def sparse_lanczos(A, k):
q = sp.random(A.shape[0], 1)
n = A.shape[0]
Q = sp.lil_matrix(np.zeros((n, k + 1)))
A = sp.lil_matrix(A)
Q[:, 0] = q / sparsenorm(q)
alpha = 0
beta = 0
for i in range(k):
if i == 0:
q = A * Q[:, i]
else:
q = A * Q[:, i] - beta * Q[:, i - 1]
alpha = q.T * Q[:, i]
q = q - Q[:, i] * alpha
q = q - Q[:, :i] * Q[:, :i].T * q # full reorthogonalization
beta = sparsenorm(q)
Q[:, i + 1] = q / beta
print(i)
Q = Q[:, :k]
Sigma = Q.T * A * Q
A2 = Q[:, :k] * Sigma[:k, :k] * Q[:, :k].T
return A2
def sparse_lanczos(A,k):
q = sp.random(A.shape[0],1)
n = A.shape[0]
Q = sp.lil_matrix(np.zeros((n,k+1)))
A = sp.lil_matrix(A)
Q[:,0] = q/sparsenorm(q)
alpha = 0
beta = 0
for i in range(k):
if i == 0:
q = A*Q[:,i]
else:
q = A*Q[:,i] - beta*Q[:,i-1]
alpha = q.T*Q[:,i]
q = q - Q[:,i]*alpha
q = q - Q[:,:i]*Q[:,:i].T*q # full reorthogonalization
beta = sparsenorm(q)
Q[:,i+1] = q/beta
print(i)
Q = Q[:,:k]
Sigma = Q.T*A*Q
A2 = Q[:,:k]*Sigma[:k,:k]*Q[:,:k].T
return A2
def mix_prop(adj, features, sparseinputs=False):
adj_column_norm = sparsenorm(adj, axis=0)
if sparseinputs:
features_row_norm = sparsenorm(features, axis=1)
else:
features_row_norm = np.linalg.norm(features, axis=1)
mix_norm = adj_column_norm * features_row_norm
norm_sum = sum(mix_norm)
return mix_norm / norm_sum
def mix_prop(adj, features, sparseinputs=False):
adj_column_norm = sparsenorm(adj, axis=0)
if sparseinputs:
features_row_norm = sparsenorm(features, axis=1)
else:
features_row_norm = np.linalg.norm(features, axis=1)
mix_norm = adj_column_norm*features_row_norm
norm_sum = sum(mix_norm)
return mix_norm / norm_sum
def column_prop(adj):
column_norm = sparsenorm(adj, axis=0)
# column_norm = pow(sparsenorm(adj, axis=0),2)
norm_sum = sum(column_norm)
return column_norm / norm_sum
def column_prop(adj):
column_norm = sparsenorm(adj, axis=0)
# column_norm = pow(sparsenorm(adj, axis=0),2)
norm_sum = sum(column_norm)
return column_norm/norm_sum
