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Astar_test.py
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from Astar import Astar, Node
import numpy as np
from typing import Tuple
class Point():
def __init__(self, coordinate: Tuple[float]) -> None:
self._x, self._y = coordinate
self.coordinate = coordinate
class dummy_graph():
def __init__(self, points, edges) -> None:
self.points = points
self.edges = edges
def get_neighbours(self, idx):
idxs = np.where(self.edges[idx] != 0)[0].tolist()
edges = [edge for edge in self.edges[idx] if edge != 0]
coordinates = [self.points[idx].coordinate for idx in idxs]
return idxs, edges, coordinates
def test_basic_path1():
points = [Point((0,0)),Point((0,1)),Point((0,2)),Point((0,3))]
edges = np.array([ [0, 1, 1, 1],
[1, 0, 1, 1],
[1, 1, 0, 1],
[1, 1, 1, 0]])
g = dummy_graph(points, edges)
a = Astar(g, (0,0), (0,3), heuristic_method="MANHATTEN")
path = a.run()
path_c = [node.coordinate for node in path]
assert path_c == [(0,0),(0,3)]
assert path[-1].g == 1
def test_basic_path2():
points = [Point((0,0)),Point((0,1)),Point((0,2)),Point((0,3))]
edges = np.array([ [0, 1, 0, 0],
[0, 0, 1, 0],
[0, 0, 0, 1],
[0, 0, 0, 0]])
g = dummy_graph(points, edges)
a = Astar(g, (0,0), (0,3), heuristic_method="EUCLIDEAN")
path = a.run()
path_c = [node.coordinate for node in path]
assert path_c == [(0,0),(0,1),(0,2),(0,3)]
assert path[-1].g == 3
def test_basic_path3():
points = [Point((0,0)),Point((0,1)),Point((0,2)),Point((0,3))]
edges = np.array([ [0, 0, 1, 0],
[0, 0, 0, 1],
[0, 1, 0, 0],
[0, 0, 0, 0]])
g = dummy_graph(points, edges)
a = Astar(g, (0,0), (0,3), heuristic_method="EUCLIDEAN")
path = a.run()
path_c = [node.coordinate for node in path]
assert path_c == [(0,0),(0,2),(0,1),(0,3)]
assert path[-1].g == 3
def test_basic_path4():
points = [Point((0,0)),Point((0,1)),Point((0,2)),Point((0,3))]
edges = np.array([ [0, 0, 1, 0],
[1, 0, 0, 0],
[0, 1, 0, 1],
[0, 0, 0, 0]])
g = dummy_graph(points, edges)
a = Astar(g, (0,1), (0,3), heuristic_method="EUCLIDEAN")
path = a.run()
path_c = [node.coordinate for node in path]
assert path_c == [(0,1),(0,2),(0,3)]
assert path[-1].g == 2
def test_basic_path5():
points = [Point((0,0)),Point((0,1)),Point((0,2)),Point((0,3))]
edges = np.array([ [0, 0, 1, 0],
[1, 0, 0, 0],
[0, 1, 0, 0],
[0, 0, 0, 0]])
g = dummy_graph(points, edges)
a = Astar(g, (0,1), (0,3), heuristic_method="EUCLIDEAN")
path = a.run()
assert path == None
def test_basic_path6():
points = [Point((0,0)),Point((0,1)),Point((0,2)),Point((0,3))]
edges = np.array([ [0, 0, 0, 0],
[1, 0, 0, 0],
[0, 1, 0, 0],
[0, 0, 1, 0]])
g = dummy_graph(points, edges)
a = Astar(g, (0,1), (0,3), heuristic_method="EUCLIDEAN")
path = a.run()
assert path == None
def test_basic_path7():
points = [Point((0,0)),Point((0,1)),Point((0,2)),Point((0,3))]
edges = np.array([ [0, 8, 1, 10],
