def random_weighted_graph_resolver(data, _):
def random_matrix(size, p):
adj_matrix = [['.'] * size for i in range(size)]
for i in range(size):
for j in range(i, size):
if i != j and random.random() < p:
# 0.4 - probability that edge between i and j exists
adj_matrix[i][j] = adj_matrix[j][i] = 1
return adj_matrix
size = int(data.split(' ')[0])
p = float(data.split(' ')[1])
if size < 0:
return "Size cannot be a negative number"
matrix = random_matrix(size, p)
graph = Graph.from_cost_matrix(matrix)
components = Algorithms.max_connected_comp(graph)
while len(components) > 1:
matrix = random_matrix(size, p)
graph = Graph.from_cost_matrix(matrix)
components = Algorithms.max_connected_comp(graph)
for i in range(size):
for j in range(i, size):
if matrix[i][j] == 1:
matrix[i][j] = matrix[j][i] = random.randrange(1, 11)
s = ""
for row in matrix:
s += f"{' '.join([f' {cell}' if len(str(cell)) == 1 else f'{cell}' for cell in row])}\n"
return s
def max_connected_comp_resolver(graph: Graph, args):
components = Algorithms.max_connected_comp(graph)
result = ""
max_component_index = 0
for i, component in enumerate(components):
result += f"{i}) {component}\n"
if len(component) == max([len(comp) for comp in components]):
max_component_index = i
result += f"Largest component have an index of {max_component_index}\n"
return result