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Set#

class ivy.data_classes.array.set._ArrayWithSet[source]#

Bases: ABC

_abc_impl = <_abc._abc_data object>#
unique_all(*, axis=None, by_value=True)[source]#

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

Parameters:

  • self (Array) – input array.

  • axis (Optional[int], default: None) – the axis to apply unique on. If None, the unique elements of the flattened x are returned.

  • by_value (bool, default: True) – If False, the unique elements will be sorted in the same order that they occur in ‘’x’’. Otherwise, they will be sorted by value.

Return type:

Tuple[Array, Array, Array, Array]

Returns:

ret – a namedtuple (values, indices, inverse_indices, counts). The details can be found in the docstring for ivy.unique_all.

Examples

>>> x = ivy.randint(0, 10, shape=(2, 2), seed=0)
>>> z = x.unique_all()
>>> print(z)
Results(values=ivy.array([1, 2, 5, 9]),
        indices=ivy.array([3, 2, 1, 0]),
        inverse_indices=ivy.array([[3, 2], [1, 0]]),
       counts=ivy.array([1, 1, 1, 1]))
unique_counts()[source]#

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

Parameters:

self (Array) – input array. If x has more than one dimension, the function must flatten x and return the unique elements of the flattened array.

Return type:

Tuple[Array, Array]

Returns:

ret – a namedtuple (values, counts) whose

  • first element must have the field name values and must be an

array containing the unique elements of x. The array must have the same data type as x. - second element must have the field name counts and must be an array containing the number of times each unique element occurs in x. The returned array must have same shape as values and must have the default array index data type.

Examples

>>> x = ivy.array([0., 1., 2. , 1. , 0.])
>>> y = x.unique_counts()
>>> print(y)
Results(values=ivy.array([0.,1.,2.]),counts=ivy.array([2,2,1]))
unique_inverse()[source]#

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

Parameters:

self (Array) – input array. If x has more than one dimension, the function must flatten x and return the unique elements of the flattened array.

Return type:

Tuple[Array, Array]

Returns:

ret – a namedtuple (values, inverse_indices) whose

  • first element must have the field name values and must be an array containing the unique elements of x. The array must have the same data type as x.

  • second element must have the field name inverse_indices and must be an array containing the indices of values that reconstruct x. The array must have the same shape as x and must have the default array index data type.

Examples

>>> x = ivy.array([0.3,0.4,0.7,0.4,0.2,0.8,0.5])
>>> y = x.unique_inverse()
>>> print(y)
Results(values=ivy.array([0.2, 0.3, 0.4, 0.5, 0.7, 0.8]),
        inverse_indices=ivy.array([1, 2, 4, 2, 0, 5, 3]))
unique_values(*, out=None)[source]#

Return the unique elements of an input array x. .. admonition:: Data-dependent output shape

class:

important

The shapes of two of the output arrays for this function depend on the data values in the input array; hence, array libraries which build computation graphs (e.g., JAX, Dask, etc.) may find this function difficult to implement without knowing array values. Accordingly, such libraries may choose to omit this function. See data-dependent-output-shapes section for more details.

Note

Uniqueness should be determined based on value equality (i.e., x_i == x_j). For input arrays having floating-point data types, value-based equality implies the following behavior. - As nan values compare as False, nan values

should be considered distinct.

  • As -0 and +0 compare as True, signed zeros should not be considered distinct, and the corresponding unique element will be implementation-dependent (e.g., an implementation could choose to return -0 if -0 occurs before +0).

Parameters:

  • x (ivy.Array or ivy.NativeArray) – Input array. If x has more than one dimension, the function must flatten x and return the unique elements of the flattened array.

  • out (ivy.Array, optional) – Optional output array, for writing the result to. It must have a shape that the inputs broadcast to.

Return type:

Array

Returns:

ivy.Array – An array containing the set of unique elements in x. The returned array must have the same data type as x. .. note:

The order of unique elements is not specified and may vary
between implementations.

Raises:

TypeError – If x is not an instance of ivy.Array or ivy.NativeArray.

Examples

>>> import ivy
>>> x = ivy.array([1, 2, 2, 3, 4, 4, 4])
>>> print(x.unique_values())
ivy.array([1, 2, 3, 4])

>>> x = ivy.array([[1, 2], [3, 4]])
>>> print(x.unique_values())
ivy.array([1, 2, 3, 4])

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

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