Removed unnecessary A_Xinv_B function.

theano/sandbox/linalg/ops.py

@@ -863,36 +863,6 @@ def spectral_radius_bound(X, log2_exponent):
             2 ** (-log2_exponent))
 
 
-class A_Xinv_b(Op):
-    """Product of form a inv(X) b"""
-    def make_node(self, a, X, b):
-        assert imported_scipy, (
-            "Scipy not available. Scipy is needed for the A_Xinv_b op")
-        a = as_tensor_variable(a)
-        X = as_tensor_variable(X)
-        b = as_tensor_variable(b)
-        assert a.ndim == 2
-        assert X.ndim == 2
-        assert b.ndim == 2
-        o = theano.tensor.matrix(dtype=X.dtype)
-        return Apply(self, [a, X, b], [o])
-
-    def perform(self, ndoe, inputs, outstor):
-        a, X, b = inputs
-        iX = numpy.linalg.inv(X)
-        z = numpy.dot(numpy.dot(a, iX), b)
-        outstor[0][0] = z
-
-    def grad(self, inputs, g_outputs):
-        gz, = g_outputs
-        a, X, b = inputs
-        iX = matrix_inverse(X)
-        ga = matrix_dot(gz, b.T, iX.T)
-        gX = -matrix_dot(iX.T, a, gz, b.T, iX.T)
-        gb = matrix_dot(iX.T, a.T, gz)
-        return [ga, gX, gb]
-
-
 class Eig(Op):
     """Compute the eigenvalues and right eigenvectors of a square array.
 

theano/sandbox/linalg/tests/test_linalg.py

@@ -33,7 +33,7 @@ from theano.sandbox.linalg.ops import (cholesky,
                                        imported_scipy,
                                        Eig,
                                        inv_as_solve,
-                                       A_Xinv_b
+                                       A_Xinv_B
                                        )
 from theano.sandbox.linalg import eig, eigh, eigvalsh
 from nose.plugins.skip import SkipTest
@@ -674,39 +674,3 @@ def test_eigvalsh_grad():
     b = 10 * numpy.eye(5, 5) + rng.randn(5, 5)
     tensor.verify_grad(lambda a, b: eigvalsh(a, b).dot([1, 2, 3, 4, 5]),
                        [a, b], rng=numpy.random)
-
-
-class test_A_Xinv_b():
-    def test_A_Xinv_b(self):
-        x = tensor.matrix()
-        y = tensor.matrix()
-        z = tensor.matrix()
-        m = A_Xinv_b()(x, y, z)
-        f = function([x, y, z], m)
-        X = [[1, 1], [1, 1]]
-        Y = [[2, 1], [3, 4]]
-        Z = [[1, 1], [1, 1]]
-        assert numpy.allclose(f(X, Y, Z), [[0.4, 0.4], [0.4, 0.4]])
-
-    def test_shape_conflict(self):
-        x = tensor.matrix()
-        y = tensor.matrix()
-        z = tensor.matrix()
-        m = A_Xinv_b()(x, y, z)
-        f = function([x, y, z], m)
-        X = [[1, 1, 1], [1, 1, 1]]
-        Y = [[2, -9], [3, 4]]
-        Z = [[1, 1], [1, 1]]
-        assert_raises(ValueError, f, X, Y, Z)
-
-    def test_grad(self):
-        x = tensor.matrix()
-        y = tensor.matrix()
-        z = tensor.matrix()
-        m = A_Xinv_b()(x, y, z)
-        f = function([x, y, z], m)
-        X = numpy.asarray([[1, 1], [1, 1]], dtype='float32')
-        Y = numpy.asarray([[2, 1], [3, 4]], dtype='float32')
-        Z = numpy.asarray([[1, 1], [1, 1]], dtype='float32')
-        theano.tests.unittest_tools.verify_grad(A_Xinv_b(),
-                                                [X, Y, Z])