tensor.xor

#tensor.xor

    fn xor(self: @Tensor<T>, other: @Tensor<T>) -> Tensor<usize>;

Computes the logical XOR of two tensors element-wise. The input tensors must have either:

• Exactly the same shape

• The same number of dimensions and the length of each dimension is either a common length or 1.

Args

• self(@Tensor<T>) - The first tensor to be compared

• other(@Tensor<T>) - The second tensor to be compared

Panics

• Panics if the shapes are not equal or broadcastable

Returns

A new Tensor<usize> of booleans (0 or 1) with the same shape as the broadcasted inputs.

Examples

Case 1: Compare tensors with same shape

use core::array::{ArrayTrait, SpanTrait};

use orion::operators::tensor::{TensorTrait, Tensor, U32Tensor};

fn xor_example() -> Tensor<usize> {
    let tensor_1 = TensorTrait::<u32>::new(
        shape: array![3, 3].span(), data: array![0, 1, 2, 3, 4, 5, 6, 7, 8].span(),
    );

    let tensor_2 = TensorTrait::<u32>::new(
        shape: array![3, 3].span(), data: array![0, 1, 2, 3, 4, 5, 9, 1, 5].span(),
    );

    return tensor_1.xor(@tensor_2);
}
>>> [0,0,0,0,0,0,0,0,0]

Case 2: Compare tensors with different shapes

use core::array::{ArrayTrait, SpanTrait};

use orion::operators::tensor::{TensorTrait, Tensor, U32Tensor};

fn xor_example() -> Tensor<usize> {
    let tensor_1 = TensorTrait::<u32>::new(
        shape: array![3, 3].span(), data: array![0, 1, 2, 3, 4, 5, 6, 7, 8].span(),
    );

    let tensor_2 = TensorTrait::<u32>::new(
        shape: array![1, 3].span(), data: array![0, 1, 2].span(),
    );

    return tensor_1.xor(@tensor_2);
}
>>> [0,0,0,1,0,0,1,0,0]