def _testBenchmark(self):
print("file", "vertices", "edges", "time", sep=', ')
for filename in [
"tinyDG.txt", "mediumDG.txt", "largeDG.txt", "XtraLargeDG.txt"
]:
graph = parseGraph(filename, AdjacencyList(), True)
before_time = default_timer()
mst = prim(graph, 0)
time = default_timer() - before_time
print(filename, graph.V, graph.E, time, sep=', ')
def group(alg, k, data):
graph = Graph()
for coordinates in data:
graph.add_connected_vertice(*coordinates)
if alg == 'prim':
mst = prim(graph, k)
else:
mst = kruskal(graph, k)
return mst
def group(alg, k, data):
graph = Graph()
for coordinates in data:
graph.add_connected_vertice(*coordinates)
if alg == 'prim':
mst = prim(graph, k)
else:
mst = kruskal(graph, k)
return mst
def main():
n = int(input('Podaj liczbę wierzchołków: '))
G = Graph(n)
G.rand_graph()
G.choose_biggest_comp()
G.assign_weights()
print(f"Wylosowano graf o {G.n} wierzchołkach:")
print(G)
print(f"Podaj wierzchołek startowy od 0 do {G.n - 1}: ")
graph = nx.from_numpy_matrix(G.adj)
graph.edges(data=True)
plt.subplot(111)
pos=nx.circular_layout(graph)
nx.draw_networkx(graph, pos)
labels = nx.get_edge_attributes(graph, 'weight')
nx.draw_networkx_edge_labels(graph, pos, edge_labels=labels)
plt.show()
#start = int(input(f'Podaj wierzchołek startowy od 0 do {G.n - 1}: '))
start = int(input())
D = Dijkstra()
D.do_dijkstra(G.adj, G.adj, start)
print(f"START: s = {start}")
for v in range(G.n):
print(f'd({v}) = {D.ds[v]} ==> [{get_ps(v, D)}]')
print("\nMacierz odległości:")
matrix = make_distance_matrix(G)
print(matrix)
centr = get_center(matrix)
print(f"\nCentrum grafu: {centr}")
print(f"Suma w centrum grafu: {sum(matrix[centr])}")
minimax = get_minimax_center(matrix)
print(f"\nCentrum minimax: {minimax}")
print(f"Odległość do najdalszego wierzchołka: {max(matrix[minimax])}")
print("\nMinimalne drzewo rozpinające:")
[mst, sum_mst] = prim(G)
print(mst)
print(f"Suma wag krawędzi: {sum_mst}")
plt.subplot(111)
graph = nx.from_numpy_matrix(G.adj)
mst = nx.from_numpy_matrix(mst)
pos = nx.circular_layout(graph)
colors = []
for u,v in graph.edges():
if [u,v] not in mst.edges:
colors.append('b')
else:
colors.append('r')
graph.edges(data=True)
nx.draw(graph, pos, with_labels=True, edge_color=colors)
labels = nx.get_edge_attributes(graph, 'weight')
nx.draw_networkx_edge_labels(graph, pos, edge_labels=labels)
plt.show()
from tour import Tour
import random
import mst
xy = reader.read_xy("input/berlin52.tsp")
t = TwoOpt(xy)
t.optimize()
best = t.tour.node_ids[:]
best_length = t.tour.tour_length()
print("local optimum: " + str(best_length))
points = 0
mst_edges = mst.prim(t.tour.xy)
mst_connectivity = [[]] * len(t.tour.xy)
for e in mst_edges:
mst_connectivity[e[0]].append(e[1])
mst_connectivity[e[1]].append(e[0])
simultaneous_edges = 10
for i in range(50):
print("iteration: " + str(i))
new_edges = mst.get_new_mst_edges(t.tour, mst_edges)
improved = False
ee = random.sample(new_edges, simultaneous_edges)
for e in ee:
m = basic.midpoint(xy, e[0], e[1])
def test_prim_groups(self):
three_groups = prim(self.g, 3)
self.assertGroupsEqual(three_groups, self.three_groups)
def test_prim(self):
mst = prim(self.g)
self.assertGroupsEqual(mst, self.mst)
def testTinyDG(self):
graph = parseGraph("tinyDG.txt", AdjacencyList(), True)
mst = prim(graph, 0)
self.assertEqual(mst, ([None, 5, 0, 1, 5, 7, 3, 2
], [0, .32, .26, .29, .35, .28, .52, .34]))