def main():
g = WeightedGraph()
gr = WeightedGraph.create_from_file("example_graph.txt")
g.graph_arr = gr.graph_arr
graph_utils.print_2d_array("Macierz odleglosci", g.get_distances_matrix())
g.get_centers()
g.draw()
def main():
g = WeightedGraph()
gr = WeightedGraph.create_from_file("example_graph.txt")
g.graph_arr = gr.graph_arr
g.prim_algorithm()
g.draw()
graph_utils.print_2d_array("Adjacency Matrix For Tree",
g.get_adjacency_matrix())
def main():
g = WeightedGraph()
graph_utils.print_2d_array("Initial data",
g.random_connected_weighted_graph())
g.draw()
length = len(g.get_adjacency_matrix())
start_vertex = int(input("Starting vertex (0-" + str(length - 1) + "): "))
dijkstra_result, distances = g.dijkstra_algorithm(start_vertex)
graph_utils.print_2d_array("\nDijkstra algorithm output", dijkstra_result)
g.draw()
def main():
graph = DirectedGraph()
n = 5
graph.correct_random(n, 0.8)
c = graph.kosaraju()
while sum(c) / n != 1:
graph.random(n, 1)
c = graph.kosaraju()
print(c)
matrix = graph.get_adjacency_matrix()
weights = graph.get_adjacency_matrix()
for x in range(n):
for y in range(n):
if weights[x][y] != 0:
weights[x][y] = random.randint(-5, 10)
graph_utils.print_2d_array("Random digraph", matrix)
graph_utils.print_2d_array("Weights on digraph", weights)
graph_utils.print_2d_array("Johnson's Matrix",
WeightedGraph.johnson(matrix, weights))
print("\nTest z zajęć \n")
test_matrix = [[0, 1, 1], [1, 0, 0], [0, 1, 0]]
test_weights = [[0, -1, -4], [4, 0, 0], [0, 2, 0]]
graph_utils.print_2d_array(
"Johnson's Matrix", WeightedGraph.johnson(test_matrix, test_weights))
def main():
graph_arr = graph.create_from_file().graph_arr
graph_utils.print_2d_array("Initial data", graph_arr)
graph_utils.print_2d_array("Adjacency matrix", generation.save_to_adjacency_matrix(graph_arr))
graph_utils.print_2d_array("Adjacency list", generation.save_to_adjacency_list(graph_arr))
graph_utils.print_2d_array("Incidence matrix", generation.save_to_incidence_matrix(graph_arr))
def main():
graph = DirectedGraph()
graph.create_from_file_with_instance()
graph_utils.print_2d_array("Initial data", graph.graph_arr)
graph_utils.print_2d_array("Adjacency matrix",
graph.get_adjacency_matrix())
graph_utils.print_2d_array("Incidence matrix",
graph.get_incidence_matrix())
graph_utils.print_2d_array("Adjacency list", graph.get_adjacency_list())
def main():
print("Wybierz tryb generowania Losowego : G(n,l) [0] G(n,p) [1]")
way = int(input())
n = int(input("Podaj liczbe wierzchołków: "))
print(n)
if way == 0:
while 1 > 0:
l = int(input("Podaj liczbe krawędzi: "))
if l <= (n * n - n) / 2:
break
Matrix = random_gen.generate_random_matrix_with_edges(n, l)
else:
p = float(input("Podaj prawdopodobieństwo wystąpienia krawędzi: "))
Matrix = random_gen.generate_random_matrix_with_probability(n, p)
graph_utils.print_2d_array("Graf losowy", Matrix)
def main():
graph = DirectedGraph()
graph.random(5, 1)
c = graph.kosaraju()
while sum(c) / 5 != 1:
graph.random(5, 1)
c = graph.kosaraju()
matrix = graph.get_adjacency_matrix()
weights = graph.get_adjacency_matrix()
for x in range(5):
for y in range(5):
if weights[x][y] != 0:
weights[x][y] = random.randint(-5, 10)
graph_utils.print_2d_array("Random digraph", matrix)
graph_utils.print_2d_array("Weights on digraph", weights)
b, d = bellman_ford(matrix, 0, weights)
print(b)
print(d)
def main():
alfabet = ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J', 'K', 'L', 'M', 'N']
graph = DirectedGraph()
graph.create_from_file_with_instance()
graph_utils.print_2d_array("Adjacency matrix", graph.get_adjacency_matrix())
print("Błądzenie przypadkowe:")
print("#########################")
vec_rand = PageRank_random(graph, 100000, 0.15, 1)
for i in range(graph_utils.get_vertices_number(graph)):
print(alfabet[i], "==> PageRank = ", vec_rand[i])
print("#########################")
print("\n\nWektor obsadzeń")
print("#########################")
vec_it = PageRank_iteration(graph, 54, 0.15)
for i in range(graph_utils.get_vertices_number(graph)):
print(alfabet[i], "==> PageRank = ", vec_it[i])
print("#########################")
graph1 = DirectedGraph()
graph1.create_from_file_with_instance('graph1.txt')
graph_utils.print_2d_array("Adjacency matrix", graph1.get_adjacency_matrix())
print("Błądzenie przypadkowe:")
print("#########################")
vec_rand = PageRank_random(graph1, 1000000, 0.15, 1)
for i in range(graph_utils.get_vertices_number(graph1)):
print(alfabet[i], "==> PageRank = ", vec_rand[i])
print("#########################")
