def store(self, distance_hash, n):
self.order = n
self._dbl_lvl_dict = _get_dbl_level_dict(distance_hash)
sp_dijkstra = Dijkstra(self._dbl_lvl_dict, self.order)
for i in range(self.order):
self.ub_matrix.append([1] * self.order)
for i in range(self.order):
nodes = list(range(self.order))
sp = sp_dijkstra.shortest_path(nodes, i)
for index in range(self.order):
# distance, node, parent
self.ub_matrix[i][index] = sp[index][0]
self.ub_matrix[index][i] = sp[index][0]
self.search_started = [False] * n
for i in range(n):
self.lb_matrix.append([0] * n)
if n - i - 1 != 0:
self.uncalculated[i] = set(range(i + 1, n))
for k in distance_hash.keys():
x, y = k
self.lb_matrix[x][y] = distance_hash[k]
self.lb_matrix[y][x] = distance_hash[k]
self.uncalculated[min(x, y)].remove(max(x, y))
if len(self.uncalculated[min(x, y)]) == 0:
del self.uncalculated[min(x, y)]
self.sparse_matrix = SparseMatrix(distance_hash, n)
if self.max_path_length == 2:
for i in range(n):
for j in range(i + 1, n):
self._update(i, j)
else:
for i in range(n):
self._dfs(i)
def test_tushar_roy(self):
''' example from Tushar Roy's video https://www.youtube.com/watch?v=lAXZGERcDf4 '''
start_vertex = 'a'
verticies = ['a', 'b', 'c', 'd', 'e', 'f']
# edge_weights = {('a','b') : 2, ('a','f') : 4, ('a','d'):5, ('b','c') : 4, ('f','c') : 3, ('f','d') : 2, ('d','e') : 1}
edge_weights = {
('a', 'b'): 2,
('a', 'f'): 4,
('a', 'd'): 5,
('b', 'c'): 4,
('c', 'f'): 3,
('d', 'f'): 2,
('d', 'e'): 1
}
djk = Dijkstra(start_vertex, verticies, edge_weights)
# pdb.set_trace()
djk.iterate()
# self.assertRaises(ValueError, djk.iterate)
# print("parents=", djk.parents)
self.assertEqual(6, len(djk.parents)) #parents is empty
# print("parents=", djk.parents)
self.assertEqual([], djk.parents['a'])
self.assertEqual(['a'], djk.parents['b'])
self.assertEqual(['a'], djk.parents['d'])
self.assertEqual(['a'], djk.parents['f'])
self.assertEqual(['b'], djk.parents['c'])
def store_nodeLandmarks(self):
dijk_obj = Dijkstra(_get_dbl_level_dict(self.G, self.noNodes),
self.noNodes)
for landmark in self.nodeLandmarks:
if landmark in self.sp:
continue
self.sp[landmark] = dijk_obj.shortest_path(self.vertices, landmark)
class DijkstraTest(PathTest, unittest.TestCase):
def create_path(self, g, u, v):
self.path = Dijkstra(g, u, v)
def test_path_to_with_weight(self):
g = Graph.from_dict({
0: [(1, 5), (2, 10)],
1: [(4, 5)],
2: [(3, 1)],
3: [(5, 6)],
4: [(5, 5), (6, 10)],
5: [(6, 2)]
})
self.create_path(g, 0, 6)
self.assertListEqual(self.path.path_to(1), [0, 1])
self.assertListEqual(self.path.path_to(2), [0, 2])
self.assertListEqual(self.path.path_to(3), [0, 2, 3])
self.assertListEqual(self.path.path_to(4), [0, 1, 4])
self.assertListEqual(self.path.path_to(5), [0, 1, 4, 5])
def test_partial_path_with_weight(self):
g = Graph.from_dict({
0: [(1, 5), (2, 1)],
1: [(4, 5)],
2: [(3, 21)],
3: [(5, 6)],
4: [(5, 5), (6, 10)],
5: [(3, 1)]
})
self.create_path(g, 0, 3)
self.assertListEqual(self.path.path_to(1), [0, 1])
self.assertListEqual(self.path.path_to(2), [0, 2])
self.assertListEqual(self.path.path_to(3), [0, 1, 4, 5, 3])
def find_paths(self):
self.d = Dijkstra(_get_dbl_level_dict(self.G), len(self.nodes))
self.dijkPaths = {}
for each in self.sampled:
for e in each:
if self.dijkPaths.get(e, -1) == -1:
self.dijkPaths[e] = self.d.shortest_path(self.nodes, e)
def JohnsonAlgorithm(graph: WeightedDirectedGraph, verbose=True):
graph_edges_with_extra_node = graph.edges + [
WeightedEdge('new_node', 0, node) for node in graph.nodes
]
graph_with_extra_node = WeightedDirectedGraph(
graph_edges_with_extra_node.copy())
try:
bf_shortest_distances, predecessors = BellmanFord(
graph_with_extra_node, 'new_node', verbose=False)
