Random#

class ivy.data_classes.array.random._ArrayWithRandom[source]#

Bases: ABC

_abc_impl = <_abc._abc_data object>#

multinomial(population_size, num_samples, /, *, batch_size=1, replace=True, device=None, seed=None, out=None)[source]#

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

Parameters:

self (Array) – The unnormalized probabilities for all elements in population, default is uniform [batch_shape, population_size]
population_size (int) – The size of the population from which to draw samples.
num_samples (int) – Number of independent samples to draw from the population.
batch_size (int, default: 1) – Number of tensors to generate. Default is 1.
replace (bool, default: True) – Whether to replace samples once they’ve been drawn. Default is True.
device (Optional[Union[Device, NativeDevice]], default: None) – device on which to create the array ‘cuda:0’, ‘cuda:1’, ‘cpu’ etc. (Default value = 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 normal distribution.

randint(high, /, *, shape=None, device=None, dtype=None, seed=None, out=None)[source]#

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

Parameters:

self (Array) – Lowest integer that can be drawn from the distribution.
high (Union[int, Array, NativeArray]) – One above the highest integer that can be drawn from the distribution.
shape (Optional[Union[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 integer 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 – Returns an array with the given shape filled with integers from the uniform distribution in the “half-open” interval [low, high)

Examples

>>> x = ivy.array([[1, 2], [0, 5]])
>>> x.randint(10)
ivy.array([[1, 5],
           [9, 7]])

>>> x.randint(8, device='cpu')
ivy.array([[6, 5],
           [0, 5]])

>>> x.randint(9, dtype='int8')
ivy.array([[1, 2],
           [7, 7]])

>>> x.randint(14, device='cpu', dtype='int16')
ivy.array([[6, 5],
           [0, 5]])

>>> z = ivy.ones((2,2))
>>> x.randint(16, device='cpu', dtype='int64', out=z)
ivy.array([[1, 2],
           [7, 7]])

>>> x = ivy.array([1, 2, 3])
>>> y = ivy.array([23, 25, 98])
>>> x.randint(y)
ivy.array([ 5, 14, 18])

>>> x.randint(y, device='cpu')
ivy.array([20, 13, 46])

>>> x.randint(y, dtype='int32')
ivy.array([ 9, 18, 33])

>>> x.randint(y, device='cpu', dtype='int16')
ivy.array([ 9, 20, 85])

>>> z = ivy.ones((3,))
>>> x.randint(y, device='cpu', dtype='int64', out=z)
ivy.array([20, 13, 46])

random_normal(*, std=1.0, shape=None, device=None, dtype=None, seed=None, out=None)[source]#

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

Parameters:

self (Array) – The mean of the normal distribution to sample from. Default is 0.0.
std (Union[float, Array, NativeArray], default: 1.0) – The standard deviation of the normal distribution to sample from. Must be non-negative. Default is 1.0.
shape (Optional[Union[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 mean and std 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 normal distribution.

Examples

>>> x = ivy.array([[9.8, 3.4], [5.8, 7.2]])
>>> x.random_normal(std=10.2)
ivy.array([[19.   , -6.44 ],
           [ 5.72 ,  0.235]])

>>> x.random_normal(std=10.2, device='cpu')
ivy.array([[18.7 , 25.2 ],
           [27.5 , -3.22]])

>>> x.random_normal(std=14.2, dtype='float16')
ivy.array([[26.6 , 12.1 ],
           [ 4.56,  5.49]])

>>> x.random_normal(std=10.8, device='cpu', dtype='float64')
ivy.array([[ 1.02, -1.39],
           [14.2 , -1.  ]])

>>> z = ivy.ones((2,2))
>>> x.random_normal(std=11.2, device='cpu', dtype='float64', out=z)
ivy.array([[ 7.72, -8.32],
           [ 4.95, 15.8 ]])

>>> x = ivy.array([8.7, 9.3])
>>> y = ivy.array([12.8, 14.5])
>>> x.random_normal(std=y)
ivy.array([-10.8,  12.1])

>>> x.random_normal(std=y, device='cpu')
ivy.array([ 13. , -26.9])

>>> x.random_normal(std=y, dtype='float16')
ivy.array([14.3  , -0.807])

>>> x.random_normal(std=y, device='cpu', dtype='float64')
ivy.array([21.3 ,  3.85])

>>> z = ivy.ones((2,))
>>> x.random_normal(std=y, device='cpu', dtype='float64', out=z)
ivy.array([ 4.32, 42.2 ])

random_uniform(*, 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. ])

shuffle(axis=0, /, *, seed=None, out=None)[source]#

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

Parameters:

self (Array) – Input array. Should have a numeric data type.
axis (Optional[int], default: 0) – The axis which x is shuffled along. Default is 0.
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 – An array object, shuffled along the first dimension.

Examples

>>> x = ivy.array([5, 2, 9])
>>> y = x.shuffle()
>>> print(y)
ivy.array([2, 5, 9])