Delete the old unmaintained copy of scan in sandbox.

theano/sandbox/scan_module/__init__.py

-"""
-This module provides the Scan Op.
-
-Scanning is a general form of recurrence, which can be used for looping.
-The idea is that you *scan* a function along some input sequence, producing
-an output at each time-step that can be seen (but not modified) by the
-function at the next time-step. Technically, the function can see the
-previous K  time-steps of your outputs and L time steps (from the past and
-future) of your inputs.
-
-So for example, ``sum()`` could be computed by scanning the ``z+x_i``
-function over a list, given an initial state of ``z=0``.
-
-Special cases:
-
-* A *reduce* operation can be performed by returning only the last
-  output of a ``scan``.
-* A *map* operation can be performed by applying a function that
-  ignores previous steps of the outputs.
-
-Often a for-loop can be expressed as a ``scan()`` operation, and ``scan`` is
-the closest that theano comes to looping. The advantage of using ``scan``
-over for loops is that it allows the number of iterations to be a part of
-the symbolic graph.
-
-The Scan Op should typically be used by calling any of the following
-functions: ``scan()``, ``map()``, ``reduce()``, ``foldl()``,
-``foldr()``.
-
-"""
-
-
-__docformat__ = 'restructedtext en'
-__authors__ = ("Razvan Pascanu "
-               "Frederic Bastien "
-               "James Bergstra "
-               "Pascal Lamblin "
-               "Arnaud Bergeron ")
-__copyright__ = "(c) 2010, Universite de Montreal"
-__contact__ = "Razvan Pascanu <r.pascanu@gmail>"
-
-from .scan import scan

theano/sandbox/tests/test_scan.py

-import theano
-import numpy
-from theano.sandbox import scan
-
-
-def test_001():
-    x0 = theano.tensor.fvector('x0')
-    state = theano.tensor.unbroadcast(
-        theano.tensor.shape_padleft(x0), 0)
-    out, _ = scan.scan(lambda x: x+numpy.float32(1),
-                           states=state,
-                           n_steps=5)
-    fn = theano.function([x0], out[0])
-    val_x0 = numpy.float32([1, 2, 3])
-    assert numpy.all(fn(val_x0) == val_x0 + 5)
-
-
-def test_002():
-    x0 = theano.tensor.fvector('x0')
-    state = theano.tensor.alloc(
-        theano.tensor.constant(numpy.float32(0)),
-        6,
-        x0.shape[0])
-    state = theano.tensor.set_subtensor(state[0], x0)
-
-    out, _ = scan.scan(lambda x: x+numpy.float32(1),
-                           states=state,
-                           n_steps=5)
-    fn = theano.function([x0], out)
-    val_x0 = numpy.float32([1, 2, 3])
-    assert numpy.all(fn(val_x0)[-1] == val_x0 + 5)
-    assert numpy.all(fn(val_x0)[0] == val_x0)
-
-
-def test_003():
-    x0 = theano.tensor.fvector('x0')
-    sq = theano.tensor.fvector('sq')
-    state = theano.tensor.alloc(
-        theano.tensor.constant(numpy.float32(0)),
-        6,
-        x0.shape[0])
-    state = theano.tensor.set_subtensor(state[0], x0)
-
-    out, _ = scan.scan(lambda s, x: x+s,
-                           sequences=sq,
-                           states=state,
-                           n_steps=5)
-    fn = theano.function([sq, x0], out)
-    val_x0 = numpy.float32([1, 2, 3])
-    val_sq = numpy.float32([1, 2, 3, 4, 5])
-    assert numpy.all(fn(val_sq, val_x0)[-1] == val_x0 + 15)
-    assert numpy.all(fn(val_sq, val_x0)[0] == val_x0)
-
-
-def test_004():
-    sq = theano.tensor.fvector('sq')
-    nst = theano.tensor.iscalar('nst')
-    out, _ = scan.scan(lambda s: s+numpy.float32(1),
-                           sequences=sq,
-                           states=[],
-                           n_steps=nst)
-    fn = theano.function([sq, nst], out)
-    val_sq = numpy.float32([1, 2, 3, 4, 5])
-    assert numpy.all(fn(val_sq, 5) == val_sq + 1)
-
-
-def test_005():
-    sq = theano.tensor.fvector('sq')
-    nst = theano.tensor.iscalar('nst')
-    out, _ = scan.scan(lambda s: s+numpy.float32(1),
-                       sequences=sq,
-                       states=[None],
-                       n_steps=nst)
-    fn = theano.function([sq, nst], out)
-    val_sq = numpy.float32([1, 2, 3, 4, 5])
-    assert numpy.all(fn(val_sq, 5) == val_sq + 1)
-
-
-if __name__ == '__main__':
-    test_001()
-    test_002()
-    test_003()
-    test_004()
-    test_005()
-