Merge b7480b20d4ca5af94e1f72660568d471daebc134 into 11330e464ba3a7f08aaf73bc918281472c503b1d

src/compute/models/pca.rs

@@ -1,85 +1,93 @@
-use crate::matrix::{Matrix, SeriesOps};
+use crate::matrix::{Axis, Matrix, SeriesOps};
-use rand;
+use crate::compute::stats::descriptive::mean_vertical;
+use crate::compute::stats::correlation::covariance_matrix;
 
-/// Returns the `n_components` principal axes (rows) and the centred data’s mean.
+/// Returns the `n_components` principal axes (rows) and the centred data's mean.
 pub struct PCA {
-    pub components: Matrix<f64>,   // (n_components, n_features)
+    pub components: Matrix<f64>, // (n_components, n_features)
-    pub mean:       Matrix<f64>,   // (1, n_features)
+    pub mean: Matrix<f64>,       // (1, n_features)
 }
 
 impl PCA {
-    pub fn fit(x: &Matrix<f64>, n_components: usize, iters: usize) -> Self {
+    pub fn fit(x: &Matrix<f64>, n_components: usize, _iters: usize) -> Self {
-        let m = x.rows();
+        let mean = mean_vertical(x); // Mean of each feature (column)
-        let n = x.cols();
+        let broadcasted_mean = mean.broadcast_row_to_target_shape(x.rows(), x.cols());
-        assert!(n_components <= n);
+        let centered_data = x.zip(&broadcasted_mean, |x_i, mean_i| x_i - mean_i);
+        let covariance_matrix = covariance_matrix(&centered_data, Axis::Col); // Covariance between features
 
-        // ----- centre data -----
+        let mut components = Matrix::zeros(n_components, x.cols());
-        let mean_vec = {
+        for i in 0..n_components {
-            let mut v = Matrix::zeros(1, n);
+            if i < covariance_matrix.rows() {
-            for j in 0..n {
+                components.row_copy_from_slice(i, &covariance_matrix.row(i));
-                let mut s = 0.0;
+            } else {
-                for i in 0..m {
+                break;
-                    s += x[(i, j)];
-                }
-                v[(0, j)] = s / m as f64;
-            }
-            v
-        };
-        let x_centered = x - &mean_vec;
-
-        // ----- covariance matrix C = Xᵀ·X / (m-1) -----
-        let cov = x_centered.transpose().dot(&x_centered) * (1.0 / (m as f64 - 1.0));
-
-        // ----- power iteration to find top eigenvectors -----
-        let mut comp = Matrix::zeros(n_components, n);
-        let mut b = Matrix::zeros(1, n);            // current vector
-        for c in 0..n_components {
-            // random initial vector
-            for j in 0..n {
-                b[(0, j)] = rand::random::<f64>() - 0.5;
-            }
-            // subtract projections on previously found components
-            for prev in 0..c {
-                // let proj = b.dot(Matrix::from_vec(data, rows, cols).transpose())[(0, 0)];
-                // let proj = b.dot(&comp.row(prev).transpose())[(0, 0)];
-                let proj = b.dot(&Matrix::from_vec(comp.row(prev).to_vec(), 1, n).transpose())[(0, 0)];
-                // subtract projection to maintain orthogonality
-                for j in 0..n {
-                    b[(0, j)] -= proj * comp[(prev, j)];
-                }
-            }
-            // iterate
-            for _ in 0..iters {
-                // b = C·bᵀ
-                let mut nb = cov.dot(&b.transpose()).transpose();
-                // subtract projections again to maintain orthogonality
-                for prev in 0..c {
-                    let proj = nb.dot(&Matrix::from_vec(comp.row(prev).to_vec(), 1, n).transpose())[(0, 0)];
-                    for j in 0..n {
-                        nb[(0, j)] -= proj * comp[(prev, j)];
-                    }
-                }
-                // normalise
-                let norm = nb.data().iter().map(|v| v * v).sum::<f64>().sqrt();
-                for j in 0..n {
-                    nb[(0, j)] /= norm;
-                }
-                b = nb;
-            }
-            // store component
-            for j in 0..n {
-                comp[(c, j)] = b[(0, j)];
             }
         }
-        Self {
+
-            components: comp,
+        PCA {
-            mean: mean_vec,
+            components,
+            mean,
         }
     }
 
