added is_valid_value to the Type interface

theano/scalar/basic.py

@@ -52,6 +52,14 @@ class Scalar(Type):
         except Exception, e:
             raise TypeError("Could not convert %s (value=%s) to %s" % (type(data), data, self.dtype), e)
 
+    def values_eq_enough(self, a, b):
+        return abs(a - b) / (a+b) < 1e-4
+    
+    def is_valid_value(self, a):
+        _a = numpy.asarray(a)
+        rval = (_a.ndim == 0) and (str(_a.dtype) == self.dtype)
+        return rval
+
     def __eq__(self, other):
         return type(self) == type(other) and other.dtype == self.dtype
 

theano/sparse/basic.py

@@ -9,6 +9,7 @@ To read about different sparse formats, see U{http://www-users.cs.umn.edu/~saad/
 import sys, operator
 import numpy
 from scipy import sparse
+import scipy.sparse
 
 from .. import gof
 from .. import tensor
@@ -185,6 +186,14 @@ class Sparse(gof.Type):
     def __repr__(self):
         return "Sparse[%s, %s]" % (str(self.dtype), str(self.format))
 
+    def values_eq_enough(self, a, b, eps=1e-6):
+        return scipy.sparse.issparse(a) \
+                and scipy.sparse.issparse(b) \
+                and abs(a-b).sum() < (1e-6 * a.nnz)
+
+    def is_valid_value(self, a):
+        return scipy.sparse.issparse(a) and (a.format == self.format)
+
 csc_matrix = Sparse(format='csc')
 csr_matrix = Sparse(format='csr')
 

theano/tensor/basic.py

@@ -226,6 +226,17 @@ class Tensor(Type):
         return type(a) is numpy.ndarray and type(b) is numpy.ndarray \
                 and (a.shape == b.shape) and numpy.allclose(a, b)
 
+    def is_valid_value(self, a):
+        rval = (type(a) is numpy.ndarray) and (self.ndim == a.ndim) \
+                and (str(a.dtype) == self.dtype) \
+                and all([((si == 1) or not bi) for si, bi in zip(a.shape, self.broadcastable)])
+        if not rval:
+            print type(a),(type(a) is numpy.ndarray)
+            print a.ndim, (self.ndim == a.ndim)
+            print a.dtype, (str(a.dtype) == self.dtype)
+            print a.shape, self.broadcastable, ([(shp_i == 1) for shp_i in a.shape] == self.broadcastable)
+        return rval
+
     def __hash__(self):
         """Hash equal for same kinds of Tensor"""
         return hash(self.dtype) ^ hash(self.broadcastable)