Merge pull request #625 from nouiz/mixed

doc/tutorial/python.txt

@@ -8,5 +8,5 @@ Python tutorial
 In this documentation, we suppose that reader know python. Here is a small list of python tutorials/exercices if you know know it or need a refresher:
 
   * `Python Challenge <http://www.pythonchallenge.com/>`__
-  * `Dive into Python <http://diveintopython.org/>`__
+  * `Dive into Python <http://diveintopython.net/>`__
   * `Google Python Class <http://code.google.com/edu/languages/google-python-class/index.html>`__

theano/misc/check_blas.py

@@ -190,6 +190,7 @@ if __name__ == "__main__":
         GTX580/3.2        0.20s
         GTX480/3.2        0.24s
         GTX480/3.0        0.27s
+        M2070/4.1         0.27s
         GTX470/3.2        0.29s
         M2070/3.2         0.32s
         GTX470/3.0        0.34s

theano/misc/tests/test_pycuda_example.py

@@ -61,9 +61,9 @@ def test_pycuda_elemwise_source_module():
 
             val1 = numpy.asarray(numpy.random.rand(*shape), dtype='float32')
             val2 = numpy.asarray(numpy.random.rand(*shape), dtype='float32')
-            assert (f(val1, val2) == f2(val1, val2)).all()
-            assert (f(val1, val2) == f3(val1, val2)).all()
-            assert (f(val1, val2) == f4(val1, val2)).all()
+            assert numpy.allclose(f(val1, val2), f2(val1, val2))
+            assert numpy.allclose(f(val1, val2), f3(val1, val2))
+            assert numpy.allclose(f(val1, val2), f4(val1, val2))
             #print f(val1,val2)
             #print f2(val1,val2)
 

theano/sparse/basic.py

@@ -704,6 +704,14 @@ class CSM(gof.Op):
         g_data = csm_grad(self.kmap)(data, csm_data(g_out), csm_indices(g_out))
         return [g_data, None, None, None]
 
+    def infer_shape(self, node, shapes):
+        if self.kmap is None:
+            # node.inputs[3] is of lenght as we only support sparse matrix.
+            return [(node.inputs[3][0], node.inputs[3][1])]
+        else:
+            return node.env.shape_feature.default_infer_shape(node, shapes)
+
+
 CSC = CSM('csc')
 CSR = CSM('csr')
 

theano/sparse/tests/test_basic.py

@@ -153,6 +153,22 @@ class SparseInferShapeTester(utt.InferShapeTester):
                                                config.floatX, 3))],
                                 GetItemScalar)
 
+    def test_csm(self):
+        for sparsetype in ('csr', 'csc'):
+            x = tensor.vector()
+            y = tensor.ivector()
+            z = tensor.ivector()
+            s = tensor.ivector()
+            call = getattr(sp, sparsetype + '_matrix')
+            spm = call(random_lil((300, 400), config.floatX, 5))
+            out = CSM(sparsetype)(x, y, z, s)
+            self._compile_and_check([x, y, z, s],
+                                    [out],
+                                    [spm.data, spm.indices, spm.indptr,
+                                     spm.shape],
+                                    CSM
+            )
+
     def test_csm_grad(self):
         for sparsetype in ('csr', 'csc'):
             x = tensor.vector()
@@ -262,10 +278,6 @@ class SparseInferShapeTester(utt.InferShapeTester):
                                config.floatX, 3))],
                 StructuredDot)
 
-    def test_csm(self):
-        # We also need the grad of CSM to be implemetned.
-        raise SkipTest('infer_shape not implemented for CSM')
-
     def test_structured_dot_grad(self):
         # We also need the grad of CSM to be implemetned.
         raise SkipTest('infer_shape not implemented for the grad'

theano/tensor/tests/test_basic.py

@@ -2441,10 +2441,14 @@ class T_subtensor(unittest.TestCase, utt.TestOptimizationMixin):
         n = self.shared(numpy.asarray(5, dtype=self.dtype))
         self.assertRaises(TypeError, n.__getitem__, [0,0])
 
-    def test_err_invalid_2list(self):
-        # TODO the error message is not clear
-        n = self.shared(numpy.ones((3,3), dtype=self.dtype)*5)
-        self.assertRaises(TypeError, n.__getitem__, ([0,0],[1,1]))
+    def test_err_invalid_not_2d(self):
+        n = self.shared(numpy.ones((3, 3, 3), dtype=self.dtype) * 5)
+        self.assertRaises(NotImplementedError, n.__getitem__,
+                          ([0, 0, 0], [1, 1, 1], [2, 2, 2]))
+
+    def test_err_invalid_2list_dtype(self):
+        n = self.shared(numpy.ones((3, 3), dtype=self.dtype) * 5)
+        self.assertRaises(TypeError, n.__getitem__, ([0., 0], [1, 1]))
 
     def test_err_bound_list(self):
         n = self.shared(numpy.ones((2,3),dtype=self.dtype)*5)