Feature/collider to dict

.gitignore

@@ -6,3 +6,5 @@ _auto_examples/
 _autosummary/
 .idea/
 .coverage
+
+*.pclprof

benchmarks/benchmark_gjk.py

@@ -1,14 +1,114 @@
-# python -m cProfile -s tottime benchmarks/benchmark_gjk.py > profile
-# python -m cProfile -o program.prof benchmarks/benchmark_gjk.py
 import timeit
-from functools import partial
 import numpy as np
-from distance3d import colliders
-from distance3d.gjk import gjk
+from distance3d import colliders, random
+from distance3d.gjk import gjk_distance_original, gjk_intersection_jolt, gjk_distance_jolt, \
+    gjk_nesterov_accelerated_primitives, gjk_nesterov_accelerated
+from numba import config
 
+iterations = 200
+shapes = []
+random_state = np.random.RandomState(84)
+shape_names = list(["sphere", "ellipsoid", "capsule", "cone", "cylinder", "box"])
 
-random_state = np.random.RandomState(32)
-collider1 = colliders.ConvexHullVertices(random_state.randn(1000, 3))
-collider2 = colliders.ConvexHullVertices(random_state.randn(1000, 3))
-times = timeit.repeat(partial(gjk, collider1=collider1, collider2=collider2), repeat=10, number=1000)
-print(f"Mean: {np.mean(times):.5f}; Std. dev.: {np.std(times):.5f}")
+for _ in range(iterations):
+    shape1 = shape_names[random_state.randint(len(shape_names))]
+    args1 = random.RANDOM_GENERATORS[shape1](random_state)
+    shape2 = shape_names[random_state.randint(len(shape_names))]
+    args2 = random.RANDOM_GENERATORS[shape2](random_state)
+    collider1 = colliders.COLLIDERS[shape1](*args1)
+    collider2 = colliders.COLLIDERS[shape2](*args2)
+
+    shapes.append((collider1, collider2))
+
+
+def benchmark_original():
+    for i in range(iterations):
+        gjk_distance_original(shapes[i][0], shapes[i][1])
+
+
+def benchmark_jolt_intersection():
+    for i in range(iterations):
+        gjk_intersection_jolt(shapes[i][0], shapes[i][1])
+
+
+def benchmark_jolt_distance():
+    for i in range(iterations):
+        gjk_distance_jolt(shapes[i][0], shapes[i][1])
+
+
+def benchmark_nesterov_accelerated():
+    for i in range(iterations):
+        gjk_nesterov_accelerated(shapes[i][0], shapes[i][1], use_nesterov_acceleration=False)
+
+
+def benchmark_nesterov_accelerated_with_acceleration():
+    for i in range(iterations):
+        gjk_nesterov_accelerated(shapes[i][0], shapes[i][1], use_nesterov_acceleration=True)
+
+
+def benchmark_nesterov_accelerated_primitives():
+    for i in range(iterations):
+        gjk_nesterov_accelerated_primitives(shapes[i][0], shapes[i][1], use_nesterov_acceleration=False)
+
+
+def benchmark_nesterov_accelerated_primitives_with_acceleration():
+    for i in range(iterations):
+        gjk_nesterov_accelerated_primitives(shapes[i][0], shapes[i][1], use_nesterov_acceleration=True)
+
+
+times = timeit.repeat(benchmark_original, repeat=10, number=1)
+print(f"Original Mean: {np.mean(times):.5f}; Std. dev.: {np.std(times):.5f}")
+
+print("\nJolt:")
+
+config.DISABLE_JIT = True
+times = timeit.repeat(benchmark_jolt_intersection, repeat=10, number=1)
+print(f"Jolt intersection Mean: {np.mean(times):.5f}; Std. dev.: {np.std(times):.5f}")
+config.DISABLE_JIT = False
+
+times = timeit.repeat(benchmark_jolt_intersection, repeat=10, number=1)
+print(f"Jolt intersection with Numba Mean: {np.mean(times):.5f}; Std. dev.: {np.std(times):.5f}")
+
+config.DISABLE_JIT = True
+times = timeit.repeat(benchmark_jolt_distance, repeat=10, number=1)
+print(f"Jolt distance Mean: {np.mean(times):.5f}; Std. dev.: {np.std(times):.5f}")
+config.DISABLE_JIT = False
+
+times = timeit.repeat(benchmark_jolt_distance, repeat=10, number=1)
+print(f"Jolt distance with Numba Mean: {np.mean(times):.5f}; Std. dev.: {np.std(times):.5f}")
+
+
+print("\nNesterov accelerated:")
+times = timeit.repeat(benchmark_nesterov_accelerated, repeat=10, number=1)
+print(f"Nesterov Mean: {np.mean(times):.5f}; Std. dev.: {np.std(times):.5f}")
+
+config.DISABLE_JIT = True
+times = timeit.repeat(benchmark_nesterov_accelerated, repeat=10, number=1)
+print(f"Nesterov with Numba Mean: {np.mean(times):.5f}; Std. dev.: {np.std(times):.5f}")
+config.DISABLE_JIT = False
+
+times = timeit.repeat(benchmark_nesterov_accelerated_with_acceleration, repeat=10, number=1)
+print(f"Nesterov with acceleration Mean: {np.mean(times):.5f}; Std. dev.: {np.std(times):.5f}")
+
+config.DISABLE_JIT = True
+times = timeit.repeat(benchmark_nesterov_accelerated_with_acceleration, repeat=10, number=1)
+print(f"Nesterov with acceleration and Numba Mean: {np.mean(times):.5f}; Std. dev.: {np.std(times):.5f}")
+config.DISABLE_JIT = False
+
+
+print("\nNesterov accelerated Primitives:")
+times = timeit.repeat(benchmark_nesterov_accelerated_primitives, repeat=10, number=1)
+print(f"Nesterov Mean: {np.mean(times):.5f}; Std. dev.: {np.std(times):.5f}")
+
+config.DISABLE_JIT = True
+times = timeit.repeat(benchmark_nesterov_accelerated_primitives, repeat=10, number=1)
+print(f"Nesterov with Numba Mean: {np.mean(times):.5f}; Std. dev.: {np.std(times):.5f}")
+config.DISABLE_JIT = False
+
+times = timeit.repeat(benchmark_nesterov_accelerated_primitives_with_acceleration, repeat=10, number=1)
+print(f"Nesterov with acceleration Mean: {np.mean(times):.5f}; Std. dev.: {np.std(times):.5f}")
+
+config.DISABLE_JIT = True
+times = timeit.repeat(benchmark_nesterov_accelerated_primitives_with_acceleration, repeat=10, number=1)
+print(f"Nesterov with acceleration and Numba Mean: {np.mean(times):.5f}; Std. dev.: {np.std(times):.5f}")
+config.DISABLE_JIT = False