except Exception as e:
return str(e)
for edge in graph.edges:
edge.weight = edge.weight + bf_shortest_distances[
edge.head] - bf_shortest_distances[edge.tail]
all_pairs_shortest_distance = {}
for source_node in graph.nodes:
if verbose:
print('\nShortest Distance with vertex ' + str(source_node) +
' as the source:\n')
dijkstra_shortest_distances, predecessors = Dijkstra(graph,
source_node,
verbose=verbose)
dijkstra_shortest_distances = {
k:
(v + bf_shortest_distances[k] - bf_shortest_distances[source_node])
for k, v in dijkstra_shortest_distances.items()
}
all_pairs_shortest_distance[source_node] = dijkstra_shortest_distances
return all_pairs_shortest_distance
def main():
i = No("I")
a = No("A")
b = No("B")
c = No("C")
d = No("D")
e = No("E")
f = No("F")
t = No("T")
i.Conectar(a, 6)
i.Conectar(b, 2)
a.Conectar(c, 4)
a.Conectar(e, 2)
b.Conectar(a, 4)
b.Conectar(c, 3)
b.Conectar(d, 7)
c.Conectar(e, 2)
c.Conectar(d, 3)
d.Conectar(t, 2)
e.Conectar(t, 4)
e.Conectar(f, 7)
f.Conectar(t, 3)
algoritmo = Dijkstra()
caminho_mais_curto = algoritmo.EncontrarCaminhoMaisCurto(i, t)
print(*InverterCaminho(caminho_mais_curto).items(), sep="\n")
def __init__(self, edge_list, order_val, k):
self.edge_list = edge_list
self.k = k
self.order_val = order_val
self.dbl_dict = _get_dbl_level_dict(self.edge_list)
self.d = Dijkstra(self.dbl_dict, self.order_val)
self.landmarks = None
self.dijk_dict = {}
def generate_shortest_path_test(edges):
print("\n### Generate shortest path test")
try:
d = Dijkstra(make_dict_graph(edges), "O", "T")
distance_total, path = d.generate_shortest_path()
print(f" {path} \n")
except:
print("Error")
def store_edgeLandmarks(self):
dijk_obj = Dijkstra(_get_dbl_level_dict(self.G, self.noNodes),
self.noNodes)
for (x, y) in self.edgeLandmarks:
if x not in self.sp:
self.sp[x] = dijk_obj.shortest_path(self.vertices, x)
if y not in self.sp:
self.sp[y] = dijk_obj.shortest_path(self.vertices, y)
def test_one_point(self):
''' test simple 1 point '''
start_vertex = 0
verticies = [0]
edge_weights = {}
djk = Dijkstra(start_vertex, verticies, edge_weights)
djk.iterate()
# self.assertRaises(ValueError, djk.iterate)
self.assertEqual(1, len(djk.parents)) #parents is empty
self.assertEqual([], djk.parents[0])
self.assertEqual({0: 0}, djk.distances)
def main(stdscr, filename, player_x, player_y):
curses.noecho()
stdscr.keypad(True)
for y, line in enumerate(open(filename, 'r').read()[:-1].split('\n')):
for x, char in enumerate(line):
if char == 'A':
goal_x, goal_y = x, y
dungeon = Dungeon(filename)
dijkstra = Dijkstra(dungeon, goal_x, goal_y)
dijkstra.find()
while True:
stdscr.clear()
#dijkstra.find()
path = list(dijkstra.pathFrom(player_x, player_y))
for y, line in enumerate(dungeon.cells):
for x, cell in enumerate(line):
if cell in path[1:]:
stdscr.addch(y, x, '.')
elif x == player_x and y == player_y:
stdscr.addch(y, x, '@')
else:
stdscr.addch(y, x, cell.char)
if len(path) != 0:
dist = path[0].dist
else:
dist = float('inf')
stdscr.addstr(20, 0, 'A: %s' % str(dist))
key = stdscr.getkey()
if key == 'q':
break
elif key == 'h' or key == 'a':
player_x -= 1
if player_x < 0:
player_x += 1
elif key == 'l' or key == 'd':
player_x += 1
if player_x >= len(dungeon.cells[0]):
player_x -= 1
elif key == 'k' or key == 'w':
player_y -= 1
if player_y < 0:
player_y += 1
elif key == 'j' or key == 's':
player_y += 1
if player_y > len(dungeon.cells) - 1:
player_y -= 1
def dijkstra_algorithm_test(edges):
print("\n### Dijkstra alogrith test")
try:
d = Dijkstra(make_dict_graph(edges), "O", "T")
distance_total, prev = d.dijkstra_algorithm()
print(f" Total distance: {distance_total}")
for p in prev:
if prev[p] != None:
print(f" {prev[p]} ")
except:
print("Error...")
