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Utility#

class ivy.data_classes.array.utility._ArrayWithUtility[source]#

Bases: ABC

_abc_impl = <_abc._abc_data object>#
all(*, axis=None, keepdims=False, out=None)[source]#

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

Parameters:

  • self (Array) – input array.

  • axis (Optional[Union[int, Sequence[int]]], default: None) – axis or axes along which to perform a logical AND reduction. By default, a logical AND reduction must be performed over the entire array. If a tuple of integers, logical AND reductions must be performed over multiple axes. A valid axis must be an integer on the interval [-N, N), where N is the rank(number of dimensions) of self. If an axis is specified as a negative integer, the function must determine the axis along which to perform a reduction by counting backward from the last dimension (where -1 refers to the last dimension). If provided an invalid axis, the function must raise an exception. Default None.

  • keepdims (bool, default: False) – If True, the reduced axes (dimensions) must be included in the result as singleton dimensions, and, accordingly, the result must be compatible with the input array (see broadcasting). Otherwise, if False, the reduced axes(dimensions) must not be included in the result. Default: False.

  • 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 – if a logical AND reduction was performed over the entire array, the returned array must be a zero-dimensional array containing the test result; otherwise, the returned array must be a non-zero-dimensional array containing the test results. The returned array must have a data type of bool.

Examples

>>> x = ivy.array([0, 1, 2])
>>> y = x.all()
>>> print(y)
ivy.array(False)

>>> x = ivy.array([[[0, 1], [0, 0]], [[1, 2], [3, 4]]])
>>> y = x.all(axis=1)
>>> print(y)
ivy.array([[False, False],
       [ True,  True]])
any(*, axis=None, keepdims=False, out=None)[source]#

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

Parameters:

  • self (Array) – input array.

  • axis (Optional[Union[int, Sequence[int]]], default: None) – axis or axes along which to perform a logical OR reduction. By default, a logical OR reduction must be performed over the entire array. If a tuple of integers, logical OR reductions must be performed over multiple axes. A valid axis must be an integer on the interval [-N, N), where N is the rank(number of dimensions) of self. If an axis is specified as a negative integer, the function must determine the axis along which to perform a reduction by counting backward from the last dimension (where -1 refers to the last dimension). If provided an invalid axis, the function must raise an exception. Default: None.

  • keepdims (bool, default: False) – If True, the reduced axes (dimensions) must be included in the result as singleton dimensions, and, accordingly, the result must be compatible with the input array (see broadcasting). Otherwise, if False, the reduced axes(dimensions) must not be included in the result. Default: False.

  • 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 – if a logical OR reduction was performed over the entire array, the returned array must be a zero-dimensional array containing the test result; otherwise, the returned array must be a non-zero-dimensional array containing the test results. The returned array must have a data type of bool.

Examples

>>> x = ivy.array([0, 1, 2])
>>> y = x.any()
>>> print(y)
ivy.array(True)

>>> x = ivy.array([[[0, 1], [0, 0]], [[1, 2], [3, 4]]])
>>> y = x.any(axis=2)
>>> print(y)
ivy.array([[ True, False],
       [ True,  True]])

This should have hopefully given you an overview of the utility submodule, if you have any questions, please feel free to reach out on our discord!

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