'Estimate the spherical Wishart distribution parameters.
Parameters
X : array-like, shape (n_samples, n_features)
nk : array-like, shape (n_components,)
xk : array-like, shape (n_components, n_features)
sk : array-like, shape (n_components,)'

def _estimate_wishart_spherical(self, nk, xk, sk):

    (_, n_features) = xk.shape
    self.degrees_of_freedom_ = (self.degrees_of_freedom_prior_ + nk)
    diff = (xk - self.mean_prior_)
    self.covariances_ = (self.covariance_prior_ + (nk * (sk + ((self.mean_precision_prior_ / self.mean_precision_) * np.mean(np.square(diff), 1)))))
    self.covariances_ /= self.degrees_of_freedom_