def testDijkstra():
filename = "testG1.txt"
sparG = SparseGraph(5, True)
ReadGraph(sparG, filename)
#sparG.show()
dij = Dijkstra(sparG, 0)
for i in range(sparG.V()):
if (dij.hasPathTo(i)):
dij.showPath(i)
else:
print "No Path To %d " % i
def test_two_point(self):
''' test simple 2 point '''
start_vertex = 0
verticies = [0, 1]
edge_weights = {(0, 1): 1}
djk = Dijkstra(start_vertex, verticies, edge_weights)
# pdb.set_trace()
djk.iterate()
# self.assertRaises(ValueError, djk.iterate)
# print("parents=", djk.parents)
self.assertEqual(2, len(djk.parents)) #parents is empty
self.assertEqual([], djk.parents[0])
self.assertEqual([0], djk.parents[1])
self.assertEqual({0: 0, 1: 1}, djk.distances)
def main(filename):
dungeon = Dungeon(filename)
for y, line in enumerate(open(filename, 'r').read()[:-1].split('\n')):
for x, char in enumerate(line):
if char == 'A':
Ax, Ay = x, y
elif char == 'B':
Bx, By = x, y
dijkstra = Dijkstra(dungeon, Ax, Ay)
dijkstra.find()
path = dijkstra.pathFrom(Bx, By)
print(list(path))
def test_dijkstra(self):
graph = [[(2, 1), (5, 2)], [(2, 0), (4, 2), (6, 3), (10, 4)],
[(5, 0), (4, 1), (2, 3)], [(2, 2), (6, 1), (1, 5)],
[(10, 1), (3, 5), (5, 6)], [(1, 3), (3, 4), (9, 6)],
[(5, 4), (9, 5)]]
djk = Dijkstra()
actual = djk.dijkstra(graph, 0)
expected = [0, 2, 5, 7, 11, 8, 16]
assert actual == expected
actual = djk.dijkstra(graph, 0, 6)
expected = [0, 2, 3, 5, 4, 6]
assert actual == expected
def min_directional_distance(edge_id, adj_list):
"""Find the shortest directional distance from the specified edge to all the other edges in the graph.
:param edge_id: the ID of the source edge
:param adj_list: the adjacency list, e.g., G = [{1: 0, 26: 1, 50: 1}, {16: 1, 2: 0, 27: 1}, ...] indicates that
the edge 0 is incident with the edges 1, 26, and 50, and distances from those edges are
G[0][1] = 0, G[0][26] = 1, and G[0][50] = 1.
:return: a dictionary of shortest directional distances from the specified edge
"""
num_lines = len(
adj_list
) // 2 # the total number of line segments (i.e., undirected edges)
source_id_1 = edge_id
source_id_2 = edge_id + num_lines
# Run dijkstra twice, respectively based on the two directed source edges
D1, P1 = Dijkstra(adj_list, source_id_1)
D2, P2 = Dijkstra(adj_list, source_id_2)
# Compare D1 and D2 to get the shortest distance for each directed edge
dict_min_dist = {}
for k in D1:
if k in D2:
dict_min_dist[k] = min(D1[k], D2[k])
else:
dict_min_dist[k] = D1[k]
for k in D2:
if k in D1:
continue
else:
dict_min_dist[k] = D2[k]
# Compare the pairs of flipped edges to find the real shortest distance for the line segment
dict_real_min_dist = {}
for k, v in dict_min_dist.items():
# find the ID of the opposite edge, assuming k is no smaller than the total number of line segments
i = k - num_lines
# find the ID of the opposite edge, assuming k is smaller than the total number of line segments
j = k + num_lines
if i >= 0:
if i in dict_min_dist:
dict_real_min_dist[i] = min(dict_min_dist[i], v)
else:
dict_real_min_dist[i] = v
else:
if j in dict_min_dist:
dict_real_min_dist[k] = min(v, dict_min_dist[j])
else:
dict_real_min_dist[k] = v
return dict_real_min_dist
def test_four_point_a(self):
''' test simple 4 point '''
start_vertex = 0
verticies = [0, 1, 2, 3]
edge_weights = {(0, 1): 1, (0, 2): 1, (1, 3): 1, (2, 3): 2}
djk = Dijkstra(start_vertex, verticies, edge_weights)
# pdb.set_trace()
djk.iterate()
# self.assertRaises(ValueError, djk.iterate)
# print("parents=", djk.parents)
self.assertEqual(4, len(djk.parents)) #parents is empty
self.assertEqual([], djk.parents[0])
self.assertEqual([0], djk.parents[1])
self.assertEqual([0], djk.parents[2])
self.assertEqual([1], djk.parents[3])
self.assertEqual({0: 0, 1: 1, 2: 1, 3: 2}, djk.distances)
class Isochroner():
def __init__(self, grapher, starting_vertex):
self.grapher = grapher
self.dijkstra = Dijkstra(grapher.graph)
self.starting_vertex = starting_vertex
self.geoms_to_lines = self.assign_geoms_to_lines()
def assign_geoms_to_lines(self):
geom_aggregator = []
for edge in self.grapher.edges:
geom_aggregator.append([edge.line(), edge.source(), edge.target()])
bus_lines = [row[0] for row in geom_aggregator]
bus_lines = set(bus_lines)
geom_to_lines = {line: [] for line in bus_lines}
for row in geom_aggregator:
geom_to_lines[row[0]].append((row[1], row[2]))
return geom_to_lines
def is_line_in_edge(self, line, edge):
return edge in self.geoms_to_lines[line]
def create_isochrones(self):
isochrones = []
start = self.starting_vertex
for vertex in self.grapher.graph.vertices():
if vertex != start:
isochrones.append(self.dijkstra.min_path(start, vertex))
return isochrones
def __init__(self, locations, latitude_min, latitude_max,
longitude_min, longitude_max):
center_latitude = latitude_min + (latitude_max - latitude_min) // 2
