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Nabushikajudicaelclair
Nabushika
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judicaelclair
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Added command line arguments to analyzer (#51)
* Adding cmd options for analyser Able to import custom modules inheriting from Algorithm Added docs in main Fixing rebase problems Added more generator options * Reworked main.py & polished code base Signed-off-by: [ 大鳄 ] Asew <[email protected]> * Added AlgorithmManager Signed-off-by: [ 大鳄 ] Asew <[email protected]> * Wired analyzer to use AlgorithmManager Signed-off-by: [ 大鳄 ] Asew <[email protected]> * Polished main.py Signed-off-by: [ 大鳄 ] Asew <[email protected]> * Bug fix Signed-off-by: [ 大鳄 ] Asew <[email protected]> * Polished test code + arg parsing Signed-off-by: [ 大鳄 ] Asew <[email protected]> * Cleanup and import error fix Signed-off-by: [ 大鳄 ] Asew <[email protected]> * Import path cleanup Signed-off-by: [ 大鳄 ] Asew <[email protected]> * Polished command line interface Signed-off-by: [ 大鳄 ] Asew <[email protected]> * Attempt at fixing CI error Signed-off-by: [ 大鳄 ] Asew <[email protected]> * Moved constants file to resolve import error Signed-off-by: [ 大鳄 ] Asew <[email protected]> * Tweaked paths Signed-off-by: [ 大鳄 ] Asew <[email protected]> * Implemented multiple algorithms per imported file Signed-off-by: [ 大鳄 ] Asew <[email protected]> * Name conflict detection Signed-off-by: [ 大鳄 ] Asew <[email protected]> * Removed stale files Signed-off-by: [ 大鳄 ] Asew <[email protected]> * Polished error handling Signed-off-by: [ 大鳄 ] Asew <[email protected]> Co-authored-by: [ 大鳄 ] Asew <[email protected]>
1 parent 8ea33f4 commit 81a75da

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‎requirements.txt‎

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torch
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numpy
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matplotlib
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nptyping
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panda3d
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pandas
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sklearn
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matplotlib
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torchvision
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memory_profiler
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seaborn
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dill
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natsort
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screeninfo
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opencv-python
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lru-dict
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lru-dict

