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提交 4684329b authored 作者: Pascal Lamblin's avatar Pascal Lamblin
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Reformat, update and clarify some of the doc.

上级 7e67387e
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正在显示 包含 78 行增加 和 67 行删除
theano/tensor/raw_random.py
浏览文件 @ 4684329b
......@@ -13,10 +13,11 @@ from theano.compile import optdb
class RandomStateType(gof.Type):
"""A Type wrapper for numpy.RandomState
The reason this exists (and `Generic` doesn't suffice) is that RandomState objects that
would appear to be equal do not compare equal with the '==' operator. This Type exists to
provide an equals function that is used by DebugMode.
The reason this exists (and `Generic` doesn't suffice) is that
RandomState objects that would appear to be equal do not compare
equal with the '==' operator. This Type exists to provide an equals
function that is used by DebugMode.
"""
def __str__(self):
return 'RandomStateType'
......@@ -53,12 +54,14 @@ class RandomFunction(gof.Op):
def __init__(self, fn, outtype, inplace=False, ndim_added=0 ):
"""
:param fn: a member function of numpy.RandomState
Technically, any function with a signature like the ones in numpy.random.RandomState
will do. This function must accept the shape (sometimes called size) of the output as
the last positional argument.
Technically, any function with a signature like the ones in
numpy.random.RandomState will do. This function must accept
the shape (sometimes called size) of the output as the last
positional argument.
:type fn: string or function reference. A string will be interpreted as the name of a
member function of numpy.random.RandomState.
:type fn: string or function reference. A string will
be interpreted as the name of a member function of
numpy.random.RandomState.
:param outtype: the theano Type of the output
......@@ -96,8 +99,6 @@ class RandomFunction(gof.Op):
self.fn = getattr(numpy.random.RandomState, fn)
else:
self.fn = fn
#backport
#self.fn = getattr(numpy.random.RandomState, fn) if isinstance(fn, str) else fn
self.outtype = outtype
self.inplace = inplace
if self.inplace:
......@@ -162,9 +163,10 @@ class RandomFunction(gof.Op):
assert type(r) == numpy.random.RandomState
r_orig = r
# If shape == [], that means numpy will compute the correct shape,
# numpy uses shape "None" to represent that. Else, numpy expects a tuple.
# TODO: compute the appropriate shape?
# If shape == [], that means no shape is enforced, and numpy is
# trusted to draw the appropriate number of samples, numpy uses
# shape "None" to represent that. Else, numpy expects a tuple.
# TODO: compute the appropriate shape, and pass it to numpy.
if len(shape) == 0:
shape = None
else:
......@@ -200,7 +202,6 @@ class RandomFunction(gof.Op):
raise ValueError('Shape mismatch: "out" should have shape starting with %s (plus %i extra dimensions), but the value produced by "perform" has shape %s'\
% (shape, self.ndim_added, rval.shape))
out[0] = rval
def grad(self, inputs, outputs):
......@@ -238,7 +239,8 @@ def _infer_ndim(ndim, shape, *args):
% (ndim, shape_ndim, shape))
elif shape is None:
# The shape will be computed at runtime, but we need to know ndim
# The number of drawn samples will be determined automatically,
# but we need to know ndim
v_shape = tensor.constant([], dtype='int64')
if ndim is None:
ndim = args_ndim
......@@ -276,11 +278,14 @@ def uniform(random_state, size=None, low=0.0, high=1.0, ndim=None):
def binomial(random_state, size=None, n=1, prob=0.5, ndim=None):
"""
Sample n times with probability of success prob for each trial, return the number of
successes.
Sample n times with probability of success prob for each trial,
return the number of successes.
If the size argument is ambiguous on the number of dimensions, ndim
may be a plain integer to supplement the missing information.
If the size argument is ambiguous on the number of dimensions, the first argument may be a
plain integer to supplement the missing information.
If size is None, the output shape will be determined by the shapes
of n and prob.
