Merged

doc/install.txt

@@ -14,8 +14,7 @@ Requirements
 ------------
 
 In order to use Theano, the following libraries and software will need
-to be installed (MacOS and Windows users should refer to platform-specific
-instructions below for detailed installation steps):
+to be installed:
 
     Linux, Mac OS X or Windows operating system
         We develop mainly on 64-bit Linux machines. 32-bit architectures are
@@ -395,7 +394,7 @@ Windows V1 (Installing from Scratch)
   You can keep the default install options (except for the installation directory).
 
 - Install Mercurial. You can download it
-  `here <http://mercurial.selenic.com/downloads>`_. You may get either the command
+  `here <http://mercurial.selenic.com/downloads>`__. You may get either the command
   line Windows version or the TortoiseHG GUI version: it does not matter as
   far as installing Theano is concerned.
 
@@ -451,7 +450,7 @@ compile GotoBLAS2 (ATLAS may work too, but was not tested, and is
 usually reported to be slower and more difficult to compile -- especially
 on Windows).
 GotoBLAS2 can be downloaded
-`here <http://www.tacc.utexas.edu/tacc-projects/gotoblas2/downloads>`_
+`here <http://www.tacc.utexas.edu/tacc-projects/gotoblas2/downloads>`__
 after registering on the website (we tested v1.13).
 To compile it, you will also need to install MSYS and Perl,
 as described below.
@@ -539,8 +538,7 @@ Windows: Using the GPU
 
 Please note that these are tentative instructions (we have not yet been able to
 get the GPU to work under Windows with Theano).
-Please report your own successes / failures on the
-`theano-users <http://groups.google.com/group/theano-users>`_ mailing list.
+Please report your own successes / failures on the `theano-users`_ mailing list.
 
 Those are instructions for the 32-bit version of Python (the one that comes
 with Python(x,y) is 32-bit).
@@ -562,7 +560,7 @@ is the correct Python installation path):
     .. code-block:: cfg
 
       [cuda]
-      nvccflags=-LC:\Python27\libs
+      nvccflags=-LC:\Python26\libs
 
 Then
   
@@ -606,25 +604,83 @@ It could probably also run with `Cygwin <http://www.cygwin.com/>`__,
 but this has not been tested yet.
 
 - From `the MinGW files <http://sourceforge.net/projects/mingw/files/>`__,
-  download the latest version of the ``Automated MinGW Installer``
-  (``mingw-get-inst``) and install it with all optional components (although
-  you probably do not need them all: if you feel like figuring out exactly
-  which ones are required, let us know!).
+  download the latest version of the ``Automated MinGW Installer`` and install
+  it (keeping default options).
+
+- From `the MinGW files <http://sourceforge.net/projects/mingw/files/>`__,
+  download the latest ``MSYS Base System`` executable file and run it
+  (note that the latest version of MSYS Base System may not contain an
+  executable file, in which case it is easier to just use an
+  older version, e.g. MSYS-1.0.11.exe).
+  This will install MSYS (you can keep the default install options).
+  It will also run a post-install script where it will ask you about the
+  location of MinGW (typically ``c:/MinGW``).
+
+- From `the MinGW files <http://sourceforge.net/projects/mingw/files/>`__,
+  download the current version of ``GCC Version 4`` (full package with
+  binaries, e.g.
+  gcc-full-4.4.0-mingw32-bin-2.tar.lzma). Unpack it (you may use
+  `7-Zip <http://www.7-zip.org>`__ to unpack files with the
+  .lzma extension), copying the content into the root directory
+  of your MinGW installation (if you obtain a .tar file, make
+  sure you expand it as well, either with `7-Zip <http://www.7-zip.org>`__
+  or through the ``tar`` command on the MSYS command line).
+
+- If you are familiar with vi, you may find useful to download and install
+  ``MSYS vim`` (this is done in a similar way to GCC 4).
+  This is strictly optional and mostly helpful to edit configuration files
+  from within MSYS.
+
+- Run MSYS (Start/Programs/MinGW/MSYS/MSYS) and check the installation
+  by verifying that the proper version of GCC is found:
+
+    .. code-block:: bash
+
+        gcc --version
+
+  You may also decide to change the location of your home directory by
+  adding a line at the beginning of msys.bat, that would look like
+  ``set HOME=C:\My\Home\For\MinGW`` (you can also set a global ``HOME``
+  environment variable within Windows, but this could affect more programs).
 