‎src/algorithms/algorithm_manager.py‎

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from algorithms.algorithm import Algorithm
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from utility.compatibility import HAS_OMPL
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from typing import Optional, List, Type, Dict, Any, Tuple
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import importlib.util
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import inspect
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import os
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import sys
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import copy
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import traceback
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# planner testing
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from algorithms.basic_testing import BasicTesting
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from algorithms.classic.testing.a_star_testing import AStarTesting
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from algorithms.classic.testing.combined_online_lstm_testing import CombinedOnlineLSTMTesting
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from algorithms.classic.testing.dijkstra_testing import DijkstraTesting
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from algorithms.classic.testing.wavefront_testing import WavefrontTesting
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from algorithms.classic.testing.way_point_navigation_testing import WayPointNavigationTesting
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# planner implementations
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from algorithms.classic.graph_based.a_star import AStar
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from algorithms.classic.graph_based.bug1 import Bug1
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from algorithms.classic.graph_based.bug2 import Bug2
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from algorithms.classic.graph_based.dijkstra import Dijkstra
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from algorithms.classic.graph_based.potential_field import PotentialField
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from algorithms.classic.sample_based.sprm import SPRM
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from algorithms.classic.sample_based.rt import RT
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from algorithms.classic.sample_based.rrt import RRT
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from algorithms.classic.sample_based.rrt_star import RRT_Star
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from algorithms.classic.sample_based.rrt_connect import RRT_Connect
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from algorithms.classic.graph_based.wavefront import Wavefront
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from algorithms.lstm.LSTM_tile_by_tile import OnlineLSTM
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from algorithms.lstm.a_star_waypoint import WayPointNavigation
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from algorithms.lstm.combined_online_LSTM import CombinedOnlineLSTM
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if HAS_OMPL:
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from algorithms.classic.sample_based.ompl_rrt import OMPL_RRT
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from algorithms.classic.sample_based.ompl_prmstar import OMPL_PRMstar
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from algorithms.classic.sample_based.ompl_lazyprmstar import OMPL_LazyPRMstar
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from algorithms.classic.sample_based.ompl_rrtstar import OMPL_RRTstar
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from algorithms.classic.sample_based.ompl_rrtsharp import OMPL_RRTsharp
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from algorithms.classic.sample_based.ompl_rrtx import OMPL_RRTXstatic
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from algorithms.classic.sample_based.ompl_informedrrt import OMPL_InformedRRT
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from algorithms.classic.sample_based.ompl_kpiece1 import OMPL_KPIECE1
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from algorithms.classic.sample_based.ompl_ltlplanner import OMPL_LTLPlanner
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from algorithms.classic.sample_based.ompl_pdst import OMPL_PDST
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from algorithms.classic.sample_based.ompl_sst import OMPL_SST
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from algorithms.classic.sample_based.ompl_aitstar import OMPL_AITstar
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from algorithms.classic.sample_based.ompl_anytimepathshortening import OMPL_AnytimePathShortening
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from algorithms.classic.sample_based.ompl_bfmt import OMPL_BFMT
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from algorithms.classic.sample_based.ompl_biest import OMPL_BiEST
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from algorithms.classic.sample_based.ompl_rrtconnect import OMPL_RRTConnect
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from algorithms.classic.sample_based.ompl_trrt import OMPL_TRRT
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from algorithms.classic.sample_based.ompl_birlrt import OMPL_BiRLRT
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from algorithms.classic.sample_based.ompl_bitrrt import OMPL_BiTRRT
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from algorithms.classic.sample_based.ompl_bitstar import OMPL_BITstar
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from algorithms.classic.sample_based.ompl_bkpiece1 import OMPL_BKPIECE1
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from algorithms.classic.sample_based.ompl_syclop import OMPL_Syclop
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from algorithms.classic.sample_based.ompl_cforest import OMPL_CForest
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from algorithms.classic.sample_based.ompl_est import OMPL_EST
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from algorithms.classic.sample_based.ompl_fmt import OMPL_FMT
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from algorithms.classic.sample_based.ompl_lazylbtrrt import OMPL_LazyLBTRRT
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from algorithms.classic.sample_based.ompl_lazyprm import OMPL_LazyPRM
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from algorithms.classic.sample_based.ompl_lazyrrt import OMPL_LazyRRT
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from algorithms.classic.sample_based.ompl_lbkpiece1 import OMPL_LBKPIECE1
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from algorithms.classic.sample_based.ompl_lbtrrt import OMPL_LBTRRT
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from algorithms.classic.sample_based.ompl_prm import OMPL_PRM
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from algorithms.classic.sample_based.ompl_spars import OMPL_SPARS
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from algorithms.classic.sample_based.ompl_spars2 import OMPL_SPARS2
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from algorithms.classic.sample_based.ompl_vfrrt import OMPL_VFRRT
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from algorithms.classic.sample_based.ompl_prrt import OMPL_pRRT
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from algorithms.classic.sample_based.ompl_tsrrt import OMPL_TSRRT
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from algorithms.classic.sample_based.ompl_psbl import OMPL_pSBL
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from algorithms.classic.sample_based.ompl_sbl import OMPL_SBL
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from algorithms.classic.sample_based.ompl_stride import OMPL_STRIDE
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from algorithms.classic.sample_based.ompl_qrrt import OMPL_QRRT
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def static_class(cls):
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if getattr(cls, "_static_init_", None):
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cls._static_init_()
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return cls
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@static_class
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class AlgorithmManager():
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MetaData = Tuple[Type[Algorithm], Type[BasicTesting], Tuple[List[Any], Dict[str, Any]]]
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builtins: Dict[str, MetaData]
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@classmethod
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def _static_init_(cls):
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cls.builtins = {
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"A*": (AStar, AStarTesting, ([], {})),
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"Global Way-point LSTM": (WayPointNavigation, WayPointNavigationTesting, (
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[], {"global_kernel": (CombinedOnlineLSTM, ([], {})), "global_kernel_max_it": 100})),
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"LSTM Bagging": (CombinedOnlineLSTM, CombinedOnlineLSTMTesting, ([], {})),
