Set

ivy.unique_all(x)

Returns the unique elements of an input array x, the first occurring indices for each unique element in x, the indices from the set of unique elements that reconstruct x, and the corresponding counts for each unique element in x.

Data-dependent output shape

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).

As signed zeros are not distinct, using inverse_indices to reconstruct the input array is not guaranteed to return an array having the exact same values.

Each nan value should have a count of one, while the counts for signed zeros should be aggregated as a single count.

Parameters

x (Union[Array, NativeArray]) – 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[Union[Array, NativeArray], Union[Array, NativeArray], Union[Array, NativeArray], Union[Array, NativeArray]]

Returns

ret – a namedtuple (values, indices, inverse_indices, 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 indices and must be an array containing the indices (first occurrences) of x that result in values. The array must have the same shape as values and must have the default array index data type.

• third 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.

• fourth 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.

Note

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

This function conforms to the Array API Standard. This docstring is an extension of the docstring in the standard.

Both the description and the type hints above assumes an array input for simplicity, but this function is nestable, and therefore also accepts ivy.Container instances in place of any of the arguments.

Functional Examples

With ivy.Array input:

>>> x = ivy.random_normal(mean=0.0, std=1.0, shape=(2, 2))
>>> print(x)
ivy.array([[0.607,1.14],[0.735,0.667]])ivy.array([0.607,0.667,0.735,1.14])

>>> values, indices, inverse_indices, counts = ivy.unique_all(x)
>>> print(values)
ivy.array([0,3,2,1])ivy.array([[0,3],[2,1]])

>>> print(indices)
ivy.array([1,1,1,1])

>>> print(inverse_indices)
ivy.array([[1.52,0.381,0.857],[-0.0396,0.14,-0.166],[1.58,-0.828,-0.144]])

>>> print(counts)
ivy.array([-0.828,-0.166,-0.144,-0.0396,0.14,0.381,0.857,1.52,1.58])

>>> x = ivy.random_normal(mean=0.0, std=1.0, shape=(3, 3))
>>> print(x)
ivy.array([[-0.40501155,  1.77361575, -1.97776199],
           [-0.36831157,  0.89148434, -0.9512272 ],
           [ 0.67542176, -0.41985657,  0.23478023]])

>>> values, indices, inverse_indices, counts = ivy.unique_all(x)
>>> print(values)
ivy.array([-1.97776199, -0.9512272 , -0.41985657, -0.40501155, -0.36831157,
            0.23478023,  0.67542176,  0.89148434,  1.77361575])

>>> print(indices)
ivy.array([2, 5, 7, 0, 3, 8, 6, 4, 1])

>>> print(inverse_indices)
ivy.array([[3, 8, 0],
           [4, 7, 1],
           [6, 2, 5]])

>>> print(counts)
ivy.array([1, 1, 1, 1, 1, 1, 1, 1, 1])

With ivy.NativeArray input:

>>> x = ivy.native_array([[ 2.1141,  0.8101,  0.9298,  0.8460],
...                       [-1.2119, -0.3519, -0.6252,  0.4033],
...                       [ 0.7443,  0.2577, -0.3707, -0.0545],
...                       [-0.3238,  0.5944,  0.0775, -0.4327]])
>>> print(x)
ivy.array([[ 2.1141,  0.8101,  0.9298,  0.8460],
           [-1.2119, -0.3519, -0.6252,  0.4033],
           [ 0.7443,  0.2577, -0.3707, -0.0545],
           [-0.3238,  0.5944,  0.0775, -0.4327]])

>>> x[range(4), range(4)] = ivy.nan #Introduce NaN values
>>> print(x)
ivy.array([[    nan,  0.8101,  0.9298,  0.8460],
           [-1.2119,     nan, -0.6252,  0.4033],
           [ 0.7443,  0.2577,     nan, -0.0545],
           [-0.3238,  0.5944,  0.0775,     nan]])

