def partitioning(G, k):
A = nx.adjacency_matrix(G)
# Alpah cut
M = Handler.alphaCut(A, 1)
# eigen calculation
eigenvalues, eigenvectors = np.linalg.eig(M)
# define K
partitionSize = k
# tempEigenValues = np.absolute(eigenvalues)
tempEigenValues = eigenvalues
idx = tempEigenValues.argsort()[:partitionSize][::]
eigenValues = tempEigenValues[idx]
eigenVectors = eigenvectors[:, idx]
z = eigenVectors
# normalize the matrix
for i in range(0, eigenVectors.shape[0]):
total = 0
for j in range(0, eigenVectors.shape[1]):
total += abs(eigenVectors.item((i, j)))**2
if (total > 0):
z[i] = z[i] / (total**(1 / 2))
# z = np.matrix.transpose(z)
# find k means paritions
kmeans = KMeans(n_clusters=partitionSize, random_state=0).fit(z)
lables = kmeans.labels_
array = Handler.indexArray(G.nodes(), partitionSize, lables)
np.savetxt('test_1.txt', array, fmt='%r')
print(lables)
# New partition array
partitionArray = []
# get each laplacian matrix
for k in array:
# sort = tempEigenvalues
if (len(k) > 1):
print(k)
H = G.subgraph(k)
r = nx.connected_components(H)
for i in r:
partitionArray.append(i)
else:
partitionArray.append(k)
matrix, edgecut1 = Handler.conectivityMatrix(partitionArray, G)
print(edgecut1)
edgecut2 = 0
q = queue.Queue()
partitionQueue = queue.Queue()
partitionQueue.put(partitionArray)
q.put(matrix)
alpha = Handler.alphaCut(matrix, 0)
partitionCount = 1
part = []
part.append(partitionArray)
while (partitionCount != partitionSize):
if (q.empty() is False):
matrix = q.get()
if (matrix.shape[0] > 1):
alpha = Handler.alphaCut(matrix, 0)
eigenvalues, eigenvectors = np.linalg.eig(alpha)
tempEigenValues = np.absolute(eigenvalues)
idx = tempEigenValues.argsort()[:2][::]
eigenValues = tempEigenValues[idx]
eigenVectors = eigenvectors[:, idx]
z = eigenVectors
# normalize the matrix
for i in range(0, eigenVectors.shape[0]):
total = 0
for j in range(0, eigenVectors.shape[1]):
total += abs(eigenVectors.item((i, j)))**2
if (total > 0):
z[i] = z[i] / (total**(1 / 2))
# norm = np.linalg.norm(z,axis=1,ord=2)
# print(norm)
# z = z.astype(np.float)/norm[:,None]
# print(z)
# find k means paritions
kmeans = KMeans(n_clusters=2, random_state=0).fit(z)
w = 0
p1, p2 = [], []
partition = partitionQueue.get()
for p in kmeans.labels_:
if (p == 0):
p1.append(partition[w])
else:
p2.append(partition[w])
w += 1
put1, tempedge1 = Handler.conectivityMatrix(p1, G)
put2, tempedge2 = Handler.conectivityMatrix(p2, G)
edgecut2 += tempedge1 + tempedge2
part.pop(0)
if (len(p1) >= len(p2)):
partitionQueue.put(p2)
partitionQueue.put(p1)
q.put(put2)
q.put(put1)
part.append(p2)
part.append(p1)
else:
partitionQueue.put(p1)
partitionQueue.put(p2)
q.put(put1)
q.put(put2)
part.append(p1)
part.append(p2)
partitionCount += 1
partition = []
for p in part:
partTemp = []
for par in p:
for part in par:
partTemp.append(part)
partition.append(partTemp)
return partition
if 0 in tempEigenvectors:
counter=collections.Counter(sort)
p = list(counter.values())[-1]
kmeans = KMeans(n_clusters=p+1, random_state=0).fit(tempEigenvectors[:,[list(tempEigenvalues).index(0)]])
lables = kmeans.labels_
