def __init__(self, **kwargs):
dictlist.replace(kwargs, lambda x: (x, ) if isinstance(x, int) else x)
self.n_pars = n_pars_by_partition(kwargs)
# Create two representations of the parameters of the object. The first
# is the symbolic theano variable (of which the type is GPU/CPU
# specific), the second either a gnumpy or numpy array (depending on
# GPU/CPU again). Also set a default size for testing.
if GPU:
self.data = gnumpy.zeros(self.n_pars)
self.flat = theano.sandbox.cuda.fvector('parameters')
else:
self.data = np.empty(self.n_pars).astype(theano.config.floatX)
self.flat = T.vector('parameters')
self.flat.tag.test_value = self.data
# Go through parameters and assign space and variable.
self.views = array_partition_views(self.data, kwargs)
# Make sure the keys are legit -- that they do not overwrite
# anything.
for key in kwargs:
if hasattr(self, key):
raise ValueError("%s is an illegal name for a variable")
variables = array_partition_views(self.flat, kwargs)
variables = dictlist.copy(variables, dct_maker=attrdict.AttrDict)
self.__dict__.update(variables)
def __init__(self, **kwargs):
dictlist.replace(kwargs, lambda x: (x,) if isinstance(x, int) else x)
self.n_pars = n_pars_by_partition(kwargs)
# Create two representations of the parameters of the object. The first
# is the symbolic theano variable (of which the type is GPU/CPU
# specific), the second either a gnumpy or numpy array (depending on
# GPU/CPU again). Also set a default size for testing.
if GPU:
self.data = gnumpy.zeros(self.n_pars)
self.flat = theano.sandbox.cuda.fvector('parameters')
else:
self.data = np.empty(self.n_pars).astype(theano.config.floatX)
self.flat = T.vector('parameters')
self.flat.tag.test_value = self.data
# Go through parameters and assign space and variable.
self.views = array_partition_views(self.data, kwargs)
# Make sure the keys are legit -- that they do not overwrite
# anything.
for key in kwargs:
if hasattr(self, key):
raise ValueError("%s is an illegal name for a variable")
variables = array_partition_views(self.flat, kwargs)
variables = dictlist.copy(variables, dct_maker=attrdict.AttrDict)
self.__dict__.update(variables)
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 test_copy():
tree = make_dictlist()
tree2 = dictlist.copy(tree, dct_maker=AttrDict)
print tree2
def test_copy():
tree = make_dictlist()
tree2 = dictlist.copy(tree, dct_maker=AttrDict)
print tree2
