Removed sandbox/solve.py as a solve Op that uses scipy can already be found in tensor/slinalg.py

theano/sandbox/solve.py

-from __future__ import absolute_import, print_function, division
-
-import unittest
-import sys
-
-import numpy
-import scipy.linalg
-
-import theano
-from theano import gof, tensor, scalar
-from theano.tests import unittest_tools as utt
-
-
-class Solve(gof.Op):
-    """
-    Find the solution to the linear equation Ax=b.
-
-    A is a 2d matrix and b is a 1d or 2d matrix.
-    It use numpy.solve to find the solution.
-
-    """
-
-    # TODO: Add class options to use the performance-enhancing flags
-    #     sym_pos, lower, overwrite_a, overwrite_b
-
-    # TODO: Add C code that calls the underlying LAPACK routines
-    #      and keeps a memory workspace from call to call as a non-default Op
-    #      output
-
-    __props__ = ()
-
-    def make_node(self, A, b):
-        A_ = tensor.as_tensor_variable(A)
-        b_ = tensor.as_tensor_variable(b)
-        if A_.broadcastable != (False, False):
-            raise TypeError("A must be a matrix", A_.type)
-        if b_.broadcastable not in ((False,), (True, False), (False, False)):
-            raise TypeError("b must be a matrix or vector", b_.type)
-        odtype = scalar.upcast(A_.dtype, b_.dtype)
-        otype = tensor.TensorType(broadcastable=b_.broadcastable, dtype=odtype)
-        return gof.Apply(op=self, inputs=[A_, b_], outputs=[otype()])
-
-    def perform(self, node, inp, out):
-        A, b = inp
-        output, = out
-        ret = scipy.linalg.solve(A, b)
-        if ret.dtype != node.outputs[0].dtype:
-            print("WARNING: Solve.perform() required cast.", file=sys.stderr)
-            ret = theano._asarray(ret, dtype=node.outputs[0].dtype)
-        output[0] = ret
-
-solve = Solve()
-
-
-# TODO: test dtype conversion
-# TODO: test that invalid types are rejected by make_node
-# TODO: test that each valid type for A and b works correctly
-
-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.arange(5, dtype=float)
-        x = scipy.linalg.solve(A, b)
-        Ax = numpy.dot(A, x)
-        are = tensor.numeric_grad.abs_rel_err(Ax, b)
-        self.assertTrue(numpy.all(are < 1.0e-5), (are, Ax, b))
-        # print A,b
-        # print numpy.dot(A,x)