 - If you do not have them already, install the latest versions of
   `Python 2.x <http://www.python.org/download/windows>`__ and
   corresponding `NumPy <http://sourceforge.net/projects/numpy/files/>`__
   then `SciPy <http://sourceforge.net/projects/scipy/files/>`__
-  packages (simply use the executable installers). Note that we experienced
-  issues with the 64 bit version of Python, and thus recommend using the 32 bit
-  version instead (if you managed to get Theano to work with 64 bit Python under
-  Windows, please let us know!).
+  packages (simply use the executable installers).
+
+- Ensure that the Python installation directory and its ``Scripts`` sub-directory
+  are in your system path. This may be done by
+  modifying the global ``PATH`` Windows environment variables, or by creating a ``.profile`` file in
+  your MinGW home, containing the line
+  ``export PATH=$PATH:/c/Python26:/c/Python26/Scripts``
+  (for Python 2.6).
+
+- Install a ``BLAS`` library. Note that although the following instructions
+  will give you a generic way to build your own library, there may exist
+  better (more optimized) versions of BLAS available for your system, but
+  these have not been tested for Windows at this time.
+  To build BLAS, download the latest version of `LAPACK <http://www.netlib.org/lapack/>`__
+  (typically lapack.tgz), then issue the following commands in MSYS
+  (for LAPACK 3.2.1):
+  
+    .. code-block:: bash
+
+        tar zxvf lapack.tgz
+        cd lapack-3.2.1
+        gfortran -shared -O3 -o libblas.dll BLAS/SRC/*.f
+        cp libblas.dll /mingw/lib
+        mv libblas.dll /mingw/bin
+
+- Edit (or create) your ``$HOME/.theanorc`` and add the following section:
+
+    .. code-block:: cfg
+
+        [blas]
+        ldflags = -lblas
 
-- Ensure that the Python installation directory and its ``Scripts``
-  sub-directory are in your system path. This may be done by
-  modifying the global ``PATH`` Windows environment variables, or by creating
-  a ``.profile`` file in your MinGW home, containing a line like
-  ``export PATH=$PATH:/c/Python27:/c/Python27/Scripts``.
+- Install `Mercurial <http://mercurial.selenic.com/downloads/>`__
+  (you can use the regular Windows release, you do not need TortoiseHg).
 
 - In order to run Theano's test-suite, you will need `nose
   <http://somethingaboutorange.com/mrl/projects/nose>`__.
@@ -635,15 +691,17 @@ but this has not been tested yet.
 
         python setup.py install
 
-- Install Theano using the above :ref:`install_bleeding_edge` installation
-  instructions
-  (using ``easy_install`` and has not been tested yet).
+- Install Theano using the above :ref:`install_bleeding_edge` installation instructions
+  (using ``easy_install`` will require additional packages and has not been
+  tested yet, while the latest official Theano release is also untested at this
+  time).
   In particular, do not forget to make the Theano package accessible from
   Python, e.g. by adding to your ``.profile`` a line like
   ``export PYTHONPATH=PYTHONPATH:$HOME/Theano``.
 
-- Please note that at this time, the test suite may be broken under Windows.
-  In particular, many tests may fail while running the test-suite,
+- Please note that at this time, some tests (launched using ``nosetests``) are
+  still failing under Windows: we are working on fixing them.
+  It may also happen that many tests may fail while running the test-suite,
   due to insufficient memory resources (in which case you will probably get an
   error of the type ``"Not enough storage is available to
   process this command"``): one workaround is to run nosetests

theano/tensor/basic.py

@@ -33,6 +33,9 @@ def _info(*msg):
 def _warn(*msg):
     _logger.warn(' '.join(msg))
 
