/
random.py
681 lines (638 loc) · 25.1 KB
/
random.py
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# global
from typing import Optional, Union, List, Dict
# local
import ivy
from ivy.data_classes.container.base import ContainerBase
class _ContainerWithRandomExperimental(ContainerBase):
# dirichlet
@staticmethod
def static_dirichlet(
alpha: ivy.Container,
/,
*,
key_chains: Optional[Union[List[str], Dict[str, str], ivy.Container]] = None,
to_apply: Union[bool, ivy.Container] = True,
prune_unapplied: Union[bool, ivy.Container] = False,
map_sequences: Union[bool, ivy.Container] = False,
size: Optional[Union[ivy.Shape, ivy.NativeShape, ivy.Container]] = None,
dtype: Optional[Union[ivy.Dtype, ivy.NativeDtype, ivy.Container]] = None,
seed: Optional[Union[int, ivy.Container]] = None,
out: Optional[ivy.Container] = None,
) -> ivy.Container:
"""ivy.Container static method variant of ivy.dirichlet. This method
simply wraps the function, and so the docstring for ivy.dirichlet also
applies to this method with minimal changes.
Parameters
----------
alpha
Sequence of floats of length k
size
optional container including ints or tuple of ints,
Output shape for the arrays in the input container.
dtype
output container array data type. If ``dtype`` is ``None``, the output data
type will be the default floating-point data type. Default ``None``
seed
A python integer. Used to create a random seed distribution
out
optional output container, for writing the result to.
Returns
-------
ret
container including the drawn samples.
Examples
--------
>>> alpha = ivy.Container(a=ivy.array([7,6,5]), \
b=ivy.array([8,9,4]))
>>> size = ivy.Container(a=3, b=5)
>>> ivy.Container.static_dirichlet(alpha, size)
{
a: ivy.array(
[[0.43643127, 0.32325703, 0.24031169],
[0.34251311, 0.31692529, 0.3405616 ],
[0.5319725 , 0.22458365, 0.24344385]]
),
b: ivy.array(
[[0.26588406, 0.61075421, 0.12336174],
[0.51142915, 0.25041268, 0.23815817],
[0.64042903, 0.25763214, 0.10193883],
[0.31624692, 0.46567987, 0.21807321],
[0.37677699, 0.39914594, 0.22407707]]
)
}
"""
return ContainerBase.cont_multi_map_in_function(
"dirichlet",
alpha,
key_chains=key_chains,
to_apply=to_apply,
prune_unapplied=prune_unapplied,
map_sequences=map_sequences,
size=size,
dtype=dtype,
out=out,
)
def dirichlet(
self: ivy.Container,
/,
*,
size: Optional[Union[ivy.Shape, ivy.NativeShape, ivy.Container]] = None,
dtype: Optional[Union[ivy.Dtype, ivy.NativeDtype, ivy.Container]] = None,
seed: Optional[Union[int, ivy.Container]] = None,
out: Optional[ivy.Container] = None,
) -> ivy.Container:
"""ivy.Container instance method variant of ivy.dirichlet. This method
simply wraps the function, and so the docstring for ivy.shuffle also
applies to this method with minimal changes.
Parameters
----------
self
Sequence of floats of length k
size
optional container including ints or tuple of ints,
Output shape for the arrays in the input container.
dtype
output container array data type. If ``dtype`` is ``None``, the output data
type will be the default floating-point data type. Default ``None``
seed
A python integer. Used to create a random seed distribution
out
optional output container, for writing the result to.
Returns
-------
ret
container including the drawn samples.
