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//! Tensor creation functions analogous to `np.zeros`, `np.ones`, etc.
use crate::error::{CoreError, Result};
use crate::{Float, Scalar};
use super::{Tensor, compute_strides};
impl<T: Scalar> Tensor<T> {
/// Create a tensor filled with zeros.
///
/// ```
/// # use scivex_core::tensor::Tensor;
/// let t = Tensor::<f64>::zeros(vec![2, 3]);
/// assert_eq!(t.shape(), &[2, 3]);
/// assert!(t.iter().all(|&x| x == 0.0));
/// ```
pub fn zeros(shape: Vec<usize>) -> Self {
let numel: usize = shape.iter().product();
let strides = compute_strides(&shape);
Self {
data: vec![T::zero(); numel],
shape,
strides,
}
}
/// Create a tensor filled with ones.
///
/// ```
/// # use scivex_core::tensor::Tensor;
/// let t = Tensor::<f64>::ones(vec![2, 3]);
/// assert!(t.iter().all(|&x| x == 1.0));
/// ```
pub fn ones(shape: Vec<usize>) -> Self {
let numel: usize = shape.iter().product();
let strides = compute_strides(&shape);
Self {
data: vec![T::one(); numel],
shape,
strides,
}
}
/// Create a tensor filled with a constant value.
///
/// ```
/// # use scivex_core::tensor::Tensor;
/// let t = Tensor::full(vec![2, 2], 7_i32);
/// assert!(t.iter().all(|&x| x == 7));
/// ```
pub fn full(shape: Vec<usize>, value: T) -> Self {
let numel: usize = shape.iter().product();
let strides = compute_strides(&shape);
Self {
data: vec![value; numel],
shape,
strides,
}
}
/// Create a 1-D tensor with values `[0, 1, 2, ..., n-1]`.
///
/// ```
/// # use scivex_core::tensor::Tensor;
/// let t = Tensor::<i32>::arange(5);
/// assert_eq!(t.as_slice(), &[0, 1, 2, 3, 4]);
/// ```
pub fn arange(n: usize) -> Self {
let data: Vec<T> = (0..n).map(T::from_usize).collect();
let strides = compute_strides(&[n]);
Self {
data,
shape: vec![n],
strides,
}
}
/// Create an identity matrix of size `n x n`.
///
/// ```
/// # use scivex_core::tensor::Tensor;
/// let eye = Tensor::<f64>::eye(3);
/// assert_eq!(eye.shape(), &[3, 3]);
/// assert_eq!(*eye.get(&[0, 0]).unwrap(), 1.0);
/// assert_eq!(*eye.get(&[0, 1]).unwrap(), 0.0);
/// ```
pub fn eye(n: usize) -> Self {
let mut data = vec![T::zero(); n * n];
for i in 0..n {
data[i * n + i] = T::one();
}
let strides = compute_strides(&[n, n]);
Self {
data,
shape: vec![n, n],
strides,
}
}
}
impl<T: Float> Tensor<T> {
/// Create a 1-D tensor with `n` evenly spaced values from `start` to `end`
/// (inclusive).
///
/// Returns an error if `n < 2`.
///
/// ```
/// # use scivex_core::tensor::Tensor;
/// let t = Tensor::<f64>::linspace(0.0, 1.0, 5).unwrap();
/// assert_eq!(t.shape(), &[5]);
/// ```
pub fn linspace(start: T, end: T, n: usize) -> Result<Self> {
if n < 2 {
return Err(CoreError::InvalidArgument {
reason: "linspace requires n >= 2",
});
}
let step = (end - start) / T::from_usize(n - 1);
let data: Vec<T> = (0..n).map(|i| start + step * T::from_usize(i)).collect();
let strides = compute_strides(&[n]);
Ok(Self {
data,
shape: vec![n],
strides,
})
}
}
#[cfg(test)]
#[allow(clippy::float_cmp)]
mod tests {
use super::*;
#[test]
fn test_zeros() {
let t = Tensor::<f64>::zeros(vec![3, 4]);
assert_eq!(t.shape(), &[3, 4]);
assert_eq!(t.numel(), 12);
assert!(t.iter().all(|&x| x == 0.0));
}
#[test]
fn test_ones() {
let t = Tensor::<f32>::ones(vec![2, 2]);
assert!(t.iter().all(|&x| x == 1.0));
}
#[test]
fn test_full() {
let t = Tensor::full(vec![2, 3], 7_i32);
assert!(t.iter().all(|&x| x == 7));
}
#[test]
fn test_arange() {
let t = Tensor::<i32>::arange(5);
assert_eq!(t.as_slice(), &[0, 1, 2, 3, 4]);
assert_eq!(t.shape(), &[5]);
}
#[test]
fn test_arange_zero() {
let t = Tensor::<i32>::arange(0);
assert!(t.is_empty());
assert_eq!(t.shape(), &[0]);
}
#[test]
fn test_eye() {
let t = Tensor::<f64>::eye(3);
assert_eq!(t.shape(), &[3, 3]);
assert_eq!(*t.get(&[0, 0]).unwrap(), 1.0);
assert_eq!(*t.get(&[1, 1]).unwrap(), 1.0);
assert_eq!(*t.get(&[2, 2]).unwrap(), 1.0);
assert_eq!(*t.get(&[0, 1]).unwrap(), 0.0);
assert_eq!(*t.get(&[1, 0]).unwrap(), 0.0);
}
#[test]
fn test_linspace() {
let t = Tensor::<f64>::linspace(0.0, 1.0, 5).unwrap();
assert_eq!(t.shape(), &[5]);
assert_eq!(*t.get(&[0]).unwrap(), 0.0);
assert_eq!(*t.get(&[4]).unwrap(), 1.0);
assert!((t.as_slice()[2] - 0.5).abs() < 1e-15);
}
#[test]
fn test_linspace_invalid() {
assert!(Tensor::<f64>::linspace(0.0, 1.0, 1).is_err());
}
}