(self, data)
return logwts.T
Compute cluster weights for specified points under GMM.
        Args:
            data: An N x D array of points
        Returns:
            A K x 1 array of average cluster log probabilities.
Compute cluster weights for specified points under GMM.
        Args:
            data: An N x D array of points
        Returns:
            A K x 1 array of average cluster log probabilities.
Compute cluster weights for specified points under GMM . Args : data : An N x D array of points Returns : A K x 1 array of average cluster log probabilities .
def clusterwts(self, data):
        """
        Compute cluster weights for specified points under GMM.
        Args:
            data: An N x D array of points
        Returns:
            A K x 1 array of average cluster log probabilities.
        """
        # Compute probability of each point under each cluster.
        logobs = self.estep(data)

        # Renormalize to get cluster weights.
        logwts = logobs - logsum(logobs, axis=1)

        # Average the cluster probabilities.
        logwts = logsum(logwts, axis=0) - np.log(data.shape[0])
        return logwts.T
def clusterwts ( self , data ) : # Compute probability of each point under each cluster. logobs = self . estep ( data ) # Renormalize to get cluster weights. logwts = logobs - logsum ( logobs , axis = 1 ) # Average the cluster probabilities. logwts = logsum ( logwts , axis = 0 ) - np . log ( data . shape [ 0 ] ) return logwts . T