Merge tag '0.6.0' into debian/sid

1 files changed, 59 insertions, 0 deletions

diff --git a/test/test_dr.py b/test/test_dr.py
new file mode 100644
index 0000000..c5df287
--- /dev/null
+++ b/test/test_dr.py
@@ -0,0 +1,59 @@
+"""Tests for module dr on Dimensionality Reduction """
+
+# Author: Remi Flamary <remi.flamary@unice.fr>
+#
+# License: MIT License
+
+import numpy as np
+import ot
+import pytest
+
+try:  # test if autograd and pymanopt are installed
+    import ot.dr
+    nogo = False
+except ImportError:
+    nogo = True
+
+
+@pytest.mark.skipif(nogo, reason="Missing modules (autograd or pymanopt)")
+def test_fda():
+
+    n_samples = 90  # nb samples in source and target datasets
+    np.random.seed(0)
+
+    # generate gaussian dataset
+    xs, ys = ot.datasets.make_data_classif('gaussrot', n_samples)
+
+    n_features_noise = 8
+
+    xs = np.hstack((xs, np.random.randn(n_samples, n_features_noise)))
+
+    p = 1
+
+    Pfda, projfda = ot.dr.fda(xs, ys, p)
+
+    projfda(xs)
+
+    np.testing.assert_allclose(np.sum(Pfda**2, 0), np.ones(p))
+
+
+@pytest.mark.skipif(nogo, reason="Missing modules (autograd or pymanopt)")
+def test_wda():
+
+    n_samples = 100  # nb samples in source and target datasets
+    np.random.seed(0)
+
+    # generate gaussian dataset
+    xs, ys = ot.datasets.make_data_classif('gaussrot', n_samples)
+
+    n_features_noise = 8
+
+    xs = np.hstack((xs, np.random.randn(n_samples, n_features_noise)))
+
+    p = 2
+
+    Pwda, projwda = ot.dr.wda(xs, ys, p, maxiter=10)
+
+    projwda(xs)
+
+    np.testing.assert_allclose(np.sum(Pwda**2, 0), np.ones(p))