graphssl.py 文件源码

def constructEucleadianGaussianKernelNoPca(self):

        self.pwdis=pairwise_distances(self.allVectors)

        maccs=[]
        ks=[3,5,7,10,12,15,20,22,25,27,30,33,35,37,40,43,45,47,50,53,55,57,60,65]
        for k in ks:
            sigmas=[1,1.5,2,2.5,3,3.5]
            accs=[]
            for sigma in sigmas:
                self.pwdis=-1*self.pwdis/(2*sigma*sigma)
                self.pwdis=np.exp(self.pwdis)
                self.D=np.zeros(self.pwdis.shape)
                for i in range(0,self.pwdis.shape[0]):
                    l1=self.pwdis[i].tolist()
                    #print 'l1 is ',l1,'\n\n'
                    allnearestNeighbours=sorted(range(len(l1)),key=lambda i : l1[i])
                    #now set the all the weights except for k+1 to 0
                    self.pwdis[i,allnearestNeighbours[k:]]=0
                    self.D[i,i]=sum(self.pwdis[i])

                    #here we make no trnasformation on the dataset, as this is simply the 
                print 'accuracy for constructEucleadianGaussianKernel with k=',k,' and sigma =',sigma,' is \n'
                accs.append(self.labelPropogation())
            maccs.append(np.mean(accs))

        plt.figure()   
        plt.plot(ks,maccs)
        plt.title('Accuarcy vs k for Eucledian Gaussian Kernel')
        plt.savefig(pp,format='pdf')           
        plt.close()