gmm_ridge.py 文件源码

def learn(self,x,y):
        '''
        Function that learns the GMM with ridge regularizationb from training samples
        Input:
            x : the training samples
            y :  the labels
        Output:
            the mean, covariance and proportion of each class, as well as the spectral decomposition of the covariance matrix
        '''

        ## Get information from the data
        C = sp.unique(y).shape[0]
        #C = int(y.max(0))  # Number of classes
        n = x.shape[0]  # Number of samples
        d = x.shape[1]  # Number of variables
        eps = sp.finfo(sp.float64).eps

        ## Initialization
        self.ni = sp.empty((C,1))    # Vector of number of samples for each class
        self.prop = sp.empty((C,1))  # Vector of proportion
        self.mean = sp.empty((C,d))  # Vector of means
        self.cov = sp.empty((C,d,d)) # Matrix of covariance
        self.Q = sp.empty((C,d,d)) # Matrix of eigenvectors
        self.L = sp.empty((C,d)) # Vector of eigenvalues
        self.classnum = sp.empty(C).astype('uint8')

        ## Learn the parameter of the model for each class
        for c,cR in enumerate(sp.unique(y)):

            j = sp.where(y==(cR))[0]

            self.classnum[c] = cR # Save the right label
            self.ni[c] = float(j.size)    
            self.prop[c] = self.ni[c]/n
            self.mean[c,:] = sp.mean(x[j,:],axis=0)
            self.cov[c,:,:] = sp.cov(x[j,:],bias=1,rowvar=0)  # Normalize by ni to be consistent with the update formulae

            # Spectral decomposition
            L,Q = linalg.eigh(self.cov[c,:,:])
            idx = L.argsort()[::-1]
            self.L[c,:] = L[idx]
            self.Q[c,:,:]=Q[:,idx]