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"CAE Online LSTM": (
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OnlineLSTM, BasicTesting, ([], {"load_name": "caelstm_section_cae_training_house_100_model"})),
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"Online LSTM": (OnlineLSTM, BasicTesting, (
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[],
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{"load_name": "tile_by_tile_training_uniform_random_fill_3000_block_map_3000_house_3000_model"})),
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"SPRM": (SPRM, BasicTesting, ([], {})),
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"RT": (RT, BasicTesting, ([], {})),
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"RRT": (RRT, BasicTesting, ([], {})),
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"RRT*": (RRT_Star, BasicTesting, ([], {})),
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"RRT-Connect": (RRT_Connect, BasicTesting, ([], {})),
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"Wave-front": (Wavefront, WavefrontTesting, ([], {})),
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"Dijkstra": (Dijkstra, DijkstraTesting, ([], {})),
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"Bug1": (Bug1, BasicTesting, ([], {})),
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"Bug2": (Bug2, BasicTesting, ([], {})),
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"Potential Field": (PotentialField, BasicTesting, ([], {}))
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}
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if HAS_OMPL:
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cls.builtins.update({
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"OMPL RRT": (OMPL_RRT, BasicTesting, ([], {})),
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"OMPL PRM*": (OMPL_PRMstar, BasicTesting, ([], {})),
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"OMPL Lazy PRM*": (OMPL_LazyPRMstar, BasicTesting, ([], {})),
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"OMPL RRT*": (OMPL_RRTstar, BasicTesting, ([], {})),
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"OMPL RRT#": (OMPL_RRTsharp, BasicTesting, ([], {})),
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"OMPL RRTX": (OMPL_RRTXstatic, BasicTesting, ([], {})),
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"OMPL KPIECE1": (OMPL_KPIECE1, BasicTesting, ([], {})),
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"OMPL LazyLBTRRT": (OMPL_LazyLBTRRT, BasicTesting, ([], {})),
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"OMPL LazyPRM": (OMPL_LazyPRM, BasicTesting, ([], {})),
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"OMPL LazyRRT": (OMPL_LazyRRT, BasicTesting, ([], {})),
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"OMPL LBKPIECE1": (OMPL_LBKPIECE1, BasicTesting, ([], {})),
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"OMPL LBTRRT": (OMPL_LBTRRT, BasicTesting, ([], {})),
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"OMPL PRM": (OMPL_PRM, BasicTesting, ([], {})),
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"OMPL SBL": (OMPL_SBL, BasicTesting, ([], {})),
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"OMPL STRIDE": (OMPL_STRIDE, BasicTesting, ([], {})),
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"OMPL PDST": (OMPL_PDST, BasicTesting, ([], {})),
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"OMPL SST": (OMPL_SST, BasicTesting, ([], {})),
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"OMPL BiEst": (OMPL_BiEST, BasicTesting, ([], {})),
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"OMPL TRRT": (OMPL_TRRT, BasicTesting, ([], {})),
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"OMPL RRTConnect": (OMPL_RRTConnect, BasicTesting, ([], {})),
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"OMPL BITstar": (OMPL_BITstar, BasicTesting, ([], {})),
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"OMPL BKPIECE1": (OMPL_BKPIECE1, BasicTesting, ([], {})),
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"OMPL EST": (OMPL_EST, BasicTesting, ([], {})),
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# "OMPL LTLPlanner": (OMPL_LTLPlanner, BasicTesting, ([], {})),
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# "OMPL AITstar": (OMPL_AITstar, BasicTesting, ([], {})),
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# "OMPL AnytimePathShortening": (OMPL_AnytimePathShortening, BasicTesting, ([], {})),
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# "OMPL BFMT": (OMPL_BFMT, BasicTesting, ([], {})),
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# "OMPL BiRLRT": (OMPL_BiRLRT, BasicTesting, ([], {})),
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# "OMPL BiTRRT": (OMPL_BiTRRT, BasicTesting, ([], {})),
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# "OMPL Syclop ": (OMPL_Syclop, BasicTesting, ([], {})),
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# "OMPL CForest": (OMPL_CForest, BasicTesting, ([], {})),
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# "OMPL FMT": (OMPL_FMT, BasicTesting, ([], {})),
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# "OMPL SPARS": (OMPL_SPARS, BasicTesting, ([], {})),
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# "OMPL SPARS2": (OMPL_SPARS2, BasicTesting, ([], {})),
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# "OMPL VFRRT": (OMPL_VFRRT, BasicTesting, ([], {})),
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# "OMPL pRRT": (OMPL_pRRT, BasicTesting, ([], {})),
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# "OMPL TSRRT": (OMPL_TSRRT, BasicTesting, ([], {})),
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# "OMPL pSBL": (OMPL_pSBL, BasicTesting, ([], {})),
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# "OMPL QRRT": (OMPL_QRRT, BasicTesting, ([], {})),
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})
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@staticmethod
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def load_all(ids: List[str]) -> List[List[Tuple[str, MetaData]]]:
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"""
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Returns a list of algorithms from a list of names or file paths.
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For each element in `ids`, if string is the display name
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of a built-in algorithm, then we return that algorithm. Otherwise,
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we return the result of AlgorithmManager.try_load_from_file().
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"""
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algs: List[List[Tuple[str, MetaData]]] = []
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for alg in ids:
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if alg in AlgorithmManager.builtins:
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algs.append([copy.deepcopy((alg, AlgorithmManager.builtins[alg]))])
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else:
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algs.append(AlgorithmManager.try_load_from_file(alg))
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return algs
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@staticmethod
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def try_load_from_file(path: str) -> List[Tuple[str, MetaData]]:
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if not os.path.exists(path):
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msg = "File '{}' does not exist".format(path)
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print(msg, file=sys.stderr)
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return []
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try:
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spec = importlib.util.spec_from_file_location("custom_loaded", path)
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module = importlib.util.module_from_spec(spec)
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spec.loader.exec_module(module)
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# return all classes that inherit from "Algorithm"
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algs = []
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for name in dir(module):
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if name.startswith("_"):
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continue
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cls = getattr(module, name)
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if inspect.isclass(cls) and cls is not Algorithm and issubclass(cls, Algorithm):
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name = cls.name if "name" in cls.__dict__ else os.path.basename(path) + " ({})".format(name)
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testing = cls.testing if "testing" in cls.__dict__ else BasicTesting
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algs.append((name, (cls, testing, ([], {}))))
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return algs
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except:
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msg = "Failed to load algorithms from file '{}', reason:\n{}".format(path, traceback.format_exc())
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print(msg, file=sys.stderr)
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return []

