Function smartnoise_runtime::components::covariance::matrix_cross_covariance

pub fn matrix_cross_covariance(
    left: &ArrayD<Float>, 
    right: &ArrayD<Float>, 
    delta_degrees_of_freedom: usize
) -> Result<ArrayD<Float>>

Construct cross-covariance matrix from pair of data matrices.

Element (i,j) of the cross-covariance matrix will be the covariance of the column i of left and column j of right

Arguments

• left - One of the two matrices for which you want the cross-covariance matrix.
• right - One of the two matrices for which you want the cross-covariance matrix.
• delta_degrees_of_freedom - 0 for population, 1 for finite sample correction

Return

Full cross-covariance matrix.

Example

use ndarray::{ArrayD, arr2};
use smartnoise_runtime::components::covariance::matrix_cross_covariance;

let left = arr2(&[ [1., 3., 5.,], [2., 4., 6.] ]).into_dyn();
let right = arr2(&[ [2., 4., 6.], [1., 3., 5.] ]).into_dyn();

let cross_covar = matrix_cross_covariance(&left, &right, 1).unwrap();
let left_covar = matrix_cross_covariance(&left, &left, 1).unwrap();

// cross-covariance of left and right matrices
assert_eq!(cross_covar, arr2(&[ [-0.5, -0.5, -0.5], [-0.5, -0.5, -0.5], [-0.5, -0.5, -0.5] ]).into_dyn());

// cross-covariance of left with itself is equivalent to the standard covariance matrix
assert_eq!(left_covar, arr2(&[ [0.5, 0.5, 0.5], [0.5, 0.5, 0.5], [0.5, 0.5, 0.5] ]).into_dyn());