arrays = Handler.indexArray(k,p+1,lables)
for i in arrays:
partitionArray.append(i)
else:
partitionArray.append(k)
else:
partitionArray.append(k)
matrix = Handler.conectivityMatrix(partitionArray,G)
q = queue.Queue()
partitionQueue = queue.Queue();
partitionQueue.put(partitionArray);
q.put(matrix)
alpha = Handler.alphaCut(matrix,0)
partitionCount = 1
part =[]
part.append(partitionArray)
while(partitionCount!=partitionSize):
if(q.empty() is False):
matrix = q.get()
if(matrix.shape[0]>1):
alpha = Handler.alphaCut(matrix,0)
eigenvalues, eigenvectors = np.linalg.eig(alpha)
if (sort.size > 2):
if 0 in tempEigenvectors:
counter = collections.Counter(sort)
p = list(counter.values())[-1]
kmeans = KMeans(n_clusters=p + 1, random_state=0).fit(
tempEigenvectors[:, [list(tempEigenvalues).index(0)]])
lables = kmeans.labels_
arrays = Handler.indexArray(k, p + 1, lables)
for i in arrays:
partitionArray.append(i)
else:
partitionArray.append(k)
else:
partitionArray.append(k)
matrix, edgecut1 = Handler.conectivityMatrix(partitionArray, G)
edgecut2 = 0
q = queue.Queue()
partitionQueue = queue.Queue()
partitionQueue.put(partitionArray)
q.put(matrix)
alpha = Handler.alphaCut(matrix, 0)
partitionCount = 1
part = []
part.append(partitionArray)
while (partitionCount != partitionSize):
if (q.empty() is False):
matrix = q.get()
if (matrix.shape[0] > 1):
alpha = Handler.alphaCut(matrix, 0)
if (0 in tempEigenvectors) & (p > 1):
index = []
for t in range(0, len(tempEigenvalues)):
if (tempEigenvalues[t] == 0):
index.append(t)
kmeans = KMeans(n_clusters=p, random_state=0).fit(tempEigenvectors[:, index])
lables = kmeans.labels_
arrays = Handler.indexArray(k, p, lables)
for i in arrays:
partitionArray.append(i)
else:
partitionArray.append(k)
else:
partitionArray.append(k)
np.savetxt('test_colombo.txt', partitionArray,fmt='%r')
matrix,edgecut1 = Handler.conectivityMatrix(partitionArray,G)
edgecut2 = 0
put,edgeConectivity = Handler.conectivityMatrix(partitionArray,G)
alpha = Handler.alphaCut(matrix,0)
partitionCount = 1
part =[]
part.append(partitionArray)
while(len(part)!=partitionSize):
if(len(part)>0):
max , id = 0,0
for i in range(0,len(part)):
if len(part[i])>max:
max = len(part[i])
id = i
matrix, edgeCount = Handler.conectivityMatrix(part[id], G)
partition = part[id]
with open("test_5.txt") as f:
datas = f.read()
# print(data.split(']')[0])
partitionArray = []
for k in range(0, 238):
partition_data = datas.split('\n')[k].replace('{',
'').replace('}',
'').split(', ')
tempartition = []
for i in partition_data:
tempartition.append(int(i))
partitionArray.append(tempartition)
adjecencyMatrix, edgecut = Handler.conectivityMatrix(partitionArray, G, data)
# sort the partition array
sorter = []
for e in partitionArray:
sorter.append(len(e))
sorter = np.asarray(sorter)
idSorter = sorter.argsort()[::-1][:]
tempPartitionArray = np.asarray(partitionArray)
tempPartitionArray = tempPartitionArray[idSorter]
partitionArray = []
for a in tempPartitionArray:
partitionArray.append(a)
tempPartitionArray = partitionArray.copy()
dict = dict()
for i in range(0, partitionSize):