Examples
--------
>>> alpha = ivy.Container(a=ivy.array([7,6,5]), \
b=ivy.array([8,9,4]))
>>> size = ivy.Container(a=3, b=5)
>>> alpha.dirichlet(size)
{
a: ivy.array(
[[0.43643127, 0.32325703, 0.24031169],
[0.34251311, 0.31692529, 0.3405616 ],
[0.5319725 , 0.22458365, 0.24344385]]
),
b: ivy.array(
[[0.26588406, 0.61075421, 0.12336174],
[0.51142915, 0.25041268, 0.23815817],
[0.64042903, 0.25763214, 0.10193883],
[0.31624692, 0.46567987, 0.21807321],
[0.37677699, 0.39914594, 0.22407707]]
)
}
"""
return self.static_dirichlet(
self,
size=size,
dtype=dtype,
out=out,
)
@staticmethod
def static_beta(
alpha: ivy.Container,
beta: Union[int, float, ivy.Container, ivy.Array, ivy.NativeArray],
/,
*,
shape: Optional[Union[ivy.Shape, ivy.NativeShape, ivy.Container]] = None,
key_chains: Optional[Union[List[str], Dict[str, str], ivy.Container]] = None,
to_apply: Union[bool, ivy.Container] = True,
prune_unapplied: Union[bool, ivy.Container] = False,
map_sequences: Union[bool, ivy.Container] = False,
device: Optional[Union[str, ivy.Container]] = None,
dtype: Optional[Union[str, ivy.Container]] = None,
seed: Optional[Union[int, ivy.Container]] = None,
out: Optional[ivy.Container] = None,
) -> ivy.Container:
"""ivy.Container static method variant of ivy.beta. This method simply
wraps the function, and so the docstring for ivy.beta also applies to
this method with minimal changes.
Parameters
----------
x
Input array or container. Should have a numeric data type.
alpha
The alpha parameter of the distribution.
beta
The beta parameter of the distribution.
shape
The shape of the output array. Default is ``None``.
key_chains
The key-chains to apply or not apply the method to. Default is ``None``.
to_apply
If True, the method will be applied to key_chains, otherwise key_chains
will be skipped. Default is ``True``.
prune_unapplied
Whether to prune key_chains for which the function was not applied.
Default is ``False``.
map_sequences
Whether to also map method to sequences (lists, tuples).
Default is ``False``.
device
The device to place the output array on. Default is ``None``.
dtype
The data type of the output array. Default is ``None``.
seed
A python integer. Used to create a random seed distribution
out
optional output container, for writing the result to. It must have a shape
that the inputs broadcast to.
Returns
-------
ret
A container object, with values drawn from the beta distribution.
"""
return ContainerBase.cont_multi_map_in_function(
"beta",
alpha,
beta,
shape=shape,
key_chains=key_chains,
to_apply=to_apply,
prune_unapplied=prune_unapplied,
map_sequences=map_sequences,
device=device,
dtype=dtype,
seed=seed,
out=out,
)
def beta(
self: ivy.Container,
beta: Union[int, float, ivy.Container, ivy.Array, ivy.NativeArray],
/,
*,
shape: Optional[Union[ivy.Shape, ivy.NativeShape, ivy.Container]] = None,
key_chains: Optional[Union[List[str], Dict[str, str], ivy.Container]] = None,
to_apply: Union[bool, ivy.Container] = True,
prune_unapplied: Union[bool, ivy.Container] = False,
map_sequences: Union[bool, ivy.Container] = False,
device: Optional[Union[str, ivy.Container]] = None,
dtype: Optional[Union[str, ivy.Container]] = None,
seed: Optional[Union[int, ivy.Container]] = None,
out: Optional[ivy.Container] = None,
) -> ivy.Container:
"""ivy.Container instance method variant of ivy.beta. This method
simply wraps the function, and so the docstring for ivy.beta also
applies to this method with minimal changes.
Parameters
----------
self
Input container. Should have a numeric data type.
alpha
The alpha parameter of the distribution.
beta
The beta parameter of the distribution.
shape
The shape of the output array. Default is ``None``.
key_chains
The key-chains to apply or not apply the method to. Default is ``None``.
to_apply
If True, the method will be applied to key_chains, otherwise key_chains
will be skipped. Default is ``True``.
prune_unapplied
Whether to prune key_chains for which the function was not applied.
Default is ``False``.
map_sequences
Whether to also map method to sequences (lists, tuples).
Default is ``False``.
device
The device to place the output array on. Default is ``None``.
dtype
The data type of the output array. Default is ``None``.
seed
A python integer. Used to create a random seed distribution
out
optional output container, for writing the result to. It must have a shape
that the inputs broadcast to.
Returns
-------
ret
A container object, with values drawn from the beta distribution.
