Adding test coverage for Covariance

And storing the input data used for fit()

Author: perimosocordiae

.gitignore

@@ -3,3 +3,4 @@ build/
 dist/
 *.egg-info
 .coverage
+htmlcov/

metric_learn/covariance.py

@@ -26,5 +26,6 @@ def fit(self, X, y=None):
     X: data matrix, (n x d)
     y: unused, optional
     """
+    self.X = X
     self.M = np.cov(X.T)
     return self

test/metric_learn_test.py

@@ -6,7 +6,7 @@
 from numpy.testing import assert_array_almost_equal
 
 from metric_learn import (
-    LMNN, NCA, LFDA,
+    LMNN, NCA, LFDA, Covariance,
     LSML_Supervised, ITML_Supervised, SDML_Supervised, RCA_Supervised)
 # Import this specially for testing.
 from metric_learn.lmnn import python_LMNN
@@ -32,6 +32,16 @@ def setUpClass(self):
     np.random.seed(1234)
 
 
+class TestCovariance(MetricTestCase):
+  def test_iris(self):
+    cov = Covariance()
+    cov.fit(self.iris_points)
+
+    csep = class_separation(cov.transform(), self.iris_labels)
+    # deterministic result
+    self.assertAlmostEqual(csep, 0.73068122)
+
+
 class TestLSML(MetricTestCase):
   def test_iris(self):
     lsml = LSML_Supervised(num_constraints=200)