If you are familiar with Python Numpy, do check out this For Numpy User Doc after you go through this tutorial.
You can use play.integer32.com to immediately try out the examples.
Just create your first 2x3 floating-point ndarray
use ndarray::prelude::*;
fn main() {
let a = array![
[1.,2.,3.],
[4.,5.,6.],
];
assert_eq!(a.ndim(), 2); // get the number of dimensions of array a
assert_eq!(a.len(), 6); // get the number of elements in array a
assert_eq!(a.shape(), [2, 3]); // get the shape of array a
assert_eq!(a.is_empty(), false); // check if the array has zero elements
println!("{:?}", a);
}This code will create a simple array and output to stdout:
[[1.0, 2.0, 3.0],
[4.0, 5.0, 6.0]], shape=[2, 3], strides=[3, 1], layout=C (0x1), const ndim=2
Now let's create more arrays. How about try make a zero array with dimension of (3, 2, 4)?
use ndarray::prelude::*;
use ndarray::Array;
fn main() {
let a = Array::zeros((3, 2, 4).f());
println!("{:?}", a);
}gives
| let a = Array::zeros((3, 2, 4).f());
| - ^^^^^^^^^^^^ cannot infer type for type parameter `A`
Note that the compiler needs to infer the element type and dimensionality from context. In this case the compiler failed to do that. Now we give it the type and let it infer dimensionality
use ndarray::prelude::*;
use ndarray::Array;
fn main() {
let a = Array::<f64, _>::zeros((3, 2, 4).f());
println!("{:?}", a);
}and now it works:
[[[0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 0.0]],
[[0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 0.0]],
[[0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 0.0]]], shape=[3, 2, 4], strides=[1, 3, 6], layout=F (0x2), const ndim=3
We can also specify its dimensionality
use ndarray::prelude::*;
use ndarray::{Array, Ix3};
fn main() {
let a = Array::<f64, Ix3>::zeros((3, 2, 4).f());
println!("{:?}", a);
}Ix3 stands for 3D array.
And now we are type checked. Try change the code above to Array::<f64, Ix3>::zeros((3, 2, 4, 5).f());
and compile, see what happens.
The from_elem method can be handy here:
use ndarray::{Array, Ix3};
fn main() {
let a = Array::<bool, Ix3>::from_elem((3, 2, 4), false);
println!("{:?}", a);
}linspace - Create a 1-D array with 11 elements with values 0., …, 5.
use ndarray::prelude::*;
use ndarray::{Array, Ix3};
fn main() {
let a = Array::<f64, _>::linspace(0., 5., 11);
println!("{:?}", a);
}The output is:
[0.0, 0.5, 1.0, 1.5, 2.0, 2.5, 3.0, 3.5, 4.0, 4.5, 5.0], shape=[11], strides=[1], layout=C | F (0x3), const ndim=1
And there are also range, logspace, ones, eye and so on you can choose to use.
use ndarray::prelude::*;
use ndarray::Array;
use std::f64::INFINITY as inf;
fn main() {
let a = array![
[10.,20.,30., 40.,],
];
let b = Array::range(0., 4., 1.); // [0., 1., 2., 3, ]
assert_eq!(&a + &b, array![[10., 21., 32., 43.,]]); // Allocates a new array. Note the explicit `&`.
assert_eq!(&a - &b, array![[10., 19., 28., 37.,]]);
assert_eq!(&a * &b, array![[0., 20., 60., 120.,]]);
assert_eq!(&a / &b, array![[inf, 20., 15., 13.333333333333334,]]);
}Try remove all the & sign in front of a and b, does it still compile? Why?
