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Improve comments for clarity in logistic regression, stats overview, PCA, correlation, descriptive statistics, and matrix tests

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This commit is contained in:
Palash Tyagi 2025-08-02 21:59:22 +01:00
parent 5509416d5f
commit 7720312354
7 changed files with 22 additions and 78 deletions
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2
examples/logistic_regression.rs
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@ -16,7 +16,7 @@ fn student_passing_example() {
// Hours studied for each student
let hours = vec![1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0];
// 0 = fail, 1 = pass
// Label: 0 denotes failure and 1 denotes success
let passed = vec![0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0];
let x = Matrix::from_vec(hours.clone(), hours.len(), 1);

6
examples/stats_overview.rs
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@ -6,9 +6,9 @@ use rustframe::matrix::{Axis, Matrix};
/// Demonstrates some of the statistics utilities in Rustframe.
///
/// The example is split into three parts:
/// 1. Basic descriptive statistics on a small data set.
/// 2. Covariance and correlation calculations.
/// 3. Simple inferential tests (t-test and chi-square).
/// - Basic descriptive statistics on a small data set
/// - Covariance and correlation calculations
/// - Simple inferential tests (t-test and chi-square)
fn main() {
descriptive_demo();
println!("\n-----\n");

16
src/compute/models/pca.rs
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@ -44,11 +44,7 @@ mod tests {
#[test]
fn test_pca_basic() {
// Simple 2D data, points along y=x line
// Data:
// 1.0, 1.0
// 2.0, 2.0
// 3.0, 3.0
// Simple 2D data with points along the y = x line
let data = Matrix::from_rows_vec(vec![1.0, 1.0, 2.0, 2.0, 3.0, 3.0], 3, 2);
let (_n_samples, _n_features) = data.shape();
@ -71,15 +67,7 @@ mod tests {
assert!((pca.components.get(0, 0) - 1.0).abs() < EPSILON);
assert!((pca.components.get(0, 1) - 1.0).abs() < EPSILON);
// Test transform
// Centered data:
// -1.0, -1.0
// 0.0, 0.0
// 1.0, 1.0
// Projected: (centered_data * components.transpose())
// (-1.0 * 1.0 + -1.0 * 1.0) = -2.0
// ( 0.0 * 1.0 + 0.0 * 1.0) = 0.0
// ( 1.0 * 1.0 + 1.0 * 1.0) = 2.0
// Test transform: centered data projects to [-2.0, 0.0, 2.0]
let transformed_data = pca.transform(&data);
assert_eq!(transformed_data.rows(), 3);
assert_eq!(transformed_data.cols(), 1);

38
src/compute/stats/correlation.rs
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@ -137,10 +137,7 @@ mod tests {
#[test]
fn test_covariance_scalar_same_matrix() {
// M =
// 1,2
// 3,4
// mean = 2.5
// Matrix with rows [1, 2] and [3, 4]; mean is 2.5
let data = vec![1.0, 2.0, 3.0, 4.0];
let m = Matrix::from_vec(data.clone(), 2, 2);
@ -152,10 +149,7 @@ mod tests {
#[test]
fn test_covariance_scalar_diff_matrix() {
// x =
// 1,2
// 3,4
// y = 2*x
// Matrix x has rows [1, 2] and [3, 4]; y is two times x
let x = Matrix::from_vec(vec![1.0, 2.0, 3.0, 4.0], 2, 2);
let y = Matrix::from_vec(vec![2.0, 4.0, 6.0, 8.0], 2, 2);
@ -167,10 +161,7 @@ mod tests {
#[test]
fn test_covariance_vertical() {
// M =
// 1,2
// 3,4
// cols are [1,3] and [2,4], each var=1, cov=1
// Matrix with rows [1, 2] and [3, 4]; columns are [1,3] and [2,4], each var=1, cov=1
let m = Matrix::from_rows_vec(vec![1.0, 2.0, 3.0, 4.0], 2, 2);
let cov_mat = covariance_vertical(&m);
@ -184,10 +175,7 @@ mod tests {
#[test]
fn test_covariance_horizontal() {
// M =
// 1,2
// 3,4
// rows are [1,2] and [3,4], each var=0.25, cov=0.25
// Matrix with rows [1,2] and [3,4], each var=0.25, cov=0.25
let m = Matrix::from_rows_vec(vec![1.0, 2.0, 3.0, 4.0], 2, 2);
let cov_mat = covariance_horizontal(&m);
@ -201,10 +189,7 @@ mod tests {
#[test]
fn test_covariance_matrix_vertical() {
// Test with a simple 2x2 matrix
// M =
// 1, 2
// 3, 4
// Test with a simple 2x2 matrix with rows [1, 2] and [3, 4]
// Expected covariance matrix (vertical, i.e., between columns):
// Col1: [1, 3], mean = 2
// Col2: [2, 4], mean = 3
@ -212,9 +197,7 @@ mod tests {
// Cov(Col2, Col2) = ((2-3)^2 + (4-3)^2) / (2-1) = (1+1)/1 = 2
// Cov(Col1, Col2) = ((1-2)*(2-3) + (3-2)*(4-3)) / (2-1) = ((-1)*(-1) + (1)*(1))/1 = (1+1)/1 = 2
// Cov(Col2, Col1) = 2
// Expected:
// 2, 2
// 2, 2
// Expected matrix filled with 2
let m = Matrix::from_rows_vec(vec![1.0, 2.0, 3.0, 4.0], 2, 2);
let cov_mat = covariance_matrix(&m, Axis::Col);
@ -226,10 +209,7 @@ mod tests {
#[test]
fn test_covariance_matrix_horizontal() {
// Test with a simple 2x2 matrix
// M =
// 1, 2
// 3, 4
// Test with a simple 2x2 matrix with rows [1, 2] and [3, 4]
// Expected covariance matrix (horizontal, i.e., between rows):
// Row1: [1, 2], mean = 1.5
// Row2: [3, 4], mean = 3.5
@ -237,9 +217,7 @@ mod tests {
// Cov(Row2, Row2) = ((3-3.5)^2 + (4-3.5)^2) / (2-1) = (0.25+0.25)/1 = 0.5
// Cov(Row1, Row2) = ((1-1.5)*(3-3.5) + (2-1.5)*(4-3.5)) / (2-1) = ((-0.5)*(-0.5) + (0.5)*(0.5))/1 = (0.25+0.25)/1 = 0.5
// Cov(Row2, Row1) = 0.5
// Expected:
// 0.5, -0.5
// -0.5, 0.5
// Expected matrix: [[0.5, -0.5], [-0.5, 0.5]]
let m = Matrix::from_rows_vec(vec![1.0, 2.0, 3.0, 4.0], 2, 2);
let cov_mat = covariance_matrix(&m, Axis::Row);

