Move tests for kron fron sandbox/tests/test_kron.py to theano.tensor.tests.test_slinalg.py

theano/sandbox/linalg/tests/test_kron.py

-from nose.plugins.skip import SkipTest
-import numpy
-
-import theano
-from theano import tensor, function
-from theano.tests import unittest_tools as utt
-from theano.sandbox.linalg.kron import kron
-
-floatX = theano.config.floatX
-
-try:
-    import scipy.linalg
-    imported_scipy = True
-except ImportError:
-    imported_scipy = False
-
-
-class TestKron(utt.InferShapeTester):
-
-    rng = numpy.random.RandomState(43)
-
-    def setUp(self):
-        super(TestKron, self).setUp()
-        self.op = kron
-
-    def test_perform(self):
-        if not imported_scipy:
-            raise SkipTest('kron tests need the scipy package to be installed')
-
-        for shp0 in [(2,), (2, 3), (2, 3, 4), (2, 3, 4, 5)]:
-            for shp1 in [(6,), (6, 7), (6, 7, 8), (6, 7, 8, 9)]:
-                if len(shp0) + len(shp1) == 2:
-                    continue
-                x = tensor.tensor(dtype='floatX',
-                                  broadcastable=(False,) * len(shp0))
-                y = tensor.tensor(dtype='floatX',
-                                  broadcastable=(False,) * len(shp1))
-                f = function([x, y], kron(x, y))
-                a = numpy.asarray(self.rng.rand(*shp0)).astype(floatX)
-                b = self.rng.rand(*shp1).astype(floatX)
-                out = f(a, b)
-                assert numpy.allclose(out, scipy.linalg.kron(a, b))
-
-    def test_numpy_2d(self):
-        for shp0 in [(2, 3)]:
-            for shp1 in [(6, 7)]:
-                if len(shp0) + len(shp1) == 2:
-                    continue
-                x = tensor.tensor(dtype='floatX',
-                                  broadcastable=(False,) * len(shp0))
-                y = tensor.tensor(dtype='floatX',
-                                  broadcastable=(False,) * len(shp1))
-                f = function([x, y], kron(x, y))
-                a = numpy.asarray(self.rng.rand(*shp0)).astype(floatX)
-                b = self.rng.rand(*shp1).astype(floatX)
-                out = f(a, b)
-                assert numpy.allclose(out, numpy.kron(a, b))
-
-
-if __name__ == "__main__":
-    t = TestKron('setUp')
-    t.setUp()
-    t.test_perform()
-    t.test_infer_shape()

theano/tensor/tests/test_slinalg.py

@@ -21,8 +21,8 @@ from theano.tensor.slinalg import ( Cholesky,
                                     Eigvalsh,
                                     EigvalshGrad,
                                     eigvalsh,
-                                    expm
-                                    )
+                                    expm,
+                                    kron)
 
 from nose.plugins.skip import SkipTest
 from nose.plugins.attrib import attr
@@ -281,3 +281,45 @@ def test_expm_grad_3():
     A = rng.randn(5, 5)
 
     tensor.verify_grad(expm, [A], rng=rng)
+
+
+class TestKron(utt.InferShapeTester):
+
+    rng = numpy.random.RandomState(43)
+
+    def setUp(self):
+        super(TestKron, self).setUp()
+        self.op = kron
+
+    def test_perform(self):
+        if not imported_scipy:
+            raise SkipTest('kron tests need the scipy package to be installed')
+
+        for shp0 in [(2,), (2, 3), (2, 3, 4), (2, 3, 4, 5)]:
+            for shp1 in [(6,), (6, 7), (6, 7, 8), (6, 7, 8, 9)]:
+                if len(shp0) + len(shp1) == 2:
+                    continue
+                x = tensor.tensor(dtype='floatX',
+                                  broadcastable=(False,) * len(shp0))
+                y = tensor.tensor(dtype='floatX',
+                                  broadcastable=(False,) * len(shp1))
+                f = function([x, y], kron(x, y))
+                a = numpy.asarray(self.rng.rand(*shp0)).astype(config.floatX)
+                b = self.rng.rand(*shp1).astype(config.floatX)
+                out = f(a, b)
+                assert numpy.allclose(out, scipy.linalg.kron(a, b))
+
+    def test_numpy_2d(self):
+        for shp0 in [(2, 3)]:
+            for shp1 in [(6, 7)]:
+                if len(shp0) + len(shp1) == 2:
+                    continue
+                x = tensor.tensor(dtype='floatX',
+                                  broadcastable=(False,) * len(shp0))
+                y = tensor.tensor(dtype='floatX',
+                                  broadcastable=(False,) * len(shp1))
+                f = function([x, y], kron(x, y))
+                a = numpy.asarray(self.rng.rand(*shp0)).astype(config.floatX)
+                b = self.rng.rand(*shp1).astype(config.floatX)
+                out = f(a, b)
+                assert numpy.allclose(out, numpy.kron(a, b))