]
    Preconditions: [((0:obj).dirtiness<=0.489)(?x0:obj), ((0:obj).pose_y<=-0.291)(?x0:obj), ((0:obj).wetness<=0.5)(?x0:obj), IsBoxColor(?x0:obj, ?x1:box), NOT-((0:obj).held<=0.5)(?x0:obj), NOT-((0:robot).gripper_rot<=0.5)(?x3:robot)]
    Add Effects: [IsShelfColor(?x0:obj, ?x2:shelf)]
    Delete Effects: [IsBoxColor(?x0:obj, ?x1:box)]
    Side Predicates: []
    Option Spec: Paint(?x3:robot)
STRIPS-Op9:
    Parameters: [?x0:robot]
    Preconditions: []
    Add Effects: []
    Delete Effects: []
    Side Predicates: []
    Option Spec: Place(?x0:robot)
STRIPS-Op10:
    Parameters: [?x0:obj, ?x1:robot]
    Preconditions: [((0:obj).dirtiness<=0.489)(?x0:obj), ((0:obj).pose_y<=-0.291)(?x0:obj), ((0:obj).wetness<=0.5)(?x0:obj), NOT-((0:obj).held<=0.5)(?x0:obj)]
    Add Effects: []
    Delete Effects: [((0:obj).wetness<=0.5)(?x0:obj)]
    Side Predicates: []
    Option Spec: Wash(?x1:robot)
STRIPS-Op11:
    Parameters: [?x0:robot]
    Preconditions: []
    Add Effects: []
    Delete Effects: []
    Side Predicates: []
    Option Spec: Dry(?x0:robot)
STRIPS-Op12:
    Parameters: [?x0:robot]
    Preconditions: []
    Add Effects: []
    Delete Effects: []
    Side Predicates: []
    Option Spec: Paint(?x0:robot)
STRIPS-Op13:
    Parameters: [?x0:robot]
    Preconditions: []
    Add Effects: []
    Delete Effects: []
    Side Predicates: []
    Option Spec: Wash(?x0:robot)
STRIPS-Op14:
    Parameters: [?x0:obj, ?x1:box, ?x2:shelf, ?x3:robot]
    Preconditions: [((0:obj).dirtiness<=0.489)(?x0:obj), ((0:obj).pose_y<=-0.291)(?x0:obj), ((0:obj).wetness<=0.5)(?x0:obj), ((0:robot).gripper_rot<=0.25)(?x3:robot), IsShelfColor(?x0:obj, ?x2:shelf), NOT-((0:obj).held<=0.5)(?x0:obj)]
    Add Effects: [IsBoxColor(?x0:obj, ?x1:box)]
    Delete Effects: [IsShelfColor(?x0:obj, ?x2:shelf)]
    Side Predicates: []
    Option Spec: Paint(?x3:robot)
STRIPS-Op15:
    Parameters: [?x0:lid, ?x1:robot]
    Preconditions: [NOT-((0:robot).fingers<=0.5)(?x1:robot)]
    Add Effects: []
    Delete Effects: []
    Side Predicates: []
    Option Spec: OpenLid(?x1:robot, ?x0:lid)
STRIPS-Op16:
    Parameters: [?x0:obj, ?x1:robot]
    Preconditions: [((0:obj).dirtiness<=0.489)(?x0:obj), ((0:obj).wetness<=0.5)(?x0:obj), NOT-((0:robot).fingers<=0.5)(?x1:robot)]
    Add Effects: []
    Delete Effects: []
    Side Predicates: []
    Option Spec: Pick(?x1:robot, ?x0:obj)

Doing sampler learning...

Learning neural sampler for NSRT Op0
Generated 36 positive and 158 negative examples
Fitting classifier...
Reduced dataset size from 194 to 72
Training MLPClassifier on 72 datapoints
Loss: 0.20256, iter: 10000/10000
Loaded best model with loss: 0.20256
Fitting regressor...
Training NeuralGaussianRegressor on 36 datapoints
Loss: -5.48067, iter: 10000/10000
Loaded best model with loss: -6.35800

Learning neural sampler for NSRT Op1
Generated 17 positive and 119 negative examples
Fitting classifier...
Reduced dataset size from 136 to 34
Training MLPClassifier on 34 datapoints
Loss: 0.00000, iter: 10000/10000
Loaded best model with loss: 0.00000
Fitting regressor...
Training NeuralGaussianRegressor on 17 datapoints
Loss: -4.15818, iter: 10000/10000
Loaded best model with loss: -6.18063

Learning neural sampler for NSRT Op2
Generated 22 positive and 197 negative examples
Fitting classifier...
Reduced dataset size from 219 to 44
Training MLPClassifier on 44 datapoints
Loss: 0.00000, iter: 10000/10000
Loaded best model with loss: 0.00000
Fitting regressor...
Training NeuralGaussianRegressor on 22 datapoints
Loss: -4.95730, iter: 10000/10000
Loaded best model with loss: -5.97599

Learning neural sampler for NSRT Op3
Generated 39 positive and 215 negative examples
Fitting classifier...
Reduced dataset size from 254 to 78
Training MLPClassifier on 78 datapoints
Loss: 0.00000, iter: 10000/10000
Loaded best model with loss: 0.00000
Fitting regressor...
Training NeuralGaussianRegressor on 39 datapoints
Loss: -3.79286, iter: 10000/10000
Loaded best model with loss: -4.61683

Learning neural sampler for NSRT Op4
Generated 10 positive and 130 negative examples
Fitting classifier...
Reduced dataset size from 140 to 20
Training MLPClassifier on 20 datapoints
Loss: 0.00000, iter: 10000/10000
Loaded best model with loss: 0.00000
Fitting regressor...
Training NeuralGaussianRegressor on 10 datapoints
Loss: -4.56750, iter: 10000/10000
Loaded best model with loss: -6.23316

Learning neural sampler for NSRT Op5
Generated 37 positive and 157 negative examples
Fitting classifier...
Reduced dataset size from 194 to 74
Training MLPClassifier on 74 datapoints
Loss: 0.25510, iter: 10000/10000
Loaded best model with loss: 0.25510
Fitting regressor...
Training NeuralGaussianRegressor on 37 datapoints
Loss: -4.15138, iter: 10000/10000
Loaded best model with loss: -6.39153

Learning neural sampler for NSRT Op6
Generated 22 positive and 199 negative examples
Fitting classifier...
Reduced dataset size from 221 to 44
Training MLPClassifier on 44 datapoints
Loss: 0.00000, iter: 10000/10000
Loaded best model with loss: 0.00000
Fitting regressor...
Training NeuralGaussianRegressor on 22 datapoints
Loss: -4.47457, iter: 10000/10000
Loaded best model with loss: -5.41650

Learning neural sampler for NSRT Op7
Generated 29 positive and 225 negative examples
Fitting classifier...
Reduced dataset size from 254 to 58
Training MLPClassifier on 58 datapoints
Loss: 0.00000, iter: 10000/10000
Loaded best model with loss: 0.00000
Fitting regressor...
Training NeuralGaussianRegressor on 29 datapoints
Loss: -5.16226, iter: 10000/10000
Loaded best model with loss: -5.33952

