Merge pull request #2013 from nouiz/mixed

doc/library/tensor/basic.txt

@@ -586,6 +586,14 @@ dimensions, see :meth:`_tensor_py_operators.dimshuffle`.
     broadcastable. When performing the function, if the length of `x`
     along that dimension is not 1, a ``ValueError`` will be raised.
 
+.. function:: patternbroadcast(x, broadcastable)
+
+    Change `x` broadcastable pattern to
+    `broadcastable`. `broadcastable` must be iterable. For example,
+    `patternbroadcast(x, (True, False))` will make the first dimension
+    of `x` broadcastable and the second dimension not broadcastable,
+    so x will now be a `row`.
+
 .. function:: flatten(x, outdim=1)
 
     Similar to :func:`reshape`, but the shape is inferred from the shape of `x`.

theano/configdefaults.py

@@ -11,10 +11,18 @@ _logger = logging.getLogger('theano.configdefaults')
 
 config = TheanoConfigParser()
 
+def floatX_convert(s):
+    if s == "32":
+        return "float32"
+    elif s == "64":
+        return "float64"
+    else:
+        return s
+
 AddConfigVar('floatX',
-        "Default floating-point precision for python casts",
-        EnumStr('float64', 'float32'),
-        )
+             "Default floating-point precision for python casts",
+             EnumStr('float64', 'float32', convert=floatX_convert,),
+)
 
 AddConfigVar('cast_policy',
         "Rules for implicit type casting",

theano/configparser.py

@@ -305,7 +305,11 @@ class EnumStr(ConfigParam):
                 raise ValueError('Valid values for an EnumStr parameter '
                         'should be strings', val, type(val))
 
+        convert = kwargs.get("convert", None)
+
         def filter(val):
+            if convert:
+                val = convert(val)
             if val in self.all:
                 return val
             else:

theano/sandbox/cuda/tests/test_opt.py

@@ -8,7 +8,7 @@ from nose.plugins.skip import SkipTest
 import theano
 from theano.compile.pfunc import pfunc
 from theano import config, tensor
-import theano.sandbox.linalg.tests.test_linalg
+import theano.tensor.tests.test_nlinalg
 
 from theano.tests import unittest_tools as utt
 
@@ -393,14 +393,14 @@ def test_erfinvgpu():
     assert numpy.allclose(f(xv),f2(xv))
 
 
-class test_diag(theano.sandbox.linalg.tests.test_linalg.test_diag):
+class test_diag(theano.tensor.tests.test_nlinalg.test_diag):
     mode = mode_with_gpu
     shared = staticmethod(cuda.shared_constructor)
     floatX = 'float32'
     type = CudaNdarrayType
 
     def __init__(self, name):
-        super(theano.sandbox.linalg.tests.test_linalg.test_diag,
+        super(theano.tensor.tests.test_nlinalg.test_diag,
               self).__init__(name)
 
 

theano/sandbox/multinomial.py

@@ -249,12 +249,12 @@ class GpuMultinomialFromUniform(MultinomialFromUniform, GpuOp):
 
         fail = sub['fail']
         return """
-        if (PyArray_NDIM(%(pvals)s) != 2)
+        if (CudaNdarray_NDIM(%(pvals)s) != 2)
         {
             PyErr_Format(PyExc_TypeError, "pvals wrong rank");
             %(fail)s;
         }
-        if (PyArray_NDIM(%(unis)s) != 1)
+        if (CudaNdarray_NDIM(%(unis)s) != 1)
         {
             PyErr_Format(PyExc_TypeError, "unis wrong rank");
             %(fail)s;

theano/tensor/tests/test_opt.py

@@ -4208,7 +4208,7 @@ def test_local_upcast_elemwise_constant_inputs():
     old = theano.config.floatX
     theano.config.floatX = 'float32'
     try:
-        v = lvector() / 2
+        v = lvector()
         function([v], theano.tensor.basic.true_div(v, 2))
     finally:
         theano.config.floatX = old