sf_kmeans.py 文件源码

def _rss(self, data):
        """ Residual Sum of Square distances of all data points from their cluster centers """
        if self.metric == 'euclidean':
            distances = cdist(data, self.cluster_centers_, metric='euclidean')
        elif self.metric == 'mahalanobis':
            #covar_matrix = self.covariance(labels=self.labels_, cluster_centers=self.cluster_centers_, data=data)
            covar_matrices = self.covariances(self.labels_,
                                            cluster_centers=self.cluster_centers_, data=data)[0]
            self._inv_covar_matrices = self._matrix_inverses(covar_matrices)
            distances = cdist(data, self.cluster_centers_, metric='mahalanobis', VI=self._inv_covar_matrices)
        distances = distances.min(axis=1)
        distances = distances ** 2
        assert distances.shape[0] == data.shape[0]
        return distances.sum()