Learning neural sampler for NSRT Op8
Generated 13 positive and 219 negative examples
Fitting classifier...
Reduced dataset size from 232 to 26
Training MLPClassifier on 26 datapoints
Loss: 0.00000, iter: 10000/10000
Loaded best model with loss: 0.00000
Fitting regressor...
Training NeuralGaussianRegressor on 13 datapoints
Loss: -5.37543, iter: 10000/10000
Loaded best model with loss: -5.93992

Learning neural sampler for NSRT Op9
Generated 59 positive and 0 negative examples
Fitting classifier...
Training MLPClassifier on 59 datapoints
Loss: 0.00000, iter: 10000/10000
Loaded best model with loss: 0.00000
Fitting regressor...
Training NeuralGaussianRegressor on 59 datapoints
Loss: -4.91708, iter: 10000/10000
Loaded best model with loss: -5.21930

Learning neural sampler for NSRT Op10
Generated 49 positive and 81 negative examples
Fitting classifier...
Reduced dataset size from 130 to 98
Training MLPClassifier on 98 datapoints
Loss: 0.00000, iter: 10000/10000
Loaded best model with loss: 0.00000
Fitting regressor...
Training NeuralGaussianRegressor on 49 datapoints
Loss: -5.05712, iter: 10000/10000
Loaded best model with loss: -6.12726

Learning neural sampler for NSRT Op11
Generated 51 positive and 0 negative examples
Fitting classifier...
Training MLPClassifier on 51 datapoints
Loss: 0.00000, iter: 10000/10000
Loaded best model with loss: 0.00000
Fitting regressor...
Training NeuralGaussianRegressor on 51 datapoints
Loss: -5.30057, iter: 10000/10000
Loaded best model with loss: -6.57513

Learning neural sampler for NSRT Op12
Generated 48 positive and 0 negative examples
Fitting classifier...
Training MLPClassifier on 48 datapoints
Loss: 0.00000, iter: 10000/10000
Loaded best model with loss: 0.00000
Fitting regressor...
Training NeuralGaussianRegressor on 48 datapoints
Loss: -0.80113, iter: 10000/10000
Loaded best model with loss: -0.80114

Learning neural sampler for NSRT Op13
Generated 11 positive and 0 negative examples
Fitting classifier...
Training MLPClassifier on 11 datapoints
Loss: 0.00000, iter: 10000/10000
Loaded best model with loss: 0.00000
Fitting regressor...
Training NeuralGaussianRegressor on 11 datapoints
Loss: -4.75521, iter: 10000/10000
Loaded best model with loss: -6.37288

Learning neural sampler for NSRT Op14
Generated 11 positive and 221 negative examples
Fitting classifier...
Reduced dataset size from 232 to 22
Training MLPClassifier on 22 datapoints
Loss: 0.00000, iter: 10000/10000
Loaded best model with loss: 0.00000
Fitting regressor...
Training NeuralGaussianRegressor on 11 datapoints
Loss: -4.67728, iter: 10000/10000
Loaded best model with loss: -6.00697

Learning neural sampler for NSRT Op16
Generated 24 positive and 0 negative examples
Fitting classifier...
Training MLPClassifier on 24 datapoints
Loss: 0.00000, iter: 10000/10000
Loaded best model with loss: 0.00000
Fitting regressor...
Training NeuralGaussianRegressor on 24 datapoints
Loss: -4.59058, iter: 10000/10000
Loaded best model with loss: -4.88084