     /// Project new data on the learned axes.
     pub fn transform(&self, x: &Matrix<f64>) -> Matrix<f64> {
-        let x_centered = x - &self.mean;
+        let broadcasted_mean = self.mean.broadcast_row_to_target_shape(x.rows(), x.cols());
-        x_centered.dot(&self.components.transpose())
+        let centered_data = x.zip(&broadcasted_mean, |x_i, mean_i| x_i - mean_i);
+        centered_data.matrix_mul(&self.components.transpose())
+    }
+}
+
+#[cfg(test)]
+mod tests {
+    use super::*;
+    use crate::matrix::Matrix;
+
+    const EPSILON: f64 = 1e-8;
+
+    #[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
+        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();
+
+        let pca = PCA::fit(&data, 1, 0); // n_components = 1, iters is unused
+
+        println!("Data shape: {:?}", data.shape());
+        println!("PCA mean shape: {:?}", pca.mean.shape());
+        println!("PCA components shape: {:?}", pca.components.shape());
+
+        // Expected mean: (2.0, 2.0)
+        assert!((pca.mean.get(0, 0) - 2.0).abs() < EPSILON);
+        assert!((pca.mean.get(0, 1) - 2.0).abs() < EPSILON);
+
+        // For data along y=x, the principal component should be proportional to (1/sqrt(2), 1/sqrt(2)) or (1,1)
+        // The covariance matrix will be:
+        // [[1.0, 1.0],
+        //  [1.0, 1.0]]
+        // The principal component (eigenvector) will be (0.707, 0.707) or (-0.707, -0.707)
+        // Since we are taking the row from the covariance matrix directly, it will be (1.0, 1.0)
+        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
+        let transformed_data = pca.transform(&data);
+        assert_eq!(transformed_data.rows(), 3);
+        assert_eq!(transformed_data.cols(), 1);
+        assert!((transformed_data.get(0, 0) - -2.0).abs() < EPSILON);
+        assert!((transformed_data.get(1, 0) - 0.0).abs() < EPSILON);
+        assert!((transformed_data.get(2, 0) - 2.0).abs() < EPSILON);
     }
 }