"""
n = tensor.as_tensor_variable(n)
prob = tensor.as_tensor_variable(prob)
......@@ -291,13 +296,14 @@ def binomial(random_state, size=None, n=1, prob=0.5, ndim=None):
def normal(random_state, size=None, avg=0.0, std=1.0, ndim=None):
"""
Usage: normal(random_state, size,
Sample from a normal distribution centered on avg with
the specified standard deviation (std)
the specified standard deviation (std).
If the size argument is ambiguous on the number of
dimensions, the first argument may be a plain integer
to supplement the missing information.
If the size argument is ambiguous on the number of dimensions, ndim
may be a plain integer to supplement the missing information.
If size is None, the output shape will be determined by the shapes
of avg and std.
"""
avg = tensor.as_tensor_variable(avg)
std = tensor.as_tensor_variable(std)
......@@ -386,12 +392,13 @@ def random_integers_helper(random_state, low, high, size):
def random_integers(random_state, size=None, low=0, high=1, ndim=None):
"""
Usage: random_integers(random_state, size, low=0, high=1)
Sample a random integer between low and high, both inclusive.
If the size argument is ambiguous on the number of
dimensions, the first argument may be a plain integer
to supplement the missing information.
If the size argument is ambiguous on the number of dimensions, ndim
may be a plain integer to supplement the missing information.
If size is None, the output shape will be determined by the shapes
of low and high.
"""
low = tensor.as_tensor_variable(low)
high = tensor.as_tensor_variable(high)
......@@ -410,7 +417,7 @@ def permutation_helper(random_state, n, shape):
output shape will be (p,q,n), because each permutation is of size n.
If you wish to perform a permutation of the elements of an existing vector,
see shuffle (to be implemented).
see shuffle_row_elements.
"""
# n should be a 0-dimension array
assert n.shape == ()
......@@ -437,10 +444,11 @@ def permutation(random_state, size=None, n=1, ndim=None):
will be generated, and the output shape will be (p,q,n), because each
permutation is of size n.
Theano tries to infer the number of dimensions from the length of the size argument, but you
may always specify it with the `ndim` parameter.
Theano tries to infer the number of dimensions from the length of
the size argument and the shape of n, but you may always specify it
with the `ndim` parameter.
.. note::
.. note::
Note that the output will then be of dimension ndim+1.
"""
ndim, size = _infer_ndim(ndim, size)
......@@ -537,14 +545,16 @@ def multinomial_helper(random_state, n, pvals, size):
def multinomial(random_state, size=None, n=1, pvals=[0.5, 0.5], ndim=None):
"""
Sample n times from a multinomial distribution defined by probabilities pvals,
as many times as required by size. For instance, if size=(p,q), p*q
samples will be drawn, and the output shape will be (p,q,len(pvals)).
Sample n times from a multinomial distribution defined by
probabilities pvals, as many times as required by size. For
instance, if size=(p,q), p*q samples will be drawn, and the output
shape will be (p,q,len(pvals)).
Theano tries to infer the number of dimensions from the length of the size argument, but you
may always specify it with the `ndim` parameter.
Theano tries to infer the number of dimensions from the length of
the size argument and the shapes of n and pvals, but you may always
specify it with the `ndim` parameter.
.. note::
.. note::
Note that the output will then be of dimension ndim+1.
"""
n = tensor.as_tensor_variable(n)
......@@ -572,44 +582,44 @@ class RandomStreamsBase(object):
def binomial(self, size=None, n=1, prob=0.5, ndim=None):
"""
Sample n times with probability of success prob for each trial, return the number of
successes.
Sample n times with probability of success prob for each trial,
return the number of successes.
If the size argument is ambiguous on the number of dimensions, the first argument may be a
plain integer to supplement the missing information.
If the size argument is ambiguous on the number of dimensions,
ndim may be a plain integer to supplement the missing
information.
