Function standardize_columns 

pub fn standardize_columns(x: &Tensor) -> Result<Tensor, MattenMlprepError>

Standardizes each column to zero mean and unit (population) standard deviation: out[i,j] = (x[i,j] - mean_j) / std_j.

std_j uses the population formula (divide by n), matching scikit-learn’s StandardScaler.

Errors

• MattenMlprepError::ExpectedMatrix if x is not rank-2.
• MattenMlprepError::ZeroVariance if any column is constant.
• MattenMlprepError::DynamicTensor (with the dynamic feature) if x is dynamic.

use matten::Tensor;
use matten_mlprep::standardize_columns;

// Column 0: [1, 3] -> mean 2, std 1 -> [-1, 1]; column 1: [10, 20] -> [-1, 1].
let x = Tensor::new(vec![1.0, 10.0, 3.0, 20.0], &[2, 2]);
let z = standardize_columns(&x).unwrap();
assert_eq!(z.as_slice(), &[-1.0, -1.0, 1.0, 1.0]);

Examples found in repository

examples/standardize_columns.rs (line 8)

fn main() {
    let x = Tensor::new(vec![1.0, 10.0, 2.0, 20.0, 3.0, 30.0], &[3, 2]);
    let z = standardize_columns(&x).expect("two non-constant columns");
    println!("input  shape {:?}: {:?}", x.shape(), x.as_slice());
    println!("z-score      {:?}: {:?}", z.shape(), z.as_slice());
}