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

examples/logistic_regression.rs

@@ -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);

examples/stats_overview.rs

@@ -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.
+///   - Basic descriptive statistics on a small data set
-///   2. Covariance and correlation calculations.
+///   - Covariance and correlation calculations
-///   3. Simple inferential tests (t-test and chi-square).
+///   - Simple inferential tests (t-test and chi-square)
 fn main() {
     descriptive_demo();
     println!("\n-----\n");

src/compute/models/pca.rs

@@ -44,11 +44,7 @@ mod tests {
 
     #[test]
     fn test_pca_basic() {
-        // Simple 2D data, points along y=x line
+        // Simple 2D data with points along the y = x line
-        // Data:
-        // 1.0, 1.0
-        // 2.0, 2.0
-        // 3.0, 3.0
         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
+        // Test transform: centered data projects to [-2.0, 0.0, 2.0]
-        // 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
         let transformed_data = pca.transform(&data);
         assert_eq!(transformed_data.rows(), 3);
         assert_eq!(transformed_data.cols(), 1);

src/compute/stats/correlation.rs

@@ -137,10 +137,7 @@ mod tests {
 
     #[test]
     fn test_covariance_scalar_same_matrix() {
-        // M =
+        // Matrix with rows [1, 2] and [3, 4]; mean is 2.5
-        // 1,2
-        // 3,4
-        // mean = 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 =
+        // Matrix x has rows [1, 2] and [3, 4]; y is two times x
-        // 1,2
-        // 3,4
-        // y = 2*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 =
+        // Matrix with rows [1, 2] and [3, 4]; columns are [1,3] and [2,4], each var=1, cov=1
-        // 1,2
-        // 3,4
-        // cols 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 =
+        // Matrix with rows [1,2] and [3,4], each var=0.25, cov=0.25
-        // 1,2
-        // 3,4
-        // rows are [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
+        // Test with a simple 2x2 matrix with rows [1, 2] and [3, 4]
-        // M =
-        // 1, 2
-        // 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:
+        // Expected matrix filled with 2
-        // 2, 2
-        // 2, 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
+        // Test with a simple 2x2 matrix with rows [1, 2] and [3, 4]
-        // M =
-        // 1, 2
-        // 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:
+        // Expected matrix: [[0.5, -0.5], [-0.5, 0.5]]
-        // 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);
 

src/compute/stats/descriptive.rs

@@ -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:
+        // columns contain sequences increasing by four starting at 1 through 4
-        //  1,  5,  9, 13, 17, 21
-        //  2,  6, 10, 14, 18, 22
-        //  3,  7, 11, 15, 19, 23
-        //  4,  8, 12, 16, 20, 24
 
         let er0 = vec![1., 5., 9., 13., 17., 21.];
         let er50 = vec![3., 7., 11., 15., 19., 23.];

src/matrix/mat.rs

@@ -1028,9 +1028,7 @@ mod tests {
 
     #[test]
     fn test_from_rows_vec() {
-        // Representing:
+        // Matrix with rows [1, 2, 3] and [4, 5, 6]
-        // 1 2 3
-        // 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:
+        // Column-major data representing a 3x3 matrix of sequential integers
-        // 1 4 7
-        // 2 5 8
-        // 3 6 9
         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:
+        // Column-major data representing a 2x4 matrix of sequential integers
-        // 1 3 5 7
-        // 2 4 6 8
         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:
+        // Matrix with columns forming a 3x3 sequence
-        // 1 4 7
-        // 2 5 8
-        // 3 6 9
         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:
+        // Resulting data should be [3, 6, 9]
-        // 3 6 9
         assert_eq!(matrix.rows(), 1);
         assert_eq!(matrix.cols(), 3);
         assert_eq!(matrix.data(), &[3, 6, 9]);

src/matrix/seriesops.rs

@@ -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
+        // 3x3 column-major matrix containing a few NaN values
-        // 1.0  4.0  7.0
-        // 2.0  NaN  8.0
-        // 3.0  6.0  NaN
         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
+        // 4x4 column-major matrix with NaNs inserted at positions where index % 5 == 0
-        // 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
         // first make array with 16 elements
         FloatMatrix::from_vec(
             (0..16)