merge

doc/library/tensor/basic.txt

@@ -293,6 +293,7 @@ them perfectly, but a dscalar otherwise.
     :rtype: :class:`TensorVariable` or :class:`TensorConstant`
 
 
+
 TensorType and TensorVariable
 =============================
 
@@ -581,6 +582,42 @@ dimensions, see :meth:`_tensor_py_operators.dimshuffle`
     have shape (2, 60).
 
 
+.. function:: zeros_like(x)
+
+    :param x: tensor that has same shape as output
+
+    Returns a tensor filled with 0s that has same shape as `x`.
+
+.. function:: ones_like(x)
+
+    :param x: tensor that has same shape as output
+
+    Returns a tensor filled with 1s that has same shape as `x`.
+
+.. function:: fill(a,b)
+
+    :param a: tensor that has same shape as output 
+    :param b: theano scalar or value with which you want to fill the output
+
+    Create a matrix by filling the shape of `a` with `b`
+
+.. function:: eye(n, m = None, k = 0, dtype='float64')
+
+    :param n: number of rows in output (value or theano scalar)
+    :param m: number of columns in output (value or theano scalar)
+    :param k: Index of the diagonal: 0 refers to the main diagonal, 
+              a positive value refers to an upper diagonal, and a 
+              negative value to a lower diagonal. It can be a theano 
+              scalar.
+    :returns: An array where all elements are equal to zero, except for the `k`-th
+              diagonal, whose values are equal to one.
+
+.. function:: identity_like(x)
+
+    :param x: tensor
+    :returns: A tensor of same shape as `x` that is filled with 0s everywhere
+              except for the main diagonal, whose values are equal to one. The output
+              will have same dtype as `x`.
 
 Reductions 
 ==========
@@ -743,6 +780,7 @@ Casting
     Return the imaginary components of Tensor x. 
     For non-complex `x` this function returns zeros_like(x).
 
+
 Comparisons
 ------------
 

theano/tensor/basic.py

@@ -1702,6 +1702,37 @@ def zeros_like(model):
     #return Zeros(model.type.ndim)(shape(model))
     return fill(model, constant(0.0, dtype=model.type.dtype))
 
+class Eye(gof.Op):
+    def __init__(self, dtype='float64'):
+        self.dtype = dtype
+    def make_node(self,n,m,k):
+        n = as_tensor_variable(n)
+        m = as_tensor_variable(m)
+        k = as_tensor_variable(k)
+        return gof.Apply(self, [n,m,k], [TensorType(dtype = self.dtype, broadcastable = (False,False))()])
+
+    def perform(self, node, (n,m,k), (out,)):
+        out[0] = numpy.eye(n,m,k)
+
+    def grad(self, (n,m,k),(gout,)):
+        return [None, None, None]
+
+    def __eq__(self,other):
+        return type(self) == type(other) and self.dtype == other.dtype
+
+    def __hash__(self):
+        return hash(self.dtype) ^ hash(type(self))
+
+
+def eye(n, m=None, k = 0, dtype = 'float64'):
+    if m == None:
+        m = n
+    localop = Eye(dtype)
+    return localop(n,m,k)
+
+def identity_like(x):
+    return eye(x.shape[0], x.shape[1], k=0, dtype = x.dtype)
+
 if 0:
     ## COMMENTED OUT FEB 17 2010
     ## TODO (DOCUMENT AND WRITE TESTS) OR DELETE

theano/tensor/tests/test_basic.py

@@ -1143,6 +1143,13 @@ class test_bitwise(unittest.TestCase):
         v = fn(l, r)
         self.failUnless(numpy.all(v == (~l)), (l, r, v))
 
+    def test_eye(self):
+        n = iscalar()
+        m = iscalar()
+        k = iscalar()
+        fn = theano.function([m,n,k],eye(m,n,k) )
+        self.failUnless(numpy.all(fn(5,6,1) == numpy.eye(5,6,1)))
+
 
 class T_add(unittest.TestCase):
     def setUp(self):