Skip to content

P
pytensor
提交 966aa9bd authored 作者: Iban Harlouchet's avatar Iban Harlouchet
浏览文件
操作

numpydoc for theano/sandbox/rng_mrg.py

上级 f2c57a5f
隐藏空白字符变更
内嵌 并排
正在显示 包含 88 行增加 和 68 行删除
theano/sandbox/rng_mrg.py
浏览文件 @ 966aa9bd
"""
Implementation of MRG31k3p random number generator for Theano
Implementation of MRG31k3p random number generator for Theano.
Generator code in SSJ package (L'Ecuyer & Simard)
Generator code in SSJ package (L'Ecuyer & Simard).
http://www.iro.umontreal.ca/~simardr/ssj/indexe.html
"""
......@@ -39,11 +39,14 @@ def matVecModM(A, s, m):
def multMatVect(v, A, m1, B, m2):
"""
multiply the first half of v by A with a modulo of m1
and the second half by B with a modulo of m2
Multiply the first half of v by A with a modulo of m1 and the second half
by B with a modulo of m2.
Notes
-----
The parameters of dot_modulo are passed implicitly because passing them
explicitly takes more time than running the function's C-code.
Note: The parameters of dot_modulo are passed implicitly because passing
them explicitly takes more time then running the function's C-code.
"""
if multMatVect.dot_modulo is None:
A_sym = tensor.lmatrix('A')
......@@ -76,7 +79,8 @@ class DotModulo(Op):
Efficient and numerically stable implementation of a dot product followed
by a modulo operation. This performs the same function as matVecModM.
We do this 2 times on 2 triple inputs and concatenating the output
We do this 2 times on 2 triple inputs and concatenating the output.
"""
__props__ = ()
......@@ -1014,9 +1018,12 @@ def guess_n_streams(size, warn=False):
"""
Return a guess at a good number of streams.
:param warn:
If True, warn when a guess cannot be made (in which case we
return 60 * 256).
Parameters
----------
warn : bool, optional
If True, warn when a guess cannot be made (in which case we
return 60 * 256).
"""
# TODO: a smart way of choosing the number of streams, see #612.
# Note that this code was moved out of `MRG_RandomStreams` so that it can
......@@ -1048,22 +1055,25 @@ def guess_n_streams(size, warn=False):
class MRG_RandomStreams(object):
"""Module component with similar interface to numpy.random (numpy.random.RandomState)"""
"""
Module component with similar interface to numpy.random
(numpy.random.RandomState).
Parameters
----------
seed : int or list of 6 int
A default seed to initialize the random state.
If a single int is given, it will be replicated 6 times.
The first 3 values of the seed must all be less than M1 = 2147483647,
and not all 0; and the last 3 values must all be less than
M2 = 2147462579, and not all 0.
"""
def updates(self):
return list(self.state_updates)
def __init__(self, seed=12345, use_cuda=None):
"""
:type seed: int or list of 6 int.
:param seed: a default seed to initialize the random state.
If a single int is given, it will be replicated 6 times.
The first 3 values of the seed must all be less than M1 = 2147483647,
and not all 0; and the last 3 values must all be less than
M2 = 2147462579, and not all 0.
"""
# A list of pairs of the form (input_r, output_r), representing the
# update rules of all the random states generated by this RandomStreams.
self.state_updates = []
......@@ -1107,14 +1117,18 @@ class MRG_RandomStreams(object):
raise TypeError("seed should be 1 integer or 6 integers")
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.
"""
Re-initialize each random stream.
:type seed: None or integer in range 0 to 2**30
Parameters
----------
seed : None or integer in range 0 to 2**30
Each random stream will be assigned a unique state that depends
deterministically on this value.
:rtype: None
Returns
-------
None
"""
if seed is None:
......@@ -1133,14 +1147,20 @@ class MRG_RandomStreams(object):
old_r.set_value(rstates, borrow=True)
def inc_rstate(self):
