gmm_ridge.py 文件源码

def BIC(self,x,y,tau=None):
        '''
        Computes the Bayesian Information Criterion of the model
        '''
        ## Get information from the data
        C,d = self.mean.shape
        n = x.shape[0]

        ## Initialization
        if tau is None:
            TAU=self.tau
        else:
            TAU=tau

        ## Penalization
        P = C*(d*(d+3)/2) + (C-1)
        P *= sp.log(n)

        ## Compute the log-likelihood
        L = 0
        for c in range(C):
            j = sp.where(y==(c+1))[0]
            xi = x[j,:]
            invCov,logdet = self.compute_inverse_logdet(c,TAU)
            cst = logdet - 2*sp.log(self.prop[c]) # Pre compute the constant
            xi -= self.mean[c,:]
            temp = sp.dot(invCov,xi.T).T
            K = sp.sum(xi*temp,axis=1)+cst
            L +=sp.sum(K)
            del K,xi

        return L + P