>>> values, indices, inverse_indices, counts = ivy.unique_all(x)
>>> print(values)
ivy.array([-1.2119, -0.6252,  0.4033,     nan,     nan,     nan,     nan, -0.3238,
           -0.0545,  0.0775,  0.2577,  0.5944,  0.7443,  0.8101,  0.8460,  0.9298])

>>> print(indices)
ivy.array([ 4,  6,  7,  0,  5, 10, 15, 12, 11, 14,  9, 13,  8,  1,  3,  2])

>>> print(inverse_indices)
ivy.array([[ 3, 13, 15, 14],
           [ 0,  3,  1,  2],
           [12, 10,  3,  8],
           [ 7, 11,  9,  3]])

>>> print(counts)
ivy.array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1])

Instance Method Examples

With ivy.Array input:

>>> x = ivy.array([[ 2.1141,  0.8101,  0.9298,  0.8460],
...                [-1.2119, -0.3519, -0.6252,  0.4033],
...                [ 0.7443,  0.2577, -0.3707, -0.0545],
...                [-0.3238,  0.5944,  0.0775, -0.4327]])
>>> print(x)
ivy.array([[ 2.1141,  0.8101,  0.9298,  0.8460],
           [-1.2119, -0.3519, -0.6252,  0.4033],
           [ 0.7443,  0.2577, -0.3707, -0.0545],
           [-0.3238,  0.5944,  0.0775, -0.4327]])

>>> x[range(4), range(4)] = ivy.nan #Introduce NaN values
>>> print(x)
ivy.array([[    nan,  0.8101,  0.9298,  0.8460],
           [-1.2119,     nan, -0.6252,  0.4033],
           [ 0.7443,  0.2577,     nan, -0.0545],
           [-0.3238,  0.5944,  0.0775,     nan]])

>>> values, indices, inverse_indices, counts = x.unique_all()
>>> print(values)
ivy.array([-1.2119, -0.6252,  0.4033,     nan,     nan,     nan,     nan, -0.3238,
           -0.0545,  0.0775,  0.2577,  0.5944,  0.7443,  0.8101,  0.8460,  0.9298])

>>> print(indices)
ivy.array([ 4,  6,  7,  0,  5, 10, 15, 12, 11, 14,  9, 13,  8,  1,  3,  2])

>>> print(inverse_indices)
ivy.array([[ 3, 13, 15, 14],
           [ 0,  3,  1,  2],
           [12, 10,  3,  8],
           [ 7, 11,  9,  3]])

>>> print(counts)
ivy.array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1])

With ivy.NativeArray input:

>>> x = ivy.native_array([[-2.176,  0.889,  1.175, -0.763],
...                       [-0.071,  1.262, -0.456, -2.114],
...                       [-0.349,  0.615, -0.594, -1.335],
...                       [ 0.212,  0.457, -0.827,  0.209]])
>>> print(x)
ivy.array([[-2.176,  0.889,  1.175, -0.763],
           [-0.071,  1.262, -0.456, -2.114],
           [-0.349,  0.615, -0.594, -1.335],
           [ 0.212,  0.457, -0.827,  0.209]])

>>> x[range(4), range(4)] = ivy.nan #Introduce NaN values
>>> print(x)
ivy.array([[   nan,  0.889,  1.175, -0.763],
           [-0.071,    nan, -0.456, -2.114],
           [-0.349,  0.615,    nan, -1.335],
           [ 0.212,  0.457, -0.827,    nan]])

>>> values, indices, inverse_indices, counts = x.unique_all()
>>> print(values)
ivy.array([-2.114, -1.335, -0.827, -0.763, -0.456,
           -0.349, -0.071,  0.212,  0.457,  0.615,
            0.889,  1.175,    nan,    nan,    nan,
              nan])

>>> print(indices)
ivy.array([ 7, 11, 14,  3,  6,  8,  4, 12, 13,  9,  1,  2,  0,  5, 10, 15])

>>> print(inverse_indices)
ivy.array([[12, 10, 11,  3],
           [ 6, 12,  4,  0],
           [ 5,  9, 12,  1],
           [ 7,  8,  2, 12]])

>>> print(counts)
ivy.array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1])

ivy.unique_counts(x)

Returns the unique elements of an input array x and the corresponding counts for each unique element in x.

