--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/pylearn/sandbox/_test_random_transformation.py	Wed Nov 12 21:57:54 2008 -0500
@@ -0,0 +1,84 @@
+from random_transformation import row_random_transformation
+
+import unittest
+from theano import compile
+from theano import gradient
+
+from theano.sparse import _is_dense, _is_sparse, _is_dense_result, _is_sparse_result
+from theano.sparse import _mtypes, _mtype_to_str
+from theano.sparse import as_sparse
+
+from theano.tensor import as_tensor
+from theano.scalar import as_scalar
+
+import random
+import numpy.random
+
+class T_RowRandomTransformation(unittest.TestCase):
+    def setUp(self):
+        random.seed(44)
+        numpy.random.seed(44)
+
+    def test_basic(self):
+        rows = 4
+        cols = 20
+        fakeseed = 0
+        length = 3 
+        md = numpy.random.rand(rows, cols)
+        for mtype in _mtypes:
+            m = as_sparse(mtype(md))
+            o = row_random_transformation(m, length, initial_seed=fakeseed)
+            y = compile.eval_outputs([o])
+            expected = "[[ 0.88239119  1.03244463 -1.29297503]\n [ 0.02644961  1.50119695 -0.025081  ]\n [-0.60741013  1.25424625  0.30119422]\n [-1.08659967 -0.35531544 -1.38915467]]"
+            self.failUnless(str(y) == expected)
+
+    def test_length(self):
+        """ Test that if length is increased, we obtain the same results
+        (except longer). """
+
+        for i in range(10):
+            mtype = random.choice(_mtypes)
+            rows = random.randint(1, 20)
+            cols = random.randint(1, 20)
+            fakeseed = random.randint(0, 100)
+            length = random.randint(1, 10)
+            extralength = random.randint(1, 10)
+
+            m = as_sparse(mtype(numpy.random.rand(rows, cols)))
+            o1 = row_random_transformation(m, length, initial_seed=fakeseed)
+            o2 = row_random_transformation(m, length + extralength, initial_seed=fakeseed)
+
+            y1 = compile.eval_outputs([o1])
+            y2 = compile.eval_outputs([o2])
+
+            self.failUnless((y1 == y2[:,:length]).all())
+
+    def test_permute(self):
+        """ Test that if the order of the rows is permuted, we obtain the same results. """
+        for i in range(10):
+            mtype = random.choice(_mtypes)
+            rows = random.randint(2, 20)
+            cols = random.randint(1, 20)
+            fakeseed = random.randint(0, 100)
+            length = random.randint(1, 10)
+
+            permute = numpy.random.permutation(rows)
+
+
+            m1 = numpy.random.rand(rows, cols)
+            m2 = m1[permute]
+            for r in range(rows):
+                self.failUnless((m2[r] == m1[permute[r]]).all())
+            s1 = as_sparse(mtype(m1))
+            s2 = as_sparse(mtype(m2))
+            o1 = row_random_transformation(s1, length, initial_seed=fakeseed)
+            o2 = row_random_transformation(s2, length, initial_seed=fakeseed)
+            y1 = compile.eval_outputs([o1])
+            y2 = compile.eval_outputs([o2])
+
+            self.failUnless(y1.shape == y2.shape)
+            for r in range(rows):
+                self.failUnless((y2[r] == y1[permute[r]]).all())
+
+if __name__ == '__main__':
+    unittest.main()