applied formatting

src/compute/mod.rs

@@ -1,4 +1,3 @@
-
 pub mod models;
 
 pub mod stats;

src/compute/models/dense_nn.rs

@@ -349,7 +349,8 @@ mod tests {
                 assert!(
                     (output[(i, j)] - expected[(i, j)]).abs() < 1e-9,
                     "Tanh forward output mismatch at ({}, {})",
-                    i, j
+                    i,
+                    j
                 );
             }
         }
@@ -366,7 +367,8 @@ mod tests {
                 assert!(
                     (output[(i, j)] - expected[(i, j)]).abs() < 1e-9,
                     "ReLU derivative output mismatch at ({}, {})",
-                    i, j
+                    i,
+                    j
                 );
             }
         }
@@ -383,7 +385,8 @@ mod tests {
                 assert!(
                     (output[(i, j)] - expected[(i, j)]).abs() < 1e-9,
                     "Tanh derivative output mismatch at ({}, {})",
-                    i, j
+                    i,
+                    j
                 );
             }
         }
@@ -401,7 +404,10 @@ mod tests {
         assert_eq!(matrix.cols(), cols);
 
         for val in matrix.data() {
-            assert!(*val >= -limit && *val <= limit, "Xavier initialized value out of range");
+            assert!(
+                *val >= -limit && *val <= limit,
+                "Xavier initialized value out of range"
+            );
         }
     }
 
@@ -417,7 +423,10 @@ mod tests {
         assert_eq!(matrix.cols(), cols);
 
         for val in matrix.data() {
-            assert!(*val >= -limit && *val <= limit, "He initialized value out of range");
+            assert!(
+                *val >= -limit && *val <= limit,
+                "He initialized value out of range"
+            );
         }
     }
 
@@ -436,7 +445,8 @@ mod tests {
                 assert!(
                     (output_gradient[(i, j)] - expected_gradient[(i, j)]).abs() < 1e-9,
                     "BCE gradient output mismatch at ({}, {})",
-                    i, j
+                    i,
+                    j
                 );
             }
         }
@@ -462,16 +472,22 @@ mod tests {
 
         let before_preds = model.predict(&x);
         // BCE loss calculation for testing
-        let before_loss = -1.0 / (y.rows() as f64) * before_preds.zip(&y, |yh, yv| {
-            yv * yh.ln() + (1.0 - yv) * (1.0 - yh).ln()
-        }).data().iter().sum::<f64>();
+        let before_loss = -1.0 / (y.rows() as f64)
+            * before_preds
+                .zip(&y, |yh, yv| yv * yh.ln() + (1.0 - yv) * (1.0 - yh).ln())
+                .data()
+                .iter()
+                .sum::<f64>();
 
         model.train(&x, &y);
 
         let after_preds = model.predict(&x);
-        let after_loss = -1.0 / (y.rows() as f64) * after_preds.zip(&y, |yh, yv| {
-            yv * yh.ln() + (1.0 - yv) * (1.0 - yh).ln()
-        }).data().iter().sum::<f64>();
+        let after_loss = -1.0 / (y.rows() as f64)
+            * after_preds
+                .zip(&y, |yh, yv| yv * yh.ln() + (1.0 - yv) * (1.0 - yh).ln())
+                .data()
+                .iter()
+                .sum::<f64>();
 
         assert!(
             after_loss < before_loss,

src/compute/models/mod.rs

@@ -1,7 +1,7 @@
+pub mod activations;
+pub mod dense_nn;
+pub mod gaussian_nb;
+pub mod k_means;
 pub mod linreg;
 pub mod logreg;
-pub mod dense_nn;
-pub mod k_means;    
 pub mod pca;
-pub mod gaussian_nb;
-pub mod activations;

src/compute/models/pca.rs

@@ -1,6 +1,6 @@
-use crate::matrix::{Axis, Matrix, SeriesOps};
-use crate::compute::stats::descriptive::mean_vertical;
 use crate::compute::stats::correlation::covariance_matrix;
+use crate::compute::stats::descriptive::mean_vertical;
+use crate::matrix::{Axis, Matrix, SeriesOps};
 
 /// Returns the `n_components` principal axes (rows) and the centred data's mean.
 pub struct PCA {
@@ -24,10 +24,7 @@ impl PCA {
             }
         }
 
-        PCA {
-            components,
-            mean,
-        }
+        PCA { components, mean }
     }
 
     /// Project new data on the learned axes.

src/compute/stats/correlation.rs

@@ -94,7 +94,7 @@ pub fn covariance_matrix(x: &Matrix<f64>, axis: Axis) -> Matrix<f64> {
         }
         Axis::Row => {
             let mean_matrix = mean_horizontal(x); // n_samples x 1
-            // Manually create a matrix by broadcasting the column vector across columns
+                                                  // Manually create a matrix by broadcasting the column vector across columns
             let mut broadcasted_mean = Matrix::zeros(n_samples, n_features);
             for r in 0..n_samples {
                 let mean_val = mean_matrix.get(r, 0);

src/compute/stats/mod.rs

@@ -1,7 +1,7 @@
+pub mod correlation;
 pub mod descriptive;
 pub mod distributions;
-pub mod correlation;
 
+pub use correlation::*;
 pub use descriptive::*;
 pub use distributions::*;
-pub use correlation::*;

src/matrix/boolops.rs

@@ -171,7 +171,7 @@ mod tests {
     #[test]
     fn test_bool_ops_count_overall() {
         let matrix = create_bool_test_matrix(); // Data: [T, F, T, F, T, F, T, F, F]
-        // Count of true values: 4
+                                                // Count of true values: 4
         assert_eq!(matrix.count(), 4);
 
         let matrix_all_false = BoolMatrix::from_vec(vec![false; 5], 5, 1); // 5x1
@@ -211,7 +211,7 @@ mod tests {
     #[test]
     fn test_bool_ops_1xn_matrix() {
         let matrix = BoolMatrix::from_vec(vec![true, false, false, true], 1, 4); // 1 row, 4 cols
-        // Data: [T, F, F, T]
+                                                                                 // Data: [T, F, F, T]
 
         assert_eq!(matrix.any_vertical(), vec![true, false, false, true]);
         assert_eq!(matrix.all_vertical(), vec![true, false, false, true]);
@@ -229,7 +229,7 @@ mod tests {
     #[test]
     fn test_bool_ops_nx1_matrix() {
         let matrix = BoolMatrix::from_vec(vec![true, false, false, true], 4, 1); // 4 rows, 1 col
-        // Data: [T, F, F, T]
+                                                                                 // Data: [T, F, F, T]
 
         assert_eq!(matrix.any_vertical(), vec![true]); // T|F|F|T = T
         assert_eq!(matrix.all_vertical(), vec![false]); // T&F&F&T = F

src/matrix/mod.rs

@@ -1,7 +1,7 @@
+pub mod boolops;
 pub mod mat;
 pub mod seriesops;
-pub mod boolops;
 
+pub use boolops::*;
 pub use mat::*;
 pub use seriesops::*;
-pub use boolops::*;