Learned NSRTs:
NSRT-Op0:
    Parameters: [?x0:obj, ?x1:robot]
    Preconditions: [((0:obj).pose_y<=-0.291)(?x0:obj), NOT-((0:robot).fingers<=0.5)(?x1:robot)]
    Add Effects: [((0:robot).gripper_rot<=0.25)(?x1:robot), NOT-((0:obj).held<=0.5)(?x0:obj)]
    Delete Effects: [NOT-((0:robot).fingers<=0.5)(?x1:robot)]
    Side Predicates: []
    Option Spec: Pick(?x1:robot, ?x0:obj)
NSRT-Op10:
    Parameters: [?x0:obj, ?x1:robot]
    Preconditions: [((0:obj).dirtiness<=0.489)(?x0:obj), ((0:obj).pose_y<=-0.291)(?x0:obj), ((0:obj).wetness<=0.5)(?x0:obj), NOT-((0:obj).held<=0.5)(?x0:obj)]
    Add Effects: []
    Delete Effects: [((0:obj).wetness<=0.5)(?x0:obj)]
    Side Predicates: []
    Option Spec: Wash(?x1:robot)
NSRT-Op11:
    Parameters: [?x0:robot]
    Preconditions: []
    Add Effects: []
    Delete Effects: []
    Side Predicates: []
    Option Spec: Dry(?x0:robot)
NSRT-Op12:
    Parameters: [?x0:robot]
    Preconditions: []
    Add Effects: []
    Delete Effects: []
    Side Predicates: []
    Option Spec: Paint(?x0:robot)
NSRT-Op13:
    Parameters: [?x0:robot]
    Preconditions: []
    Add Effects: []
    Delete Effects: []
    Side Predicates: []
    Option Spec: Wash(?x0:robot)
NSRT-Op14:
    Parameters: [?x0:obj, ?x1:box, ?x2:shelf, ?x3:robot]
    Preconditions: [((0:obj).dirtiness<=0.489)(?x0:obj), ((0:obj).pose_y<=-0.291)(?x0:obj), ((0:obj).wetness<=0.5)(?x0:obj), ((0:robot).gripper_rot<=0.25)(?x3:robot), IsShelfColor(?x0:obj, ?x2:shelf), NOT-((0:obj).held<=0.5)(?x0:obj)]
    Add Effects: [IsBoxColor(?x0:obj, ?x1:box)]
    Delete Effects: [IsShelfColor(?x0:obj, ?x2:shelf)]
    Side Predicates: []
    Option Spec: Paint(?x3:robot)
NSRT-Op15:
    Parameters: [?x0:lid, ?x1:robot]
    Preconditions: [NOT-((0:robot).fingers<=0.5)(?x1:robot)]
    Add Effects: []
    Delete Effects: []
    Side Predicates: []
    Option Spec: OpenLid(?x1:robot, ?x0:lid)
NSRT-Op16:
    Parameters: [?x0:obj, ?x1:robot]
    Preconditions: [((0:obj).dirtiness<=0.489)(?x0:obj), ((0:obj).wetness<=0.5)(?x0:obj), NOT-((0:robot).fingers<=0.5)(?x1:robot)]
    Add Effects: []
    Delete Effects: []
    Side Predicates: []
    Option Spec: Pick(?x1:robot, ?x0:obj)
NSRT-Op1:
    Parameters: [?x0:obj, ?x1:robot]
    Preconditions: [((0:obj).dirtiness<=0.489)(?x0:obj), ((0:obj).pose_y<=-0.291)(?x0:obj), NOT-((0:obj).held<=0.5)(?x0:obj)]
    Add Effects: [((0:obj).wetness<=0.5)(?x0:obj)]
    Delete Effects: []
    Side Predicates: []
    Option Spec: Dry(?x1:robot)
NSRT-Op2:
    Parameters: [?x0:obj, ?x1:shelf, ?x2:robot]
    Preconditions: [((0:obj).dirtiness<=0.489)(?x0:obj), ((0:obj).pose_y<=-0.291)(?x0:obj), ((0:obj).wetness<=0.5)(?x0:obj), NOT-((0:obj).held<=0.5)(?x0:obj)]
    Add Effects: [IsShelfColor(?x0:obj, ?x1:shelf)]
    Delete Effects: []
    Side Predicates: []
    Option Spec: Paint(?x2:robot)
NSRT-Op3:
    Parameters: [?x0:obj, ?x1:shelf, ?x2:robot]
    Preconditions: [((0:obj).dirtiness<=0.489)(?x0:obj), ((0:obj).pose_y<=-0.291)(?x0:obj), ((0:robot).gripper_rot<=0.25)(?x2:robot), NOT-((0:obj).held<=0.5)(?x0:obj)]
    Add Effects: [InShelf(?x0:obj, ?x1:shelf), NOT-((0:robot).fingers<=0.5)(?x2:robot)]
    Delete Effects: [((0:obj).pose_y<=-0.291)(?x0:obj), ((0:robot).gripper_rot<=0.25)(?x2:robot), NOT-((0:obj).held<=0.5)(?x0:obj)]
    Side Predicates: []
    Option Spec: Place(?x2:robot)
NSRT-Op4:
    Parameters: [?x0:obj, ?x1:robot]
    Preconditions: [((0:obj).pose_y<=-0.291)(?x0:obj), ((0:obj).wetness<=0.5)(?x0:obj), NOT-((0:obj).held<=0.5)(?x0:obj)]
    Add Effects: [((0:obj).dirtiness<=0.489)(?x0:obj)]
    Delete Effects: [((0:obj).wetness<=0.5)(?x0:obj)]
    Side Predicates: []
    Option Spec: Wash(?x1:robot)
NSRT-Op5:
    Parameters: [?x0:obj, ?x1:robot]
    Preconditions: [((0:obj).pose_y<=-0.291)(?x0:obj), NOT-((0:robot).fingers<=0.5)(?x1:robot)]
    Add Effects: [NOT-((0:obj).held<=0.5)(?x0:obj), NOT-((0:robot).gripper_rot<=0.5)(?x1:robot)]
    Delete Effects: [NOT-((0:robot).fingers<=0.5)(?x1:robot)]
    Side Predicates: []
    Option Spec: Pick(?x1:robot, ?x0:obj)
NSRT-Op6:
    Parameters: [?x0:obj, ?x1:box, ?x2:robot]
    Preconditions: [((0:obj).dirtiness<=0.489)(?x0:obj), ((0:obj).pose_y<=-0.291)(?x0:obj), ((0:obj).wetness<=0.5)(?x0:obj), NOT-((0:obj).held<=0.5)(?x0:obj)]
    Add Effects: [IsBoxColor(?x0:obj, ?x1:box)]
    Delete Effects: []
    Side Predicates: []
    Option Spec: Paint(?x2:robot)
NSRT-Op7:
    Parameters: [?x0:obj, ?x1:box, ?x2:robot]
    Preconditions: [((0:obj).pose_y<=-0.291)(?x0:obj), NOT-((0:obj).held<=0.5)(?x0:obj), NOT-((0:robot).gripper_rot<=0.5)(?x2:robot)]
    Add Effects: [InBox(?x0:obj, ?x1:box), NOT-((0:robot).fingers<=0.5)(?x2:robot)]
    Delete Effects: [((0:obj).pose_y<=-0.291)(?x0:obj), NOT-((0:obj).held<=0.5)(?x0:obj), NOT-((0:robot).gripper_rot<=0.5)(?x2:robot)]
    Side Predicates: []
    Option Spec: Place(?x2:robot)
NSRT-Op8:
    Parameters: [?x0:obj, ?x1:box, ?x2:shelf, ?x3:robot]
    Preconditions: [((0:obj).dirtiness<=0.489)(?x0:obj), ((0:obj).pose_y<=-0.291)(?x0:obj), ((0:obj).wetness<=0.5)(?x0:obj), IsBoxColor(?x0:obj, ?x1:box), NOT-((0:obj).held<=0.5)(?x0:obj), NOT-((0:robot).gripper_rot<=0.5)(?x3:robot)]
    Add Effects: [IsShelfColor(?x0:obj, ?x2:shelf)]
    Delete Effects: [IsBoxColor(?x0:obj, ?x1:box)]
    Side Predicates: []
    Option Spec: Paint(?x3:robot)
NSRT-Op9:
    Parameters: [?x0:robot]
    Preconditions: []
    Add Effects: []
    Delete Effects: []
    Side Predicates: []
    Option Spec: Place(?x0:robot)

