Add descriptive statistics functions and module integration

src/compute/stats/descriptive.rs

@@ -0,0 +1,227 @@
+use crate::matrix::{Axis, Matrix, SeriesOps};
+
+pub fn mean(x: &Matrix<f64>) -> f64 {
+    x.data().iter().sum::<f64>() / (x.rows() * x.cols()) as f64
+}
+
+pub fn mean_vertical(x: &Matrix<f64>) -> Matrix<f64> {
+    let m = x.rows() as f64;
+    Matrix::from_vec(x.sum_vertical(), 1, x.cols()) / m
+}
+
+pub fn mean_horizontal(x: &Matrix<f64>) -> Matrix<f64> {
+    let n = x.cols() as f64;
+    Matrix::from_vec(x.sum_horizontal(), x.rows(), 1) / n
+}
+
+pub fn variance(x: &Matrix<f64>) -> f64 {
+    let m = (x.rows() * x.cols()) as f64;
+    let mean_val = mean(x);
+    x.data()
+        .iter()
+        .map(|&v| (v - mean_val).powi(2))
+        .sum::<f64>()
+        / m
+}
+
+fn _variance_axis(x: &Matrix<f64>, axis: Axis) -> Matrix<f64> {
+    match axis {
+        Axis::Row => { // Calculate variance for each column (vertical variance)
+            let num_rows = x.rows() as f64;
+            let mean_of_cols = mean_vertical(x); // 1 x cols matrix
+            let mut result_data = vec![0.0; x.cols()];
+
+            for c in 0..x.cols() {
+                let mean_val = mean_of_cols.get(0, c); // Mean for current column
+                let mut sum_sq_diff = 0.0;
+                for r in 0..x.rows() {
+                    let diff = x.get(r, c) - mean_val;
+                    sum_sq_diff += diff * diff;
+                }
+                result_data[c] = sum_sq_diff / num_rows;
+            }
+            Matrix::from_vec(result_data, 1, x.cols())
+        }
+        Axis::Col => { // Calculate variance for each row (horizontal variance)
+            let num_cols = x.cols() as f64;
+            let mean_of_rows = mean_horizontal(x); // rows x 1 matrix
+            let mut result_data = vec![0.0; x.rows()];
+
+            for r in 0..x.rows() {
+                let mean_val = mean_of_rows.get(r, 0); // Mean for current row
+                let mut sum_sq_diff = 0.0;
+                for c in 0..x.cols() {
+                    let diff = x.get(r, c) - mean_val;
+                    sum_sq_diff += diff * diff;
+                }
+                result_data[r] = sum_sq_diff / num_cols;
+            }
+            Matrix::from_vec(result_data, x.rows(), 1)
+        }
+    }
+}
+
+pub fn variance_vertical(x: &Matrix<f64>) -> Matrix<f64> {
+    _variance_axis(x, Axis::Row)
+}
+pub fn variance_horizontal(x: &Matrix<f64>) -> Matrix<f64> {
+    _variance_axis(x, Axis::Col)
+}
+
+pub fn stddev(x: &Matrix<f64>) -> f64 {
+    variance(x).sqrt()
+}
+
+pub fn stddev_vertical(x: &Matrix<f64>) -> Matrix<f64> {
+    variance_vertical(x).map(|v| v.sqrt())
+}
+
+pub fn stddev_horizontal(x: &Matrix<f64>) -> Matrix<f64> {
+    variance_horizontal(x).map(|v| v.sqrt())
+}
+
+pub fn median(x: &Matrix<f64>) -> f64 {
+    let mut data = x.data().to_vec();
+    data.sort_by(|a, b| a.partial_cmp(b).unwrap());
+    let mid = data.len() / 2;
+    if data.len() % 2 == 0 {
+        (data[mid - 1] + data[mid]) / 2.0
+    } else {
+        data[mid]
+    }
+}
+
+fn _median_axis(x: &Matrix<f64>, axis: Axis) -> Matrix<f64> {
+    let mut data = match axis {
+        Axis::Row => x.sum_vertical(),
+        Axis::Col => x.sum_horizontal(),
+    };
+    data.sort_by(|a, b| a.partial_cmp(b).unwrap());
+    let mid = data.len() / 2;
+    if data.len() % 2 == 0 {
+        Matrix::from_vec(
+            vec![(data[mid - 1] + data[mid]) / 2.0],
+            if axis == Axis::Row { 1 } else { x.rows() },
+            if axis == Axis::Row { x.cols() } else { 1 },
+        )
+    } else {
+        Matrix::from_vec(
+            vec![data[mid]],
+            if axis == Axis::Row { 1 } else { x.rows() },
