e7392399dfa3d64a87eb31dfdfc532ada310ea59,metric_learn/_util.py,,_initialize_metric_mahalanobis,#Any#Any#Any#Any#Any#Any#,597

Before Change

                        "require the {} to be strictly positive definite."
                        .format(*((matrix_name,) * 3)))
    if return_inverse:
      M_inv = np.dot(u / s, u.T)
      return M, M_inv
    else:
      return M

After Change

                        "require the `{}` to be strictly positive definite."
                        .format(*((matrix_name,) * 2)))
    elif not cov_is_definite:
      warnings.warn("The covariance matrix is not invertible: "
                    "using the pseudo-inverse instead."
                    "To make the covariance matrix invertible"
                    " you can remove any linearly dependent features and/or "
                    "reduce the dimensionality of your input, "
                    "for instance using `sklearn.decomposition.PCA` as a "
                    "preprocessing step.")
    M = _pseudo_inverse_from_eig(w, V)
    if return_inverse:
      return M, M_inv

Instances