def test_nonEmptySet(self):
Cu_mock = [[13], [14], [15], [16, 17], [18, 19], [20, 21, 22],
[23, 24, 25], [1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12],
[26, 27, 28, 29, 30]]
G = graphFromList(Cu_mock).to_undirected()
G2, I, C_D = collapse_graph(G)
G2, R_D = renameGraph(G2)
drawNetwork(G2, [], 'test images/abcd.png')
max_T = 5
T_size = 5
# greedyselect 1
response1 = greedySelect(Cu_mock, max_T, T_size, rd.uniform(0, 0.99))
bought = translate(response1, C_D, R_D)
drawNetwork(G2, bought, 'test images/abcd2.png')
Cu_mock += [[31, 32, 33, 34, 35], [36, 37, 38, 39, 40]]
T_size = 15
G = graphFromList(Cu_mock).to_undirected()
G2, I, C_D = collapse_graph(G)
G2, R_D = renameGraph(G2)
drawNetwork(G2, [], 'test images/test2.png')
# greedyselect 2
response2 = greedySelect(Cu_mock, max_T, T_size, 1)
bought2 = translate(response2, C_D, R_D)
drawNetwork(G2, bought2, 'test images/test2.1.png')
def test_custom(self):
max_T, T_size, Cu = constructCu()
G = graphFromList(Cu).to_undirected()
G2, I, C_D = collapse_graph(G)
G2, R_D = renameGraph(G2)
drawNetwork(G2, [], 'test images/test1.0.png')
print(max_T)
Loop = True
index = 1
while Loop:
alpha = float(input('Introduce an alpha to test: '))
response1 = greedySelect(Cu, max_T, T_size, alpha)
bought = translate(response1, C_D, R_D)
drawNetwork(G2, bought, 'test images/test1.' + str(index) + '.png')
index += 1
Loop = int(
input(
'Introduce 1 if you want to try again, otherwise introduce 0: '
))
def test_MetaTreeConstruct(self):
G, t_max = randomGraph(200)
G2, I, C_D = collapse_graph(G, t_max)
G2, R_D = renameGraph(G2)
inverse_R_D = {value: key for key, value in R_D.items()}
drawNetwork(G, [], 'test2.png')
drawNetwork(G2, [], 'test_collapse2.png')
l_I = [inverse_R_D[item] for item in I]
M, M_D = constructMetaTree(G2, l_I)
drawNetwork(M, [], 'test_metaTree.png')
def test_custom(self):
max_T, T_size, Cu = constructCu()
At, Av = subSetSelect(len(Cu), T_size - 1, Cu, 5)
print('T_size', T_size)
print('Av total size: ', 1 + sum(list(map(lambda x: len(x), Av))))
print('At total size: ', 1 + sum(list(map(lambda x: len(x), At))))
G = graphFromList(Cu).to_undirected()
G2, I, C_D = collapse_graph(G)
G2, R_D = renameGraph(G2)
drawNetwork(G2, [], 'test1.1.png')
bought1 = translate(At, C_D, R_D)
bought2 = translate(Av, C_D, R_D)
drawNetwork(G2, bought1, 'test1.2.png')
drawNetwork(G2, bought2, 'test1.3.png')
from maximum_carnage.src.utils.graph_utils import initial_utility
from maximum_carnage.test.utils.utils import drawNetwork, randomGraph3
import networkx as nx
import itertools
import random as rd
import os
if __name__ == '__main__':
test = True
iteration = 0
while test:
print('##########iteration ' + str(iteration) + '#############')
iteration += 1
n = 10
G = randomGraph3(n, 1/n)
drawNetwork(G, [], 'graphs/images/graph_' + str(n) + '_' + str(iteration) + '.png')
nx.write_gpickle(G, os.path.dirname(os.path.abspath(__file__)) + './graphs/pickle/graph_' + str(n) + '_' +
str(iteration) + '.pickle')
alpha = rd.uniform(1/2, 3)
beta = rd.uniform(1/2, 5)
print('alpha: ', alpha, '/ beta: ', beta)
for i in range(0, G.number_of_nodes()):
G2 = G.copy()
print('------------ node' + str(i) + ' -------------')
strategies = []
utilities = []
utilities2 = []
aux = list(range(0, G.number_of_nodes()))
aux.remove(i)
for L in range(0, int(G.number_of_nodes()/alpha)):
def test_MetaTreeConstruct2(self):
target_size = 10
G, Imm = randomGraph2(500, target_size)
drawNetwork(G, [], 'test1.png')
M, M_D = constructMetaTree(G, list(Imm))
drawNetwork(M, [], 'test1_metaTree.png')
def test_collapse(self):
G, t_max = randomGraph(50)
G2, I, C_D = collapse_graph(G, t_max)
G2, R_D = renameGraph(G2)
drawNetwork(G, [], 'test.png')
drawNetwork(G2, [], 'test_collapse.png')