130cbadff294b686e466d430f26b2d069f6bbf59,metric_learn/sdml.py,_BaseSDML,_fit,#_BaseSDML#Any#Any#,65

Before Change

    // set up (the inverse of) the prior M
    if self.use_cov:
      X = np.vstack({tuple(row) for row in pairs.reshape(-1, pairs.shape[2])})
      prior_inv = np.atleast_2d(np.cov(X, rowvar=False))
    else:
      prior_inv = np.identity(pairs.shape[2])
    diff = pairs[:, 0] - pairs[:, 1]
    loss_matrix = (diff.T * y).dot(diff)

After Change

    // set up (the inverse of) the prior M
    // if the prior is the default (identity), we raise a warning just in case
    if self.prior is None:
      // TODO:
      //  replace prior=None by prior="identity" in v0.6.0 and remove the
      //  warning
      msg = ("Warning, no prior was set (`prior=None`). As of version 0.5.0, "
             "the default prior will now be set to "
             ""identity", instead of "covariance". If you still want to use "
             "the inverse of the covariance matrix as a prior, "
             "set prior="covariance". This warning will disappear in "
             "v0.6.0, and `prior` parameter"s default value will be set to "
             ""identity".")
      warnings.warn(msg, ChangedBehaviorWarning)
      prior = "identity"
    else:
      prior = self.prior
    _, prior_inv = _initialize_metric_mahalanobis(pairs, prior,
                                                  return_inverse=True,
                                                  strict_pd=True,
                                                  matrix_name="prior")

Instances