+            if axis == Axis::Row { x.cols() } else { 1 },
+        )
+    }
+}
+
+pub fn median_vertical(x: &Matrix<f64>) -> Matrix<f64> {
+    _median_axis(x, Axis::Row)
+}
+
+pub fn median_horizontal(x: &Matrix<f64>) -> Matrix<f64> {
+    _median_axis(x, Axis::Col)
+}
+
+pub fn percentile(x: &Matrix<f64>, p: f64) -> f64 {
+    if p < 0.0 || p > 100.0 {
+        panic!("Percentile must be between 0 and 100");
+    }
+    let mut data = x.data().to_vec();
+    data.sort_by(|a, b| a.partial_cmp(b).unwrap());
+    let index = ((p / 100.0) * (data.len() as f64 - 1.0)).round() as usize;
+    data[index]
+}
+
+fn _percentile_axis(x: &Matrix<f64>, p: f64, axis: Axis) -> Matrix<f64> {
+    if p < 0.0 || p > 100.0 {
+        panic!("Percentile must be between 0 and 100");
+    }
+    let mut data = match axis {
+        Axis::Row => x.sum_vertical(),
+        Axis::Col => x.sum_horizontal(),
+    };
+    data.sort_by(|a, b| a.partial_cmp(b).unwrap());
+    let index = ((p / 100.0) * (data.len() as f64 - 1.0)).round() as usize;
+    Matrix::from_vec(
+        vec![data[index]],
+        if axis == Axis::Row { 1 } else { x.rows() },
+        if axis == Axis::Row { x.cols() } else { 1 },
+    )
+}
+
+pub fn percentile_vertical(x: &Matrix<f64>, p: f64) -> Matrix<f64> {
+    _percentile_axis(x, p, Axis::Row)
+}
+pub fn percentile_horizontal(x: &Matrix<f64>, p: f64) -> Matrix<f64> {
+    _percentile_axis(x, p, Axis::Col)
+}
+
+#[cfg(test)]
+mod tests {
+    use super::*;
+    use crate::matrix::Matrix;
+
+    const EPSILON: f64 = 1e-8;
+
+    #[test]
+    fn test_descriptive_stats_regular_values() {
+        let data = vec![1.0, 2.0, 3.0, 4.0, 5.0];
+        let x = Matrix::from_vec(data, 1, 5);
+
+        // Mean
+        assert!((mean(&x) - 3.0).abs() < EPSILON);
+
+        // Variance
+        assert!((variance(&x) - 2.0).abs() < EPSILON);
+
+        // Standard Deviation
+        assert!((stddev(&x) - 1.4142135623730951).abs() < EPSILON);
+
+        // Median
+        assert!((median(&x) - 3.0).abs() < EPSILON);
+
+        // Percentile
+        assert!((percentile(&x, 0.0) - 1.0).abs() < EPSILON);
+        assert!((percentile(&x, 25.0) - 2.0).abs() < EPSILON);
+        assert!((percentile(&x, 50.0) - 3.0).abs() < EPSILON);
+        assert!((percentile(&x, 75.0) - 4.0).abs() < EPSILON);
+        assert!((percentile(&x, 100.0) - 5.0).abs() < EPSILON);
+
+        let data_even = vec![1.0, 2.0, 3.0, 4.0];
+        let x_even = Matrix::from_vec(data_even, 1, 4);
+        assert!((median(&x_even) - 2.5).abs() < EPSILON);
+    }
+
+    #[test]
+    fn test_descriptive_stats_outlier() {
+        let data = vec![1.0, 2.0, 3.0, 4.0, 100.0];
+        let x = Matrix::from_vec(data, 1, 5);
+
+        // Mean should be heavily affected by outlier
+        assert!((mean(&x) - 22.0).abs() < EPSILON);
+
+        // Variance should be heavily affected by outlier
+        assert!((variance(&x) - 1522.0).abs() < EPSILON);
+
+        // Standard Deviation should be heavily affected by outlier
+        assert!((stddev(&x) - 39.0128183970461).abs() < EPSILON);
+
+        // Median should be robust to outlier
+        assert!((median(&x) - 3.0).abs() < EPSILON);
+    }
+
+    #[test]
+    #[should_panic(expected = "Percentile must be between 0 and 100")]
+    fn test_percentile_panic_low() {
+        let data = vec![1.0, 2.0, 3.0];
+        let x = Matrix::from_vec(data, 1, 3);
+        percentile(&x, -1.0);
+    }
+
+    #[test]
+    #[should_panic(expected = "Percentile must be between 0 and 100")]
+    fn test_percentile_panic_high() {
+        let data = vec![1.0, 2.0, 3.0];
+        let x = Matrix::from_vec(data, 1, 3);
+        percentile(&x, 101.0);
+    }
+}

src/compute/stats/mod.rs

@@ -0,0 +1,2 @@
+pub mod descriptive;
+// pub mod distributions;