Data-dependent output shape

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 (Union[Array, NativeArray]) – 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[Union[Array, NativeArray], Union[Array, NativeArray]]

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.

• .. note:: – The order of unique elements is not specified and may vary between implementations.

This function conforms to the Array API Standard. This docstring is an extension of the docstring in the standard.

Both the description and the type hints above assumes an array input for simplicity, but this function is nestable, and therefore also accepts ivy.Container instances in place of any of the arguments.

Examples

With ivy.Array input:

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

>>> x = ivy.asarray([1,2,3,4],[2,3,4,5],[3,4,5,6])
>>> y = unique_counts(x)
>>> print(y)
Tuple([1,2,3,4,5,6],[1,2,3,3,2,1])

With ivy.NativeArray input:

>>> x = ivy.native_array([0.2,0.3,0.4,0.2,1.4,2.3,0.2])
>>> y = ivy.unique_counts(x)
>>> print(y)
Tuple([0.2,0.3,0.4,1.4,2.3],[3,1,1,1,1]

With ivy.Container input:

>>> x = ivy.Container(a=ivy.array([0., 1., 3. , 2. , 1. , 0.]),
...                   b=ivy.array([1, 2, 1, 3, 4, 1, 3]))
>>> y = ivy.unique_counts(x)
>>> print(y)
{
    a: (list[2],<classivy.array.array.Array>shape=[4]),
    b: (list[2],<classivy.array.array.Array>shape=[4])
}

ivy.unique_inverse(x)

Returns the unique elements of an input array x, and the indices from the

set of unique elements that reconstruct x.

Data-dependent output shape

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).

As signed zeros are not distinct, using inverse_indices to reconstruct the input array is not guaranteed to return an array having the exact same values.

Parameters

x (Union[Array, NativeArray]) – 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[Union[Array, NativeArray], Union[Array, NativeArray]]

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.

Note

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

This function conforms to the Array API Standard. This docstring is an extension of the docstring in the standard.

Both the description and the type hints above assumes an array input for simplicity, but this function is nestable, and therefore also accepts ivy.Container instances in place of any of the arguments.

Examples

With ivy.Array input:

>>> x = ivy.array([4,5,3,2,4,1,3])
>>> y = ivy.unique_inverse(x)
>>> print(y)
Results(values=ivy.array([1, 2, 3, 4, 5]),
inverse_indices=ivy.array([3, 4, 2, 1, 3, 0, 2]))

With ivy.NativeArray input:

>>> x = ivy.native_array([0.5,0.3,0.8,0.2,1.2,2.4,0.3])
>>> y = ivy.ivy.unique_inverse(x)
>>> print(y)
Results(values=ivy.array([0.2, 0.3, 0.5, 0.8, 1.2, 2.4]),
inverse_indices=ivy.array([2, 1, 3, 0, 4, 5, 1]))

With ivy.Container input:

>>> x = ivy.Container(a=ivy.array([1., 4., 3. , 5. , 3. , 7.]),
...                   b=ivy.array([3, 2, 6, 3, 7, 4, 9]))
>>> y = ivy.ivy.unique_inverse(x)
>>> print(y)
{
    a: (list[2], <class ivy.array.array.Array> shape=[5]),
    b: (list[2], <class ivy.array.array.Array> shape=[6])
}

ivy.unique_values(x, /, *, out=None)

Returns the unique elements of an input array x.

Data-dependent output shape

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

Return type

Array

Returns

ret – 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.

This function conforms to the Array API Standard. This docstring is an extension of the docstring in the standard.

Both the description and the type hints above assumes an array input for simplicity, but this function is nestable, and therefore also accepts ivy.Container instances in place of any of the arguments.