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)