def spectral_coclustering(A, use_k_means=False, k=3):
D1 = np.sum(A, 1)
D2 = np.sum(A, 0)
D1_root = np.diag(np.abs((D1)**(-0.5)))
D2_root = np.diag(np.abs((D2)**(-0.5)))
An = dot(D1_root, dot(A, D2_root))
#print An
t1 = time()
U, S, V = svd(An)
V = np.transpose(V)
#print U, S, V
print "%s" % (time() - t1)
n_eigen = np.ceil(np.log2(k))
z = np.vstack((dot(D1_root, U[:, 1:n_eigen+1]),
dot(D2_root, V[:,1:n_eigen+1])))
if use_k_means:
_, labels, _ = k_means(z, k)
else:
t1 = time()
labels = ap(z)
print "%s" % (time() - t1)
r_labels = labels[:A.shape[0]]
c_labels = labels[A.shape[0]:]
return r_labels, c_labels
def spectral_coclustering(A, use_k_means=False, k=3):
D1 = np.sum(A, 1)
D2 = np.sum(A, 0)
D1_root = np.diag(np.abs((D1)**(-0.5)))
D2_root = np.diag(np.abs((D2)**(-0.5)))
An = dot(D1_root, dot(A, D2_root))
#print An
t1 = time()
U, S, V = svd(An)
V = np.transpose(V)
#print U, S, V
print "%s" % (time() - t1)
n_eigen = np.ceil(np.log2(k))
z = np.vstack(
(dot(D1_root, U[:, 1:n_eigen + 1]), dot(D2_root, V[:, 1:n_eigen + 1])))
if use_k_means:
_, labels, _ = k_means(z, k)
else:
t1 = time()
labels = ap(z)
print "%s" % (time() - t1)
r_labels = labels[:A.shape[0]]
c_labels = labels[A.shape[0]:]
return r_labels, c_labels
def k_means_plus(*args, **kwargs):
"""k means with smart initialization.
Notes
-----
This function requires scikits-learn
See Also
--------
kmeans
"""
return skcluster.k_means(*args, **kwargs)[1]
def k_means_plus(*args, **kwargs):
"""k means with smart initialization.
Notes
-----
This function requires scikits-learn
See Also
--------
kmeans
"""
return skcluster.k_means(*args, **kwargs)[1]
import numpy as np
from numpy.linalg import svd
from numpy import dot
from time import time
from scipy.cluster.hierarchy import dendrogram, linkage
#fast version
from scikits.learn.utils.extmath import fast_svd
from scikits.learn.cluster import k_means, affinity_propagation
if __name__ == "__main__":
A = np.loadtxt('town.csv', delimiter=",")
_, labels, _ = k_means(A, 3)
print labels
import numpy as np
from numpy.linalg import svd
from numpy import dot
from time import time
from scipy.cluster.hierarchy import dendrogram, linkage
#fast version
from scikits.learn.utils.extmath import fast_svd
from scikits.learn.cluster import k_means, affinity_propagation
if __name__ == "__main__":
A = np.loadtxt('town.csv', delimiter=",")
_, labels, _ = k_means(A, 3)
print labels