small update

doc/library/tensor/basic.txt

@@ -770,7 +770,7 @@ Creating Tensor
     if mode=wrap, values in a (and thus Ba) may be any (signed) integer; modular arithmetic is used to map integers outside the range [0, n-1] back into that range; and then the new array is constructed as above;
     if mode=clip, values in a (and thus Ba) may be any (signed) integer; negative integers are mapped to 0; values greater than n-1 are mapped to n-1; and then the new array is constructed as above.
 
-    Parameters :    
+    Parameters : 
     a : int array
         This array must contain integers in [0, n-1], where n is the number of choices, unless mode=wrap or mode=clip, in which cases any integers are permissible. 
     choices : sequence of arrays
@@ -782,10 +782,10 @@ Creating Tensor
         ‘raise’ : an exception is raised
         ‘wrap’ : value becomes value mod n
         ‘clip’ : values < 0 are mapped to 0, values > n-1 are mapped to n-1
-    Returns :   
+    Returns :
     merged_array : array
         The merged result.
-    Raises :    
+    Raises :
         ValueError: shape mismatch
         If a and each choice array are not all broadcastable to the same shape.
 

theano/tensor/basic.py

@@ -5095,19 +5095,13 @@ def swapaxes(y, axis1, axis2):
 
 
 def choose(a, choices, out=None, mode='raise'):
+    # This is done to keep the same function signature then NumPy.
     assert out is None
     return Choose(mode)(a, choices)
 
-class Choose(Op):
-
-    def __init__(self, mode):
-        self.mode = mode
-
-    def __hash__(self):
-        return hash((type(self), self.props()))
 
-    def __eq__(self, other):
-        return (type(self) == type(other) and self.props() == other.props())
+class Choose(Op):
+    __props__ = ('mode',)
 
     def infer_shape(self, node, shapes):
         if isinstance(node.inputs[1], tuple):
@@ -5118,9 +5112,6 @@ class Choose(Op):
         else:
             return[(shapes[0])]
 
-    def props(self):
-        return self.mode
-
     def make_node(self, a, choices):
         from theano import typed_list
         a = as_tensor_variable(a)
@@ -5133,26 +5124,4 @@ class Choose(Op):
     def perform(self, node, inputs, (z, )):
         a = inputs[0]
         choice = inputs[1]
-        z[0] = numpy.choose(a, choice, mode=self.mode)      
-
-"""
-import theano
-from theano import tensor as T
-from theano import function
-from theano.tensor.basic import choose
-import numpy as np
-x = T.tensor4(dtype='int64')
-y = T.tensor4(dtype='int64')
-z = T.tensor4(dtype='int64')
-p = T.tensor4(dtype='int64')
-w = choose(x,(y,z,p))
-f = function([x,y,z,p], w)
-a = np.array([0, 1, 2]).reshape((3,1,1,1))
-c1 = np.array([1, 2, 3]).reshape((1,3,1,1))
-c2 = np.array([-1, -2, -3, -4, -5]).reshape((1,1,5,1))
-c3 = np.array([1, 2, 4]).reshape((1,1,1,3))
-"""
-
-
-
-
+        z[0] = numpy.choose(a, choice, mode=self.mode)