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Generate.py
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Generate.py
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import random
import math
import constants as k
import numpy as np
from collections import defaultdict
from Christofides import christofides
from Edge import Edge
import os
def euclidean_distance(a, b):
return math.sqrt((a[1] - b[1]) ** 2 + (a[0] - b[0]) ** 2)
class DistanceTable:
def __init__(self, vertices, edges=None):
size = len(vertices)
self.vertices = vertices
self.edges = edges
if not self.edges:
self.edges = [[1 for n in range(m)] for m in range(size)]
self.paths = [[[Edge(x, y)] for x in range(size)] for y in range(size)]
self.distances = [[0 if x == y else float("inf") for x in range(size)] for y in range(size)]
self._update()
def _update(self, changed_edges=None):
if changed_edges:
changed = changed_edges
for c in changed_edges:
point_a = self.vertices[c.a]
point_b = self.vertices[c.b]
d = euclidean_distance(point_a, point_b)
self.distances[c.a][c.b] = d
self.distances[c.b][c.a] = d
self.paths[c.a][c.b] = [Edge(c.a, c.b)]
self.paths[c.b][c.a] = [Edge(c.b, c.a)]
else:
changed = []
self.old_changed = []
for i1, v1 in enumerate(self.vertices):
for i2, v2 in enumerate(self.vertices[:i1]):
if self.edges[max(i1, i2)][min(i1, i2)]:
d = euclidean_distance(v1, v2)
self.distances[i1][i2] = d
self.distances[i2][i1] = d
self.paths[i1][i2] = [Edge(i1, i2)]
self.paths[i2][i1] = [Edge(i2, i1)]
changed.append(Edge(i1, i2))
while changed:
recently_changed = changed
self.old_changed += recently_changed
changed = []
for change1 in recently_changed:
for change2 in self.old_changed:
do = False
if change1.a == change2.a:
a = max(change1.b, change2.b)
b = min(change1.b, change2.b)
c = change1.a
do = True
elif change1.b == change2.a:
a = change1.a
b = change2.b
c = change1.b
do = True
elif change1.a == change2.b:
a = change2.a
b = change1.b
c = change1.a
do = True
if change1.b == change2.b:
a = max(change1.a, change2.a)
b = min(change1.a, change2.a)
c = change1.b
do = True
if do:
new_d = self.distances[a][c] + self.distances[c][b]
if new_d < self.distances[a][b]:
self.distances[a][b] = new_d
self.distances[b][a] = new_d
self.paths[a][b] = self.paths[a][c] + self.paths[c][b]
self.paths[b][a] = self.paths[b][c] + self.paths[c][a]
changed.append(Edge(a, b))
def add_edges(self, new_edges):
for edge in new_edges:
large = max(edge.a, edge.b)
small = min(edge.a, edge.b)
self.edges[large][small] = 1
self._update(changed_edges = new_edges)
def __str__(self):
return str(self.distances)
def get_minimum_spanning_distance(distance_matrix, size):
unconnected = {}
for i in range(1, size):
unconnected[i] = 0
max_distance = 0
def get_distance(x):
y = unconnected[x]
return distance_matrix[x][y]
while unconnected:
closest = min(unconnected, key=lambda x: get_distance(x))
max_distance = max(max_distance, get_distance(closest))
del unconnected[closest]
for i in unconnected:
if distance_matrix[i][closest] < distance_matrix[i][unconnected[i]]:
unconnected[i] = closest
return max_distance
class Graph:
max_x = 100
max_y = 100
# Note that Vertices are [Y coords, X coords]
def __init__(self, number_of_vertices, number_of_edges, vertices=None):
self.n = number_of_vertices
self.e = number_of_edges
self.create_vertices(vertices)
self.edges = self.create_edges()
self.distance_table = None
def create_vertex(self):