Planning succeeded! Found plan of length 21 after 1 skeletons, discovering 0 failures
Task 1 / 50: SOLVED
Planning succeeded! Found plan of length 23 after 1 skeletons, discovering 0 failures
Task 2 / 50: SOLVED
Planning succeeded! Found plan of length 17 after 1 skeletons, discovering 0 failures
Task 3 / 50: SOLVED
Planning succeeded! Found plan of length 26 after 1 skeletons, discovering 0 failures
Task 4 / 50: SOLVED
Planning succeeded! Found plan of length 19 after 1 skeletons, discovering 0 failures
Task 5 / 50: SOLVED
Planning succeeded! Found plan of length 25 after 1 skeletons, discovering 0 failures
Task 6 / 50: SOLVED
Planning succeeded! Found plan of length 19 after 1 skeletons, discovering 0 failures
Task 7 / 50: SOLVED
Planning succeeded! Found plan of length 24 after 1 skeletons, discovering 0 failures
Task 8 / 50: SOLVED
Planning succeeded! Found plan of length 17 after 1 skeletons, discovering 0 failures
Task 9 / 50: SOLVED
Planning succeeded! Found plan of length 22 after 1 skeletons, discovering 0 failures
Task 10 / 50: SOLVED
Planning succeeded! Found plan of length 22 after 1 skeletons, discovering 0 failures
Task 11 / 50: SOLVED
Planning succeeded! Found plan of length 22 after 1 skeletons, discovering 0 failures
Task 12 / 50: SOLVED
Planning succeeded! Found plan of length 21 after 1 skeletons, discovering 0 failures
Task 13 / 50: SOLVED
Planning succeeded! Found plan of length 22 after 1 skeletons, discovering 0 failures
Task 14 / 50: SOLVED
Planning succeeded! Found plan of length 23 after 1 skeletons, discovering 0 failures
Task 15 / 50: SOLVED
Planning succeeded! Found plan of length 25 after 1 skeletons, discovering 0 failures
Task 16 / 50: SOLVED
Planning succeeded! Found plan of length 21 after 1 skeletons, discovering 0 failures
Task 17 / 50: SOLVED
Planning succeeded! Found plan of length 26 after 1 skeletons, discovering 0 failures
Task 18 / 50: SOLVED
Planning succeeded! Found plan of length 16 after 1 skeletons, discovering 0 failures
Task 19 / 50: SOLVED
Planning succeeded! Found plan of length 22 after 1 skeletons, discovering 0 failures
Task 20 / 50: SOLVED
Planning succeeded! Found plan of length 22 after 1 skeletons, discovering 0 failures
Task 21 / 50: SOLVED
Planning succeeded! Found plan of length 21 after 1 skeletons, discovering 0 failures
Task 22 / 50: SOLVED
Planning succeeded! Found plan of length 19 after 1 skeletons, discovering 0 failures
Task 23 / 50: SOLVED
Planning succeeded! Found plan of length 25 after 1 skeletons, discovering 0 failures
Task 24 / 50: SOLVED
Planning succeeded! Found plan of length 20 after 1 skeletons, discovering 0 failures
Task 25 / 50: SOLVED
Planning succeeded! Found plan of length 27 after 1 skeletons, discovering 0 failures
Task 26 / 50: SOLVED
Planning succeeded! Found plan of length 19 after 1 skeletons, discovering 0 failures
Task 27 / 50: SOLVED
Planning succeeded! Found plan of length 26 after 1 skeletons, discovering 0 failures
Task 28 / 50: SOLVED
Planning succeeded! Found plan of length 21 after 1 skeletons, discovering 0 failures
Task 29 / 50: SOLVED
Planning succeeded! Found plan of length 23 after 1 skeletons, discovering 0 failures
Task 30 / 50: SOLVED
Planning succeeded! Found plan of length 18 after 1 skeletons, discovering 0 failures
Task 31 / 50: SOLVED
Planning succeeded! Found plan of length 24 after 1 skeletons, discovering 0 failures
Task 32 / 50: SOLVED
Planning succeeded! Found plan of length 20 after 1 skeletons, discovering 0 failures
Task 33 / 50: SOLVED
Planning succeeded! Found plan of length 25 after 1 skeletons, discovering 0 failures
Task 34 / 50: SOLVED
Planning succeeded! Found plan of length 20 after 1 skeletons, discovering 0 failures
Task 35 / 50: SOLVED
Planning succeeded! Found plan of length 27 after 1 skeletons, discovering 0 failures
Task 36 / 50: SOLVED
Planning succeeded! Found plan of length 19 after 1 skeletons, discovering 0 failures
Task 37 / 50: SOLVED
Planning succeeded! Found plan of length 24 after 1 skeletons, discovering 0 failures
Task 38 / 50: SOLVED
Planning succeeded! Found plan of length 20 after 1 skeletons, discovering 0 failures
Task 39 / 50: SOLVED
Planning succeeded! Found plan of length 25 after 1 skeletons, discovering 0 failures
Task 40 / 50: SOLVED
Task 41 / 50: Approach failed to solve with error: Planning timed out in backtracking!
Planning succeeded! Found plan of length 18 after 1 skeletons, discovering 0 failures
Task 42 / 50: SOLVED
Planning succeeded! Found plan of length 19 after 1 skeletons, discovering 0 failures
Task 43 / 50: SOLVED
Planning succeeded! Found plan of length 24 after 1 skeletons, discovering 0 failures
Task 44 / 50: SOLVED
Planning succeeded! Found plan of length 19 after 1 skeletons, discovering 0 failures
Task 45 / 50: SOLVED
Planning succeeded! Found plan of length 26 after 1 skeletons, discovering 0 failures
Task 46 / 50: SOLVED
Planning succeeded! Found plan of length 20 after 1 skeletons, discovering 0 failures
Task 47 / 50: SOLVED
Planning succeeded! Found plan of length 23 after 1 skeletons, discovering 0 failures
Task 48 / 50: SOLVED
Planning succeeded! Found plan of length 17 after 1 skeletons, discovering 0 failures
Task 49 / 50: SOLVED
Planning succeeded! Found plan of length 25 after 1 skeletons, discovering 0 failures
Task 50 / 50: SOLVED
Tasks solved: 49 / 50
Average time for successes: 0.38344 seconds
Test results: defaultdict(<class 'float'>, {'num_solved': 49, 'num_total': 50, 'avg_suc_time': 0.38344391024842556, 'avg_skeletons_optimized': 1.0, 'min_skeletons_optimized': 0.0, 'max_skeletons_optimized': 1.0, 'avg_nodes_expanded':<PHONE_NUMBER>22449, 'avg_plan_length': 21.816326530612244, 'avg_execution_failures': 0.0, 'learning_time':<PHONE_NUMBER>852722})
Wrote out test results to results/painting__grammar_search_invention__0__all__.pkl

discouraging result, with non-debug grammar and lid sometimes open:
nondebug_lidopen.log

closing because a lot of stuff here is obsolete


WIP: feat: optimize tree performance

Types

[x] 🎉 New Features

Background or solution
we found that the refresh tree node is very slow

this is because we create many Path objs

Changelog
optimize tree performance

我来接着搞


Evaluate model on PR

On a PR including changes to the notebook (or this workflow), run the notebook print model accuracy data back to the PR.
Working sample

@dmgardiner25 LMK your thoughts -- I find this helpful when demonstrating how ML Devs interact w/ reviewers. Note also inclusion of Python cache.


Fix decoding a @Persisted property throws a DecodingError.keyNotFound when key is missing

Fix decoding a @Persisted property will incorrectly throw a DecodingError.keyNotFound for an optional property if the key is missing.
Fixes this issue https://github.com/realm/realm-cocoa/issues/7358

Hi, @dianaafanador3
Is there any planned date for a new release including this fix?

Hi @barisozr a new version of realm has been released today with the fix https://github.com/realm/realm-cocoa/releases/tag/v10.12.0.


Testing v1.8.1

Tested on: #704

[x] Ceremony

[x] Registering participant waits until data has been updated (data has been invalidated manually with the dev-button on the homepage)
[x] Run two ceremonies on Rococo with 1 Newbie and 2 Bootstrappers. Check that the newbie becomes a reputable in the second ceremony.
[x] claimReward works for: rococo, solonode local < v18, and solonode local = v18

[x] Send Flow

[x] Send transaction scanned from invoice

[x] Receive Flow:

[x] shows pending incoming transfer

[x] ProfilePage:

[x] remove all account brings brings you back to entry page
[x] Creating first account works
[x] Importing first account works

[x] Qr-scanner

[x] scanning contact works when navigated from homepage and from contact page
[x] scanning invoice works when navigated from homepage and from send page
[x] scanning  invoice from contact page works

v1.8.1 is released


How do I make the bots split and "W"?

^

You press E for bots split and R for bots feed

easy change case to 87 on ur scripte     ( # case 87:
emitSplit();
break; )
on ur scripte  line 219 on tampermokey

E  split R w


Eio using MPSC queue?

https://github.com/ocaml-multicore/lockfree/blob/8c87c8bc675ac7537992e857705b7ee3a65ee2db/README.md?plain=1#L16
It is mentioned in the README.md that eio uses the MPSC queue. I didn't see lockfree being a dependency for eio: https://opam.ocaml.org/packages/eio/. Does that mean that eio has a copy of this data structure? If the API is compatible, can eio use the MPSC queue provided by lockfree @talex5?

Yes, Eio should ideally use it from lockfree. We talked about this in the team meeting yesterday and @talex5 agreed. cc/ @lyrm

Does that mean that eio has a copy of this data structure?

lockfree copied it from Eio. And without adding my copyright to its license file (see https://github.com/ocaml-multicore/eio/blob/82886514b710f9fc4fec133e53db7f4772c66a88/LICENSE.md).
This implementation was specialised for Eio by dropping support for multiple consumers. But for the lockfree project it might be better to provide the more general version.

Thanks @talex5. Can we fix the LICENSE please @lyrm @Sudha247?

