def sum_weight(state, letter):
# maximum = 0
weight = cube.path(state)[letter]
count = 0
for i in range(len(weight.values())):
count = weight.values()[i] + count
return count
def onnela_clustering(state):
sum_AB = 0.0
C_o = 0.0
letter_sum = 0.0
partial = 0.0
for node in state:
for letterA in state:
if letterA != node:
for letterB in state:
if letterA != letterB and letterB != node:
w1 = cube.path(state)[node][letterA]
w1A = w1 * 0.20
# print 'For i={}, j={}, k={}'.format(node, letterA, letterB)
# print "({},{}:{})".format(node,letterB,w1A)
w2 = cube.path(state)[node][letterB]
w2A = w2 * 0.20
# print "({},{}:{})".format(node,letterB,w2A)
w3 = cube.path(state)[letterA][letterB]
w3A = w3 * 0.20
# print "({},{}:{})".format(letterA,letterB,w3A)
partial = (w1A * w2A * w3A)**float(1 / 3)
# print partial
sum_AB = partial + sum_AB
# print "({},{},{}):{}".format(node,letterA,letterB,sum_AB)
letter_sum = sum_AB + letter_sum
# print ' '
# print 'End of first iteration for {}, {}. This is the partial sum: {}'.format(node, letterA, letter_sum)
# print ' '
# print 'Start New iterations. For i={}, j={}'.format(node, letterA)
C_node = letter_sum / (23 * 22)
C_o = C_node + C_o
# print 'End of first iteration for node {}. This is the average clustering of node {}: {}'.format(node,node, C_node)
sum_AB = 0.0
letter_sum = 0.0
partial = 0.0
return C_o
def barrat_clustering(state):
graph = cube.network(state)
sum_AB = 0
for letter in state:
k = nx.degree(graph)[letter]
w_sum = np.sum(cube.path(state)[letter].values())
partial = 0
partial_sum = 0
for letterB in graph.nodes():
for letterC in graph.nodes():
if (letter, letterB) in graph.edges() and (
letter, letterC) in graph.edges():
wA = cube.path(state)[letter][letterB]
wB = cube.path(state)[letter][letterC]
partial = ((wA + wB) * 0.50) * (w_sum)**-1 + partial
partial_sum = partial + partial_sum
sum_AB = ((k)**-1) * ((k - 1)**-1) * partial_sum + sum_AB
return sum_AB
def zhang_clustering(state):
graph = cube.network(state)
C_z = 0
for letter in state:
sum_numerator = 0
for letterB in graph.nodes():
partial_sum = 0
for letterC in graph.nodes():
if (letter, letterB) in graph.edges() and (
letter, letterC) in graph.edges():
wA = cube.path(state)[letter][letterB] * 0.20
wB = cube.path(state)[letter][letterC] * 0.20
wC = cube.path(state)[letterB][letterC] * 0.20
wB_array = cube.path(state)[letter].values()
prod = wA * wB * wC
partial_sum = prod + partial_sum
sum_numerator = partial_sum + sum_numerator
A_squared = (np.sum(wB_array))**2.0
A_squared_not = (np.sum([element**2 for element in wB_array]))
C_z = sum_numerator * (A_squared - A_squared_not)**-1 + C_z
return C_z
def network(string):
pocketcube = nx.Graph()
for letterA in solved_state:
pocketcube.add_node(letterA)
edges = pocketcube.edges()
listahan = cube.path(solved_state)[letterA]
# for letterB in listahan:
# pair = (letterA, letterB)
pocketcube.add_weighted_edges_from(edges)
return pocketcube