def matrix_multiply(A, B):
n1, k1 = shape(A)
n2, k2 = shape(B)
if k1 != n2:
raise ArithmeticError("incompatible shapes!")
return generateMatrix(n1, k2, partial(matrix_product_entry, A, B))
def matrix_multiply(A, B):
n1, k1 = shape(A)
n2, k2 = shape(B)
if k1 != n2:
raise ArithmeticError("incompatible shapes!")
return generateMatrix(n1, k2, partial(matrix_product_entry, A, B))
def correlationMatrix(data):
_, numCols = shape(data)
def matrixEntry(i, j):
return correlation(getCol(data, i), getCol(data, j))
return generateMatrix(numCols, numCols, matrixEntry)
def correlationMatrix(data):
_, numCols = shape(data)
def matrixEntry(i, j):
return correlation(getCol(data, i), getCol(data, j))
return generateMatrix(numCols, numCols, matrixEntry)
def rescale(dataMatrix):
means, stdevs = scale(dataMatrix)
def rescaled(i, j):
if(stdevs[j] > 0):
return (dataMatrix[i][j] - means[j]) / stdevs[j]
else:
return dataMatrix[i][j]
nRows, nCols = shape(dataMatrix)
return generateMatrix(nRows, nCols, rescaled)
if id not in [source_id, target_id]:
users[id]["betweenness_centrality"] += contrib
for user in users:
print("User ID : ", user["id"], " Betweenness Centrality : ",
user["betweenness_centrality"])
print()
"""
Closeness Centrality
"""
for user in users:
user["closeness_centrality"] = 1 / farness(user)
print("Closeness Centrality")
for user in users:
print("User ID : ", user["id"], " Closeness Centrality : ",
user["closeness_centrality"])
print()
"""
Eigenvector Centralities,
"""
n = len(users)
adjacency_matrix = generateMatrix(n, n, entry_fn)
eigenvector_centralities, _ = find_eigenvector(adjacency_matrix)
print("Eigenvector Centrality")
for user_id, centrality in enumerate(eigenvector_centralities):
print("User ID : ", user_id, " Eigenvector Centrality : ", centrality)
print()
def deMeanMatrix(A):
nr, nc = shape(A)
colMean, _ = scale(A)
return generateMatrix(nr, nc, lambda i, j: A[i][j] - colMean[j])
users[id]["betweenness_centrality"] += contrib
for user in users:
print("User ID : ", user["id"], " Betweenness Centrality : ", user["betweenness_centrality"])
print()
"""
Closeness Centrality
"""
for user in users:
user["closeness_centrality"] = 1 / farness(user)
print("Closeness Centrality")
for user in users:
print("User ID : ", user["id"], " Closeness Centrality : ", user["closeness_centrality"])
print()
"""
Eigenvector Centralities,
"""
n = len(users)
adjacency_matrix = generateMatrix(n, n, entry_fn)
eigenvector_centralities, _ = find_eigenvector(adjacency_matrix)
print("Eigenvector Centrality")
for user_id, centrality in enumerate(eigenvector_centralities):
print("User ID : ", user_id, " Eigenvector Centrality : ", centrality)
print()
def deMeanMatrix(A):
nr, nc = shape(A)
colMean, _ = scale(A)
return generateMatrix(nr, nc, lambda i, j: A[i][j] - colMean[j])