+#This is needed as we will hide it later
+python_complex=complex
+
 def check_equal_numpy(x, y):
     """
     Returns True iff x and y are equal (checks the dtype and
@@ -388,6 +391,20 @@ def get_constant_value(v):
                 ret = get_constant_value(ret)
                 #join can cast implicitly its input in some case.
                 return theano._asarray(ret, dtype=v.type.dtype)
+            if (v.owner.inputs[0].owner and
+                isinstance(v.owner.inputs[0].owner.op,
+                           theano.tensor.opt.MakeVector) and
+                # MakeVector normally accept only scalar as input.
+                # We put this check in case there is change in the future
+                all(var.ndim==0 for var in v.owner.inputs[0].owner.inputs)):
+
+                # The index list 'idx_list' should have length one
+                # since joining scalar variables results in a 1D vector.
+                assert len(v.owner.op.idx_list) == 1
+                ret = v.owner.inputs[0].owner.inputs[v.owner.op.idx_list[0]]
+                ret = get_constant_value(ret)
+                #MakeVector can cast implicitly its input in some case.
+                return theano._asarray(ret, dtype=v.type.dtype)
     raise TypeError(v)
 
 
@@ -1505,7 +1522,7 @@ class SpecifyShape(Op):
     L{Op} put into the graph the user provided shape
 
     In the case where this op stay in the final graph, we assert the shape.
-    For this the output of this op must be used in the graph. This is not 
+    For this the output of this op must be used in the graph. This is not
     the case most of the time if we only take the shape of the output.
     Maybe there is other optimization that will mess with this.
 
@@ -1524,12 +1541,12 @@ class SpecifyShape(Op):
             x = as_tensor_variable(x)
         shape = as_tensor_variable(shape)
         return Apply(self, [x, shape], [x.type()])
-    
+
     def perform(self, node, (x,shape ), (out, )):
         assert numpy.all(x.shape==shape), ("got shape", x.shape,
                                            "expected", shape)
         out[0] = x
-        
+
     def infer_shape(self, node, (xshape, sshape)):
         new_shape=[]
         for dim in range(node.inputs[0].ndim):
@@ -2276,7 +2293,7 @@ def std(input, axis=None):
     :type axis: None or int or (list of int) (see `Sum`)
     """
     return sqrt(var(input=input, axis=axis))
- 
+
 if 0:
     ## COMMENTED OUT FEB 17 2010
     ## TODO (DOCUMENT AND WRITE TESTS) OR DELETE
@@ -3269,11 +3286,18 @@ def stack(*tensors):
         raise Exception('theano.tensor.stack(*tensors) must have at least one parameter')
     # If all tensors are scalars of the same type, call make_vector.
     # It makes the graph simpler, by not adding DimShuffles and Rebroadcasts
-    if numpy.all([isinstance(t, Variable) and\
-                  isinstance(t.type, TensorType) and\
-                  t.ndim==0 and t.type==tensors[0].type\
+    if isinstance(tensors[0], (numpy.number, float, int, python_complex)):
+        tensors=list(tensors)
+        tensors[0]=as_tensor_variable(tensors[0])
+    if numpy.all([isinstance(t, (numpy.number, float, int, python_complex))#in case their is direct int
+                  or (isinstance(t, Variable) and
+                      isinstance(t.type, TensorType) and
+                      t.ndim==0 and
+                      t.type.__class__==tensors[0].type.__class__)
                   for t in tensors]):
-        return theano.tensor.opt.MakeVector(scal.upcast(*[i.dtype for i in tensors]))(*tensors)
+        tensors = map(as_tensor_variable,tensors)#in case their is direct int
+        dtype = scal.upcast(*[i.dtype for i in tensors])
+        return theano.tensor.opt.MakeVector(dtype)(*tensors)
     return join(0, *[shape_padleft(t, 1) for t in tensors])
 
 @constructor

theano/tensor/tests/test_basic.py

@@ -1552,6 +1552,36 @@ class T_Join_and_Split(unittest.TestCase):
         assert len([n for n in e if isinstance(n, Join)]) == 0
         assert f.maker.env.outputs[0].dtype == config.floatX
 
