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random_uniform#

ivy.random_uniform(*, low=0.0, high=1.0, shape=None, device=None, dtype=None, seed=None, out=None)[source]#

Draws samples from a uniform distribution. Samples are uniformly distributed over the half-open interval [low, high) (includes low, but excludes high). In other words, any value within the given interval is equally likely to be drawn by uniform.

Parameters:

  • low (Union[float, NativeArray, Array], default: 0.0) – Lower boundary of the output interval. All values generated will be greater than or equal to low. If array, must have same shape as high.

  • high (Union[float, NativeArray, Array], default: 1.0) – Upper boundary of the output interval. All the values generated will be less than high. If array, must have same shape as low.

  • shape (Optional[Union[Array, Shape, NativeShape]], default: None) – If the given shape is, e.g (m, n, k), then m * n * k samples are drawn. Can only be specified when low and high are numeric values, else exception will be raised. Default is None, where a single value is returned.

  • device (Optional[Union[Device, NativeDevice]], default: None) – device on which to create the array ‘cuda:0’, ‘cuda:1’, ‘cpu’ etc. (Default value = None).

  • dtype (Optional[Union[Dtype, NativeDtype]], default: None) – output array data type. If dtype is None, the output array data type will be the default floating-point data type. Default None

  • seed (Optional[int], default: None) – A python integer. Used to create a random seed distribution

  • out (Optional[Array], default: None) – optional output array, for writing the result to. It must have a shape that the inputs broadcast to.

Return type:

Array

Returns:

ret – Drawn samples from the parameterized uniform distribution.

Examples

>>> ivy.random_uniform()
ivy.array(0.26431865)

>>> ivy.random_uniform(shape=3)
ivy.array([0.475, 0.878, 0.861])

>>> ivy.random_uniform(shape=(2,3))
ivy.array([[0.929 , 0.545 , 0.789 ],
           [0.519 , 0.0435, 0.381 ]])

>>> ivy.random_uniform(low=3.0, high=6.0)
ivy.array(3.4608004)

>>> ivy.random_uniform(low=1.0, high=2.0, shape=(2,1))
ivy.array([[1.85],
           [1.81]])

>>> z = ivy.zeros(())
>>> ivy.random_uniform(low=1.0, high=2.0, out=z)
ivy.array(1.8458502)

>>> ivy.random_uniform(low=1.0, high=2.0, shape=(2,2), device='cpu')
ivy.array([[1.81, 1.8 ],
           [1.32, 1.43]])

>>> ivy.random_uniform(low=1.0, high=2.0, shape=(2,2), device='cpu',
...                    dtype='int32')
ivy.array([[1, 1],
           [1, 1]])

>>> z = ivy.zeros((1,2))
>>> ivy.random_uniform(low=1.0, high=2.0, shape=(1,2), device='cpu',
...                    dtype='float64', out=z)
ivy.array([[1.34, 1.02]])

>>> x = ivy.array([4.8, 5.6])
>>> y = ivy.array([9.8, 7.4])
>>> ivy.random_uniform(low=x, high=y)
ivy.array([0.475, 0.878])

>>> z = ivy.zeros((2,))
>>> ivy.random_uniform(low=x, high=y, out=z, seed=42)
ivy.array([6.67270088, 7.31128597])

>>> ivy.random_uniform(low=x, high=y, device='cpu')
ivy.array([6.88, 6.75])

>>> ivy.random_uniform(low=x, high=y, device='cpu', dtype='float64')
ivy.array([8.62, 6.47])

>>> z = ivy.zeros((2,))
>>> ivy.random_uniform(low=x, high=y, device='cpu', dtype='float64', out=z)
ivy.array([5. , 7.3])
Array.random_uniform(self, /, *, high=1.0, shape=None, device=None, dtype=None, seed=None, out=None)[source]#

ivy.Array instance method variant of ivy.random_uniform. This method simply wraps the function, and so the docstring for ivy.random_uniform also applies to this method with minimal changes.

Parameters:

  • self (Array) – Lower boundary of the output interval. All values generated will be greater than or equal to low. If array, must have same shape as high.

  • high (Union[float, Array, NativeArray], default: 1.0) – Upper boundary of the output interval. All the values generated will be less than high. If array, must have same shape as low.

