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use crate::check;
use crate::check::TensorCheck;
use crate::{
backend::Backend, cartesian_grid, Float, Int, Shape, Tensor, TensorData, TensorPrimitive,
};
use core::ops::Range;
impl<B> Tensor<B, 1, Int>
where
B: Backend,
{
/// Returns a new integer tensor on the specified device.
///
/// # Arguments
///
/// * `range` - The range of values to generate.
/// * `device` - The device to create the tensor on.
pub fn arange(range: Range<i64>, device: &B::Device) -> Self {
Tensor::new(B::int_arange(range, device))
}
/// Returns a new integer tensor on the specified device.
///
/// # Arguments
///
/// * `range` - The range of values to generate.
/// * `step` - The step between each value.
pub fn arange_step(range: Range<i64>, step: usize, device: &B::Device) -> Self {
Tensor::new(B::int_arange_step(range, step, device))
}
/// Create a one hot tensor from an index tensor.
///
/// # Arguments
///
/// * `num_classes` - The number of classes to use in encoding.
///
/// # Example
///
/// ```rust
/// use burn_tensor::backend::Backend;
/// use burn_tensor::{Tensor, Int};
///
/// fn example<B: Backend>() {
/// let device = B::Device::default();
/// let indices: Tensor<B, 1, Int> = Tensor::from_ints([0, 1, 2, 3], &device);
/// let one_hot = indices.one_hot(4);
/// println!("{}", one_hot.to_data());
/// // [[1, 0, 0, 0], [0, 1, 0, 0], [0, 0, 1, 0], [0, 0, 0, 1]]
/// }
/// ```
pub fn one_hot(self, num_classes: usize) -> Tensor<B, 2, Int> {
check!(TensorCheck::one_hot_tensor(self.clone(), num_classes));
let [num_samples] = self.dims();
let indices = self.unsqueeze_dim(1);
let values = indices.ones_like();
Tensor::zeros([num_samples, num_classes], &indices.device()).scatter(1, indices, values)
}
}
impl<const D: usize, B> Tensor<B, D, Int>
where
B: Backend,
{
/// Create a tensor from integers (i32), placing it on a given device.
///
/// # Example
///
/// ```rust
/// use burn_tensor::backend::Backend;
/// use burn_tensor::{Tensor, Int};
///
/// fn example<B: Backend>() {
/// let device = B::Device::default();
/// let _x: Tensor<B, 1, Int> = Tensor::from_ints([1, 2], &device);
/// let _y: Tensor<B, 2, Int> = Tensor::from_ints([[1, 2], [3, 4]], &device);
/// }
/// ```
pub fn from_ints<A: Into<TensorData>>(ints: A, device: &B::Device) -> Self {
Self::from_data(ints.into().convert::<i32>(), device)
}
/// Returns a new tensor with the same shape and device as the current tensor and the data
/// cast to Float.
///
/// # Example
///
/// ```rust
/// use burn_tensor::backend::Backend;
/// use burn_tensor::{Int, Tensor};
///
/// fn example<B: Backend>() {
/// let device = Default::default();
/// let int_tensor = Tensor::<B, 1, Int>::arange(0..5, &device);
/// let float_tensor = int_tensor.float();
/// }
/// ```
pub fn float(self) -> Tensor<B, D, Float> {
Tensor::new(TensorPrimitive::Float(B::int_into_float(self.primitive)))
}
/// Generates a cartesian grid for the given tensor shape on the specified device.
/// The generated tensor is of dimension `D2 = D + 1`, where each element at dimension D contains the cartesian grid coordinates for that element.
///
/// # Arguments
///
/// * `shape` - The shape specifying the dimensions of the tensor.
/// * `device` - The device to create the tensor on.
///
/// # Panics
///
/// Panics if `D2` is not equal to `D+1`.
///
/// # Examples
///
/// ```rust
/// use burn_tensor::Int;
/// use burn_tensor::{backend::Backend, Shape, Tensor};
/// fn example<B: Backend>() {
/// let device = Default::default();
/// let result: Tensor<B, 3, _> = Tensor::<B, 2, Int>::cartesian_grid([2, 3], &device);
/// println!("{}", result);
/// }
/// ```
pub fn cartesian_grid<S: Into<Shape>, const D2: usize>(
shape: S,
device: &B::Device,
) -> Tensor<B, D2, Int> {
cartesian_grid::<B, S, D, D2>(shape, device)
}
}