"""
return self.gen(binomial, size, n, prob, ndim=ndim)
def uniform(self, size=None, low=0.0, high=1.0, ndim=None):
"""
Sample a tensor of given size whose element from a uniform distribution between low and high.
Sample a tensor of given size whose element from a uniform
distribution between low and high.
If the size argument is ambiguous on the number of
dimensions, the first argument may be a plain integer
to supplement the missing information.
If the size argument is ambiguous on the number of dimensions,
ndim may be a plain integer to supplement the missing
information.
"""
return self.gen(uniform, size, low, high, ndim=ndim)
def normal(self, size=None, avg=0.0, std=1.0, ndim=None):
"""
Usage: normal(random_state, size,
Sample from a normal distribution centered on avg with
the specified standard deviation (std)
the specified standard deviation (std).
If the size argument is ambiguous on the number of
dimensions, the first argument may be a plain integer
to supplement the missing information.
If the size argument is ambiguous on the number of dimensions,
ndim may be a plain integer to supplement the missing
information.
"""
return self.gen(normal, size, avg, std, ndim=ndim)
def random_integers(self, size=None, low=0, high=1, ndim=None):
"""
Usage: random_integers(random_state, size, low=0, high=1)
Sample a random integer between low and high, both inclusive.
If the size argument is ambiguous on the number of
dimensions, the first argument may be a plain integer
to supplement the missing information.
If the size argument is ambiguous on the number of dimensions,
ndim may be a plain integer to supplement the missing
information.
"""
return self.gen(random_integers, size, low, high, ndim=ndim)
......@@ -620,8 +630,9 @@ class RandomStreamsBase(object):
will be generated, and the output shape will be (p,q,n), because each
permutation is of size n.
Theano tries to infer the number of dimensions from the length of the size argument, but you
may always specify it with the `ndim` parameter.
Theano tries to infer the number of dimensions from the length
of the size argument and the shape of n, but you may always
specify it with the `ndim` parameter.
.. note::
Note that the output will then be of dimension ndim+1.
......@@ -630,12 +641,14 @@ class RandomStreamsBase(object):
def multinomial(self, size=None, n=1, pvals=[0.5, 0.5], ndim=None):
"""
Sample n times from a multinomial distribution defined by probabilities pvals,
as many times as required by size. For instance, if size=(p,q), p*q
samples will be drawn, and the output shape will be (p,q,len(pvals)).
Sample n times from a multinomial distribution defined by
probabilities pvals, as many times as required by size. For
instance, if size=(p,q), p*q samples will be drawn, and the
output shape will be (p,q,len(pvals)).
Theano tries to infer the number of dimensions from the length of the size argument, but you
may always specify it with the `ndim` parameter.
Theano tries to infer the number of dimensions from the length
of the size argument and the shapes of n and pvals, but you may
always specify it with the `ndim` parameter.
.. note::
Note that the output will then be of dimension ndim+1.
......@@ -645,8 +658,8 @@ class RandomStreamsBase(object):
def shuffle_row_elements(self, input):
"""Return a variable with every row (rightmost index) shuffled.
This uses permutation random variable internally, available via the ``.permutation``
attribute of the return value.
This uses permutation random variable internally, available via
the ``.permutation`` attribute of the return value.
"""
perm = self.permutation(size=input.shape[:-1], n=input.shape[-1], ndim=input.ndim-1)
shuffled = tensor.permute_row_elements(input, perm)
......
theano/tensor/tests/test_raw_random.py
浏览文件 @ 4684329b
......@@ -649,8 +649,6 @@ class T_random_function(unittest.TestCase):
rng2, val2 = g(rng1, n_val, pvals_val)
numpy_val2 = numpy.asarray([numpy_rng.multinomial(n=nv, pvals=pv)
for nv, pv in zip(n_val, pvals_val)])
print 'val2 =', val2
print 'numpy_val2 =', numpy_val2
assert numpy.all(val2 == numpy_val2)
self.assertRaises(ValueError, g, rng2, n_val[:-1], pvals_val[:-1])
......
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