We should fix the license bit.
Lockfree itself doesn't depend on yojson. It's needed for integrating the benchmarks with ocaml-cb. I wonder if we can restrict it to be an optional dependency (cc @bartoszmodelski).

Created https://github.com/ocaml-multicore/lockfree/pull/50


Running New Sustainability Tests Fail Due to remote CO2.js update

Which Umbraco.Community.Sustainability version are you using?
1.0.3
Which Umbraco version are you using? For example: 10.8.0 - don't just write v10
13.4
Bug summary
Running tests in the package currently fails with a Json deserialization error.

This appears to be happening due to the internal library calling a remote CDN copy of the latest version of the CO2.js library. It appears this library has been updated to a new release 0.16 in the past few days which has changed the structure of the internal model coming through by default, despite the release notes saying this new model should be opt-in only.
Steps to reproduce

Install Sustainability Package on a site with pages
Choose a page in the back office
Run a fresh Sustainability test against the page. It should error

Expected result / actual result
No response

Resolved in v1.0.4/v2.0.4


Tutorial Island Giant Rat listed with wrong HP in combat overlay

Describe the bug
Tutorial island giant rat shows up with 5 hp in the combat overlay, it actually has 3.
To Reproduce
Steps to reproduce the behavior:

Go to tutorial island
Enter combat with a giant rat with the combat overlay enabled
HP is tracked starting from 5/5

Expected behavior
Max HP listed as 5, rat actually has 3.
Environment (please complete the following information):

OS: Debian
RuneLite version: 1.5.27
Launcher version: 1.6.2

This bug can be fixed once #8463 is merged by replacing the entry for NPC ID 3313 with
  "3313": {
    "name": "Giant rat",
    "hitpoints": 3,
    "combatLevel": 3,
    "slayerLevel": 1,
    "attackLevel": 1,
    "strengthLevel": 1,
    "defenceLevel": 1,
    "rangeLevel": 1,
    "magicLevel": 1,
    "stabDef": -100,
    "slashDef": -100,
    "crushDef": -100,
    "rangeDef": -100,
  }

Courtesy of Mod Ash
What would be the stats you see if you cast monster examine on a rat on tutorial island?
Level 1 in the stats other than 3 HP, with -100 defence bonuses in stab, slash, crush and ranged.— Mod Ash (@JagexAsh) August 13, 2019

I've updated the "Giant rat" wiki page with the tutorial island variant separated from the other level 3 rats. This issue will be fixed in the client when next our wiki scraper is run automatically, which looks to be in about 1 hour.


[1.10.2] Server-Crash on Linux

Seems like for some reason CraftTweaker has a X11 problem?
Why does it even need X11?
CraftTweaker: 3.0.15
Forge: <IP_ADDRESS>9
OS: Debian 8.6 (64Bit)
Crashlog: http://pastebin.com/VNPAzYc5
fml-server-latest: http://pastebin.com/WZvwRFSv
minetweaker.log is empty.
If you need any other informations, let me know!

I see that this bug was fixed a month ago. Could we please get a new build? I get the same error with a headless server. 3.0.15 seems to be the latest release, but it looks like the bug was fixed after that.

3.0.13 is the latest version that can be used on a linux server.
For now, use that version and wait for an update, jaredlll08 is realy busy atm.

Thanks for the info, and the hard work. This is a really useful mod.


fix: use types from eslint for flat config

To avoid TS errors when using this plugin with flat config

I fixed the typescript version to 5.0.2 because @typescript-eslint/typescript-estree is incompatible with the latest (5.5.2)

released


Enable caching to accelerate Front Door traffic

Rule request
Suggested rule change
Use caching for each Front Door route rule to reduce latency and minimize calls to backend systems.
Applies to the following
The rule applies to the following:

Resource type: Microsoft.Network/frontDoors

Additional context
Caching with Azure Front Door

@BernieWhite
Since almost two years has passed by and this is left untouched, I'd like to contribute to this one.
Front Door declaration without caching is something we should not have unless explicitly stated as this is one of the main reasons using Front Door in the first place.


Add Stripe Payments (WIP)

:construction_worker: Purpose

Allow purchasing credits with stripe
Complete refactor of all API tests

:rocket: Changes

Adds a stripe payment api call
stripe-go dependency added

:warning: Breaking Changes

None

@bobheadxi I'm looking to finally get the API test refactor done. Can you take a look at the tests in routes_rtfs_test.go and let me know how they look? If they've got your approval, I'm going to continue with the refactor.

test refactoring was more involved than i expected it to be, and is being done in a separate branch https://github.com/RTradeLtd/Temporal/tree/tests/refactor

broke this branch pretty badly due to some overly eager test refactoring, opening up a new pr


Wildcard in Reference matches same file itself

Please select the area your bug applies to. (Multiple selections are Possible. You can leave blank if you're not sure.)

[ ] Workspace.  VSCode workspace, vaults, Intellisense/autocomplete, Dendron settings
[ ] Lookup.  Dendron's Lookup Command
[x] Views.  Dendron Preview, Tree View, Side Panels in the UI
[ ] Schema.  Dendron Schemas
[ ] Pod.  Data import from / export to Dendron
[ ] Publish.  External Site Publish
[ ] Markdown.  Markdown features, such as syntax support and features

Describe the bug
I would like to create a monthly review to the journal. To do this, I have added the following reference to my template:
![[daily.journal.2022.07.*]]

Unfortunately this seems to also include the file itself (which is named daily.journal.2022.07.review.
I guess, that inclusion of the file itself in a wildcard reference does not make any sense to me.
To Reproduce
Use a wildcard reference which references the file itself as well.
Expected behavior
A wildcard reference should not reference itself.
Screenshots
If applicable, add screenshots to help explain your problem.
Desktop (please complete the following information)

OS: [e.g. iOS]
Browser (if with published websites; include version): [e.g. chrome, safari]
Versions (all that apply to bug):

Dendron Extension:
VS Code version:
Dendron CLI:

Additional context
Add any other context about the problem here.
Dendron Log file
Please attach the output of > Dendron:Dev: Open Logs here

Thank you for the report.
While I don't think it is a bug (* reference is doing what it is expected to do), I understand the need to do a pattern matching that is more specific than a simple wildcard in your use case.
My interim suggestion is to put your reviews in a separate hierarchy, or use the note daily.journal.2022.07 itself as a review note, but I understand that that is not what you are going for and we need to support these use cases.
Could you create a separate feature request to support a more fine-grained pattern in the references? (or just convert this issue to a feature request?)

please see: #3743.


Add: Airbnb

Company
Airbnb
Description
Airbnb operates an online marketplace focused on short-term homestays and experiences.
Link
https://ourcollective.us/airbnb-connect-software-engineering-apprenticeship/
Location
San Francisco Bay Area
Featured Image
https://upload.wikimedia.org/wikipedia/commons/6/69/Airbnb_Logo_Bélo.svg
Thanks for reading my issue.

@zliwow Thanks for sharing this apprenticeship! Do you know if there's a new iteration of this apprenticeship available? Looks like the application deadline already passed back in early 2022, and it's unclear if this will repeat again.

Thanks for addressing my issue. You are correct, a the moment, Airbnb hasn't updated it. Though their apprenticeship is a known program, I thought I put it out there so people can keep an eye out for potential updates.

