Fixed several issues I encountered (missing or confusing imports, missing

doc/extending/ctype.txt

@@ -162,7 +162,7 @@ it, it's best to publish it somewhere.
 
 .. code-block:: python
 
-    def c_init(self, name, sub):
+    def c_init(name, sub):
         return """
         %(name)s = 0.0;
         """ % dict(name = name)
@@ -191,7 +191,7 @@ called, without knowing for sure which of the two.
 
 .. code-block:: python
 
-    def c_extract(self, name, sub):
+    def c_extract(name, sub):
         return """
         if (!PyFloat_Check(py_%(name)s)) {
             PyErr_SetString(PyExc_TypeError, "expected a float");
@@ -229,7 +229,7 @@ API) and we put it in our double variable that we declared previously.
 
 .. code-block:: python
 
-    def c_sync(self, name, sub):
+    def c_sync(name, sub):
         return """
         Py_XDECREF(py_%(name)s);
         py_%(name)s = PyFloat_FromDouble(%(name)s);
@@ -291,7 +291,7 @@ than sorry.
 
 .. code-block:: python
 
-    def c_cleanup(self, name, sub):
+    def c_cleanup(name, sub):
         return ""
     double.c_cleanup = c_cleanup
 
@@ -339,6 +339,9 @@ and call it:
 .. theano/tests/test_tutorial.py:T_extending.test_extending_2
 
 .. code-block:: python
+   
+   from theano import function
+   from theano.tensor import double
 
    x, y, z = double('x'), double('y'), double('z')
    a = add(x, y)
@@ -430,7 +433,7 @@ Final version
 
    class Double(gof.Type):
 
-       def filter(self, x, strict=False):
+       def filter(self, x, strict=False, allow_downcast = False):
            if strict and not isinstance(x, float):
                raise TypeError('Expected a float!')
            return float(x)

doc/extending/optimization.txt

@@ -469,6 +469,8 @@ Here are a few examples of how to use a Query on optdb to produce an
 Optimizer:
 
 .. code-block:: python
+   
+   from theano.compile import optdb
 
    # This is how the optimizer for the fast_run mode is defined
    fast_run = optdb.query(Query(include = ['fast_run']))

doc/extending/type.txt

@@ -113,7 +113,7 @@ must define ``filter`` and shall override ``values_eq_approx``.
 
     # Note that we shadow Python's function ``filter`` with this
     # definition.
-    def filter(x, strict=False):
+    def filter(x, strict=False, allow_downcast = False):
         if strict:
             if isinstance(x, float):
                 return x
@@ -278,7 +278,7 @@ Final version
 
    class Double(gof.Type):
 
-       def filter(self, x, strict=False):
+       def filter(self, x, strict=False, allow_downcast = False):
            if strict and not isinstance(x, float):
                raise TypeError('Expected a float!')
            return float(x)

doc/tutorial/examples.txt

@@ -149,7 +149,7 @@ logistic is: :math:`ds(x)/dx = s(x) \cdot (1 - s(x))`.
 .. theano/tests/test_tutorial.py:T_examples.test_examples_5
 
 >>> x = T.dmatrix('x')
->>> s = 1 / (1 + T.exp(-x))
+>>> s = T.sum(1 / (1 + T.exp(-x)))
 >>> gs = T.grad(s, x)
 >>> dlogistic = function([x], gs)
 >>> dlogistic([[0, 1], [-1, -2]])
@@ -321,7 +321,7 @@ for the purpose of one particular function.
 .. theano/tests/test_tutorial.py:T_examples.test_examples_8
 
 >>> fn_of_state = state * 2 + inc
->>> foo = lscalar()    # the type (lscalar) must match the shared variable we
+>>> foo = T.lscalar()    # the type (lscalar) must match the shared variable we
 >>>                    # are replacing with the ``givens`` list
 >>> skip_shared = function([inc, foo], fn_of_state,
         givens=[(state, foo)])
@@ -394,7 +394,7 @@ not affected by calling the returned function.  So for example, calling
 ``g`` multiple times will return the same numbers.
 
 >>> g_val0 = g()  # different numbers from f_val0 and f_val1
->>> g_val0 = g()  # same numbers as g_val0 !!!
+>>> g_val1 = g()  # same numbers as g_val0 !!!
 
 An important remark is that a random variable is drawn at most once during any
 single function execution.  So the ``nearly_zeros`` function is guaranteed to

doc/tutorial/using_gpu.txt

@@ -88,7 +88,7 @@ file and run it.
         r = f()
     print 'Looping %d times took'%iters, time.time() - t0, 'seconds'
     print 'Result is', r
-    print 'Used the','cpu' if any( [isinstance(x.op,T.Elemwise) for x in f.maker.env.toposort()]) else 'gpu'
+    print 'Used the','cpu' if numpy.any( [isinstance(x.op,T.Elemwise) for x in f.maker.env.toposort()]) else 'gpu'
 
 The program just computes the exp() of a bunch of random numbers.
 Note that we use the `shared` function to
@@ -145,7 +145,7 @@ after the T.exp(x) is replaced by a GPU version of exp().
     print 'Looping %d times took'%iters, time.time() - t0, 'seconds'
     print 'Result is', r
     print 'Numpy result is', numpy.asarray(r)
-    print 'Used the','cpu' if any( [isinstance(x.op,T.Elemwise) for x in f.maker.env.toposort()]) else 'gpu'
+    print 'Used the','cpu' if numpy.any( [isinstance(x.op,T.Elemwise) for x in f.maker.env.toposort()]) else 'gpu'
 
 The output from this program is
 
@@ -200,7 +200,7 @@ that it has the un-wanted side-effect of really slowing things down.
     print 'Looping %d times took'%iters, time.time() - t0, 'seconds'
     print 'Result is', r
     print 'Numpy result is', numpy.asarray(r)
-    print 'Used the','cpu' if any( [isinstance(x.op,T.Elemwise) for x in f.maker.env.toposort()]) else 'gpu'
+    print 'Used the','cpu' if numpy.any( [isinstance(x.op,T.Elemwise) for x in f.maker.env.toposort()]) else 'gpu'
 
 Running this version of the code takes just under 0.05 seconds, over 140x faster than
 the CPU implementation!

theano/compile/io.py

@@ -184,7 +184,7 @@ class In(SymbolicInput):
     # try to keep it synchronized.
     def __init__(self, variable, name=None, value=None, update=None,
             mutable=None, strict=False, allow_downcast=False, autoname=True,
-            implicit=None):
+            implicit=None, borrow=False):
         if implicit is None:
             implicit = (isinstance(value, gof.Container) or
                     isinstance(value, SharedVariable))