+    def test_stack_scalar_make_vector_dtype(self):
+        '''Test that calling stack() on scalars instantiates MakeVector,
+        event when the scalar don't have the same dtype.'''
+        a = tensor.iscalar('a')
+        b = tensor.lscalar('b')
+        s = stack(a, b, a, b)
+        f = function([a,b], s)
+        val = f(1,2)
+        self.failUnless(numpy.all(val == [1,2,1,2]))
+        e = f.maker.env.toposort()
+        assert len([n for n in e if isinstance(n.op,opt.MakeVector)]) > 0
+        assert len([n for n in e if isinstance(n, Join)]) == 0
+        assert f.maker.env.outputs[0].dtype == 'int64'
+
+    def test_stack_scalar_make_vector_constant(self):
+        '''Test that calling stack() on scalars instantiates MakeVector,
+        event when the scalar are simple int type.'''
+        a = tensor.iscalar('a')
+        b = tensor.lscalar('b')
+        #test when the constant is the first element.
+        #The first element is used in a special way
+        s = stack(10,a,b, numpy.int8(3))
+        f = function([a,b], s)
+        val = f(1,2)
+        self.failUnless(numpy.all(val == [10,1,2,3]))
+        e = f.maker.env.toposort()
+        assert len([n for n in e if isinstance(n.op,opt.MakeVector)]) > 0
+        assert len([n for n in e if isinstance(n, Join)]) == 0
+        assert f.maker.env.outputs[0].dtype == 'int64'
+
     def test_join_vector(self):
         a = as_tensor_variable(numpy.array([1, 2, 3]))
         b = as_tensor_variable(numpy.array([7, 8, 9]))
@@ -3440,6 +3470,28 @@ def test_dimshuffle_duplicate():
 
     assert success
 
+class T_get_constant_value(unittest.TestCase):
+
+    def test_get_constant_value(self):
+        a = tensor.stack(1,2,3)
+        assert get_constant_value(a[0])==1
+        assert get_constant_value(a[1])==2
+        assert get_constant_value(a[2])==3
+
+        b = tensor.iscalar()
+        a = tensor.stack(b,2,3)
+        self.assertRaises(TypeError, get_constant_value, a[0])
+        assert get_constant_value(a[1])==2
+        assert get_constant_value(a[2])==3
+
+        #For now get_constant_value got throught only MakeVector and Join of scalar.
+        v = tensor.ivector()
+        a = tensor.stack(v,2,3)
+        self.assertRaises(TypeError, get_constant_value, a[0])
+        self.assertRaises(TypeError, get_constant_value, a[1])
+        self.assertRaises(TypeError, get_constant_value, a[2])
+
+
 if __name__ == '__main__':
     if 1:
         unittest.main()
@@ -3449,5 +3501,3 @@ if __name__ == '__main__':
         suite = unittest.TestLoader()
         suite = suite.loadTestsFromTestCase(testcase)
         unittest.TextTestRunner(verbosity=2).run(suite)
-
-

theano/tensor/tests/test_sharedvar.py

@@ -316,20 +316,20 @@ def makeSharedTester(shared_constructor_,
 
             #Test that we forward the input
             specify_shape_fct = theano.function([],x1_specify_shape)
-            theano.printing.debugprint(specify_shape_fct)
+            #theano.printing.debugprint(specify_shape_fct)
             assert numpy.all(self.ref_fct(specify_shape_fct())
                              ==self.ref_fct(x1_2))
             topo_specify = specify_shape_fct.maker.env.toposort()
             if theano.config.mode!='FAST_COMPILE':
-                assert len(topo_specify)==6
+                assert len(topo_specify)==4
 
             #Test that we put the shape info into the graph
             shape_constant_fct = theano.function([],x1_specify_shape.shape)
-            theano.printing.debugprint(shape_constant_fct)
+            #theano.printing.debugprint(shape_constant_fct)
             assert numpy.all(shape_constant_fct()==shape_op_fct())
             topo_cst = shape_constant_fct.maker.env.toposort()
             if theano.config.mode!='FAST_COMPILE':
-                assert len(topo_cst)==6
+                assert len(topo_cst)==2
 
             #Test that we can replace with values of the different shape
             # but that will raise an error in some case, but not all