@zliwow Thanks for sharing this apprenticeship! Do you know if there's a new iteration of this apprenticeship available? Looks like the application deadline already passed back in early 2022, and it's unclear if this will repeat again.

Thanks for addressing my issue. You are correct, a the moment, Airbnb hasn't updated it. Though their apprenticeship is a known program, I thought I put it out there so people can keep an eye out for potential updates.

@zliwow Thanks for sharing this apprenticeship! Do you know if there's a new iteration of this apprenticeship available? Looks like the application deadline already passed back in early 2022, and it's unclear if this will repeat again.

Thanks for addressing my issue. You are correct, a the moment, Airbnb hasn't updated it. Though their apprenticeship is a known program, I thought I put it out there so people can keep an eye out for potential updates.

Makes sense, thanks for sharing! I'll add it in when I have a chance! 👍🏽


request help:

Issue description
请教个问题，如果apisix 部署在 k8s 集群外部， apisix ingress controller 监听 k8s api server, 给 apisix 生成的路由信息 是 pod+port? ，那apisix怎么访问 容器内部服务？
Environment

your apisix-ingress-controller version (output of apisix-ingress-controller version --long):
your Kubernetes cluster version (output of kubectl version):
if you run apisix-ingress-controller in Bare-metal environment, also show your OS version (uname -a):

I don't fully understand what you mean.
Please describe more clearly.
Maybe you want this one https://github.com/apache/apisix-ingress-controller/blob/de9f84fce92e9ad26d487ccafa4bb7e800176776/docs/en/latest/concepts/apisix_route.md?plain=1#L120
Also, please improve the title


 This is a problem because fzf currently doesn't pass go vet. So fzf doesn't pass tests with Go 1.10.
I based this off #1218, which vendors all the dependencies. I couldn't run go vet ./... or go test ./... on Go 1.10 because of missing packages in vendor/ (for some reason make wasn't fetching them all). I recommend reviewing #1218 first. That will remove a lot of noise from this PR.
go vet fixes

add String() methods to Token, term, Delimeter, and *util.Chars so they can be printed with %s
replace %s with %v where the default string representation is good enough

Cherry-picked the commit that fixes go vet issue. Thanks.


Improve JVM thread names support

This PR brings better JVM thread names reporting.

VMStructs symbols are loaded at the same time when AGCT symbols do: it fix bug when no thread names was reported by perf engine.
Thread names storage is updated on every thread start and thread end: we can get names not only for active at the moment of dump threads , but also for all threads that had already stopped.
I slightly changed thread output format to [<thread Name> tid=<thread id>], because we need to distinguish different threads with the same name and to understand if dump contains thread labels or not.

Thank you! The changes sound good. I'll review them shortly.

Could you please take a look at updated version.
Is it also possible to install java8 on CI server? :)

I've finally managed to revise and integrate this pull request while leaving your original idea.
Feedback is welcome!


[WIP] NRF52810 Support

This PR hopes to add support for the NRF52810 series.
It fixes the issues addressed in #845 (AFAIK)
It does require updating to the CMSIS 5 header, which are also included.
An example project with an NRF52810 BSP can be found here: https://github.com/alvarop/blemesh

Btw, I also added a CMSIS 5 PR here: https://github.com/apache/mynewt-core/pull/1100
I don't see how or that you solved the issue building on any Cortex-M0 MCUs in your PR. I am not sure what is the best way to do it but I solved it adding a customized hw/cmsis-core/src/ext/mynewt_cm0_vectab.h and adding flags to compiler/arm-none-eabi-m0/compiler.yml. Leaving as suggestions!

@alvarop It's merged, please rebase!

Rebased!

LGTM


Branch cleanup

We have a lot of dangling features in various branches, and we should clean these up before the new team starts working on the repo :smile:

PR #256 and #255 deals with the additions that should be merged into development. The rest of the branches have been deleted.


Failures opening files provided as app arguments are not reflected in the UI

Describe the bug
If an attempt to open a (supported or unsupported) file via f3d by double-clicking on the file path or otherwise opening using the window manager facilities to open the path with f3d fails, the f3d ui loads normally but with a blank canvas and no errors are shown. The error is only posted to stderr, but if f3d was launched via the window manager instead of from the terminal, stderr is hidden and the user is unaware that any error took place.
To Reproduce
Steps to reproduce the behavior:

Instead of opening the file using f3d --dry-run example.glb, open an unsupported file by using your window manager's facilities for opening a file with a specific gui application
Observe that f3d opens but no file is loaded and no error is shown

Expected behavior
Any errors attempting to open a file at startup via command line arguments should result in a blocking dialog, a message box, or some other error display mechanism that reflects the error in the GUI (in addition to the error being printed to stderr). f3d should not assume that stderr is visible, or that even if stderr is visible that the user is monitoring it for output.
System Information:

OS: Ubuntu 23.04
GPU and GPU driver: N/A

F3D Information
Paste the content of f3d --version:
f3d 1.3.1

F3D - A fast and minimalist 3D viewer
Version: 1.3.1.
Build date: 2022-11-06 13:17:51.
Build system: Linux.
Compiler: GNU 12.2.0.
External rendering module: OFF.
Raytracing module: OFF.
Exodus module: ON.
OpenCASCADE module: 7.6.3 (full support).
Assimp module: 5.2.4.
Alembic module: OFF.
VTK version: 9.1.0 (build 0).
Copyright (C) 2019-2021 Kitware SAS.
Copyright (C) 2021-2022 Michael Migliore, Mathieu Westphal.
License BSD-3-Clause.
By Michael Migliore, Mathieu Westphal and Joachim Pouderoux.

Additional context
In this example and for reproduction purposes, I have recommended opening an unsupported file type with the window manager. However, this issue was actually encountered trying to open a supported file type but from a mount type that is not supported for unknown reasons, so it is not as contrived as it seems.
An HN comment suggests that this functionality is blocking on the feature to add a log output dialog to f3d. I want to argue against this from two different perspectives:

A log output is not required in order to show simple, fatal errors taking actions (such as loading a file)
A log output even if implemented does not take the place of showing a blocking dialog in the main app conveying the critical/fatal error information to the user. A log dialog should be treated a source of supplemental information but should not be considered part of the core UX feedback loop. For definitive (not asynchronous, not deferred, not supplemental, not incidental) actions, an error acting out on the user's command should be considered a fatal process requiring blocking UI feedback to the user.

f3d should not assume that stderr is visible, or that even if stderr is visible that the user is monitoring it for output.

We (the maintainers) definitely agree. Is is planned to be implemented it in the context of integrating imGui in F3D, but this is a log of work.
Related issue: https://github.com/f3d-app/f3d/issues/29

Fixed by https://github.com/f3d-app/f3d/pull/1748


get centroid where there are multiple lat longs

currently tidy_va_data is just using first lat long, but this should be re-processed to get centroid

https://github.com/geryan/va_multispecies_sdm/blob/c95734543f2a8f7e55cbed3a7b8690da74811ff5/R/tidy_va_data.R#L7


Geocoder requires an API key to use options parameters even if global key is set

I set a mapzen.js global apiKey, and was finding that the geocoder still requires its own API key to be passed for the geocoder options to work.
Here's some code to reproduce:
<script>
      L.Mapzen.apiKey = "your-mapzen-api-key";
      var map = L.Mapzen.map("map", {
        center: [47.61033,-122.31801],
        zoom: 16,
      });
      var geocoderOptions = {
        autocomplete: false
      };
      var geocoder = L.Mapzen.geocoder(geocoderOptions);
      geocoder.addTo(map);
    </script>

but could not get the geocoder to use my options. In this case, it still continued to use autocomplete in the search box when it should have switched to the search endpoint.
It did work with var geocoder = L.Mapzen.geocoder("", geocoderOptions); where I passed an empty set (or an actual key) for the geocoder API key.