"""Update self.rstate to be skipped 2^134 steps forward to the next stream start"""
"""
Update self.rstate to be skipped 2^134 steps forward to the next stream
start.
"""
#self.rstate = ff_2p134(self.rstate)
self.rstate = multMatVect(self.rstate, A1p134, M1, A2p134, M2)
assert self.rstate.dtype == numpy.int32
def get_substream_rstates(self, n_streams, dtype, inc_rstate=True):
"""Initialize a matrix in which each row is a MRG stream state,
"""
Initialize a matrix in which each row is a MRG stream state,
and they are spaced by 2**72 samples.
"""
assert isinstance(dtype, str)
assert n_streams < 2**72
......@@ -1198,27 +1218,25 @@ class MRG_RandomStreams(object):
distribution between low and high.
If the size argument is ambiguous on the number of dimensions,
ndim may be a plain integer to supplement the missing
information.
:param low:
Lower bound of the interval on which values are sampled. If
the ``dtype`` arg is provided, ``low`` will be cast into
dtype. This bound is excluded.
:param high:
Higher bound of the interval on which values are sampled.
If the ``dtype`` arg is provided, ``high`` will be cast into
dtype. This bound is excluded.
:param size:
ndim may be a plain integer to supplement the missing information.
Parameters
----------
low
Lower bound of the interval on which values are sampled.
If the ``dtype`` arg is provided, ``low`` will be cast into
dtype. This bound is excluded.
high
Higher bound of the interval on which values are sampled.
If the ``dtype`` arg is provided, ``high`` will be cast into
dtype. This bound is excluded.
size
Can be a list of integer or Theano variable (ex: the shape
of other Theano Variable)
:param dtype:
The output data type. If dtype is not specified, it will be
inferred from the dtype of low and high, but will be at
least as precise as floatX.
of other Theano Variable).
dtype
The output data type. If dtype is not specified, it will be
inferred from the dtype of low and high, but will be at
least as precise as floatX.
"""
low = as_tensor_variable(low)
......@@ -1300,15 +1318,17 @@ class MRG_RandomStreams(object):
Example : pvals = [[.98, .01, .01], [.01, .98, .01]] will
probably result in [[1,0,0],[0,1,0]].
.. note::
-`size` and `ndim` are only there keep the same signature as other
uniform, binomial, normal, etc.
todo : adapt multinomial to take that into account
Notes
-----
-`size` and `ndim` are only there keep the same signature as other
uniform, binomial, normal, etc.
TODO : adapt multinomial to take that into account
-Does not do any value checking on pvals, i.e. there is no
check that the elements are non-negative, less than 1, or
sum to 1. passing pvals = [[-2., 2.]] will result in
sampling [[0, 0]]
-Does not do any value checking on pvals, i.e. there is no
check that the elements are non-negative, less than 1, or
sum to 1. passing pvals = [[-2., 2.]] will result in
sampling [[0, 0]]
"""
if pvals is None:
raise TypeError("You have to specify pvals")
......@@ -1342,17 +1362,17 @@ class MRG_RandomStreams(object):
def normal(self, size, avg=0.0, std=1.0, ndim=None,
dtype=None, nstreams=None):
"""
:param size:
Can be a list of integers or Theano variables (ex: the shape
of another Theano Variable)
:param dtype:
The output data type. If dtype is not specified, it will be
inferred from the dtype of low and high, but will be at
least as precise as floatX.
:param nstreams:
Number of streams.
Parameters
----------
size
Can be a list of integers or Theano variables (ex: the shape
of another Theano Variable).
dtype
The output data type. If dtype is not specified, it will be
inferred from the dtype of low and high, but will be at
least as precise as floatX.
nstreams
Number of streams.
"""
# We need an even number of ]0,1[ samples. Then we split them
......
Markdown 格式
0% 或
您添加了 0 人 到此讨论。请谨慎行事。
请先完成此评论的编辑！
请 注册 或者 后发表评论