Note that
&A @ &Aproduces a newArrayB @ AconsumesB, updates it with the result, and returns itB @ &AconsumesB, updates it with the result, and returns itC @= &Aperforms an arithmetic operation in place
For more info checkout https://docs.rs/ndarray/latest/ndarray/struct.ArrayBase.html#arithmetic-operations
Some operations have _axis appended to the function name: they generally take in a parameter of type Axis as one of their inputs,
such as sum_axis:
use ndarray::{aview0, aview1, arr2, Axis};
fn main() {
let a = arr2(&[[1., 2., 3.],
[4., 5., 6.]]);
assert!(
a.sum_axis(Axis(0)) == aview1(&[5., 7., 9.]) &&
a.sum_axis(Axis(1)) == aview1(&[6., 15.]) &&
a.sum_axis(Axis(0)).sum_axis(Axis(0)) == aview0(&21.) &&
a.sum_axis(Axis(0)).sum_axis(Axis(0)) == aview0(&a.sum())
);
}use ndarray::prelude::*;
use ndarray::Array;
fn main() {
let a = array![
[10.,20.,30., 40.,],
];
let b = Array::range(0., 4., 1.); // b = [0., 1., 2., 3, ]
println!("a shape {:?}", &a.shape());
println!("b shape {:?}", &b.shape());
let b = b.into_shape((4,1)).unwrap(); // reshape b to shape [4, 1]
println!("b shape {:?}", &b.shape());
println!("{}", a.dot(&b)); // [1, 4] x [4, 1] -> [1, 1]
println!("{}", a.t().dot(&b.t())); // [4, 1] x [1, 4] -> [4, 4]
}The output is:
a shape [1, 4]
b shape [4]
b shape after reshape [4, 1]
[[200]]
[[0, 10, 20, 30],
[0, 20, 40, 60],
[0, 30, 60, 90],
[0, 40, 80, 120]]
One-dimensional arrays can be indexed, sliced and iterated over, much like numpy arrays
use ndarray::prelude::*;
use ndarray::Array;
fn main() {
let a = Array::range(0., 10., 1.);
let mut a = a.mapv(|a: f64| a.powi(3)); // numpy equivlant of `a ** 3`; https://doc.rust-lang.org/nightly/std/primitive.f64.html#method.powi
println!("{}", a);
println!("{}", a[[2]]);
println!("{}", a.slice(s![2]));
println!("{}", a.slice(s![2..5]));
a.slice_mut(s![..6;2]).fill(1000.); // numpy equivlant of `a[:6:2] = 1000`
println!("{}", a);
for i in a.iter() {
print!("{}, ", i.powf(1./3.))
}
}The output is:
[0, 1, 8, 27, 64, 125, 216, 343, 512, 729]
8
8
[8, 27, 64]
[1000, 1, 1000, 27, 1000, 125, 216, 343, 512, 729]
9.999999999999998, 1, 9.999999999999998, 3, 9.999999999999998, 4.999999999999999, 5.999999999999999, 6.999999999999999, 7.999999999999999, 8.999999999999998,
For more info about iteration see Loops, Producers, and Iterators
Let's try a 3D array with elements of type isize. This is how you index it:
use ndarray::prelude::*;
fn main() {
let a = array![
[[ 0, 1, 2], // a 3D array 2 x 2 x 3
[ 10, 12, 13]],
[[100,101,102],
[110,112,113]]
];
let a = a.mapv(|a: isize| a.pow(1)); // numpy equivlant of `a ** 1`;
// This line does nothing but illustrate mapv with isize type
println!("a -> \n{}\n", a);
println!("`a.slice(s![1, .., ..])` -> \n{}\n", a.slice(s![1, .., ..]));
println!("`a.slice(s![.., .., 2])` -> \n{}\n", a.slice(s![.., .., 2]));
println!("`a.slice(s![.., 1, 0..2])` -> \n{}\n", a.slice(s![.., 1, 0..2]));
println!("`a.iter()` ->");
for i in a.iter() {
print!("{}, ", i) // flat out to every element
}
println!("\n\n`a.outer_iter()` ->");
for i in a.outer_iter() {
print!("row: {}, \n", i) // iterate through first dimension
}
}The output is:
a ->
[[[0, 1, 2],
[10, 12, 13]],
[[100, 101, 102],
[110, 112, 113]]]
`a.slice(s![1, .., ..])` ->
[[100, 101, 102],
[110, 112, 113]]
`a.slice(s![.., .., 2])` ->
[[2, 13],
[102, 113]]
`a.slice(s![.., 1, 0..2])` ->
[[10, 12],
[110, 112]]
`a.iter()` ->
0, 1, 2, 10, 12, 13, 100, 101, 102, 110, 112, 113,
`a.outer_iter()` ->
row: [[0, 1, 2],
[10, 12, 13]],
row: [[100, 101, 102],
[110, 112, 113]],
The shape of an array can be changed with into_shape method.