‎src/algorithms/configuration/configuration.py‎

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from typing import Tuple, Callable, Type, List, Optional, Dict, Any, Union
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import copy
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from algorithms.algorithm import Algorithm
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from algorithms.basic_testing import BasicTesting
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generator_show_gen_sample: bool
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generator_house_expo: bool
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generator_size: int
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generator_obstacle_fill_min: float
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generator_obstacle_fill_max: float
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generator_min_room_size: int
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generator_max_room_size: int
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# Trainer
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trainer: bool
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# Misc
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analyzer: bool
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algorithms: Dict[str, Tuple[Type[Algorithm], Type[BasicTesting], Tuple[List[Any], Dict[str, Any]]]]
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load_simulator: bool
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clear_cache: bool
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num_dim: int
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self.generator_modify = None
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self.generator_show_gen_sample = False
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self.generator_house_expo = False
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self.generator_obstacle_fill_min = 0.1
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self.generator_obstacle_fill_max = 0.3
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self.generator_min_room_size = 3
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self.generator_max_room_size = 16
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self.generator_size = 64
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self.num_dim = 2
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self.trainer_pre_process_data_only = False
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self.trainer_bypass_and_replace_pre_processed_cache = False
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# Custom behaviour settings
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# Analyzer
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self.analyzer = False
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# Common
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from algorithms.algorithm_manager import AlgorithmManager
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self.algorithms = copy.deepcopy(AlgorithmManager.builtins)
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# Simulator
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self.load_simulator = False
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‎src/algorithms/lstm/LSTM_CAE_tile_by_tile.py‎

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from algorithms.lstm.ML_model import MLModel, EvaluationResults
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from algorithms.lstm.map_processing import MapProcessing
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from simulator.services.services import Services
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from constants import DATA_PATH
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from utility.constants import DATA_PATH
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class CAEEncoder(nn.Module):

‎src/analyze.py‎

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analyzer.analyze_algorithms()
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if __name__== '__main__':
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main()
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main()

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