FIX: make proposal for sdml formulation

Author: William de Vazelhes

metric_learn/sdml.py

@@ -58,15 +58,12 @@ def _fit(self, pairs, y):
     # set up prior M
     if self.use_cov:
       X = np.vstack({tuple(row) for row in pairs.reshape(-1, pairs.shape[2])})
-      self.M_ = pinvh(np.cov(X, rowvar = False))
+      self.M_ = np.cov(X, rowvar = False)
     else:
       self.M_ = np.identity(pairs.shape[2])
     diff = pairs[:, 0] - pairs[:, 1]
     loss_matrix = (diff.T * y).dot(diff)
-    P = self.M_ + self.balance_param * loss_matrix
-    emp_cov = pinvh(P)
-    # hack: ensure positive semidefinite
-    emp_cov = emp_cov.T.dot(emp_cov)
+    emp_cov = self.M_ + self.balance_param * loss_matrix
     _, self.M_ = graph_lasso(emp_cov, self.sparsity_param, verbose=self.verbose)
 
     self.transformer_ = transformer_from_metric(self.M_)