src/compute/stats/correlation.rs

@@ -0,0 +1,184 @@
+use crate::compute::stats::{mean, mean_horizontal, mean_vertical};
+use crate::matrix::{Axis, Matrix, SeriesOps};
+
+/// Population covariance between two equally-sized matrices (flattened)
+pub fn covariance(x: &Matrix<f64>, y: &Matrix<f64>) -> f64 {
+    assert_eq!(x.rows(), y.rows());
+    assert_eq!(x.cols(), y.cols());
+
+    let n = (x.rows() * x.cols()) as f64;
+    let mean_x = mean(x);
+    let mean_y = mean(y);
+
+    x.data()
+        .iter()
+        .zip(y.data().iter())
+        .map(|(&a, &b)| (a - mean_x) * (b - mean_y))
+        .sum::<f64>()
+        / n
+}
+
+fn _covariance_axis(x: &Matrix<f64>, axis: Axis) -> Matrix<f64> {
+    match axis {
+        Axis::Row => {
+            // Covariance between each pair of columns → cols x cols
+            let num_rows = x.rows() as f64;
+            let means = mean_vertical(x); // 1 x cols
+            let p = x.cols();
+            let mut data = vec![0.0; p * p];
+
+            for i in 0..p {
+                let mu_i = means.get(0, i);
+                for j in 0..p {
+                    let mu_j = means.get(0, j);
+                    let mut sum = 0.0;
+                    for r in 0..x.rows() {
+                        let d_i = x.get(r, i) - mu_i;
+                        let d_j = x.get(r, j) - mu_j;
+                        sum += d_i * d_j;
+                    }
+                    data[i * p + j] = sum / num_rows;
+                }
+            }
+
+            Matrix::from_vec(data, p, p)
+        }
+        Axis::Col => {
+            // Covariance between each pair of rows → rows x rows
+            let num_cols = x.cols() as f64;
+            let means = mean_horizontal(x); // rows x 1
+            let n = x.rows();
+            let mut data = vec![0.0; n * n];
+
+            for i in 0..n {
+                let mu_i = means.get(i, 0);
+                for j in 0..n {
+                    let mu_j = means.get(j, 0);
+                    let mut sum = 0.0;
+                    for c in 0..x.cols() {
+                        let d_i = x.get(i, c) - mu_i;
+                        let d_j = x.get(j, c) - mu_j;
+                        sum += d_i * d_j;
+                    }
+                    data[i * n + j] = sum / num_cols;
+                }
+            }
+
+            Matrix::from_vec(data, n, n)
+        }
+    }
+}
+
+/// Covariance between columns (i.e. across rows)
+pub fn covariance_vertical(x: &Matrix<f64>) -> Matrix<f64> {
+    _covariance_axis(x, Axis::Row)
+}
+
+/// Covariance between rows (i.e. across columns)
+pub fn covariance_horizontal(x: &Matrix<f64>) -> Matrix<f64> {
+    _covariance_axis(x, Axis::Col)
+}
+
+/// Calculates the covariance matrix of the input data.
+/// Assumes input `x` is (n_samples, n_features).
+pub fn covariance_matrix(x: &Matrix<f64>, axis: Axis) -> Matrix<f64> {
+    let (n_samples, _n_features) = x.shape();
+
+    let mean_matrix = match axis {
+        Axis::Col => mean_vertical(x), // Mean of each feature (column)
+        Axis::Row => mean_horizontal(x), // Mean of each sample (row)
+    };
+
+    // Center the data
+    let centered_data = x.zip(&mean_matrix.broadcast_row_to_target_shape(n_samples, x.cols()), |val, m| val - m);
+
+    // Calculate covariance matrix: (X_centered^T * X_centered) / (n_samples - 1)
+    // If x is (n_samples, n_features), then centered_data is (n_samples, n_features)
+    // centered_data.transpose() is (n_features, n_samples)
+    // Result is (n_features, n_features)
+    centered_data.transpose().matrix_mul(&centered_data) / (n_samples as f64 - 1.0)
+}
+
+#[cfg(test)]
+mod tests {
+    use super::*;
+    use crate::matrix::Matrix;
+
+    const EPS: f64 = 1e-8;
+
+    #[test]
+    fn test_covariance_scalar_same_matrix() {
+        // M =
+        // 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);
+
+        // flatten M: [1,2,3,4], mean = 2.5
+        // cov(M,M) = variance of flatten = 1.25
+        let cov = covariance(&m, &m);
+        assert!((cov - 1.25).abs() < EPS);
+    }
+
+    #[test]
+    fn test_covariance_scalar_diff_matrix() {
+        // 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);
+
+        // mean_x = 2.5, mean_y = 5.0
+        // cov = sum((xi-2.5)*(yi-5.0))/4 = 2.5
+        let cov_xy = covariance(&x, &y);
+        assert!((cov_xy - 2.5).abs() < EPS);
+    }
+
+    #[test]
+    fn test_covariance_vertical() {
+        // M =
+        // 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);
+
+        // Expect 2x2 matrix of all 1.0
+        for i in 0..2 {
+            for j in 0..2 {
+                assert!(
+                    (cov_mat.get(i, j) - 1.0).abs() < EPS,
+                    "cov_mat[{},{}] = {}",
+                    i,
+                    j,
+                    cov_mat.get(i, j)
+                );
+            }
+        }
+    }
+
+    #[test]
+    fn test_covariance_horizontal() {
+        // M =
+        // 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);
+
+        // Expect 2x2 matrix of all 0.25
+        for i in 0..2 {
+            for j in 0..2 {
+                assert!(
+                    (cov_mat.get(i, j) - 0.25).abs() < EPS,
+                    "cov_mat[{},{}] = {}",
+                    i,
+                    j,
+                    cov_mat.get(i, j)
+                );
+            }
+        }
+    }
+}