return random.random() * self.max_x, random.random() * self.max_y
@staticmethod
def name():
return 'Rectangular Graph'
@staticmethod
def prefix():
return ''
def create_vertices(self, vertices):
if vertices:
self.vertices = vertices
else:
self.vertices = [self.create_vertex() for n in range(self.n)]
def get_paths(self):
self.calculate_distances()
return self.distance_table.paths
def get_distance_table(self):
self.calculate_distances()
return self.distance_table.distances
def calculate_distances(self):
if not self.distance_table:
self.distance_table = DistanceTable(self.vertices, self.edges)
def edge_distance(self, edge):
self.calculate_distances()
return self.get_distance_table()[edge.a][edge.b]
def distance(self, a, b):
self.calculate_distances()
return self.get_paths()[a][b]
def __str__(self):
return str(self.vertices) + '\n' + str(self.edges)
def __repr__(self):
return str(self)
def create_edges(self):
unused_edges = set(sum(map(lambda b: [Edge(a, b) for a in range(b + 1, self.n)], range(self.n)), []))
edge_set = set()
#first connect the unconnected edges
for node in range(1, self.n):
rand = random.randint(0, node - 1)
a = max(rand, node)
b = min(rand, node)
new_edge = Edge(a, b)
edge_set.add(new_edge)
unused_edges.remove(new_edge)
while len(edge_set) < self.e:
new_edge = random.sample(unused_edges, 1)[0]
edge_set.add(new_edge)
unused_edges.remove(new_edge)
edges = [[0] * m for m in range(self.n)]
for e in edge_set:
edges[e.a][e.b] = 1
return edges
def copy(self, number_of_edges):
graph_copy = Graph(self.n, number_of_edges, self.vertices)
return graph_copy
def make_christofides_route(self):
self.calculate_distances()
result = christofides.compute(self.distance_table.distances)
solution = result['Christofides_Solution']
path = []
for index in range(1, len(solution)):
path += [Edge(solution[index - 1], solution[index])]
return path, self.get_paths()
def make_optimal_route(self):
self.calculate_distances()
solution = OfflineBruteForce(self, self.n).path
return solution, self.get_paths()
@staticmethod
def init_intervals(num_ver):
return (num_ver * (num_ver - 1))/2
@staticmethod
def bonus_intervals():
return 1
@staticmethod
def intervals(num_ver):
max_num_present = k.max_num(num_ver)
min_num_present = k.min_num(num_ver)
max_range = max_num_present - min_num_present
return [min_num_present + (num_ver - 2)] + \
[((max_range * i) / (k.num_intervals - 1)) + min_num_present for i in range(k.num_intervals)]
class UnitDiskGraph(Graph):
def __init__(self, number_of_vertices, percent_edges, vertices=None, max_distance=0):
if percent_edges > 1:
percent_edges = 1
self.max_distance = max_distance
Graph.__init__(self, number_of_vertices, percent_edges, vertices=vertices)
def create_edges(self):
edge_distance_table = DistanceTable(self.vertices)
dm = edge_distance_table.distances
if not self.max_distance:
self.max_distance = get_minimum_spanning_distance(dm, self.n)
self.full_edges = [[0 for n in range(m)] for m in range(self.n)]
unused_edges = []
for x, v1 in enumerate(self.vertices):
for y, v2 in enumerate(self.vertices[:x]):
edge = euclidean_distance(v1, v2) <= self.max_distance
if edge:
self.full_edges[x][y] = 1
unused_edges.append((x, y))
self.edges = [[0] * m for m in range(self.n)]
edge_count = 0
unconnected = set(range(1, self.n))
connected = {0}
count = 0
while unconnected:
count += 1
for u in random.sample(list(unconnected), len(unconnected)):
possible_connections = []
for c in connected:
a = max(u, c)
