moving solve to sandbox

theano/sandbox/solve.py

+import numpy
+from theano import gof, tensor
+import unittest
+
+class Solve(gof.Op):
+    """
+    Find the solution to the linear equation Ax=b,
+    where A is a 2d matrix and b is a 1d or 2d matrix.
+    It use numpy.solve to find the solution.
+    """
+
+    def make_node(self, A, b):
+        if not isinstance(A, gof.Variable) or not A.type==tensor.matrix().type:
+            raise TypeError("We expected that A had a matrix type")
+        if not isinstance(B, gof.Variable) or not B.type==tensor.matrix().type:
+            raise TypeError("We expected that B had a matrix type")
+
+        node = gof.Apply(op=self, inputs=[A, B], outputs=[tensor.matrix()])
+        return node
+
+    def perform(self, node, (A, B), (output, )):
+        ret=numpy.solve(A,B)
+        output[0]=ret
+
+    def grad(self, (theta, A, B), (gtheta,)):
+        raise NotImplementedError()
+
+solve = Solve()
+
+
+class T_solve(unittest.TestCase):
+    def setUp(self):
+        self.rng = numpy.random.RandomState(utt.fetch_seed(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 = tensor.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)
+

theano/tensor/nnet.py

@@ -1272,32 +1272,6 @@ prepend_scalar_to_each_row = Prepend_scalar_to_each_row()
 prepend_0_to_each_row = Prepend_scalar_constant_to_each_row(0.)
 prepend_1_to_each_row = Prepend_scalar_constant_to_each_row(1.)
 
-class solve(gof.Op):
-    """
-    Find the solution to the linear equation Ax=b,
-    where A is a 2d matrix and b is a 1d or 2d matrix.
-    It use numpy.solve to find the solution.
-    """
-
-    def make_node(self, A, b):
-        if not isinstance(A, gof.Variable) or not A.type==tensor.matrix().type:
-            raise TypeError("We expected that A had a matrix type")
-        if not isinstance(B, gof.Variable) or not B.type==tensor.matrix().type:
-            raise TypeError("We expected that B had a matrix type")
-
-        node = gof.Apply(op=self, inputs=[A, B], outputs=[tensor.matrix()])
-        return node
-
-    def perform(self, node, (A, B), (output, )):
-        ret=numpy.solve(A,B)
-        output[0]=ret
-
-    def grad(self, (theta, A, B), (gtheta,)):
-        raise NotImplementedError()
-
-
-
-
 logsigm_to_softplus = gof.PatternSub(
     (tensor.log, (sigmoid, 'x')),
     (tensor.neg, (softplus, (tensor.neg, 'x'))),

theano/tensor/tests/test_nnet.py

@@ -108,21 +108,6 @@ class T_prepend(unittest.TestCase):
         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(utt.fetch_seed(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 = T.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)
-
-
 class T_CrossentropyCategorical1Hot(unittest.TestCase):
     def setUp(self):
         utt.seed_rng()