  • shape (Optional[Union[Array, Shape, NativeShape]], default: None) – If the given shape is, e.g (m, n, k), then m * n * k samples are drawn. Can only be specified when low and high are numeric values, else exception will be raised. Default is None, where a single value is returned.

  • device (Optional[Union[Device, NativeDevice]], default: None) – device on which to create the array ‘cuda:0’, ‘cuda:1’, ‘cpu’ etc. (Default value = None).

  • dtype (Optional[Union[Dtype, NativeDtype]], default: None) – output array data type. If dtype is None, the output array data type will be the default floating-point data type. Default None

  • seed (Optional[int], default: None) – A python integer. Used to create a random seed distribution

  • out (Optional[Array], default: None) – optional output array, for writing the result to. It must have a shape that the inputs broadcast to.

Return type:

Array

Returns:

ret – Drawn samples from the parameterized uniform distribution.

Examples

>>> x = ivy.array([[9.8, 3.4], [5.8, 7.2]])
>>> x.random_uniform(high=10.2)
ivy.array([[9.86, 4.89],
           [7.06, 7.47]])

>>> x.random_uniform(high=10.2, device='cpu')
ivy.array([[9.86, 4.89],
           [7.06, 7.47]])

>>> x.random_uniform(high=14.2, dtype='float16')
ivy.array([[9.86, 4.89],
           [7.06, 7.47]])

>>> x.random_uniform(high=10.8, device='cpu', dtype='float64')
ivy.array([[9.86, 4.89],
           [7.06, 7.47]])

>>> z = ivy.ones((2,2))
>>> x.random_uniform(high=11.2, device='cpu', dtype='float64', out=z)
ivy.array([[10.1 ,  6.53],
           [ 7.94,  8.85]])

>>> x = ivy.array([8.7, 9.3])
>>> y = ivy.array([12.8, 14.5])
>>> x.random_uniform(y)
ivy.array([12.1, 14. ])

>>> x.random_uniform(high=y, device='cpu')
ivy.array([12.1, 14. ])

>>> x.random_uniform(high=y, dtype='float16')
ivy.array([12.1, 14. ])

>>> x.random_uniform(high=y, device='cpu', dtype='float64')
ivy.array([12.1, 14. ])

>>> z = ivy.ones((2,))
>>> x.random_uniform(high=y, device='cpu', dtype='float64', out=z)
ivy.array([12.1, 14. ])
Container.random_uniform(self, /, *, high=1.0, shape=None, key_chains=None, to_apply=True, prune_unapplied=False, map_sequences=False, device=None, dtype=None, seed=None, out=None)[source]#

ivy.Container instance method variant of ivy.random_uniform. This method simply wraps the function, and so the docstring for ivy.random_uniform also applies to this method with minimal changes.

Parameters:

  • self (Container) – Lower boundary of the output interval. All values generated will be greater than or equal to low. If array, must have same shape as high.

  • high (Union[float, Container, Array, NativeArray], default: 1.0) – Upper boundary of the output interval. All the values generated will be less than high. If array, must have same shape as low.

  • shape (Optional[Union[Shape, NativeShape, Container]], default: None) – If the given shape is, e.g (m, n, k), then m * n * k samples are drawn. Can only be specified when low and high are numeric values, else exception will be raised. Default is None, where a single value is returned.

  • key_chains (Optional[Union[List[str], Dict[str, str], Container]], default: None) – The key-chains to apply or not apply the method to. Default is None.

  • to_apply (Union[bool, Container], default: True) – If True, the method will be applied to key_chains, otherwise key_chains will be skipped. Default is True.

  • prune_unapplied (Union[bool, Container], default: False) – Whether to prune key_chains for which the function was not applied. Default is False.

  • map_sequences (Union[bool, Container], default: False) – Whether to also map method to sequences (lists, tuples). Default is False.

  • device (Optional[Union[Device, NativeDevice, Container]], default: None) – device on which to create the array ‘cuda:0’, ‘cuda:1’, ‘cpu’ etc. (Default value = None).

  • dtype (Optional[Union[Dtype, NativeDtype, Container]], default: None) – output array data type. If dtype is None, the output array data type will be the default floating-point data type. Default None

  • seed (Optional[Union[int, Container]], default: None) – A python integer. Used to create a random seed distribution

  • out (Optional[Container], default: None) – optional output array, for writing the result to. It must have a shape that the inputs broadcast to.