center_longitude = longitude_min + (longitude_max - longitude_min) // 2
self.top_left_location = Location("top_left", latitude_min, longitude_min)
self.bottom_right_location = Location("bottom_right", latitude_max, longitude_max)
self.center_location = Location("center", center_latitude, center_longitude)
self.width = calc_distance(latitude_min, center_longitude, latitude_max, center_longitude)
self.height = calc_distance(center_latitude, longitude_min, center_latitude, longitude_max)
self.locations = locations
self.dijkstra = Dijkstra(self.locations)
self.selected_citys = []
self.road_paths = []
def __init__(self, data, width, height, targets):
self.mat = np.array(data, dtype="int32").reshape(height, width)
self.potentials = {}
graph, nodes = make_graph(self)
self.pathfinding = Dijkstra(graph, nodes)
self.generate_potential_for_targets(targets)
def __init__(self, graph, vertices_count):
self.graph = graph
extra_vertex = {}
for tail in graph.keys():
for head in graph[tail]:
extra_vertex[tail] = 0
extra_vertex[head] = 0
self.graph[0] = extra_vertex
B = Bellman_ford(self.graph, vertices_count + 1, source_vertex=0)
self.p_values = B.distances
self.graph.pop(0)
self.graph_double_prime = defaultdict(dict)
for tail in graph.keys():
for head in graph[tail]:
self.graph_double_prime[tail][head] = graph[tail][head] + self.p_values[tail] - self.p_values[head]
self.shortest_paths_dist_prime = {}
for i in range(1, vertices_count +1 ):
print(i)
D = Dijkstra(self.graph_double_prime, vertices_count, source_vertex=i)
for j in D.shortest_path_dist.keys():
self.shortest_paths_dist_prime[(i, j)] = D.shortest_path_dist[j]
self.shortest_path_dist = {}
for tail, head in self.shortest_paths_dist_prime.keys():
self.shortest_path_dist[(tail, head)] = self.shortest_paths_dist_prime[(tail, head)] - self.p_values[tail] + self.p_values[head]
def create(self, method_name): if method_name == "dijkstra": return Dijkstra() elif method_name == "astar": return AStar() else: return AStar()
def johnson(network):
"""
Calculates the shortest path using Johnson's algorithm
Parameters
----------
src : str, int
An arbitrary node that does not exist in the STN.
Returns
-------
distance_matrix : List[List[int]]
A 2-D list representing the shortest distances between all the nodes
"""
distance_matrix = [[] for x in range(network.length)]
potential_function = BellmanFord.bellman_ford(network)
if not potential_function:
return False
for node_idx in range(network.length):
distance_matrix[node_idx] = Dijkstra.dijkstra(
network, node_idx, potential_function=potential_function)
if network.flag:
network.flag = False
# network.distance_matrix = distance_matrix
return distance_matrix
def path_callback(self, map):
self.map = map
if self.firstCall == False:
rospy.loginfo("+++++++++++++++++initX: " + str(self.initx) +
" initY: " + str(self.inity) + " goalX: " +
str(self.goalx) + " goalY: " + str(self.goaly))
self.firstCall = True
if self.world != 0:
dijkstra = Dijkstra(self.map)
self.path = dijkstra.planning(self.initx, self.inity,
self.goalx, self.goaly)
self.save_as_yaml(self.path)
x, y = self.path
x = x[::-1]
y = y[::-1]
my_path = Path()
my_path.header.frame_id = "map"
my_path.header.stamp = rospy.get_rostime()
size = range(len(self.path[0]))
poses = []
for i in size:
p_stamp = PoseStamped()
p_stamp.pose.position.x = x[i]
p_stamp.pose.position.y = y[i]
p_stamp.pose.position.z = 0
p_stamp.header.frame_id = "map"
p_stamp.header.stamp = rospy.get_rostime()
poses.append(p_stamp)
my_path.poses = poses
self.pathPublisher.publish(my_path)
else:
poses = []
self.path = [(self.goalx, self.goaly)]
my_path = Path()
my_path.header.frame_id = "map"
my_path.header.stamp = rospy.get_rostime()
for i in range(2):
p_stamp = PoseStamped()
p_stamp.pose.position.x = self.goalx
p_stamp.pose.position.y = self.goaly
p_stamp.pose.position.z = 0
p_stamp.header.frame_id = "map"
p_stamp.header.stamp = rospy.get_rostime()
poses.append(p_stamp)
my_path.poses = poses
self.pathPublisher.publish(my_path)
def test2():
inf = np.inf
scan=[inf, inf, inf, inf, inf, inf, 2.122950792312622, 1.8218177556991577, 1.59413743019104, 1.423231601715088, 1.323502540588379, 1.3081393241882324, 1.3145588636398315, 1.3490028381347656, 1.335472822189331, 1.319705605506897, 1.3400593996047974, 1.3491653203964233, 1.3466880321502686, 1.3696467876434326, 1.3745601177215576, 1.3833107948303223, 1.3923722505569458, 1.4062138795852661, 1.3897963762283325, 1.4243078231811523, 1.4298373460769653, 1.4416676759719849, 1.450016736984253, 1.471937656402588, 1.5024218559265137, 1.4850928783416748, 1.5270593166351318, 1.5272008180618286, 1.5350757837295532, 1.5613079071044922, 1.58877432346344, 1.6090070009231567, 1.6587506532669067, 1.6668319702148438, 1.6823045015335083, 1.711564540863037, 1.7509064674377441, 