16_variance_std/
16_variance_std.rs

1//! Statistics core-lite: population variance and standard deviation (RFC-040).
2//!
3//! Run: cargo run --example 16_variance_std
4//!
5//! `var`/`std` are POPULATION statistics (ddof = 0): var = sum((x - mean)^2) / n,
6//! std = sqrt(var). `var_axis`/`std_axis` do the same along one axis, dropping it
7//! from the shape. Quantile, percentile, histogram, covariance, and correlation are
8//! out of core scope (a possible future matten-stats companion).
9
10use matten::Tensor;
11
12fn main() {
13    // Population variance and std over all elements.
14    let v = Tensor::from_vec(vec![1.0, 2.0, 3.0, 4.0]);
15    println!("data          = {:?}", v.as_slice());
16    println!("mean          = {}", v.mean());
17    println!("var (pop)     = {}", v.var()); // 1.25
18    println!("std (pop)     = {}", v.std()); // sqrt(1.25)
19    assert_eq!(v.var(), 1.25);
20
21    // Per-axis statistics on a 2x3 matrix; the reduced axis is dropped.
22    let m = Tensor::new(vec![1.0, 2.0, 3.0, 4.0, 5.0, 6.0], &[2, 3]);
23    println!("matrix        = {:?} shape {:?}", m.as_slice(), m.shape());
24
25    let var0 = m.var_axis(0); // per column -> [3]
26    println!(
27        "var_axis(0)   = {:?} shape {:?}",
28        var0.as_slice(),
29        var0.shape()
30    );
31    assert_eq!(var0.shape(), &[3]);
32
33    let std1 = m.std_axis(1); // per row -> [2]
34    println!(
35        "std_axis(1)   = {:?} shape {:?}",
36        std1.as_slice(),
37        std1.shape()
38    );
39    assert_eq!(std1.shape(), &[2]);
40
41    // try_* forms return Result; e.g. an out-of-range axis is Err.
42    assert!(m.try_var_axis(5).is_err());
43
44    println!("Variance/std: OK");
45}
16_variance_std/16_variance_std.rs

16_variance_std/
16_variance_std.rs
