Merge pull request #1127 from abalkin/take-op

theano/tensor/basic.py

@@ -1662,21 +1662,29 @@ class _tensor_py_operators:
         # standard indexing is used; if it fails with
         # AdvancedIndexingError, advanced indexing
         advanced = False
-        for arg in args:
+        axis = None
+        for i, arg in enumerate(args):
             try:
                 arg == numpy.newaxis or Subtensor.convert(arg)
             except AdvancedIndexingError:
-                advanced = True
-                break
+                if advanced:
+                    axis = None
+                    break
+                else:
+                    advanced = True
+                    axis = i
 
         if advanced:
-            if (len(args) == 1
-                    and isinstance(args[0], (
+            if (axis is not None
+                and numpy.all(a == slice(None) for a in args[:axis])
+                and numpy.all(a == slice(None) for a in args[axis+1:])
+                and isinstance(args[axis], (
+                        numpy.ndarray,
                         list,
                         TensorVariable,
                         TensorConstant,
                         theano.tensor.sharedvar.TensorSharedVariable))):
-                return advanced_subtensor1(self, *args)
+                return self.take(arg, axis)
             else:
                 return AdvancedSubtensor()(self, *args)
         else:
@@ -1705,6 +1713,9 @@ class _tensor_py_operators:
                 return Subtensor(args)(self, *Subtensor.collapse(args,
                     lambda entry: isinstance(entry, Variable)))
 
+    def take(self, indices, axis=None, mode='raise'):
+        return take(self, indices, axis, mode)
+        
     # COPYING
     def copy(self):
         return tensor_copy(self)
@@ -6811,6 +6822,36 @@ class AdvancedIncSubtensor(Op):
                               *inputs[2:]).outputs
 advanced_inc_subtensor = AdvancedIncSubtensor()
 
+def take(a, indices, axis=None, mode='raise'):
+    a = as_tensor_variable(a)
+    indices = as_tensor_variable(indices)
+    # Reuse advanced_subtensor1 if indices is a vector
+    if indices.ndim == 1:
+        if mode == 'clip':
+            indices = clip(indices, 0, a.shape[axis]-1)
+        elif mode == 'wrap':
+            indices = indices % a.shape[axis]
+        if axis is None:
+            return advanced_subtensor1(a.flatten(), indices)
+        elif axis == 0:
+            return advanced_subtensor1(a, indices)
+        else:
+            if axis < 0:
+                axis += a.ndim
+            assert axis >= 0
+            shuffle = range(a.ndim)
+            shuffle[0] = axis
+            shuffle[axis] = 0
+            return advanced_subtensor1(
+                a.dimshuffle(shuffle), indices).dimshuffle(shuffle)
+    if axis is None:
+        shape = indices.shape
+        ndim = indices.ndim
+    else:
+        shape = concatenate([a.shape[:axis], indices.shape, a.shape[axis+1:]])
+        ndim = a.ndim + indices.ndim - 1
+    return take(a, indices.flatten(), axis, mode).reshape(shape, ndim)
+    
 #########################
 # Linalg : Dot
 #########################

theano/tensor/tests/test_basic.py

@@ -7167,6 +7167,26 @@ class TestTensorInstanceMethods(unittest.TestCase):
             assert_array_equal(X.diagonal(offset, axis1, axis2).eval({X: x}),
                                x.diagonal(offset, axis1, axis2))
 
+    def test_take(self):
+        X, _ = self.vars
+        x, _ = self.vals
+        indices = [1,0,3]
+        assert_array_equal(X.take(indices).eval({X: x}), x.take(indices))
+        indices = [1,0,1]
+        assert_array_equal(X.take(indices, 1).eval({X: x}), x.take(indices, 1))
+        indices = [-10,5,12]
+        assert_array_equal(X.take(indices, 1, mode='wrap').eval({X: x}),
+                           x.take(indices, 1, mode='wrap'))
+        assert_array_equal(X.take(indices, -1, mode='wrap').eval({X: x}),
+                           x.take(indices, -1, mode='wrap'))
+        assert_array_equal(X.take(indices, 1, mode='clip').eval({X: x}),
+                           x.take(indices, 1, mode='clip'))
+        assert_array_equal(X.take(indices, -1, mode='clip').eval({X: x}),
+                           x.take(indices, -1, mode='clip'))
+        indices = [[1,0,1], [0,1,1]]
+        assert_array_equal(X.take(indices, 1).eval({X: x}), x.take(indices, 1))
+        # Test equivalent advanced indexing
+        assert_array_equal(X[:,indices].eval({X: x}), x[:,indices])
 
 if __name__ == '__main__':