'Check the parameters of the Gaussian distribution.
Parameters
X : array-like, shape (n_samples, n_features)'

def _check_means_parameters(self, X):

    (_, n_features) = X.shape
    if (self.mean_precision_prior is None):
        self.mean_precision_prior_ = 1.0
    elif (self.mean_precision_prior > 0.0):
        self.mean_precision_prior_ = self.mean_precision_prior
    else:
        raise ValueError(("The    parameter    'mean_precision_prior'    should    be    greater    than    0.,    but    got    %.3f." % self.mean_precision_prior))
    if (self.mean_prior is None):
        self.mean_prior_ = X.mean(axis=0)
    else:
        self.mean_prior_ = check_array(self.mean_prior, dtype=[np.float64, np.float32], ensure_2d=False)
        _check_shape(self.mean_prior_, (n_features,), 'means')
