--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/onehotop.py.scalar	Thu Jun 19 16:18:37 2008 -0400
@@ -0,0 +1,64 @@
+"""
+One hot Op
+"""
+
+#from theano import tensor
+from theano.tensor import as_tensor, Tensor
+#from theano import scalar
+from theano.scalar import as_scalar
+from theano.gof import op
+from theano.gof.graph import Apply
+
+import numpy
+
+class OneHot(op.Op):
+    """
+    Construct a one-hot vector, x out of y.
+
+    @todo: Document inputs and outputs
+    @todo: Use 'bool' as output dtype? Or, at least 'int64' ? Not float64!
+    @todo: Use 'bool' as output dtype, not 'int64' ?
+    @todo: Allow this to operate on column vectors (Tensor)
+    @todo: Describe better.
+    @todo: What type is y?
+    @todo: What about operating on L{Scalar}s?
+    """
+
+    def make_node(self, x, y):
+        """
+        @type x: Vector L{Tensor} of integers
+        @param x: The entries of the one-hot vector to be one.
+        @type y: Integer L{Scalar}
+        @param y: The length (#columns) of the one-hot vectors.
+        @return: A L{Tensor} of one-hot vectors
+
+        @precondition: x < y for all entries of x
+        @todo: Check that x and y are int types
+        """
+        #x = tensor.as_tensor(x)
+        #y = scalar.as_scalar(y)
+        x = as_tensor(x)
+        y = as_scalar(y)
+        #assert x.dtype[0:3] == "int"
+        #assert y.dtype[0:3] == "int"
+        inputs = [x, y]
+        ##outputs = [tensor.Tensor("int64", broadcastable=[False, False])]
+        #outputs = [tensor.Tensor("float64", broadcastable=[False, False])]
+        #outputs = [Tensor("int64", broadcastable=[False, False])]
+        outputs = [Tensor("float64", broadcastable=[False, False]).make_result()]
+        node = Apply(op = self, inputs = inputs, outputs = outputs)
+        return node
+
+    def perform(self, node, (x, y), (out, )):
+        assert x.dtype == "int64"
+        assert type(y) == numpy.int64
+        assert x.ndim == 1
+        #out = numpy.zeros((x.shape[0], y), dtype="int64")
+        out[0] = numpy.zeros((x.shape[0], y), dtype="float64")
+        for c in range(x.shape[0]):
+            assert x[c] < y
+            out[0][c, x[c]] = 1
+
+    def grad(self, (x, y), (out_gradient, )):
+        return None, None
+one_hot = OneHot()