sf_kmeans.py 文件源码

def _inertia(self, data):
        """ Sum of distances of all data points from their cluster centers """
        distances = np.zeros((data.shape[0], self.n_clusters))
        covar_matrices = self.covariances(self.labels_, cluster_centers=self.cluster_centers_, data=data)
        self._inv_covar_matrices = self._matrix_inverses(covar_matrices)
        for k in range(self.n_clusters):
            k_dist = cdist(data, np.array([self.cluster_centers_[k]]), metric=self.metric,
                           VI=self._inv_covar_matrices[k])
            k_dist = k_dist.reshape((data.shape[0],))
            distances[:, k] = k_dist
        distances = distances.min(axis=1)
        assert distances.shape[0] == data.shape[0]
        return distances.sum()