3e38fe09a87e6ef05289f3cbe6ffa03e2dc716e8,metric_learn/sdml.py,SDML_Supervised,fit,#SDML_Supervised#Any#Any#Any#,105

Before Change

      num_classes = len(np.unique(labels))
      num_constraints = 20 * num_classes**2

    c = Constraints.random_subset(labels, self.params["num_labeled"],
                                  random_state=random_state)
    adj = c.adjacency_matrix(num_constraints, random_state=random_state)
    return SDML.fit(self, X, adj)

After Change

    self : object
        Returns the instance.
    
    y = check_array(y, ensure_2d=False)
    num_constraints = self.num_constraints
    if num_constraints is None:
      num_classes = len(np.unique(y))
      num_constraints = 20 * num_classes**2

Instances