b = min(u, c)
if self.full_edges[a][b]:
possible_connections.append(c)
if possible_connections:
c = random.choice(possible_connections)
connected.add(u)
unconnected.remove(u)
a = max(u, c)
b = min(u, c)
unused_edges.remove((a, b))
self.edges[a][b] = 1
edge_count += 1
num_edges_to_use = int(len(unused_edges) * self.e)
if num_edges_to_use:
new_edges = random.sample(unused_edges, num_edges_to_use)
for e in new_edges:
self.edges[e[0]][e[1]] = 1
return self.edges
@staticmethod
def prefix():
return 'Unit_'
@staticmethod
def name():
return 'Unit Disk Graph'
def copy(self, percent_edges):
graph_copy = UnitDiskGraph(self.n, percent_edges, vertices = self.vertices, max_distance = self.max_distance)
return graph_copy
@staticmethod
def init_intervals(num_ver):
return 1.0
@staticmethod
def bonus_intervals():
return 0
@staticmethod
def intervals(num_ver):
return np.linspace(1.0, 0.0, num=k.num_intervals)
class GridGraph(Graph):
def __init__(self, number_of_vertices, percent_edges, vertices=None, max_distance=0):
if percent_edges > 1:
percent_edges = 1
self.max_distance = max_distance
Graph.__init__(self, number_of_vertices, percent_edges, vertices=vertices)
def nearby_spots(self, spot):
for around in [(1, 0), (-1, 0), (0, 1), (0, -1)]:
x = spot[0] + around[0]
y = spot[1] + around[1]
if 0 <= x < self.max_x and 0 <= y < self.max_y:
yield x, y
def update_spots(self, spot):
for s in self.nearby_spots(spot):
if not self.spots[s[0]][s[1]]:
self.spots[s[0]][s[1]] = 1
self.possible_spots.add(s)
def create_vertex(self):
try:
return random.sample(self.possible_spots, 1)[0]
except:
print('possible_spots')
print('v', self.vertices)
print('spots', self.spots)
raise Exception
def create_vertices(self, vertices):
if vertices:
self.vertices = vertices
return
self.spots = defaultdict(lambda: defaultdict(int))
self.possible_spots = set()
v = (random.randint(0, self.max_x), random.randint(0, self.max_y))
self.spots[v[0]][v[1]] = 2
self.vertices = [v]
self.update_spots(v)
while len(self.vertices) < self.n:
v = self.create_vertex()
self.possible_spots.remove(v)
self.spots[v[0]][v[1]] = 2
self.vertices.append(v)
self.update_spots(v)
def create_edges(self):
edge_distance_table = DistanceTable(self.vertices)
dm = edge_distance_table.distances
if not self.max_distance:
self.max_distance = get_minimum_spanning_distance(dm, self.n)
self.full_edges = [[0 for n in range(m)] for m in range(self.n)]
unused_edges = []
for x, v1 in enumerate(self.vertices):
for y, v2 in enumerate(self.vertices[:x]):
edge = euclidean_distance(v1, v2) <= self.max_distance
if edge:
self.full_edges[x][y] = 1
unused_edges.append((x, y))
self.edges = [[0] * m for m in range(self.n)]
edge_count = 0
unconnected = set(range(1, self.n))
connected = {0}
count = 0
while unconnected:
count += 1
for u in random.sample(list(unconnected), len(unconnected)):
possible_connections = []
for c in connected:
a = max(u, c)
b = min(u, c)
if self.full_edges[a][b]:
possible_connections.append(c)
if possible_connections:
c = random.choice(possible_connections)
connected.add(u)
unconnected.remove(u)
a = max(u, c)
b = min(u, c)
unused_edges.remove((a, b))
self.edges[a][b] = 1
edge_count += 1
num_edges_to_use = int(len(unused_edges) * self.e)
if num_edges_to_use:
new_edges = random.sample(unused_edges, num_edges_to_use)
for e in new_edges:
self.edges[e[0]][e[1]] = 1
return self.edges
@staticmethod
def prefix():
return 'Grid_'
@staticmethod
def name():