use ndarray::prelude::*;
use ndarray::Array;
use std::iter::FromIterator;
// use ndarray_rand::RandomExt;
// use ndarray_rand::rand_distr::Uniform;
fn main() {
// Or you may use ndarray_rand crate to generate random arrays
// let a = Array::random((2, 5), Uniform::new(0., 10.));
let a = array![
[3., 7., 3., 4.],
[1., 4., 2., 2.],
[7., 2., 4., 9.]];
println!("a = \n{:?}\n", a);
// use trait FromIterator to flatten a matrix to a vector
let b = Array::from_iter(a.iter());
println!("b = \n{:?}\n", b);
let c = b.into_shape([6, 2]).unwrap(); // consume b and generate c with new shape
println!("c = \n{:?}", c);
}The output is:
a =
[[3.0, 7.0, 3.0, 4.0],
[1.0, 4.0, 2.0, 2.0],
[7.0, 2.0, 4.0, 9.0]], shape=[3, 4], strides=[4, 1], layout=C (0x1), const ndim=2
b =
[3.0, 7.0, 3.0, 4.0, 1.0, 4.0, 2.0, 2.0, 7.0, 2.0, 4.0, 9.0], shape=[12], strides=[1], layout=C | F (0x3), const ndim=1
c =
[[3.0, 7.0],
[3.0, 4.0],
[1.0, 4.0],
[2.0, 2.0],
[7.0, 2.0],
[4.0, 9.0]], shape=[6, 2], strides=[2, 1], layout=C (0x1), const ndim=2
Macro stack! is helpful for stacking arrays:
use ndarray::prelude::*;
use ndarray::{Array, Axis, stack};
fn main() {
let a = array![
[9., 7.],
[5., 2.]];
let b = array![
[1., 9.],
[5., 1.]];
println!("a vstack b = \n{:?}\n", stack![Axis(0), a, b]);
println!("a hstack b = \n{:?}\n", stack![Axis(1), a, b]);
}The output is:
a vstack b =
[[9.0, 7.0],
[5.0, 2.0],
[1.0, 9.0],
[5.0, 1.0]], shape=[4, 2], strides=[2, 1], layout=C (0x1), const ndim=2
a hstack b =
[[9.0, 7.0, 1.0, 9.0],
[5.0, 2.0, 5.0, 1.0]], shape=[2, 4], strides=[4, 1], layout=C (0x1), const ndim=2
More to see here ArrayView::split_at
use ndarray::prelude::*;
use ndarray::Axis;
fn main() {
let a = array![
[6., 7., 6., 9., 0., 5., 4., 0., 6., 8., 5., 2.],
[8., 5., 5., 7., 1., 8., 6., 7., 1., 8., 1., 0.]];
let (s1, s2) = a.view().split_at(Axis(0), 1);
println!("Split a from Axis(0), at index 1:");
println!("s1 = \n{}", s1);
println!("s2 = \n{}\n", s2);
let (s1, s2) = a.view().split_at(Axis(1), 4);
println!("Split a from Axis(1), at index 4:");
println!("s1 = \n{}", s1);
println!("s2 = \n{}\n", s2);
}The output is:
Split a from Axis(0), at index 1:
s1 =
[[6, 7, 6, 9, 0, 5, 4, 0, 6, 8, 5, 2]]
s2 =
[[8, 5, 5, 7, 1, 8, 6, 7, 1, 8, 1, 0]]
Split a from Axis(1), at index 4:
s1 =
[[6, 7, 6, 9],
[8, 5, 5, 7]]
s2 =
[[0, 5, 4, 0, 6, 8, 5, 2],
[1, 8, 6, 7, 1, 8, 1, 0]]
As in Rust we have owner ship, so we cannot simply update an element of an array while we have a shared view of it. This will help us write more robust code.
