def power_method(self):
""" Multiplies a random vector with all matrices in self.
"""
x=self.random_vector()
x/=np.linalg.norm(x)
results={}
for i in range(len(self)):
print i
#norm1=np.linalg.norm(x)
x=(self[i].transpose())*x
norm2=np.linalg.norm(x)
print norm2
results[i]=norm2,gwh.the_fle(x)
return results
if __name__=="__main__":
#Z=ProductOfAdjacencyMatrices(nx.fast_gnp_random_graph,n=100,p=0.01,directed=True)
At=AdjMatrixSequence("/Users/lentz/Desktop/BA_reduced_RT.txt",directed=False)
#At = AdjMatrixSequence(fs.dataPath("T_edgelist.txt"),directed=True,columns=(0,1,2))
#At = AdjMatrixSequence(fs.dataPath("nrw_edges_01JAN2008_31DEC2009.txt"))
#At=AdjMatrixSequence("Data/sociopatterns_hypertext_social_ijt.dat")
#At=AdjMatrixSequence("Data/sexual_contacts.dat")
#At.as_undirected()
#At = AdjMatrixSequence(fs.dataPath("D_sw_uvd_01JAN2009_31MAR2010.txt"),matr_type='dok')
#C=At.cumulated()
Z=ProductOfAdjacencyMatrices(At)
c=Z.unfold_accessibility(False)
gwh.dict2file(c,"BA-Cumu.txt")
#h=gwh.cdf2histogram(c)
#gwh.dict2file(h,"BA-Histo.txt")
#out=P.sum(1)
#inn=P.sum(0)
#mmwrite("Vir.mtx",out)
#mmwrite("Vul.mtx",inn)
