gmm_ridge.py 文件源码

def predict(self,xt,tau=None,confidenceMap=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
        '''

        MAX = sp.finfo(sp.float64).max
        E_MAX = sp.log(MAX) # Maximum value that is possible to compute with sp.exp

        ## 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        

        yp = sp.argmin(K,1)

        if confidenceMap is None:

            ## Assign the label save in classnum to the minimum value of K 
            yp = self.classnum[yp]

            return yp

        else:

            K *= -0.5
            K[K>E_MAX],K[K<-E_MAX] = E_MAX,-E_MAX
            sp.exp(K,out=K)
            K /= K.sum(axis=1).reshape(nt,1)
            K = K[sp.arange(len(K)),yp]
            #K = sp.diag(K[:,yp])

            yp = self.classnum[yp]

            return yp,K