1.7584381103515625, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, 2.650038242340088, 2.6445717811584473, 2.631784677505493, 2.6164636611938477, 2.6125850677490234, 2.5924696922302246, 2.5889229774475098, 2.5727877616882324, 2.5825552940368652, 2.5913491249084473, 2.5679054260253906, 2.575549840927124, 2.5961365699768066, 2.578033447265625, 2.563906669616699, 2.576000690460205, 2.586289644241333, 2.5772287845611572, 2.5811655521392822, 2.5825459957122803, 2.588222026824951, 2.5943994522094727, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, 2.9890737533569336, 2.9071550369262695, 2.8188626766204834, 2.7494678497314453, 2.67634916305542, 2.6266491413116455, 2.5653467178344727, 2.5333642959594727, 2.4661786556243896, 2.4012131690979004, 2.3687829971313477, 2.323002338409424, 2.2887566089630127, 2.2677664756774902, 2.296781539916992, 2.3500185012817383, 2.360821008682251, 2.453289031982422, 2.4872214794158936, 2.548398017883301, 2.6022679805755615, 2.6568949222564697, 2.7322375774383545, 2.7981326580047607, 2.8849222660064697, 2.9328646659851074, 3.0372416973114014, inf, inf, inf, inf, inf, 3.4685451984405518, 3.4392693042755127, 3.423564910888672, 3.389524221420288, 3.3799402713775635, 3.355445146560669, 3.335869073867798, 3.295499086380005, 3.285273551940918, 3.2675883769989014, 3.2415120601654053, 3.2439682483673096, 3.21799898147583, 3.207578659057617, 3.2076492309570312, 3.3214356899261475, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, 1.7193019390106201, 1.679825782775879, 1.650679349899292, 1.6179615259170532, 1.5983997583389282, 1.5532128810882568, 1.5364797115325928, 1.5386948585510254, 1.5013322830200195, 1.4845850467681885, 1.4534012079238892, 1.444892168045044, 1.4262295961380005, 1.4062203168869019, 1.4057549238204956, 1.3824982643127441, 1.367220163345337, 1.3536320924758911, 1.3183528184890747, 1.3393183946609497, 1.312820553779602, 1.3034974336624146, 1.2918084859848022, 1.2986373901367188, 1.2754086256027222, 1.271276831626892, 1.2715413570404053, 1.2496140003204346, 1.220840334892273, 1.259627103805542, 1.2267930507659912, 1.2130810022354126, 1.2276153564453125, 1.2212096452713013, 1.281704306602478, 1.3779208660125732, 1.5410783290863037, 1.7324031591415405, 1.9674873352050781, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, 2.966913938522339, 2.879478931427002, 2.812376022338867, 2.7323076725006104, 2.667478322982788, 2.613341808319092, 2.5557971000671387, 2.4918932914733887, 2.439162492752075, 2.3888156414031982, 2.3447256088256836, 2.304054021835327, 2.2600510120391846, 2.2221076488494873, 2.216076374053955, 2.2552261352539062, 2.31876802444458, 2.357189178466797, 2.396010398864746, 2.4466495513916016, 2.4985275268554688, 2.564828395843506, 2.634127140045166, 2.671659231185913, 2.7563955783843994, 2.829598903656006, 2.9014203548431396, 2.9869801998138428, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf]
o = Mapper()
prob_map = Mapper.main(o,scan)
d =Dijkstra()
d(prob_map , [36,36], [50,30])
print('\n ' + '-'*30 + "\n> Starting operation ...\n " + '-'*30 + '\n')
start_time = time.time()
final_path = d.find_path()
print('\n ' + '-'*30 + "\n> Time taken: {:.4} seconds.\n ".format(time.time() - start_time) + '-'*30 + '\n')
d.draw_final_graph(final_path)
def get_route(vehicle):
if vehicle.path_finder_algorithm == PathFinderAlgorithm.VALIDATOR:
""" DIJKSTRA """
start = time.time()
d_path, d_visited = Dijkstra.find_route_nodes(vehicle)
path = PathFinder._print_route_results(start, d_path, d_visited,
vehicle, 'Dijkstra')
""" ALT ALGORITHM """
start = time.time()
alt_path, alt_visited = Alt.find_route_nodes(vehicle)
PathFinder._print_route_results(start, alt_path, alt_visited,
vehicle, 'ALT')
""" A STAR ALGORITHM """
start = time.time()
a_star_path, a_star_visited = AStar.find_route_nodes(vehicle)
PathFinder._print_route_results(start, a_star_path, a_star_visited,
vehicle, 'A*')
if GeneralSettings.debug_print:
print "--------------------------------------------------------"
return path
if vehicle.path_finder_algorithm == PathFinderAlgorithm.DIJKSTRA:
start = time.time()
path, _ = Dijkstra.find_route_nodes(vehicle)
if GeneralSettings.debug_print:
print("Dijkstra.get_route elapsed time: %.2f ms" %
((time.time() - start) * 1000))
return path
elif vehicle.path_finder_algorithm == PathFinderAlgorithm.ALT:
start = time.time()
path, _ = Alt.find_route_nodes(vehicle)
if GeneralSettings.debug_print:
print("Alt.get_route elapsed time: %.2f ms" %
((time.time() - start) * 1000))
return path
elif vehicle.path_finder_algorithm == PathFinderAlgorithm.A_STAR:
start = time.time()
path, _ = AStar.find_route_nodes(vehicle)
if GeneralSettings.debug_print:
print("A star.get_route elapsed time: %.2f ms" %
((time.time() - start) * 1000))
return path
else:
raise ValueError("Invalid vehicles path finder algorithm value.")
