def run(self):
"""
:returns: Tuple, such as (<dict of nmis, with algorithm names as keys >)
"""
nsc = NSpecSparse(self.X, self.k, maxiter=2000)
nmf = NMF(self.X, self.k)
km = KMeans(n_clusters=self.k)
nsckm = NSpecSparseKM(self.X, self.k, maxiter=2000)
nsc_result = nsc.predict()
nmf_result = nmf.predict()
km_result = km.fit_predict(self.X)
nsckm_result = nsckm.predict()
w_nsc = nsc_result.matrices[0].todense()
w_nmf = nmf_result.matrices[0]
w_nsckm = nsckm_result.matrices # gets only the labels
arrays = {
'nsc': np.array(np.argmax(w_nsc, axis=1))[:, 0],
'nmf': np.array(np.argmax(w_nmf, axis=1)),
'km': km_result,
'nsckm': w_nsckm
}
nmi = {k: nmiscore(arrays[k], self.y) for k in arrays.keys()}
return (nmi, arrays)
def run(self):
"""
:returns: Tuple, such as (<dict of nmis, with algorithm names as keys >)
"""
nsc = NSpecSparse(self.X, self.k, maxiter=2000)
nmf = NMF(self.X, self.k)
km = KMeans(n_clusters=self.k)
nsckm = NSpecSparseKM(self.X, self.k, maxiter=2000)
nsc_result = nsc.predict()
nmf_result = nmf.predict()
km_result = km.fit_predict(self.X)
nsckm_result = nsckm.predict()
w_nsc = nsc_result.matrices[0].todense()
w_nmf = nmf_result.matrices[0]
w_nsckm = nsckm_result.matrices # gets only the labels
arrays = {
'nsc': np.array(np.argmax(w_nsc, axis=1))[:,0],
'nmf': np.array(np.argmax(w_nmf, axis=1)),
'km': km_result,
'nsckm': w_nsckm
}
nmi = {k: nmiscore(arrays[k], self.y) for k in arrays.keys()}
return (nmi, arrays)
def main():
origin,mask = load_data(4,5,0.2)
dm = origin*mask
mask1 = dm / origin
print(mask1)
print(mask)
nmf = NMF()
rec = nmf.predict(dm,mask)
print(rec-dm)
# encoding: utf-8 ''' Created on 2016年11月15日 @author: alibaba ''' import numpy as np #from basenmf import BaseNMF, NMFResult #from projective import ProjectiveNMF from nmf import NMF X=np.array([(1,2,3,4,5,6),(4,5,6,7,8,9),(1,3,5,4,2,6)])/10.0 nmf=NMF(X,2,maxiter=100) nmf_result=nmf.predict() w_nmf = nmf_result.matrices[0] print(w_nmf)