Return type:

Container

Returns:

ret – Drawn samples from the parameterized uniform distribution.

Examples

>>> x = ivy.Container(a=ivy.array([7.5,6.7,0.9]), b=ivy.array([8.7,9.8,4.5]))
>>> x.random_uniform(high=17.4)
{
    a: ivy.array([11.2, 10.5, 13.1]),
    b: ivy.array([11.2, 11.9, 6.01])
}

>>> x.random_uniform(high=10.2, device='cpu')
{
    a: ivy.array([8.55, 10.1, 4.08]),
    b: ivy.array([9.45, 9.9, 8.6])
}

>>> x.random_uniform(high=14.2, dtype='float16')
{
    a: ivy.array([12.4, 11.7, 7.25]),
    b: ivy.array([11.8, 11.8, 4.96])
}

>>> x.random_uniform(high=10.8, device='cpu', dtype='float64')
{
    a: ivy.array([8.86, 9.24, 6.43]),
    b: ivy.array([8.95, 10.1, 8.51])
}

>>> z = ivy.Container(a=ivy.zeros((3,)), b=ivy.ones((3,)))
>>> x.random_uniform(high=11.2, device='cpu', dtype='float64', out=z)
{
    a: ivy.array([9.6, 8.24, 3.67]),
    b: ivy.array([9.29, 11.2, 9.84])
}

>>> y = ivy.Container(a=10.4, b=17.4)
>>> x.random_uniform(high=y)
{
    a: ivy.array([8.24, 9.22, 1.52]),
    b: ivy.array([16.5, 13.4, 17.3])
}

>>> x.random_uniform(high=y, device='cpu')
{
    a: ivy.array([8.55, 10.1, 4.08]),
    b: ivy.array([9.45, 9.9, 8.6])
}

>>> x.random_uniform(high=y, dtype='float16')
{
    a: ivy.array([12.4, 11.7, 7.25]),
    b: ivy.array([11.8, 11.8, 4.96])
}

>>> x.random_uniform(high=y, device='cpu', dtype='float64')
{
    a: ivy.array([8.86, 9.24, 6.43]),
    b: ivy.array([8.95, 10.1, 8.51])
}

>>> z = ivy.Container(a=ivy.zeros((3,)), b=ivy.ones((3,)))
>>> x.random_uniform(high=y, device='cpu', dtype='float64', out=z)
{
    a: ivy.array([9.6, 8.24, 3.67]),
    b: ivy.array([9.29, 11.2, 9.84])
}

>>> x = ivy.Container(a=ivy.array([[9.8,7.6],[6.5,2.3]]),
...                   b=ivy.array([[0.9,2.4],[7.6,5.4]]))
>>> y = ivy.Container(a=ivy.array([[10.9,32.4],[18.7,19.6]]),
...                   b=ivy.array([[4.3,5.6],[23.4,54.3]]))
>>> x.random_uniform(high=y)
{
    a: ivy.array([[10.4, 17.],
                  [9.81, 10.9]]),
    b: ivy.array([[3.6, 4.31],
                  [18.8, 54.2]])
}

>>> x.random_uniform(high=y, device='cpu')
{
    a: ivy.array([[10.1, 7.93],
                  [7.98, 6.]]),
    b: ivy.array([[4.28, 4.65],
                  [13.9, 28.9]])
}

>>> x.random_uniform(high=y, dtype='float16')
{
    a: ivy.array([[10.6, 28.],
                  [16.4, 4.92]]),
    b: ivy.array([[3.61, 4.82],
                  [12.6, 10.2]])
}

>>> x.random_uniform(high=y, device='cpu', dtype='float64')
{
    a: ivy.array([[10.7, 28.4],
                  [9.29, 17.4]]),
    b: ivy.array([[1.88, 4.94],
                  [17., 9.68]])
}

>>> z = ivy.Container(a=ivy.zeros((2,2)), b=ivy.ones((2,2)))
>>> x.random_uniform(high=y, device='cpu', dtype='float64', out=z)
{
    a: ivy.array([[10.4, 29.8],
                  [12.1, 3.9]]),
    b: ivy.array([[3.79, 5.4],
                  [16.2, 31.7]])
}
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