sf_kmeans.py 文件源码

def free_parameters(self, data):
        """
        Compute free parameters for the model fit using K-Means
        """
        K = np.unique(self.labels_).shape[0]  # number of clusters
        n, d = data.shape
        r = (K - 1) + (K * d)
        if self.metric == 'euclidean':
            r += 1  # one parameter for variance
        elif self.metric == 'mahalanobis':
            if self.covar_type == 'full' and self.covar_tied:
                r += (d * (d + 1) * 0.5)  # half of the elements (including diagonal) in the matrix
            if self.covar_type == 'full' and not self.covar_tied:
                r += (d * (d + 1) * 0.5 * K)  # half of the elements (including diagonal) in the matrix
            if self.covar_type == 'diag' and self.covar_tied:
                r += d  # diagonal elements of the matrix
            if self.covar_type == 'diag' and not self.covar_tied:
                r += (d * K)  # diagonal elements of the matrix
            if self.covar_type == 'spher' and self.covar_tied:
                r += 1  # all diagonal elements are equal
            if self.covar_type == 'spher' and not self.covar_tied:
                r += K  # all diagonal elements are equal
        return r