randn 0.1.0

A package to populate Nalgebra's vector, matrix and vector of matrices with standard normal random variables in parallel.
Documentation 
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#![doc = include_str!("../README.md")]

use rand_distr::StandardNormal;
use rand::{Rng, thread_rng};
use nalgebra::{DVector, DMatrix};
use rayon::prelude::*;

// Helper function to generate random data
fn parallel_randn(size: usize) -> Vec<f64> {
    (0..size).into_par_iter().map(|_| {
        let mut rng = thread_rng();
        rng.sample(&StandardNormal)
    }).collect()
}

/// Generates a random vector of the specified size:
pub fn randn_vector(size: usize) -> DVector<f64> {
    DVector::from_column_slice(&parallel_randn(size))
}

/// Generates a random matrix of the specified rows and columns sizes:
pub fn randn_matrix(rows: usize, cols: usize) -> DMatrix<f64> {
    DMatrix::from_row_slice(rows, cols, &parallel_randn(rows * cols))
}

/// Generates a vector of random matrices of specified rows, columns, and number of simulations:
pub fn randn_matrices(rows: usize, cols: usize, sims: usize) -> Vec<DMatrix<f64>> {
    (0..sims).into_par_iter().map(|_| randn_matrix(rows, cols)).collect()
}

#[cfg(test)]
mod tests {
    use super::*;

    fn mean(data: &DVector<f64>) -> f64 {
        data.sum() / data.len() as f64
    }

    fn variance(data: &DVector<f64>, mean: f64) -> f64 {
        data.iter().map(|&value| (value - mean).powi(2)).sum::<f64>() / data.len() as f64
    }

    fn standard_deviation(data: &DVector<f64>, mean: f64) -> f64 {
        variance(data, mean).sqrt()
    }

    #[test]
    fn test_vector_statistics() {
        let size: usize = 50_000;
        let vec = randn_vector(size);

        let mean_value = mean(&vec);
        let variance = variance(&vec, mean_value);
        let std_dev = variance.sqrt();
        let epsilon = 0.01;

        // Assert that the mean and standard deviation are approximately 0 and 1 respectively.
        assert!(
            (mean_value - 0.0).abs() < epsilon,
            "Mean is not approximately 0: actual mean = {}",
            mean_value
        );
        assert!(
            (std_dev - 1.0).abs() < epsilon,
            "Standard deviation is not approximately 1: actual std_dev = {}",
            std_dev
        );
    }

    #[test]
    fn test_matrix_statistics() {
        let (rows, cols): (usize, usize) = (50, 1000);
        let mat = randn_matrix(rows, cols);
        let data = mat.as_slice();

        let mean_value = mean(&DVector::from_vec(data.to_vec()));
        let std_dev = standard_deviation(&DVector::from_vec(data.to_vec()), mean_value);
        let epsilon = 0.01;

        // Assert that the mean and standard deviation are approximately 0 and 1 respectively.
        assert!(
            (mean_value - 0.0).abs() < epsilon,
            "Mean is not approximately 0: actual mean = {}",
            mean_value
        );
        assert!(
            (std_dev - 1.0).abs() < epsilon,
            "Standard deviation is not approximately 1: actual std_dev = {}",
            std_dev
        );
    }
}