Added tensor.sort() op

theano/tensor/basic.py

@@ -5756,3 +5756,52 @@ def any(x, axis=None):
 
 def all(x, axis=None):
     return elemwise.All(axis)(x)
+
+
+class SortOp(theano.Op):
+    def __init__(self, kind, order=None):
+      self.kind = kind
+      self.order = order
+
+    def __eq__(self, other):
+      return type(self) == type(other) and self.order == other.order
+
+    def __hash__(self):
+        return hash(type(self)) ^ hash(self.order)
+
+    def __str__(self):
+        return self.__class__.__name__ + "{%s, %s}" % (self.kind, str(self.order))
+
+    def make_node(self, input, axis=-1):
+
+        input = theano.tensor.as_tensor_variable(input)
+        axis = theano.tensor.as_tensor_variable(axis)
+
+        return theano.Apply(self, [input, axis], [input.type()])
+
+    def perform(self, node, inputs, output_storage):
+        a = inputs[0]
+        axis = inputs[1]
+        z = output_storage[0]
+        z[0] = numpy.sort(a,axis,self.kind,self.order)
+
+    def infer_shape(self, node, inputs_shapes):
+        return [inputs_shapes[0]]
+
+    #**** It need the argsort, so we can't do it now.
+    #def grad(self, inputs, output_grads):
+
+    """
+    def R_op(self, inputs, eval_points):
+        # R_op can receive None as eval_points.
+        # That mean there is no diferientiable path through that input
+        # If this imply that you cannot compute some outputs,
+        # return None for those.
+        if eval_points[0] is None:
+            return eval_points
+        return self.grad(inputs, eval_points)
+    """
+
+
+def sort(a, axis=-1, kind='quicksort', order=None):
+    return SortOp(kind, order)(a, axis)

theano/tensor/tests/test_basic.py

@@ -34,7 +34,7 @@ from theano.tensor import (_shared, wvector, bvector, autocast_float_as,
         get_constant_value, ivector, reshape, scalar_from_tensor, scal,
         iscalars, arange,  dscalars, fvector, imatrix, numeric_grad,
         opt, ComplexError, TensorDot, lvector, true_div, max, min, Split, roll,
-        tile, patternbroadcast)
+        tile, patternbroadcast, sort, SortOp, )
 from theano.tests import unittest_tools as utt
 
 
@@ -5584,6 +5584,57 @@ def test_transpose():
     assert numpy.all(t3d == numpy.transpose(x3v, [0, 2, 1]))
 
 
+def test_sort():
+
+  testMatrix = [[4,9,1],[1,3,2]]
+  testVector = [1,10,0,2]
+
+  print "Example 1: "
+  a = theano.tensor.dmatrix()
+  w = sort(a)
+  f = theano.function([a],w)
+  print testMatrix
+  print f(testMatrix)
+  print "------------------------------"
+
+  print "Example 2: "
+  a = theano.tensor.dmatrix()
+  axis = theano.tensor.scalar()
+  w = sort(a,axis)
+  f = theano.function([a,axis],w)
+  print testMatrix
+  print f(testMatrix,1)
+  print "------------------------------"
+
+  print "Example 3: "
+  a = theano.tensor.dvector()
+  w2 = sort(a)
+  f = theano.function([a],w2)
+  print testVector
+  print f(testVector)
+  print "------------------------------"
+
+  print "Example 4: "
+  a = theano.tensor.dmatrix()
+  axis = theano.tensor.scalar()
+  l = sort(a,axis,"mergesort")
+  f = theano.function([a,axis],l)
+  print testMatrix
+  print f(testMatrix,1)
+  print "------------------------------"
+
+  print "Example 5: Check __eq__ function "
+  a = theano.tensor.dmatrix()
+  axis = theano.tensor.scalar()
+  a1 = SortOp("mergesort",[])
+  a2 = SortOp("quicksort",[])
+
+  #All the below should give true
+  print  a1 == a2
+  print  a1 == SortOp("mergesort",[])
+  print  a2 == SortOp("quicksort",[])
+
+
 if __name__ == '__main__':
     if 0:
         unittest.main()