fixed score T

Author: JesseLivezey

dca/dca.py

@@ -206,7 +206,7 @@ def _fit_projection(self, d=None, T=None, record_V=False):
             raise ValueError
         if (2 * T) > self.cross_covs.shape[0]:
             raise ValueError('T must less than or equal to the value when ' +
-                             '`estimate_cross_covariance()` was called.')
+                             '`estimate_data_statistics()` was called.')
         self.T_fit = T
 
         if self.cross_covs is None:
@@ -276,10 +276,12 @@ def score(self, X=None):
             Optional. If X is none, calculate PI from the training data.
             If X is given, calcuate the PI of X for the learned projections.
         """
+        T = self.T_fit
         if X is None:
-            cross_covs = self.cross_covs
+            cross_covs = self.cross_covs.cpu().numpy()
         else:
-            cross_covs = calc_cross_cov_mats_from_data(X, T=self.T)
+            cross_covs = calc_cross_cov_mats_from_data(X, 2 * self.T)
+        cross_covs = cross_covs[:2 * T]
         if self.block_toeplitz:
             return calc_pi_from_cross_cov_mats_block_toeplitz(cross_covs, self.coef_)
         else:

tests/test_dca.py

@@ -181,3 +181,17 @@ def test_stride_DCA(lorenz_dataset):
     model.estimate_data_statistics(X)
     ccms2 = model.cross_covs.numpy()
     assert_allclose(ccms1, ccms2)
+
+
+def test_DCA_validation_score(lorenz_dataset):
+    """Check that score() returns the same value for the training data using the
+    default value of None or passing in the training data.
+    """
+    X = lorenz_dataset
+    model = DCA(T=5, d=2)
+    model.fit(X)
+    assert_allclose(model.score(), model.score(X))
+
+    model.fit_projection(T=3)
+    assert_allclose(model.score(), model.score(X))
+