Added some documentation.

sparse.py

@@ -9,7 +9,12 @@ import tensor
 # Wrapper type
 
 def assparse(sp, **kwargs):
-    """Return SparseR version of sp"""
+    """
+    Wrapper around SparseR constructor.
+    @param sp:  A sparse matrix. assparse reads dtype and format properties
+                out of this sparse matrix.
+    @return:    SparseR version of sp.
+    """
     if isinstance(sp, SparseR):
         return sp
     else:
@@ -37,6 +42,13 @@ class SparseR(gof.result.ResultBase):
     dtype_set = set(['int', 'int32', 'int64', 'float32', 'float64'])
 
     def __init__(self, dtype, format, **kwargs):
+        """
+        Fundamental way to do create a sparse node.
+        @param dtype:   Type of numbers in the matrix.
+        @param format:  The sparse storage strategy.
+        @return         An empty SparseR instance.
+        """
+
         gof.ResultBase.__init__(self, **kwargs)
         if dtype in SparseR.dtype_set:
             self._dtype = dtype
@@ -165,8 +177,10 @@ if 0:
         def gen_outputs(self): return [SparseR()]
         def impl(x,y):
             if hasattr(x, 'getnnz'):
+                # if x is sparse, then do this.
                 return x.dot(y)
             else:
+                # if x is dense (and y is sparse), we do this
                 return y.transpose().dot(x.transpose()).transpose()
 
         def grad(self, x, y, gz):