def fit(self, X, y=None):
if self.normalize:<EOL><INDENT>X = normalize(X)<EOL><DEDENT>self._check_force_weights()<EOL>random_state = check_random_state(self.random_state)<EOL>X = self._check_fit_data(X)<EOL>(<EOL>self.cluster_centers_,<EOL>self.labels_,<EOL>self.inertia_,<EOL>self.weights_,<EOL>self.concentrations_,<EOL>self.posterior_,<EOL>) = movMF(<EOL>X,<EOL>self.n_clusters,<EOL>posterior_type=self.posterior_type,<EOL>force_weights=self.force_weights,<EOL>n_init=self.n_init,<EOL>n_jobs=self.n_jobs,<EOL>max_iter=self.max_iter,<EOL>verbose=self.verbose,<EOL>init=self.init,<EOL>random_state=random_state,<EOL>tol=self.tol,<EOL>copy_x=self.copy_x,<EOL>)<EOL>return self<EOL>
Compute mixture of von Mises Fisher clustering.

        Parameters
        ----------
        X : array-like or sparse matrix, shape=(n_samples, n_features)