"""
return self.static_beta(
self,
beta,
shape=shape,
key_chains=key_chains,
to_apply=to_apply,
prune_unapplied=prune_unapplied,
map_sequences=map_sequences,
device=device,
dtype=dtype,
seed=seed,
out=out,
)
@staticmethod
def static_poisson(
lam: ivy.Container,
*,
key_chains: Optional[Union[List[str], Dict[str, str], ivy.Container]] = None,
to_apply: Union[bool, ivy.Container] = True,
prune_unapplied: Union[bool, ivy.Container] = False,
map_sequences: Union[bool, ivy.Container] = False,
shape: Optional[Union[ivy.Shape, ivy.NativeShape, ivy.Container]] = None,
device: Optional[Union[ivy.Device, ivy.NativeDevice, ivy.Container]] = None,
dtype: Optional[Union[ivy.Dtype, ivy.NativeDtype, ivy.Container]] = None,
seed: Optional[Union[int, ivy.Container]] = None,
fill_value: Optional[Union[float, int, ivy.Container]] = 0,
out: Optional[ivy.Container] = None,
) -> ivy.Container:
"""ivy.Container static method variant of ivy.poisson. This method
simply wraps the function, and so the docstring for ivy.poisson also
applies to this method with minimal changes.
Parameters
----------
lam
Input container with rate parameter(s) describing the poisson
distribution(s) to sample.
shape
optional container including ints or tuple of ints,
Output shape for the arrays in the input container.
device
device on which to create the array 'cuda:0', 'cuda:1', 'cpu' etc.
(Default value = None).
dtype
output container array data type. If ``dtype`` is ``None``, the output data
type will be the default floating-point data type. Default ``None``
seed
A python integer. Used to create a random seed distribution.
fill_value
if lam is negative, fill the output array with this value
on that specific dimension.
out
optional output container, for writing the result to.
Returns
-------
ret
container including the drawn samples.
Examples
--------
>>> lam = ivy.Container(a=ivy.array([7,6,5]), \
b=ivy.array([8,9,4]))
>>> shape = ivy.Container(a=(2,3), b=(1,1,3))
>>> ivy.Container.static_poisson(lam, shape=shape)
{
a: ivy.array([[5, 4, 6],
[12, 4, 5]]),
b: ivy.array([[[8, 13, 3]]])
}
"""
return ContainerBase.cont_multi_map_in_function(
"poisson",
lam,
key_chains=key_chains,
to_apply=to_apply,
prune_unapplied=prune_unapplied,
map_sequences=map_sequences,
shape=shape,
device=device,
dtype=dtype,
seed=seed,
fill_value=fill_value,
out=out,
)
def poisson(
self: ivy.Container,
/,
*,
shape: Optional[Union[ivy.Shape, ivy.NativeShape, ivy.Container]] = None,
device: Optional[Union[ivy.Device, ivy.NativeDevice, ivy.Container]] = None,
dtype: Optional[Union[ivy.Dtype, ivy.NativeDtype, ivy.Container]] = None,
seed: Optional[Union[int, ivy.Container]] = None,
fill_value: Optional[Union[float, int, ivy.Container]] = 0,
out: Optional[ivy.Container] = None,
) -> ivy.Container:
"""ivy.Container instance method variant of ivy.poisson. This method
simply wraps the function, and so the docstring for ivy.poisson also
applies to this method with minimal changes.
Parameters
----------
self
Input container with rate parameter(s) describing the poisson
distribution(s) to sample.
shape
optional container including ints or tuple of ints,
Output shape for the arrays in the input container.
device
device on which to create the array 'cuda:0', 'cuda:1', 'cpu' etc.
(Default value = None).
dtype
output container array data type. If ``dtype`` is ``None``, the output data
type will be the default floating-point data type. Default ``None``
seed
A python integer. Used to create a random seed distribution.
fill_value
if lam is negative, fill the output array with this value
on that specific dimension.
out
optional output container, for writing the result to.
Returns
-------
ret
container including the drawn samples.