use ndarray::prelude::*;
use ndarray::{Array, Axis};
fn main() {
let mut a = Array::range(0., 12., 1.).into_shape([3 ,4]).unwrap();
println!("a = \n{}\n", a);
{
let (s1, s2) = a.view().split_at(Axis(1), 2);
// with s as a view sharing the ref of a, we cannot update a here
// a.slice_mut(s![1, 1]).fill(1234.);
println!("Split a from Axis(0), at index 1:");
println!("s1 = \n{}", s1);
println!("s2 = \n{}\n", s2);
}
// now we can update a again here, as views of s1, s2 are dropped already
a.slice_mut(s![1, 1]).fill(1234.);
let (s1, s2) = a.view().split_at(Axis(1), 2);
println!("Split a from Axis(0), at index 1:");
println!("s1 = \n{}", s1);
println!("s2 = \n{}\n", s2);
}The output is:
a =
[[0, 1, 2, 3],
[4, 5, 6, 7],
[8, 9, 10, 11]]
Split a from Axis(0), at index 1:
s1 =
[[0, 1],
[4, 5],
[8, 9]]
s2 =
[[2, 3],
[6, 7],
[10, 11]]
Split a from Axis(0), at index 1:
s1 =
[[0, 1],
[4, 1234],
[8, 9]]
s2 =
[[2, 3],
[6, 7],
[10, 11]]
As the usual way in Rust, a clone() call will
make a copy of your array:
use ndarray::prelude::*;
use ndarray::Array;
fn main() {
let mut a = Array::range(0., 4., 1.).into_shape([2 ,2]).unwrap();
let b = a.clone();
println!("a = \n{}\n", a);
println!("b clone of a = \n{}\n", a);
a.slice_mut(s![1, 1]).fill(1234.);
println!("a updated...");
println!("a = \n{}\n", a);
println!("b clone of a = \n{}\n", b);
}The output is:
a =
[[0, 1],
[2, 3]]
b clone of a =
[[0, 1],
[2, 3]]
a updated...
a =
[[0, 1],
[2, 1234]]
b clone of a =
[[0, 1],
[2, 3]]
Noticing that using clone() (or cloning) an Array type also copies the array's elements. It creates an independently owned array of the same type.
Cloning an ArrayView does not clone or copy the underlying elements - it just clones the view reference (as it happens in Rust when cloning a & reference).
Arrays support limited broadcasting, where arithmetic operations with array operands of different sizes can be carried out by repeating the elements of the smaller dimension array.
use ndarray::prelude::*;
fn main() {
let a = array![
[1., 1.],
[1., 2.],
[0., 3.],
[0., 4.]];
let b = array![[0., 1.]];
let c = array![
[1., 2.],
[1., 3.],
[0., 4.],
[0., 5.]];
// We can add because the shapes are compatible even if not equal.
// The `b` array is shape 1 × 2 but acts like a 4 × 2 array.
assert!(c == a + b);
}See .broadcast() for a more detailed description.
And there is a short example of it:
use ndarray::prelude::*;
fn main() {
let a = array![
[1., 2.],
[3., 4.],
];
let b = a.broadcast((3, 2, 2)).unwrap();
println!("shape of a is {:?}", a.shape());
println!("a is broadcased to 3x2x2 = \n{}", b);
}The output is:
shape of a is [2, 2]
a is broadcased to 3x2x2 =
[[[1, 2],
[3, 4]],
[[1, 2],
[3, 4]],
[[1, 2],
[3, 4]]]
Please checkout these docs for more information