class Grid(object):
"""Represents the field for the walkers
data -- contains the map representation according to config.Cell.
Use ioutil.parse_map to generate the input.
width -- the width of the given map data
height -- the height of the given map data
targets -- list of targets for which a potential should be generateted
"""
def __init__(self, data, width, height, targets):
self.mat = np.array(data, dtype="int32").reshape(height, width)
self.potentials = {}
graph, nodes = make_graph(self)
self.pathfinding = Dijkstra(graph, nodes)
self.generate_potential_for_targets(targets)
def generate_potential_for_targets(self, targets):
# Generate potential fields
for target in targets:
x, y = target["pos"]
if not self.is_empty(target["pos"]):
raise RuntimeError("Tried to place a target on an occupied cell")
p = self.pathfinding.calc_potential(x, y)
self.potentials[target["name"]] = p
# Place Target on map
self.mat[x, y] = Cell.TARGET
def is_empty(self, pos):
x, y = pos[0], pos[1]
return self.mat[x, y] in (Cell.EMPTY, Cell.TARGET)
@staticmethod
def neighbour_pos_in_direction(pos, direction):
return pos + decode_rot(direction)
@property
def width(self):
return self.mat.shape[1]
@property
def height(self):
return self.mat.shape[0]
def find_obstacles(self):
"""Returns a list of tuple containing the obstacles of the map (including the outer borders)"""
return [(x, y) for x, y in product(range(self.height), range(self.width)) if self.mat[x, y] == Cell.WALL]
def __getitem__(self, pos):
return self.mat[tuple(pos)]
def __setitem__(self, pos, val):
self.mat[tuple(pos)] = val
def runRerouting(obstacle_list, start_grid, end_grid, is_save=False):
# start and goal position
sx = start_grid[0] # [m]
sy = start_grid[1] # [m]
gx = end_grid[0] # [m]
gy = end_grid[1] # [m]
grid_size = 0.4 # [m]
robot_radius = 0.3 # [m]
# set obstacle positions
ox, oy = [], []
path = []
for i in range(len(obstacle_list)):
ox.append(obstacle_list[i][0])
oy.append(obstacle_list[i][1])
if show_animation: # pragma: no cover
plt.title("Rerouting")
plt.plot(ox, oy, ".k")
plt.plot(sx, sy, "og")
plt.plot(gx, gy, "xb")
plt.grid(True)
plt.axis("equal")
dijkstra = Dijkstra(ox, oy, grid_size, robot_radius)
rx, ry = dijkstra.planning(sx, sy, gx, gy)
for i in range(len(rx)):
path.append([i, 1])
path.append([rx[i], ry[i]])
if show_animation: # pragma: no cover
plt.plot(rx, ry, "-r")
if is_save:
fig = plt.gcf()
fig.savefig(save_path + 'task3_rerouted.png')
plt.pause(0.01)
plt.show()
plt.close('all')
return path
def setup_algos(root):
controller = root.winfo_children()[0]
cell_grid = root.winfo_children()[1].winfo_children()[0]
a_star = Astar(cell_grid)
Button(controller, text="A*", width=10,
command=a_star.trace).grid(row=0, column=8)
dijkstra = Dijkstra(cell_grid)
Button(controller, text="Dijkstra", width=10,
command=dijkstra.trace).grid(row=1, column=8)
def __init__(self):
self.fr = FlowRetrieval()
self.fs = FlowStatistics()
self.fm = FlowManagement()
self.dj = Dijkstra()
self.g = Graph()
#self.fm.deleteAllFlow()
try:
self.hostlist = self.fs.list_of_hosts()
self.switchlist = self.fs.list_of_switches()
except:
traceback.print_exc()
self.hostdict = {}
for i in range(1, len(self.hostlist) + 1):
self.hostdict[i] = self.hostlist[i - 1]
self.switchdict = {}
for i in range(1, len(self.switchlist) + 1):
self.switchdict[i] = self.switchlist[i - 1]
def x_reach(edge_id, max_cross, adj_list, na_seg_len):
"""Conduct intersection reach analysis.