This fix was just released (v0.6.9)

thanks for the fix and status note, @hanbyul-here!


Possibility to ring the "siren" (the "alarm" button from live view) via a button after integrating in home assistant

Is your feature request related to a problem? Please describe.
No
Describe the solution you'd like
In the live view of the app exist a button called "alarm" and that will sound the alarm tone via the camera speaker for 30 seconds. I will like the possibility to be able to make "alarm" sound with the cameras when the notification comes in home assistant from other sources, something like a door sensor or a "photocell barrier" placed in the region not seen by the camera.
Describe alternatives you've considered
none
Additional context
none

Thank you very much for this, somehow I have not seen it.
I've tested and is working!

@zadigre @burleanu apologies I was also looking for this but I cannot seem to find the service anyway. How do you get the alarm to trigger?
Thanks in advance
Rob

Its ok - worked it out.....


fix(ws) Optimising websocket data

https://github.com/longhorn/longhorn/issues/2372
image: wangsiye/longhorn-ui:1ecd4e5
Signed-off-by: Siye Wang<EMAIL_ADDRESS>

cc @c3y1huang

Works for me, thanks @smallteeths .


Unable connect to docker machine

OS: Windows 10 1511 x64
VMware v12
C:\Users\Fes\Downloads
λ DockerToolbox-1.12.0.exe /COMPONENTS="Docker,DockerMachine"

C:\Users\Fes\Downloads
λ docker-machine create --driver=vmwareworkstation dev
Running pre-create checks...
(dev) Default Boot2Docker ISO is out-of-date, downloading the latest release...
(dev) Latest release for github.com/boot2docker/boot2docker is v1.12.1
(dev) Downloading C:\Users\Fes\.docker\machine\cache\boot2docker.iso from https://github.com/boot2docker/boot2docker/releases/download/v1.12.1/boot2docker.iso...
(dev) 0%....10%....20%....30%....40%....50%....60%....70%....80%....90%....100%
Creating machine...
(dev) Copying C:\Users\Fes\.docker\machine\cache\boot2docker.iso to C:\Users\Fes\.docker\machine\machines\dev\boot2docker.iso...
(dev) Creating SSH key...
(dev) Creating VM...
(dev) Creating disk 'C:\Users\Fes\.docker\machine\machines\dev\dev.vmdk'
(dev) Virtual disk creation successful.
(dev) Starting dev...
(dev) Waiting for VM to come online...
Waiting for machine to be running, this may take a few minutes...
Detecting operating system of created instance...
Waiting for SSH to be available...
Error creating machine: Error detecting OS: Too many retries waiting for SSH to be available.
  Last error: Maximum number of retries (60) exceeded

C:\Users\Fes\Downloads
λ dm ls
NAME   ACTIVE   DRIVER              STATE     URL                         SWARM   DOCKER    ERRORS
dev    -        vmwareworkstation   Running   tcp://<IP_ADDRESS>:2376           Unknown   Unable to query docker version: Get https://<IP_ADDRESS>:2376/v1.15/version: x509: certificate signed by unknown authority

@Festum Can you post debug logs?

@pecigonzalo I found the problem is caused by default OpenSSHd as default ssh client in SysEnvPath. Solved by changing it to Portable Git's ssh binary.

Thanks for reporting back, i think another user just had the same issue, i will look to submit a fix to upstream docker-machine


The popover component should auto close when clicking outside of element.

Describe the bug
I am trying to use the Popover component in DotCom for the file comments within our React app prototype & it does not work as I assumed when clicking outside of the Popover component. I expected it to close the popover like the overlay, details or selection menu components do. Instead, the component stays open. Maybe there needs to be a closeOnOutsideClick prop option that the component can be passed (e.g. <Popover closeOnOutsideClick={true} />) like the Details component, if we don't always want to default to outside clicks closing the popover.
https://user-images.githubusercontent.com/15013243/174690037-13d31be9-5ab2-41f9-a0c4-17e4a11ba1cd.mov
Additional context
This is probably a good code pattern to solve this for the component

Hi!
How do you feel about using the accessible ActionMenu instead of Popover here?
https://primer.style/react/ActionMenu#with-a-custom-anchor

@siddharthkp that is a great recommendation, but it doesn't have the pre-existing styles that we are using on the Files page.
We need the directional caret. You can test it here on current production to get the look and feel.
1 example on the Diff Files page comment

2 examples on Issue comments

Final Thoughts
The ActionMenu.Anchor would work for us outside of it missing the directional caret styling. I do feel very strongly that the Popover should have an auto-dismiss closeOnOutsideClick prop at minimum.

Yep, the design change is recent. Let me get @vdepizzol and @ashygee for comments as well.

I do feel very strongly that the Popover should have an auto-dismiss closeOnOutsideClick prop at minimum.

That's fair! We definitely can consider adding that.
FYI, Popover has other accessibility issues as well. For example, it does not implement a focus trap. You might like AnchoredOverlay as a better building block (you would have to add the caret though)

@Lukeghenco for clarity, do we need the caret for visual parity with the non-React implementation? If that's not the case, is there room here for small visual updates? As @siddharthkp described we're moving away from using carets in overlay type patterns.

@langermank if we are moving away from carets as a design in overlay type patterns, I think we are good for implementing the ActionMenu in place of the Popover. If we plan to keep Popover though, the non-React implementation does close when clicking outside of the popover as shown in the pics and links for my comment above

I have moved forward with using ActionMenu for my current work though, without directional carets.

That's great to hear.
I'm going to keep this issue open for Popover and take it to the next prioritisation meeting


[Segment Replication] Fix flaky tests testSegmentReplicationStatsResponse() and testSegmentReplicationStatsWithTimeout()

Signed-off-by: Rishikesh1159<EMAIL_ADDRESS>
Description
This PR fixes flaky tests testSegmentReplicationStatsWithTimeout() and testSegmentReplicationStatsResponse().
Issues Resolved
Resolves #6261, #6255
Check List

[ ] New functionality includes testing.

[ ] All tests pass

[ ] New functionality has been documented.

[ ] New functionality has javadoc added

[ ] Commits are signed per the DCO using --signoff
[ ] Commit changes are listed out in CHANGELOG.md file (See: Changelog)

By submitting this pull request, I confirm that my contribution is made under the terms of the Apache 2.0 license.
For more information on following Developer Certificate of Origin and signing off your commits, please check here.

Codecov Report

Merging #6268 (d15da00) into main (aabafa3) will decrease coverage by 0.10%.
The diff coverage is 0.00%.

