gmm_ridge.py 文件源码

def predict(self,xt,tau=None,proba=None):
        '''
        Function that predict the label for sample xt using the learned model
        Inputs:
            xt: the samples to be classified
        Outputs:
            y: the class
            K: the decision value for each class
        '''
        ## Get information from the data
        nt = xt.shape[0]        # Number of testing samples
        C = self.ni.shape[0]    # Number of classes

        ## Initialization
        K = sp.empty((nt,C))

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

        for c in range(C):
            invCov,logdet = self.compute_inverse_logdet(c,TAU)
            cst = logdet - 2*sp.log(self.prop[c]) # Pre compute the constant

            xtc = xt-self.mean[c,:]
            temp = sp.dot(invCov,xtc.T).T
            K[:,c] = sp.sum(xtc*temp,axis=1)+cst
            del temp,xtc

        ##

        ## Assign the label save in classnum to the minimum value of K 
        yp = self.classnum[sp.argmin(K,1)]

        ## Reassign label with real value

        if proba is None:
            return yp
        else:
            return yp,K