--- a/_test_nnet_ops.py	Mon Jul 14 13:48:41 2008 -0400
+++ b/_test_nnet_ops.py	Mon Jul 14 16:48:02 2008 -0400
@@ -1,5 +1,6 @@
 
 import unittest
+import theano
 import theano._test_tensor as TT
 import numpy
 
@@ -35,6 +36,43 @@
                 return crossentropy_softmax_1hot(a, y_idx)[0:1]
         TT.verify_grad(self, Dummy(), [numpy.random.rand(3,4)])
 
+class T_prepend(unittest.TestCase):
+    def test0(self):
+        """basic functionality"""
+        x=tensor.matrix('x')
+        y=Prepend_scalar_constant_to_each_row(4.)(x)
+        f=theano.function([x],[y])
+        m=numpy.random.rand(3,5)
+        my = f(m)
+        self.failUnless(my.shape == (3, 6), my.shape)
+        self.failUnless(numpy.all( my[:,0] == 4.0))
+
+
+class T_prepend(unittest.TestCase):
+    def test0(self):
+        """basic functionality"""
+        x=tensor.matrix('x')
+        y=Prepend_scalar_to_each_row()(5.,x)
+        f=theano.function([x],[y])
+        m=numpy.ones((3,5),dtype="float32")
+        my = f(m)
+        self.failUnless(str(my.dtype) == 'float64')
+        self.failUnless(my.shape == (3, 6))
+        self.failUnless(numpy.all(my[:,0] == 5.0))
+
+class T_solve(unittest.TestCase):
+    def setUp(self):
+        self.rng = numpy.random.RandomState(666)
+
+    def test0(self):
+        A=self.rng.randn(5,5)
+        b=numpy.array(range(5),dtype=float)
+        x=numpy.linalg.solve(A,b)
+        Ax = numpy.dot(A,x)
+        are = theano.gradient.numeric_grad.abs_rel_err(Ax, b)
+        self.failUnless(numpy.all(are < 1.0e-5), (are, Ax, b))
+        #print A,b
+        #print numpy.dot(A,x)
 
 
 if __name__ == '__main__':