params.py

   def _initialise_parameters(self, X):
        """Implement k-means to find starting
            parameter values.

        Parameters:
        ------------
        X: numpy array of data points
        
        Returns:
        ----------
        tuple containing initial means and covariance
        
        _initial_means: numpy array: (C*d)
        
        _initial_cov: numpy array: (C,d*d)
        
        
        """
        n_clusters = self.C
        kmeans = KMeans(n_clusters= n_clusters, init="k-means++", max_iter=500, algorithm = 'auto')
        fitted = kmeans.fit(X)
        prediction = kmeans.predict(X)
        self._initial_means, self._initial_cov, self._initial_pi = self.calculate_mean_covariance(X, prediction)
        
        
        return (self._initial_means, self._initial_cov, self._initial_pi)