提交 2ef495d9 authored 作者: Frederic's avatar Frederic

pep8

上级 401e2a9b
"""Define RandomStreams, providing random number variables for Theano graphs."""
"""Define RandomStreams, providing random number variables for Theano
graphs.
"""
__docformat__ = "restructuredtext en"
import sys
......@@ -8,6 +11,7 @@ from theano.compile import module, In, Component
from theano.gof import Container
from theano.tensor import raw_random
class RandomStreamsInstance(object):
"""RandomStreamsInstance"""
def __init__(self, random_streams, memo, default_seed):
......@@ -18,44 +22,51 @@ class RandomStreamsInstance(object):
def initialize(self, seed=None):
"""Initialize each random stream
:param seed: each random stream will be assigned a unique state that depends
deterministically on this value.
:param seed: each random stream will be assigned a unique
state that depends deterministically on this value.
:type seed: None or integer in range 0 to 2**30
:rtype: None
"""
self.seed(seed)
def seed(self, seed=None):
"""Re-initialize each random stream
:param seed: each random stream will be assigned a unique state that depends
deterministically on this value.
:param seed: each random stream will be assigned a unique
state that depends deterministically on this value.
:type seed: None or integer in range 0 to 2**30
:rtype: None
"""
if seed is None:
seed = self.default_seed
seed = self.default_seed
#backport
#seed = self.default_seed if seed is None else seed
seedgen = numpy.random.RandomState(seed)
for old_r, new_r in self.random_streams.random_state_variables:
old_r_seed = seedgen.randint(2**30)
old_r_seed = seedgen.randint(2 ** 30)
old_r_container = self.memo[old_r].value
if old_r_container.value is None:
#the cast to int here makes it work on 32bit machines, not sure why
old_r_container.value = numpy.random.RandomState(int(old_r_seed))
#the cast to int here makes it work on 32bit machines,
#not sure why
old_r_container.value = numpy.random.RandomState(
int(old_r_seed))
else:
#the cast to int here makes it work on 32bit machines, not sure why
#the cast to int here makes it work on 32bit machines,
#not sure why
old_r_container.value.seed(int(old_r_seed))
def __getitem__(self, item):
"""Retrieve the numpy RandomState instance associated with a particular stream
"""Retrieve the numpy RandomState instance associated with a
particular stream
:param item: a variable of type RandomStateType, associated with this RandomStream
:param item: a variable of type RandomStateType, associated
with this RandomStream
:rtype: numpy RandomState (or None, before initialize)
......@@ -67,9 +78,11 @@ class RandomStreamsInstance(object):
raise KeyError(item)
def __setitem__(self, item, val):
"""Set the numpy RandomState instance associated with a particular stream
"""Set the numpy RandomState instance associated with a
particular stream
:param item: a variable of type RandomStateType, associated with this RandomStream
:param item: a variable of type RandomStateType, associated
with this RandomStream
:param val: the new value
:type val: numpy RandomState
......@@ -78,7 +91,8 @@ class RandomStreamsInstance(object):
"""
if type(val) is not numpy.random.RandomState:
raise TypeError('only values of type RandomState are permitted', val)
raise TypeError('only values of type RandomState are permitted',
val)
for old_r, new_r in self.random_streams.random_state_variables:
if item is old_r:
container = self.memo[item].value
......@@ -86,24 +100,34 @@ class RandomStreamsInstance(object):
return
raise KeyError(item)
class RandomStreams(Component, raw_random.RandomStreamsBase):
"""Module component with similar interface to numpy.random (numpy.random.RandomState)"""
"""Module component with similar interface to numpy.random
(numpy.random.RandomState)
"""
random_state_variables = []
"""A list of pairs of the form (input_r, output_r). This will be over-ridden by the module
instance to contain stream generators.
"""A list of pairs of the form (input_r, output_r). This will be
over-ridden by the module instance to contain stream
generators.
"""
default_instance_seed = None
"""Instance variable should take None or integer value. Used to seed the random number
generator that provides seeds for member streams"""
"""Instance variable should take None or integer value. Used to
seed the random number generator that provides seeds for member
streams
"""
def __init__(self, seed=None):
"""
:type seed: None or int
""":type seed: None or int
:param seed: a default seed to initialize the RandomState
instances after build. See `RandomStreamsInstance.__init__`
for more details.
:param seed: a default seed to initialize the RandomState instances after build. See
`RandomStreamsInstance.__init__` for more details.
"""
super(RandomStreams, self).__init__()
self.random_state_variables = []
......@@ -115,7 +139,7 @@ class RandomStreams(Component, raw_random.RandomStreamsBase):
if old_r in memo:
assert memo[old_r].update is new_r
else:
memo[old_r] = In(old_r,
memo[old_r] = In(old_r,
value=Container(old_r, storage=[None]),
update=new_r,
mutable=True)
......@@ -124,26 +148,28 @@ class RandomStreams(Component, raw_random.RandomStreamsBase):
"""override `Component.build` """
if self not in memo:
print 'creating RandomStreamsInstance'
memo[self] = RandomStreamsInstance(self, memo, self.default_instance_seed)
memo[self] = RandomStreamsInstance(self, memo,
self.default_instance_seed)
return memo[self]
def gen(self, op, *args, **kwargs):
"""Create a new random stream in this container.
:param op: a RandomFunction instance to
:param op: a RandomFunction instance to
:param args: interpreted by `op`
:param kwargs: interpreted by `op`
:returns: The symbolic random draw part of op()'s return value. This function stores
the updated RandomStateType Variable for use at `build` time.
:returns: The symbolic random draw part of op()'s return
value. This function stores the updated RandomStateType
Variable for use at `build` time.
:rtype: TensorVariable
"""
random_state_variable = raw_random.random_state_type()
new_r, out = op(random_state_variable, *args, **kwargs)
out.rng = random_state_variable
self.random_state_variables.append((random_state_variable, new_r))
return out
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