[0, 0, 2, 0],
[0, 0, 0, 1],
[0, 0, 0, 0]])
g = dummy_graph(points, edges)
a = Astar(g, (0,0), (0,3), heuristic_method="EUCLIDEAN")
path = a.run()
path_c = [node.coordinate for node in path]
assert path_c == [(0,0),(0,2),(0,3)]
assert path[-1].g == 2
def test_basic_path8():
points = [Point((0,0)),Point((0,1)),Point((0,2)),Point((0,3))]
edges = np.array([ [0, 2, 2, 10],
[0, 0, 0, 2],
[0, 0, 0, 2],
[0, 0, 0, 0]])
g = dummy_graph(points, edges)
a = Astar(g, (0,0), (0,3), heuristic_method="EUCLIDEAN")
path = a.run()
path_c = [node.coordinate for node in path]
assert path_c == [(0,0),(0,2),(0,3)]
assert path[-1].g == 4
def test_basic_path9():
points = [Point((0,0)),Point((0,1)),Point((0,2)),Point((0,3))]
edges = np.array([ [0, 1, 2, 10],
[0, 0, 0, 2],
[0, 0, 0, 2],
[0, 0, 0, 0]])
g = dummy_graph(points, edges)
a = Astar(g, (0,0), (0,3), heuristic_method="EUCLIDEAN")
path = a.run()
path_c = [node.coordinate for node in path]
assert path_c == [(0,0),(0,1),(0,3)]
assert path[-1].g == 3
def test_basic_path10():
points = [Point((0,0)),Point((0,1)),Point((0,2)),Point((0,3))]
edges = np.array([ [0, 1, 2, 10],
[0, 0, 0, 2],
[0, 0, 0, 2],
[0, 0, 0, 0]])
g = dummy_graph(points, edges)
a = Astar(g, (0,0), (0,3), heuristic_method="EUCLIDEAN")
path = a.run()
path_c = [node.coordinate for node in path]
assert path_c == [(0,0),(0,1),(0,3)]
assert path[-1].g == 3
def test_basic_path11():
points = [Point((0,0)),Point((0,1)),Point((0,2)),Point((0,3))]
edges = np.array([ [0, 0.9, 2, 10],
[0, 0 , 0, 2],
[0, 0 , 0, 2.1],
[0, 0 , 0, 0]])
g = dummy_graph(points, edges)
a = Astar(g, (0,0), (0,3), heuristic_method="MANHATTEN")
path = a.run()
path_c = [node.coordinate for node in path]
assert path_c == [(0,0),(0,1),(0,3)]
assert path[-1].g == 2.9
def test_basic_path12():
# This one is interesting as it shows how the heuristic fails
# by being inconsistent (because the weights are weird),
# meaning that the heurstic does not properly represent the
# weights. The solution that A* gives is therefor SUBoptimal.
# Make sure that the heuristic is always consistent with the
# weights.
# A heuristic is not consistent if c(u,v) + h(v) >= h(u)
points = [Point((0,0)),Point((0,1)),Point((0,2)),Point((0,3))]
edges = np.array([ [0, 0.9, 2, 10],
[0, 0 , 0, 2],
[0, 0 , 0, 0.8],
[0, 0 , 0, 0]])
g = dummy_graph(points, edges)
a = Astar(g, (0,0), (0,3), heuristic_method="EUCLIDEAN")
path = a.run()
path_c = [node.coordinate for node in path]
assert path_c == [(0,0),(0,1),(0,3)]
assert path[-1].g == 2.9
def test_heuristic1():
a = Astar(None, None, (1,1), heuristic_method="EUCLIDEAN")
node = Node((0,0), None, None)
assert a.heuristic(node, "EUCLIDEAN") == np.sqrt(2)
def test_heuristic2():
a = Astar(None, None, (1,1), heuristic_method="MANHATTEN")
node = Node((0,0), None, None)
assert a.heuristic(node, "MANHATTEN") == 0