:param edge_id: the ID of the source edge
:param max_cross: the maximum number of intersections allowed to cross
:param adj_list: an adjacency list, e.g., G = [{12: 0, 1: 1, 24: 1}, {0: 1, 24:1, 2: 1, 16:1}, ...]
indicates that the edge 0 is adjacent with edges 12, 1, and 24, and the cross distances from
those edges are G[0][12] = 0, G[0][1] = 1, and G[0][24] = 1.
:param na_seg_len: a list listing the length of each line segment
:return: the total street length accessible within max_cross intersections and
the list of lines that can be reached
"""
D, P = Dijkstra(adj_list, edge_id)
# find all the edges that are no more than max_cross intersections away from the source edge
list_reached_edges = [k for k, v in D.items() if v <= max_cross]
# compute the total length of the reached edges
total_len = 0
for i in list_reached_edges:
total_len += na_seg_len[i]
return total_len, list_reached_edges
def test_dijkstra():
graph = Graph()
nodes = []
while True:
str = input()
if str == "-1":
break
first, second, weight = str.split(' ')
if first not in nodes:
nodes.append(first)
if second not in nodes:
nodes.append(second)
graph.add_edge(first, second, int(weight))
dijkstra = Dijkstra(graph, nodes[0])
dijkstra.do_dijkstra()
print("weights : ")
for v in nodes:
print("%s : %s" % (v, dijkstra.get_distance(v)))
mst = []
for n in nodes:
path = dijkstra.get_path(n)
if len(path) == 1:
continue
for i in range(len(path) - 1):
edge = path[i] + " ---- " + path[i + 1]
if edge not in mst:
mst.append(edge)
print("MST : ")
for edge in mst:
print(edge)
class AStar:
def __init__(self, graph, start, target):
self._dijkstra = Dijkstra(graph, start, target, lambda f : f.distance + f.get_heuristic(target))
self._comparison = 0
self._moviments = 0
self._iterations = 0
@property
def comparison(self):
return self._comparison
@property
def moviments(self):
return self._moviments
@property
def iterations(self):
return self._iterations
def run(self):
self._dijkstra.run()
self._comparison = self._dijkstra.comparison
self._moviments = self._dijkstra.moviments
self._iterations = self._dijkstra.iterations
def main():
# First create a grid with grid.py
grid = createGrid()
# Then pass the grid to be processed by the Dijkstra algorithm
# Note that I break encapsulation of the grid.py context here.
# Good or bad...?
dijkstraGrid = Dijkstra(grid)
dijkstraGrid.printShortestPathFor('a', 'k')
# I need a fresh grid if I want to do it twice.
# See what I mean about context scoped state?
# The old grid should be clean of Dijkstra state...
grid = createGrid()
dijkstraGrid = Dijkstra(grid)
dijkstraGrid.printShortestPathFor('h', 'b')
import dijkstra
from dijkstra import Dijkstra
from adapton_list import *
dijkstra.key = dijkstra.BINARY_DOUBLE_KEY
test1 = Dijkstra(6, [0, 0, 1, 1, 2, 3, 4], [1, 2, 3, 4, 4, 5, 5], l2ll([5, 5, 5, 5, 5, 5, 5]))
trace1 = test1.compute_trace(0)
dis11 = test1.compute_dis(0, trace1)
dis12 = test1.compute_dis(0, [0, 1, 2, 3, 4, 5])
if dis11 != [0, 5, 5, 10, 10, 15]:
print "Test Case 1 Fail!"
elif dis12 != [0, 5, 5, 10, 10, 15]:
print "Test Case 1 Fail!"
else:
print "Test Case 1 Pass!"
test2 = Dijkstra(6, [0, 0, 1, 1, 2, 3, 4], [1, 2, 3, 4, 4, 5, 5], l2ll([5, 4, 5, 5, 5, 5, 5]))
trace2 = test2.compute_trace(0)
dis2 = test2.compute_dis(0, trace2)
if dis2 != [0, 5, 4, 10, 9, 14]:
print "Test Case 2 Fail!"
else:
print "Test Case 2 Pass!"
test3 = Dijkstra(6, [0, 0, 1, 1, 2, 3, 4], [1, 2, 3, 4, 4, 5, 5], l2ll([5, 4, 5, 5, 7, 5, 5]))
trace3 = test3.compute_trace(0)
dis3 = test3.compute_dis(0, trace3)
if dis3 != [0, 5, 4, 10, 10, 15]:
print "Test Case 3 Fail!"
