sf_kmeans.py 文件源码

def _matrix_inverse(self, matrix):
        """
        Computes inverse of a matrix.
        """
        matrix = np.array(matrix)
        n_features = matrix.shape[0]
        rank = np.linalg.matrix_rank(matrix)

        if rank == n_features:
            return np.linalg.inv(matrix)
        else:
            # Matrix is not full rank, so use Hadi's technique to compute inverse
            # Reference: Ali S. Hadi (1992) "Identifying Multiple Outliers in Multivariate Data" eg. 2.3, 2.4
            eigenValues, eigenVectors = np.linalg.eig(matrix)
            eigenValues = np.abs(eigenValues)  # to deal with -0 values
            idx = eigenValues.argsort()[::-1]
            eigenValues = eigenValues[idx]
            eigenVectors = eigenVectors[:, idx]

            s = eigenValues[eigenValues != 0].min()
            w = [1 / max(e, s) for e in eigenValues]
            W = w * np.eye(n_features)

            return eigenVectors.dot(W).dot(eigenVectors.T)