6
src/compute/stats/descriptive.rs
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@ -350,11 +350,7 @@ mod tests {
let data: Vec<f64> = (1..=24).map(|x| x as f64).collect();
let x = Matrix::from_vec(data, 4, 6);
// columns:
// 1, 5, 9, 13, 17, 21
// 2, 6, 10, 14, 18, 22
// 3, 7, 11, 15, 19, 23
// 4, 8, 12, 16, 20, 24
// columns contain sequences increasing by four starting at 1 through 4
let er0 = vec![1., 5., 9., 13., 17., 21.];
let er50 = vec![3., 7., 11., 15., 19., 23.];

21
src/matrix/mat.rs
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@ -1028,9 +1028,7 @@ mod tests {
#[test]
fn test_from_rows_vec() {
// Representing:
// 1 2 3
// 4 5 6
// Matrix with rows [1, 2, 3] and [4, 5, 6]
let rows_data = vec![1.0, 2.0, 3.0, 4.0, 5.0, 6.0];
let matrix = Matrix::from_rows_vec(rows_data, 2, 3);
@ -1042,19 +1040,14 @@ mod tests {
// Helper function to create a basic Matrix for testing
fn static_test_matrix() -> Matrix<i32> {
// Column-major data:
// 1 4 7
// 2 5 8
// 3 6 9
// Column-major data representing a 3x3 matrix of sequential integers
let data = vec![1, 2, 3, 4, 5, 6, 7, 8, 9];
Matrix::from_vec(data, 3, 3)
}
// Another helper for a different size
fn static_test_matrix_2x4() -> Matrix<i32> {
// Column-major data:
// 1 3 5 7
// 2 4 6 8
// Column-major data representing a 2x4 matrix of sequential integers
let data = vec![1, 2, 3, 4, 5, 6, 7, 8];
Matrix::from_vec(data, 2, 4)
}
@ -1132,10 +1125,7 @@ mod tests {
#[test]
fn test_from_cols_basic() {
// Representing:
// 1 4 7
// 2 5 8
// 3 6 9
// Matrix with columns forming a 3x3 sequence
let cols_data = vec![vec![1, 2, 3], vec![4, 5, 6], vec![7, 8, 9]];
let matrix = Matrix::from_cols(cols_data);
@ -1512,8 +1502,7 @@ mod tests {
// Delete the first row
matrix.delete_row(0);
// Should be:
// 3 6 9
// Resulting data should be [3, 6, 9]
assert_eq!(matrix.rows(), 1);
assert_eq!(matrix.cols(), 3);
assert_eq!(matrix.data(), &[3, 6, 9]);

11
src/matrix/seriesops.rs
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@ -215,20 +215,13 @@ mod tests {
// Helper function to create a FloatMatrix for SeriesOps testing
fn create_float_test_matrix() -> FloatMatrix {
// 3x3 matrix (column-major) with some NaNs
// 1.0 4.0 7.0
// 2.0 NaN 8.0
// 3.0 6.0 NaN
// 3x3 column-major matrix containing a few NaN values
let data = vec![1.0, 2.0, 3.0, 4.0, f64::NAN, 6.0, 7.0, 8.0, f64::NAN];
FloatMatrix::from_vec(data, 3, 3)
}
fn create_float_test_matrix_4x4() -> FloatMatrix {
// 4x4 matrix (column-major) with some NaNs
// 1.0 5.0 9.0 13.0
// 2.0 NaN 10.0 NaN
// 3.0 6.0 NaN 14.0
// NaN 7.0 11.0 NaN
// 4x4 column-major matrix with NaNs inserted at positions where index % 5 == 0
// first make array with 16 elements
FloatMatrix::from_vec(
(0..16)