Examples
--------
>>> lam = ivy.Container(a=ivy.array([7,6,5]), \
b=ivy.array([8,9,4]))
>>> shape = ivy.Container(a=(2,3), b=(1,1,3))
>>> lam.poisson(shape=shape)
{
a: ivy.array([[5, 4, 6],
[12, 4, 5]]),
b: ivy.array([[[8, 13, 3]]])
}
"""
return self.static_poisson(
self,
shape=shape,
device=device,
dtype=dtype,
seed=seed,
fill_value=fill_value,
out=out,
)
@staticmethod
def static_bernoulli(
probs: ivy.Container,
*,
key_chains: Optional[Union[List[str], Dict[str, str], ivy.Container]] = None,
to_apply: Union[bool, ivy.Container] = True,
prune_unapplied: Union[bool, ivy.Container] = False,
map_sequences: Union[bool, ivy.Container] = False,
logits: Optional[
Union[float, ivy.Array, ivy.NativeArray, ivy.Container]
] = None,
shape: Optional[Union[ivy.Shape, ivy.NativeShape, ivy.Container]] = None,
device: Optional[Union[ivy.Device, ivy.NativeDevice, ivy.Container]] = None,
dtype: Optional[Union[ivy.Dtype, ivy.NativeDtype, ivy.Container]] = None,
seed: Optional[Union[int, ivy.Container]] = None,
out: Optional[Union[ivy.Array, ivy.Container]] = None,
) -> ivy.Container:
"""
Parameters
----------
probs
An N-D Array representing the probability of a 1 event.
Each entry in the Array parameterizes an independent Bernoulli
distribution. Only one of logits or probs should be passed in
key_chains
The key-chains to apply or not apply the method to. Default is ``None``.
to_apply
If True, the method will be applied to key_chains, otherwise key_chains
will be skipped. Default is ``True``.
prune_unapplied
Whether to prune key_chains for which the function was not applied.
Default is ``False``.
map_sequences
Whether to also map method to sequences (lists, tuples).
Default is ``False``.
logits
An N-D Array representing the log-odds of a 1 event.
Each entry in the Array parameterizes an independent Bernoulli
distribution where the probability of an event is sigmoid
(logits). Only one of logits or probs should be passed in.
shape
If the given shape is, e.g '(m, n, k)', then 'm * n * k' samples are drawn.
(Default value = 'None', where 'ivy.shape(logits)' samples are drawn)
device
The device to place the output array on. Default is ``None``.
dtype
The data type of the output array. Default is ``None``.
seed
A python integer. Used to create a random seed distribution
out
optional output container, for writing the result to. It must have a shape
that the inputs broadcast to.
Returns
-------
ret
Drawn samples from the Bernoulli distribution
"""
return ContainerBase.cont_multi_map_in_function(
"bernoulli",
probs,
key_chains=key_chains,
to_apply=to_apply,
prune_unapplied=prune_unapplied,
map_sequences=map_sequences,
logits=logits,
shape=shape,
device=device,
dtype=dtype,
seed=seed,
out=out,
)
def bernoulli(
self: ivy.Container,
/,
*,
logits: Optional[
Union[float, ivy.Array, ivy.NativeArray, ivy.Container]
] = None,
shape: Optional[Union[ivy.Shape, ivy.NativeShape, ivy.Container]] = None,
device: Optional[Union[ivy.Device, ivy.NativeDevice, ivy.Container]] = None,
dtype: Optional[Union[ivy.Dtype, ivy.NativeDtype, ivy.Container]] = None,
seed: Optional[Union[int, ivy.Container]] = None,
out: Optional[ivy.Container] = None,
) -> ivy.Container:
"""
Parameters
----------
self
An N-D Array representing the probability of a 1 event.
Each entry in the Array parameterizes an independent
Bernoulli distribution. Only one of logits or probs should
be passed in.
logits
An N-D Array representing the log-odds of a 1 event.
Each entry in the Array parameterizes an independent Bernoulli
distribution where the probability of an event is
sigmoid(logits). Only one of logits or probs should be passed in.
shape
If the given shape is, e.g '(m, n, k)', then 'm * n * k' samples are drawn.
(Default value = 'None', where 'ivy.shape(logits)' samples are drawn)
device
device on which to create the array 'cuda:0', 'cuda:1', 'cpu' etc.
(Default value = None).
dtype
output array data type. If ``dtype`` is ``None``, the output array data
type will be the default floating-point data type. Default ``None``
seed
A python integer. Used to create a random seed distribution
out
optional output array, for writing the result to.
