def GenerateGraph(a: [], n: int):
if CheckSeries(a[:], n):
a.sort(reverse=True)
b = list(range(0, n))
g = Graph(n, 0.0, 1)
while True:
empty = True
negative = False
for x in a:
if x != 0:
empty = False
if x < 0:
negative = True
if empty:
return g
elif a[0] < 0 or a[0] >= n or negative:
return False
else:
i = 1
while i <= a[0]:
a[i] -= 1
g.addEdge(b[0], b[i])
i += 1
a[0] = 0
for i in range(n - 1):
for j in range(n - 1):
if a[j] < a[j + 1]:
a[j], a[j + 1] = a[j + 1], a[j]
b[j], b[j + 1] = b[j + 1], b[j]
else:
return False
def randomize(graph: Graph, n: int = 1):
edges = []
# check if edge
for i in range(len(graph.adjMatrix)):
for j in range(i):
if graph.adjMatrix[i][j] == 1:
edge = Edge(i, j)
edges.append(edge)
# ab, cd -> ad, cb
i = 0
while i < n:
x, y = random.randint(0,
len(edges) - 1), random.randint(
0,
len(edges) - 1)
nodes = [edges[x].start, edges[x].end, edges[y].start, edges[y].end]
edge_1 = Edge(edges[x].start, edges[y].end)
edge_2 = Edge(edges[y].start, edges[x].end)
if edge_1 not in edges and edge_2 not in edges and len(nodes) == len(
set(nodes)):
edges[x].end, edges[y].end = edges[y].end, edges[x].end
i += 1
# change adjMatrix
for i in range(len(graph.adjMatrix)):
for j in range(len(graph.adjMatrix)):
graph.adjMatrix[i][j] = 0
for val in edges:
start = val.start
end = val.end
graph.adjMatrix[start][end] = 1
graph.adjMatrix[end][start] = 1
def get_coherent_graph(n: int = 10, p: float = 0.5):
i = 0
while True:
example_graph = Graph(n, p, 1)
i += 1
# if there's only one coherent component graph is coherent
if is_one_coherent_component(Components(example_graph)):
break
if i > 10:
print("Change graph parameters")
break
example_graph.add_weight_to_edges()
return example_graph
def get_minimum_spanning_tree(start_graph: Graph):
start = random.randint(0, start_graph.size)
vertexes = list()
vertexes.append(start)
out = Graph(start_graph.size, 0, 1)
potential_edges = list()
while len(vertexes) < out.size:
for edge in start_graph.edges:
if len(out.edges) == 0 and (edge.start == start
or edge.end == start):
potential_edges.append(edge)
for tree_edge in out.edges:
if edge.start == tree_edge.start or edge.start == tree_edge.end or edge.end == tree_edge.start \
or edge.end == tree_edge.end:
potential_edges.append(edge)
potential_edges = list(set(potential_edges))
potential_edges.sort(key=lambda item: item.weight)
out.edges.append(
Edge(potential_edges[0].start, potential_edges[0].end,
potential_edges[0].weight))
out.values[potential_edges[0].start][
potential_edges[0].end] = potential_edges[0].weight
out.values[potential_edges[0].end][
potential_edges[0].start] = potential_edges[0].weight
out.addEdge(potential_edges[0].start, potential_edges[0].end)
vertexes.append(potential_edges[0].start)
vertexes.append(potential_edges[0].end)
vertexes = list(set(vertexes))
for edge in start_graph.edges:
if edge.start in vertexes and edge.end in vertexes:
start_graph.edges.remove(edge)
potential_edges.clear()
return out
from Shared.Graph import Graph from Shared.Canvas import draw_graph opening_graph = Graph(8, 15, 0) draw_graph(opening_graph)
from Shared.Graph import Graph
from Shared.EdgeRandomization import randomize
from Shared.Generator import GenerateGraph
example_graph = Graph(5, 0.5, 1)
series = [7, 5, 5, 5, 3, 3, 2, 1, 1, 0]
output = GenerateGraph(series, len(series))
example_graph.change_adj_matrix(output.adjMatrix)
example_graph.printAdjacencyMatrix()
print("\n")
randomize(example_graph, 10)
example_graph.printAdjacencyMatrix()
from Shared.Graph import Graph
# Zadanie 3.
print('Adjacency matrix - given number of vertexes and number of edges:')
g = Graph(4, 2, 0)
# last variable defines if we want to use second argument as probability(1) or as number of edges(0)
print(
'Adjacency matrix - given number of vertexes and probability of existing an edge between vertexes:'
)
f = Graph(4, 0.5, 8)
from Shared.Graph import Graph from Shared.Canvas import draw_graph from Shared.HamiltonianCycle import hamiltonian_cycle example_graph = Graph(10, 0.5, 1) hamiltonian_cycle(example_graph) draw_graph(example_graph)