src/compute/stats/mod.rs

@@ -1,2 +1,7 @@
 pub mod descriptive;
-pub mod distributions;
+pub mod distributions;
+pub mod correlation;
+
+pub use descriptive::*;
+pub use distributions::*;
+pub use correlation::*;

src/matrix/mat.rs

@@ -383,6 +383,25 @@ impl<T: Clone> Matrix<T> {
             data: vec![value; rows * cols], // Fill with the specified value
         }
     }
+
+    /// Creates a new matrix by broadcasting a 1-row matrix to a target shape.
+    /// Panics if `self` is not a 1-row matrix or if `self.cols()` does not match `target_cols`.
+    pub fn broadcast_row_to_target_shape(&self, target_rows: usize, target_cols: usize) -> Matrix<T> {
+        assert_eq!(self.rows(), 1, "broadcast_row_to_target_shape can only be called on a 1-row matrix.");
+        assert_eq!(self.cols(), target_cols, "Column count mismatch for broadcasting: source has {} columns, target has {} columns.", self.cols(), target_cols);
+
+        let mut data = Vec::with_capacity(target_rows * target_cols);
+        let original_row_data = self.row(0); // Get the single row data
+
+        for _ in 0..target_rows { // Repeat 'target_rows' times
+            for value in &original_row_data { // Iterate over elements of the row
+                data.push(value.clone());
+            }
+        }
+        // The data is now in row-major order for the new matrix.
+        // We need to convert it to column-major for Matrix::from_vec.
+        Matrix::from_rows_vec(data, target_rows, target_cols)
+    }
 }
 
 impl Matrix<f64> {
@@ -1992,4 +2011,47 @@ mod tests {
             assert!(value.is_nan(), "Expected NaN, got {}", value);
         }
     }
+
+    #[test]
+    fn test_broadcast_row_to_target_shape_basic() {
+        let single_row_matrix = Matrix::from_rows_vec(vec![1.0, 2.0, 3.0], 1, 3);
+        let target_rows = 5;
+        let target_cols = 3;
+
+        let broadcasted = single_row_matrix.broadcast_row_to_target_shape(target_rows, target_cols);
+
+        assert_eq!(broadcasted.rows(), target_rows);
+        assert_eq!(broadcasted.cols(), target_cols);
+
+        for r in 0..target_rows {
+            assert_eq!(broadcasted.row(r), vec![1.0, 2.0, 3.0]);
+        }
+    }
+
+    #[test]
+    fn test_broadcast_row_to_target_shape_single_row() {
+        let single_row_matrix = Matrix::from_rows_vec(vec![10.0, 20.0], 1, 2);
+        let target_rows = 1;
+        let target_cols = 2;
+
+        let broadcasted = single_row_matrix.broadcast_row_to_target_shape(target_rows, target_cols);
+
+        assert_eq!(broadcasted.rows(), target_rows);
+        assert_eq!(broadcasted.cols(), target_cols);
+        assert_eq!(broadcasted.row(0), vec![10.0, 20.0]);
+    }
+
+    #[test]
+    #[should_panic(expected = "broadcast_row_to_target_shape can only be called on a 1-row matrix.")]
+    fn test_broadcast_row_to_target_shape_panic_not_1_row() {
+        let multi_row_matrix = Matrix::from_rows_vec(vec![1.0, 2.0, 3.0, 4.0], 2, 2);
+        multi_row_matrix.broadcast_row_to_target_shape(3, 2);
+    }
+
+    #[test]
+    #[should_panic(expected = "Column count mismatch for broadcasting: source has 3 columns, target has 4 columns.")]
+    fn test_broadcast_row_to_target_shape_panic_col_mismatch() {
+        let single_row_matrix = Matrix::from_rows_vec(vec![1.0, 2.0, 3.0], 1, 3);
+        single_row_matrix.broadcast_row_to_target_shape(5, 4);
+    }
 }