else:
import dijkstra
from dijkstra import Dijkstra
from Adapton.Lazy import * # You need to add AdaptonPython/Source to the PYTHONPATH
from adapton_list import *
dijkstra.key = dijkstra.BINARY_DOUBLE_KEY
test1 = Dijkstra(6, [0,0,1,1,2,3,4,0], [1,2,3,4,4,5,5,0], l2ll([5,5,5,5,5,5,5,0]))
trace1 = test1.compute_trace(0)
dis11 = test1.distances_adap(0, trace1)
dis12 = test1.distances_adap(0, [0,1,2,3,4,5])
print ll2l(dis11)
if (ll2l(dis11) != [0,5,5,10,10,15]):
print "Test Case 1 Fail!"
elif (ll2l(dis12) != [0,5,5,10,10,15]):
print "Test Case 1 Fail!"
else:
print "Test Case 1 Pass!"
test2 = Dijkstra(6, [0,0,1,1,2,3,4,0], [1,2,3,4,4,5,5,0], l2ll([5,4,5,5,5,5,5,0]))
trace2 = test2.compute_trace(0)
dis2 = test2.distances_adap(0, trace2)
print "Distance: ", ll2l(dis2)
if (ll2l(dis2) != [0,5,4,10,9,14]):
print "Test Case 2 Fail!"
else:
print "Test Case 2 Pass!"
input_ad3 = l2ll([5,4,5,5,7,5,5,0])
test3 = Dijkstra(6, [0,0,1,1,2,3,4,0], [1,2,3,4,4,5,5,0], input_ad3)
trace3 = test3.compute_trace(0)
def __init__(self, graph, start, target):
self._dijkstra = Dijkstra(graph, start, target, lambda f : f.distance + f.get_heuristic(target))
self._comparison = 0
self._moviments = 0
self._iterations = 0
def test_dijkstraChallenge1(self):
args = dijkstraChallenge1()
d = Dijkstra(args[0], args[1], args[2])
d.doComplete()
actual = d.printS()
self.assertEquals(actual, args[3])
def transitive_closure(D, kind='metric', algorithm='dense', *args, **kwargs):
"""
Compute the transitive closure (All-Pairs-Shortest-Paths; APSP) using different shortest path measures
on the distance graph (adjacency matrix) with values in the ``[0,inf]`` interval.
.. math::
c_{ij} = min_{k}( metric ( a_{ik} , b_{kj} ) )
Args:
D (matrix or dict): The [D]istance matrix. Accepted formats for kind ``dense`` is a numpy array; for ``dijkstra`` is a either a numpy array, a scipy sparse matrix or edgelist dictionary.
kind (string): type of closure to compute: ``metric`` or ``ultrametric``.
algorithm (string): type of algorithm to use: ``dense`` or ``dijkstra``.
verbose (bool): Prints statements as it computes.
Returns:
C (matrix or dict): transitive closure dense matrix or a edgelist dictionary, depending on input
Examples:
>>> # using dense matrix
>>> P = np.array([
[1.,.9,.1,0.],
[.9,1.,.8,0.],
[.1,.8,1.,.6],
[0.,0.,.6,1.],
], dtype=float)
>>> D = prox2dist(P)
>>> transitive_closure(D, kind='metric', algorithm='dense', verbose=True)
[ 0. ,.11111111, 0.36111111, 1.02777778],
[ 0.11111111, 0., 0.25, 0.91666667],
[ 0.36111111, 0.25, 0., 0.66666667],
[ 1.02777778, 0.91666667, 0.66666667, 0.]
>>> # using an edgelist
>>> D = {
('a','b'): 0.11111111,
('a','c'): 9.,
('b','c'): 0.25,
('c','d'): 0.66666667,
}
>>> transitive_closure(D, kind='metric', algorithm='dijkstra', verbose=True)
>>> # using a sparse matrix
>>> Dsp = csr_matrix(D)
>>> transitive_closure(Dsp, kind='metric', algorithm='dijkstra', verbose=True)
Note:
Dense matrix is slow for large graphs. If your network is large and/or sparse, use Dijkstra.
Metric: :math:`(min,+)`
Ultrametric: :math:`(min,max)` -- also known as maximum flow.
Semantic proximity: TODO
.. math::
[ 1 + \\sum_{i=2}^{n-1} log k(v_i) ]^{-1}
"""
_check_for_metric_type(kind)
_check_for_algorithm(algorithm)
# Algorithm - Dense
if algorithm == 'dense':
# Numpy object
if (type(D).__module__ == np.__name__):
return _transitive_closure_dense_numpy(D, kind, *args, **kwargs)
else:
raise TypeError("Input is not a numpy object.")
# Dijkstra
elif algorithm == 'dijkstra':
dij = Dijkstra(*args, **kwargs)
# Numpy object
if (type(D).__module__ == np.__name__):
dij.from_numpy_matrix(D)
# Edgelist object
elif (isinstance(D, dict)):
dij.from_edgelist(D)
# Sparse Matrix
elif (ssp.issparse(D)):
dij.from_sparse_matrix(D)
else:
raise TypeError("Invalid Input. For the Dijkstra algorithm, input must be `Numpy matrix`, `Scipy Sparse Matrix` or `Edgelist dict`")
dij.all_pairs_shortest_paths(kind=kind)
return dij.get_shortest_distances_sparse()