sf_kmeans.py 文件源码

def fit(self, data):
        """
        Run K-Means on data n_init times.

        Parameters
        ----------
        data: numpy array

        Returns
        -------
        No value is returned.
        Function sets the following two object params:
            self.labels_
            self.cluster_centers_
        """
        data = np.array(data)
        labels, cluster_centers = [], []
        for i in range(self.n_init):
            if not self.warm_start:
                self.cluster_centers_ = None
                self._global_covar_matrices = None
                self._inv_covar_matrices = None
            self._fit(data)
            labels += [self.labels_]
            cluster_centers += [self.cluster_centers_]
            self.inertias_ += [self._inertia(data)]
            self.log_likelihoods_ += [self.log_likelihood(data)]
        best_idx = np.argmin(self.inertias_)
        self.labels_ = labels[best_idx]
        self.all_labels_ = labels
        self.best_log_likelihood_ = self.log_likelihoods_[best_idx]
        self.best_inertia_ = self.inertias_[best_idx]
        self.cluster_centers_ = cluster_centers[best_idx]
        if self.verbose == 1:
            print('fit: n_clusters: {}, label bin count: {}'.format(self.n_clusters, np.bincount(self.labels_, minlength=self.n_clusters)))