def array_partition_views(array, partition):
"""Return a dictlist with different items corresponding to different sub
views into an array.
This function can be used to use different parts of a one-dimensional
array as a collection of arrays with differing without the need to
reallocate. E.g the first half of an array might correspond to a matrix,
the next entry to a scalar and the rest to a vector.
The function takes two arguments. The first, ``array`` is supposed to
resemble the flat array, while the second is a dictlist partitioning the
array into views of given shapes.
Here, the leaves of ``partition`` will be treated as shapes, of which a
view into ``array`` should be contained in the result with the same path.
Parameters
----------
array : np.array or gp.garray
numpy or gnumpy array of ndim 1.
partition : dictlist
Dictlist -- that is a a dictionary containing either lists or dicts as
its elements, except leave nodes. These should be either tuples or ints
to represent shapes of arays.
Examples
--------
>>> from breze.arch.util import array_partition_views
>>> partition = {'first': (2,),
... 'second': (2, 3),}
>>> flat = np.arange(8)
>>> views = array_partition_views(flat, partition)
>>> views['first']
... # doctest: +NORMALIZE_WHITESPACE
array([0, 1])
>>> views['second']
... # doctest: +NORMALIZE_WHITESPACE
array([[2, 3, 4],
[5, 6, 7]])
"""
views = dictlist.copy(partition)
pathsshapes = sorted(list(dictlist.leafs(partition)))
n_used = 0
for path, shape in pathsshapes:
item = dictlist.get(partition, path)
shape = (item,) if isinstance(item, (int, long)) else item
size = int(np.prod(shape))
dictlist.set_(views, path, array[n_used:n_used + size].reshape(shape))
n_used += size
return views
def array_partition_views(array, partition):
"""Return a dictlist with different items corresponding to different sub
views into an array.
This function can be used to use different parts of a one-dimensional
array as a collection of arrays with differing without the need to
reallocate. E.g the first half of an array might correspond to a matrix,
the next entry to a scalar and the rest to a vector.
The function takes two arguments. The first, ``array`` is supposed to
resemble the flat array, while the second is a dictlist partitioning the
array into views of given shapes.
Here, the leaves of ``partition`` will be treated as shapes, of which a
view into ``array`` should be contained in the result with the same path.
Parameters
----------
array : np.array or gp.garray
numpy or gnumpy array of ndim 1.
partition : dictlist
Dictlist -- that is a a dictionary containing either lists or dicts as
its elements, except leave nodes. These should be either tuples or ints
to represent shapes of arays.
Examples
--------
>>> from breze.arch.util import array_partition_views
>>> partition = {'first': (2,),
... 'second': (2, 3),}
>>> flat = np.arange(8)
>>> views = array_partition_views(flat, partition)
>>> views['first']
... # doctest: +NORMALIZE_WHITESPACE
array([0, 1])
>>> views['second']
... # doctest: +NORMALIZE_WHITESPACE
array([[2, 3, 4],
[5, 6, 7]])
"""
views = dictlist.copy(partition)
pathsshapes = sorted(list(dictlist.leafs(partition)))
n_used = 0
for path, shape in pathsshapes:
item = dictlist.get(partition, path)
shape = (item, ) if isinstance(item, (int, long)) else item
size = int(np.prod(shape))
dictlist.set_(views, path, array[n_used:n_used + size].reshape(shape))
n_used += size
return views
def array_partition_views(array, partition):
views = dictlist.copy(partition)
pathsshapes = sorted(list(dictlist.leafs(partition)))
n_used = 0
for path, shape in pathsshapes:
item = dictlist.get(partition, path)
shape = (item,) if isinstance(item, (int, long)) else item
size = int(np.prod(shape))
dictlist.set_(views, path, array[n_used:n_used + size].reshape(shape))
n_used += size
return views
def array_partition_views(array, partition):
views = dictlist.copy(partition)
pathsshapes = sorted(list(dictlist.leafs(partition)))
n_used = 0
for path, shape in pathsshapes:
item = dictlist.get(partition, path)
shape = (item, ) if isinstance(item, int) else item
size = int(np.prod(shape))
dictlist.set_(views, path, array[n_used:n_used + size].reshape(shape))
n_used += size
return views
def _lookup(self, container, ident):
tensor_types = (theano.tensor.basic.TensorVariable,
theano.sandbox.cuda.var.CudaNdarrayVariable)
if isinstance(ident, tensor_types):
res = ident
elif isinstance(ident, tuple):
res = dictlist.get(container, ident)
elif isinstance(ident, str):
res = container[ident]
else:
raise ValueError('unrecognized way of pointing to expr')
return res
def _lookup(self, container, ident):
tensor_types = (theano.tensor.basic.TensorVariable,
theano.sandbox.cuda.var.CudaNdarrayVariable)
if isinstance(ident, tensor_types):
res = ident
elif isinstance(ident, tuple):
res = dictlist.get(container, ident)
elif isinstance(ident, str):
res = container[ident]
else:
raise ValueError('unrecognized way of pointing to expr')
return res
def test_dictlist_get():
tree = make_dictlist()
assert dictlist.get(tree, ('blu', 'far', 0)) == 1
assert dictlist.get(tree, ('blu', 'foo')) == (2, 4)
def test_dictlist_get():
tree = make_dictlist()
assert dictlist.get(tree, ('blu', 'far', 0)) == 1
assert dictlist.get(tree, ('blu', 'foo')) == (2, 4)