predict.py

     def predict(self, X):
        """Returns predicted labels using Bayes Rule to
        Calculate the posterior distribution
        
        Parameters:
        -------------
        X: N*d numpy array
        
        Returns:
        ----------
        labels: predicted cluster based on 
        highest responsibility gamma.
        
        """
        labels = np.zeros((X.shape[0], self.C))
        
        for c in range(self.C):
            labels [:,c] = self.pi[c] * mvn.pdf(X, self.mu[c,:], self.sigma[c])
        labels  = labels.argmax(1)
        return labels 
    
    def predict_proba(self, X):
        """Returns predicted labels
        
        Parameters:
        -------------
        X: N*d numpy array
        
        Returns:
        ----------
        labels: predicted cluster based on 
        highest responsibility gamma.
        
        """
        post_proba = np.zeros((X.shape[0], self.C))
        
        for c in range(self.C):
            # Posterior Distribution using Bayes Rule
            post_proba[:,c] = self.pi[c] * mvn.pdf(X, self.mu[c,:], self.sigma[c])
    
        return post_proba