:mega: This organization is not using Codecov’s GitHub App Integration. We recommend you install it so Codecov can continue to function properly for your repositories. Learn more
@@             Coverage Diff              @@
##               main    #6268      +/-   ##
============================================
- Coverage     70.79%   70.69%   -0.10%
+ Complexity    58897    58832      -65
============================================
  Files          4789     4789
  Lines        281788   281793       +5
  Branches      40669    40671       +2
============================================
- Hits         199481   199222     -259
- Misses        65971    66212     +241
- Partials      16336    16359      +23

Impacted Files
Coverage Δ

...cation/TransportSegmentReplicationStatsAction.java
11.11% <0.00%> (ø)

...h/indices/replication/SegmentReplicationState.java
45.25% <0.00%> (-1.72%)
:arrow_down:

...luster/routing/allocation/RoutingExplanations.java
41.37% <0.00%> (-58.63%)
:arrow_down:

...a/org/opensearch/client/cluster/SniffModeInfo.java
0.00% <0.00%> (-52.95%)
:arrow_down:

...ava/org/opensearch/action/NoSuchNodeException.java
0.00% <0.00%> (-50.00%)
:arrow_down:

...ch/transport/ReceiveTimeoutTransportException.java
50.00% <0.00%> (-50.00%)
:arrow_down:

.../opensearch/client/indices/CloseIndexResponse.java
42.50% <0.00%> (-48.75%)
:arrow_down:

.../admin/cluster/reroute/ClusterRerouteResponse.java
55.00% <0.00%> (-45.00%)
:arrow_down:

...cluster/routing/allocation/RerouteExplanation.java
65.00% <0.00%> (-35.00%)
:arrow_down:

...ensearch/client/indices/DetailAnalyzeResponse.java
20.54% <0.00%> (-34.25%)
:arrow_down:

... and 458 more

Help us with your feedback. Take ten seconds to tell us how you rate us. Have a feature suggestion? Share it here.

Gradle Check (Jenkins) Run Completed with:

RESULT: UNSTABLE ❕
TEST FAILURES:

      2 org.opensearch.cluster.service.MasterServiceTests.classMethod
      1 org.opensearch.cluster.service.MasterServiceTests.testThrottlingForMultipleTaskTypes

URL: https://build.ci.opensearch.org/job/gradle-check/11531/
CommitID: ba41b87
Please review all flaky tests that succeeded after retry and create an issue if one does not already exist to track the flaky failure.

Issue already opened for these flaky tests https://github.com/opensearch-project/OpenSearch/issues/5958


Field is validated live despite validationVisibility: blur

Reproduction
https://formkit.link/de2250d66dc389e0fcfc9b8eff0843f8
Describe the bug
When using validationVisibility: blur a field should not be be validated while the cursor is in the field. However, once the field has received the blur event, it will validate 'live' from then on.
Steps to reproduce:

Start typing
Remove all text; No error is visible, intended behaviour
Blur the field; Error becomes visible, intended behaviour
Start typing again
Remove all text; Error becomes visible again, bug!

Environment
• Browser [Chrome]
• Version [103]

Whoops forgot to search the closed issues, apparently this is intended behavior.
https://github.com/formkit/formkit/issues/279


Make device cache max age configurable

Is your feature request related to a problem? Please describe.
Currently device cache max age is hard coded to 4 hours. If the suite is taking more than 4 hours, all tests which was not executed at 4 hour mark gets skipped
Describe the solution you'd like
Make device cache max age configurable with default value as 4 hours so that the team using it can increase if required.
Describe alternatives you've considered
Split into smaller suite. Increase parallel threads.
Additional context

Update version to 1.0.2


[SPARK-49108][EXAMPLE] Add submit_pi.sh REST API example

What changes were proposed in this pull request?
This PR aims to provide submit_pi.sh example via REST API.
Why are the changes needed?
To provide the REST API feature more clearly via a working example.
Does this PR introduce any user-facing change?
No, this is a new example.
How was this patch tested?
Manual review.

Start Spark Cluster and Submit the job via submit_pi.sh script.

$ SPARK_MASTER_OPTS="-Dspark.master.rest.enabled=true" sbin/start-master.sh
$ sbin/start-worker.sh spark://$(hostname):7077

$ ./examples/src/main/scripts/submit_pi.sh
{
  "action" : "CreateSubmissionResponse",
  "message" : "Driver successfully submitted as driver-20240804234519-0000",
  "serverSparkVersion" : "4.0.0-SNAPSHOT",
  "submissionId" : "driver-20240804234519-0000",
  "success" : true
}%

Visit Spark Master UI and Spark job Executor UI.

http://localhost:8080

After completion, check the status via REST API.

$ curl http://localhost:6066/v1/submissions/status/driver-20240804234519-0000
{
  "action" : "SubmissionStatusResponse",
  "driverState" : "FINISHED",
  "serverSparkVersion" : "4.0.0-SNAPSHOT",
  "submissionId" : "driver-20240804234519-0000",
  "success" : true,
  "workerHostPort" : "<IP_ADDRESS>:61480",
  "workerId" : "worker-20240804234513-<IP_ADDRESS>-61480"
}

Was this patch authored or co-authored using generative AI tooling?
No.

Could you review this new example PR when you have some time, @viirya and @HyukjinKwon ?

Thank you, @viirya !

Merged to master.
Thank you @dongjoon-hyun @viirya @HyukjinKwon

Thank you, @yaooqinn and @HyukjinKwon !


[CHORE] update pyln-client

Codecov Report

Merging #1894 (62a45a6) into main (3a65363) will decrease coverage by 5.74%.
The diff coverage is n/a.

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@@            Coverage Diff             @@
##             main    #1894      +/-   ##
==========================================
- Coverage   54.17%   48.44%   -5.74%
==========================================
  Files          49       49
  Lines        7516     7516
==========================================
- Hits         4072     3641     -431
- Misses       3444     3875     +431

see 15 files with indirect coverage changes
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Singularity bugs

Singularity effects ghosts, objects through walls, and other things it probably shouldn't

If you don't fight against the pull as a ghost you kind of rotate around it. Sometimes it doesn't work and throw you far, this can probably be tweaked. In regular SS13 it affects objects through walls too

Closing due to ambiguity.


Handle capabilities in instance_info

This commit adds a parser for capabilities when they
are defined in instance_info.
The capabilities should be provided as a single string,
in the format key1:value1,key2:value2, this will be
parsed and sent to ironic as a
map[string]string{"key1":"value1","key2":"value2"}

LGTM, thanks!
I released v0.2.6.

LGTM, thanks!
I released v0.2.6.

Thanks! I will update the installer tomorrow


[iOS][0.40] Error handling incoming deeplinks - invalid URL: should be a string. Was: [object Object]

Hello,
I've encountered an issue handling incoming deeplinks on iOS.
I've added a url listener on componentWillMount and the handler function has one event argument which is an object that contains a url property. This works fine, and the event handler is called and starts executing. However, right afterwards an error is thrown with the message invalid URL: should be a string. Was: [object Object]. It appears to be coming from within the Linking javascript package, in the _validateURL function.
My handler function contains only a console.log which is executed..

Additional Information

React Native version: 0.40
Platform: iOS
Operating System: MacOS

Ok, so I figured out what the issue was. Turns out that there was another event listened somewhere else in the app which was incorrectly calling canOpenUrl on the event object. Apologies for the false alarm!

Hello @ggordan,
i have a same issue.
I don't understand how you did fix? Could you more explain?
Thanks