Added type constructors to sparse module: csc_dmatrix, csc_matrix, etc.

theano/sparse/basic.py

@@ -15,6 +15,7 @@ from theano import gof
 from theano import tensor
 from theano import compile
 from theano import scalar
+from theano import config
 
 #TODO: move this decorator to the compile submodule
 def register_specialize(lopt, *tags, **kwargs):
@@ -138,7 +139,7 @@ class SparseType(gof.Type):
     dtype_set = set(['int', 'int8', 'int16','int32', 'int64', 'float32', 'float64', 'complex64','complex128'])
     ndim = 2
 
-    def __init__(self, format, dtype = 'float64'):
+    def __init__(self, format, dtype):
         """
         Fundamental way to create a sparse node.
         @param dtype:   Type of numbers in the matrix.
@@ -205,8 +206,13 @@ class SparseType(gof.Type):
     def is_valid_value(self, a):
         return scipy.sparse.issparse(a) and (a.format == self.format)
 
-csc_matrix = SparseType(format='csc')
-csr_matrix = SparseType(format='csr')
+# for more dtypes, call SparseType(format, dtype)
+csc_matrix = SparseType(format='csc', dtype=config.floatX)
+csr_matrix = SparseType(format='csr', dtype=config.floatX)
+csc_dmatrix = SparseType(format='csc', dtype='float64')
+csr_dmatrix = SparseType(format='csr', dtype='float64')
+csc_fmatrix = SparseType(format='csc', dtype='float32')
+csr_fmatrix = SparseType(format='csr', dtype='float32')
 
 class _sparse_py_operators:
     T = property(lambda self: transpose(self), doc = "Return aliased transpose of self (read-only)")