print("\n\nWektor obsadzeń")
print("#########################")
vec_it = PageRank_iteration(graph1, 31, 0.15)
for i in range(graph_utils.get_vertices_number(graph1)):
print(alfabet[i], "==> PageRank = ", vec_it[i])
print("#########################")
def main():
n = 7
test_adj_matrix = [[0 for x in range(n)] for y in range(n)]
test_adj_matrix[0][1] = 1
test_adj_matrix[0][2] = 1
test_adj_matrix[0][4] = 1
test_adj_matrix[1][0] = 1
test_adj_matrix[1][2] = 1
test_adj_matrix[1][4] = 1
test_adj_matrix[1][3] = 1
test_adj_matrix[1][6] = 1
test_adj_matrix[2][5] = 1
test_adj_matrix[4][6] = 1
test_adj_matrix[3][6] = 1
test_adj_matrix[3][1] = 1
test_adj_matrix[5][1] = 1
test_adj_matrix[6][5] = 1
print("#############################################")
graph_utils.print_2d_array("Example digraph 1", test_adj_matrix)
c = DirectedGraph.kosaraju_with_adj_matrix(test_adj_matrix)
#print(c)
for i in range(len(c)):
print("Wierzchołek ", i, " należy do składowej nr: ", c[i])
print("#############################################")
print("\n\n")
test_adj_matrix1 = [[0 for x in range(n)] for y in range(n)]
test_adj_matrix1[0][1] = 1
test_adj_matrix1[0][6] = 1
test_adj_matrix1[6][0] = 1
test_adj_matrix1[6][1] = 1
test_adj_matrix1[1][2] = 1
test_adj_matrix1[2][1] = 1
test_adj_matrix1[2][3] = 1
test_adj_matrix1[2][4] = 1
test_adj_matrix1[4][3] = 1
test_adj_matrix1[4][5] = 1
test_adj_matrix1[5][1] = 1
test_adj_matrix1[5][2] = 1
print("#############################################")
graph_utils.print_2d_array("Example digraph 2", test_adj_matrix1)
c = DirectedGraph.kosaraju_with_adj_matrix(test_adj_matrix1)
print(c)
for i in range(len(c)):
print("Wierzchołek ", i, " należy do składowej nr: ", c[i])
print("#############################################")
print("\n\n#############################################")
graph = DirectedGraph()
graph_utils.print_2d_array("Random digraph", graph.correct_random(7, 0.3))
c = graph.kosaraju()
print(c)
for i in range(len(c)):
print("Wierzchołek ", i, " należy do składowej nr: ", c[i])
print("#############################################")
def main():
graph = WeightedGraph().create_from_file_with_instance()
graph.random_connected_weighted_graph(9)
graph_utils.print_2d_array("Randomly weighted connected graph", graph.get_adjacency_matrix())
graph.draw()
def main():
g = graph.create_from_file()
g.draw()
component = g.largest_component()
graph_utils.print_2d_array("largest component", component)
def main():
graph_arr = graph.create_from_file().graph_arr
adj_matrix = generation.save_to_adjacency_matrix(graph_arr)
adj_list = generation.save_to_adjacency_list(graph_arr)
inc_matrix = generation.save_to_incidence_matrix(graph_arr)
graph_utils.print_2d_array("Adjacency matrix",
generation.save_to_adjacency_matrix(graph_arr))
graph_utils.print_2d_array("Adjacency list",
generation.save_to_adjacency_list(graph_arr))
graph_utils.print_2d_array("Incidence matrix",
generation.save_to_incidence_matrix(graph_arr))
print("----------")
print("")
# from adjacency matrix:
adj_list1 = conversions.adjacency_matrix_to_adjacency_list(adj_matrix)
inc_matrix1 = conversions.adjacency_matrix_to_incident_matrix(adj_matrix)
graph_utils.print_2d_array("adj matrix to list", adj_list1)
graph_utils.print_2d_array("adj matrix to inc matrix", inc_matrix1)
# from adjacency list:
adj_matrix1 = conversions.adjacency_list_to_adjacency_matrix(adj_list)
inc_matrix1 = conversions.adjacency_list_to_incident_matrix(adj_list)
graph_utils.print_2d_array("list to adj matrix", adj_matrix1)
graph_utils.print_2d_array("list to inc matrix", inc_matrix1)
# from incidence matrix:
adj_matrix1 = conversions.incident_matrix_to_adjacency_matrix(inc_matrix)
adj_list1 = conversions.incident_matrix_to_adjacency_list(inc_matrix)
graph_utils.print_2d_array("inc matrix to adj matrix", adj_matrix1)
graph_utils.print_2d_array("inc matrix to list", adj_list1)
def main():
graph = DirectedGraph()
graph.create_from_file_with_instance()
graph_utils.print_2d_array("Random digraph", graph.correct_random(5, 0.5))
#!/bin/env python3.7
import sys
sys.path.append('../..')
from graph.graph import graph
import graph.graph_utils as graph_utils
graph1 = graph.create_from_sequence([4,2,2,3,2,1,4,2,2,2,2])
graph_utils.print_2d_array("Ciag graficzny", graph1.get_adjacency_matrix())
graph1.draw()
print("Ciag niegraficzny:")
graph_wrong = graph.create_from_sequence([4,2,2,3,2,1,4,2,2,2,1])
print("Ciag niegraficzny:")
graph_wrong2 = graph.create_from_sequence([4,-2,1,4,2,2,2,1])