It must have a shape that the inputs broadcast to.
Returns
-------
ret
Drawn samples from the Bernoulli distribution
"""
return self.static_bernoulli(
self,
logits=logits,
shape=shape,
device=device,
dtype=dtype,
seed=seed,
out=out,
)
@staticmethod
def static_gamma(
alpha: ivy.Container,
beta: Union[int, float, ivy.Container, ivy.Array, ivy.NativeArray],
/,
*,
shape: Optional[Union[ivy.Shape, ivy.NativeShape, ivy.Container]] = None,
key_chains: Optional[Union[List[str], Dict[str, str], ivy.Container]] = None,
to_apply: Union[bool, ivy.Container] = True,
prune_unapplied: Union[bool, ivy.Container] = False,
map_sequences: Union[bool, ivy.Container] = False,
device: Optional[Union[str, ivy.Container]] = None,
dtype: Optional[Union[str, ivy.Container]] = None,
seed: Optional[Union[int, ivy.Container]] = None,
out: Optional[ivy.Container] = None,
):
"""ivy.Container static method variant of ivy.gamma. This method simply
wraps the function, and so the docstring for ivy.gamma also applies to
this method with minimal changes.
Parameters
----------
alpha
First parameter of the distribution.
beta
Second parameter of the distribution.
shape
If the given shape is, e.g '(m, n, k)', then 'm * n * k' samples are drawn.
(Default value = 'None', where 'ivy.shape(logits)' samples are drawn)
key_chains
The key-chains to apply or not apply the method to. Default is ``None``.
to_apply
If True, the method will be applied to key_chains, otherwise key_chains
will be skipped. Default is ``True``.
prune_unapplied
Whether to prune key_chains for which the function was not applied.
Default is ``False``.
map_sequences
Whether to also map method to sequences (lists, tuples).
Default is ``False``.
device
device on which to create the array 'cuda:0', 'cuda:1', 'cpu' etc.
(Default value = None).
dtype
output array data type. If ``dtype`` is ``None``, the output array data
type will be the default floating-point data type. Default ``None``
seed
A python integer. Used to create a random seed distribution
out
Optional output container, for writing the result to. It must have a shape
that the inputs broadcast to.
Returns
-------
ret
Drawn samples from the parameterized gamma distribution with the shape of
the input Container.
"""
return ContainerBase.cont_multi_map_in_function(
"gamma",
alpha,
beta,
key_chains=key_chains,
to_apply=to_apply,
prune_unapplied=prune_unapplied,
map_sequences=map_sequences,
shape=shape,
device=device,
dtype=dtype,
seed=seed,
out=out,
)
def gamma(
self: ivy.Container,
beta: Union[int, float, ivy.Container, ivy.Array, ivy.NativeArray],
/,
*,
shape: Optional[Union[ivy.Shape, ivy.NativeShape, ivy.Container]] = None,
device: Optional[Union[str, ivy.Container]] = None,
dtype: Optional[Union[str, ivy.Container]] = None,
seed: Optional[Union[int, ivy.Container]] = None,
out: Optional[ivy.Container] = None,
):
"""ivy.Container method variant of ivy.gamma. This method simply wraps
the function, and so the docstring for ivy.gamma also applies to this
method with minimal changes.
Parameters
----------
self
First parameter of the distribution.
beta
Second parameter of the distribution.
shape
If the given shape is, e.g '(m, n, k)', then 'm * n * k' samples are drawn.
(Default value = 'None', where 'ivy.shape(logits)' samples are drawn)
device
device on which to create the array 'cuda:0', 'cuda:1', 'cpu' etc.
(Default value = None).
dtype
output array data type. If ``dtype`` is ``None``, the output array data
type will be the default floating-point data type. Default ``None``
seed
A python integer. Used to create a random seed distribution
out
Optional output container, for writing the result to. It must have a shape
that the inputs broadcast to.
Returns
-------
ret
Drawn samples from the parameterized gamma distribution with the shape of
the input Container.
"""
return self.static_gamma(
self,
beta,
shape=shape,
device=device,
dtype=dtype,
seed=seed,
out=out,
)