return 'Grid Graph'
def copy(self, percent_edges):
graph_copy = GridGraph(self.n, percent_edges, vertices = self.vertices, max_distance = self.max_distance)
return graph_copy
@staticmethod
def init_intervals(num_ver):
return 1.0
@staticmethod
def bonus_intervals():
return 0
@staticmethod
def intervals(num_ver):
return np.linspace(1.0, 0.0, num=k.num_intervals)
class Traversal:
def __init__(self, graph):
self.graph = graph
self.current_node = 0
self.seen = {self.current_node}
self.size = len(graph.vertices)
self.edges = [[0] * m for m in range(self.size)]
self.graph_edges = graph.edges
self.path = []
self.finished = False
temp_edges = [[0 for n in range(m)] for m in range(self.size)]
self.distance_table = DistanceTable(graph.vertices, edges = temp_edges)
self.look_around()
def see(self, spot):
large = max(self.current_node, spot)
small = min(self.current_node, spot)
if self.graph_edges[large][small] == 1:
self.seen.add(spot)
self.edges[large][small] = 1
self.distance_table.add_edges([Edge(large, small)])
def look_around(self):
for x in range(0, self.size):
if x != self.current_node:
self.see(x)
def go_to_closest_unvisited(self, to_visit):
targets = list(filter(lambda t: t in self.seen, to_visit))
distances = self.distance_table.distances
closest_target = min(targets, key=lambda t: distances[self.current_node][t])
shortest_route = self.distance_table.paths[self.current_node][closest_target]
self.path = self.path + shortest_route
self.current_node = closest_target
def get_distance(self):
distance = 0
for edge in self.path:
distance += self.graph.edge_distance(edge)
return distance
class OfflineBruteForce:
template = """NAME: {name}
TYPE: TSP
COMMENT: {name}
DIMENSION: {n_cities}
EDGE_WEIGHT_TYPE: EXPLICIT
EDGE_WEIGHT_FORMAT: LOWER_DIAG_ROW
EDGE_WEIGHT_SECTION
{matrix_s}EOF"""
in_file = '/tmp/temp.tsp'
out_file = '/tmp/out.txt'
concorde = '/Library/Concorde/concorde/TSP/concorde'
@staticmethod
def dumps_matrix(matrix, name="route"):
#Credit to jvkersch
arr = np.array(matrix)
n_cities = arr.shape[0]
width = len(str(arr.max())) + 1
assert arr.shape[0] == arr.shape[1]
assert len(arr.shape) == 2
# space delimited string
matrix_s = ""
for i, row in enumerate(arr.tolist()):
matrix_s += " ".join(["{0:>{1}}".format((int(elem)), width)
for elem in row[:i + 1]])
matrix_s += "\n"
return OfflineBruteForce.template.format(**{'name': name,
'n_cities': n_cities,
'matrix_s': matrix_s})
def __init__(self, graph, id):
id = str(id)
self.graph = graph
self.distance_table = DistanceTable(graph.vertices, graph.edges)
with open(self.in_file + id, 'w') as dest:
dest.write(self.dumps_matrix(self.distance_table.distances))
os.system(self.concorde + ' -o ' + self.out_file + id + ' ' + self.in_file + id)
with open(self.out_file + id, 'r') as out:
high_level_path = map(int, out.read().strip().split()[1:]) + [0]
self.path = []
for i in range(len(high_level_path) - 1):
self.path.append(Edge(high_level_path[i], high_level_path[i + 1]))
self.distance = self.get_distance()
def get_distance(self):
distance = 0
for edge in self.path:
distance = distance + self.graph.edge_distance(edge)
return distance
def get_low_level_distance(self):
low_path = self.get_low_level_path()
distance = 0
for edge in low_path:
distance = distance + self.graph.edge_distance(edge)
return distance
def get_low_level_path(self):
low_path = []
for e in self.path:
low_path += self.distance_table.paths[e.a][e.b]
return low_path
graph_type = GridGraph