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import networkx as nx
import numpy
import scipy.linalg
import scipy.sparse.linalg
from scipy.sparse.linalg.eigen.arpack.arpack import ArpackNoConvergence
def reduce_from_matlab(mat_path, output_dim):
mat =
A = mat['ans'].todense()
G = nx.from_numpy_matrix(A)
return reduce_graph(G, output_dim)
def reduce_graph(nx_graph, output_dim):
assert output_dim < len(nx_graph)
print 'Calculating Laplacian L'
L = nx.laplacian_matrix(nx_graph).astype('f')
print 'Calculating smallest eigenvalues of L & corresponding eigenvectors'
(E, U) = retry_eigendecomp(L, output_dim + 1, which='SM')
print 'Assembling PCA result'
# Remove the 0 eigenvalue and corresponding eigenvector
assert abs(E[0]) < 0.0001, E
E = E[1:]
U = U[:, 1:]
# Invert eigenvalues to get largest eigenvalues of L-pseudoinverse
Ep = 1/E
# Assemble into the right structure
X = numpy.zeros((output_dim, len(nx_graph)))
sqrtEp = numpy.sqrt(Ep)
for i in range(output_dim):
X[i, :] = sqrtEp[i] * U[:, i]
return X
def retry_eigendecomp(M, output_dim, tol=0, _attempt=0, **kwargs):
return scipy.sparse.linalg.eigs(M, output_dim, tol=tol, **kwargs)
except ArpackNoConvergence, e:
if _attempt > 2:
print 'Eigendecomp did not converge. Bailing.'
raise e
print e
if tol == 0:
tol = 0.000000001
new_tol = tol * 10
print 'Eigendecomp failed to converge, retrying with tolerance {}'.format(new_tol)
return retry_eigendecomp(M, output_dim, tol=new_tol, _attempt=_attempt+1)
def plot_2d(pca_output_2d):
import matplotlib.pyplot as plt
import as cm
x = pca_output_2d[0, :]
y = pca_output_2d[1, :]
colors =
plt.scatter(x, y, c=colors)
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