benchmarks/compare_gjk_implementations.py

@@ -33,7 +33,7 @@ def test_gjk_intersection_vs_gjk():
         args2 = random_function(random_state)
         collider2 = Class(*args2)
 
-        dist_original, closest_point1_original, closest_point2_original, _ = gjk.gjk_distance_original(collider1, collider2)
+        dist_original, closest_point1_original, closest_point2_original, _, _ = gjk.gjk_distance_original(collider1, collider2)
         dist_jolt, closest_point1_jolt, closest_point2_jolt, _ = gjk.gjk_distance_jolt(collider1, collider2)
         gjk_intersection_original = dist_original < 1e-7
         gjk_intersection_libccd = gjk.gjk_intersection_libccd(collider1, collider2)

benchmarks/compare_gjk_iterations.py

@@ -0,0 +1,44 @@
+import json
+import os
+import numpy as np
+from distance3d import colliders, random
+from distance3d.gjk._gjk_original import gjk_distance_iterations
+from distance3d.gjk._gjk_jolt import gjk_distance_jolt_iterations
+from distance3d.gjk._gjk_nesterov_accelerated_primitives import gjk_nesterov_accelerated_primitives_iterations
+
+iterations = 100
+random_state = np.random.RandomState(84)
+shape_names = ["sphere", "capsule", "cylinder"]
+
+original_iteration_sum = 0
+jolt_iteration_sum = 0
+nasterov_iteration_sum = 0
+
+for i in range(iterations):
+    print("Case:", i)
+
+    shape1 = shape_names[random_state.randint(len(shape_names))]
+    args1 = random.RANDOM_GENERATORS[shape1](random_state)
+    shape2 = shape_names[random_state.randint(len(shape_names))]
+    args2 = random.RANDOM_GENERATORS[shape2](random_state)
+    collider1 = colliders.COLLIDERS[shape1](*args1)
+    collider2 = colliders.COLLIDERS[shape2](*args2)
+
+    collider1.round_values(6)
+    collider2.round_values(6)
+
+    original_iterations = gjk_distance_iterations(collider1, collider2)
+    jolt_iterations = gjk_distance_jolt_iterations(collider1, collider2)
+    nasterov_iterations = gjk_nesterov_accelerated_primitives_iterations(collider1, collider2)
+
+    print("Original Iterations:", original_iterations)
+    print("Jolt Distance Iterations:", jolt_iterations)
+    print("Nesterov Iterations", nasterov_iterations)
+
+    original_iteration_sum += original_iterations
+    jolt_iteration_sum += jolt_iterations
+    nasterov_iteration_sum += nasterov_iterations
+
+print("Original Iterations per Case", original_iteration_sum / iterations)
+print("Jolt Distance Iterations per Case", jolt_iteration_sum / iterations)
+print("Nesterov Iterations per Case", nasterov_iteration_sum / iterations)

benchmarks/vis_gjk_mismatch.py

@@ -32,10 +32,11 @@
 print(f"{intersection_jolt=}")
 intersection_mpr = mpr.mpr_intersection(collider1, collider2)
 print(f"{intersection_mpr=}")
-dist_original, cp1_original, cp2_original, _ = gjk.gjk_distance_original(collider1, collider2)
+dist_original, cp1_original, cp2_original, _, _ = gjk.gjk_distance_original(collider1, collider2)
 print(f"{dist_original=}, {cp1_original=}, {cp2_original=}")
 dist_jolt, cp1_jolt, cp2_jolt, _ = gjk.gjk_distance_jolt(collider1, collider2)
 print(f"{dist_jolt=}, {cp1_jolt=}, {cp2_jolt=}")
+
 fig = pv.figure()
 collider1.make_artist((1, 0, 0))
 collider2.make_artist((0, 1, 0))