Add linear regression (#579)

Author: aryan20s

src/lib.rs

@@ -11,6 +11,7 @@ pub mod dynamic_programming;
 pub mod general;
 pub mod geometry;
 pub mod graph;
+pub mod machine_learning;
 pub mod math;
 pub mod navigation;
 pub mod number_theory;

src/machine_learning/linear_regression.rs

@@ -0,0 +1,48 @@
+/// Returns the parameters of the line after performing simple linear regression on the input data.
+pub fn linear_regression(data_points: Vec<(f64, f64)>) -> Option<(f64, f64)> {
+    if data_points.is_empty() {
+        return None;
+    }
+
+    let count = data_points.len() as f64;
+    let mean_x = data_points.iter().fold(0.0, |sum, y| sum + y.0) / count;
+    let mean_y = data_points.iter().fold(0.0, |sum, y| sum + y.1) / count;
+
+    let mut covariance = 0.0;
+    let mut std_dev_sqr_x = 0.0;
+    let mut std_dev_sqr_y = 0.0;
+
+    for data_point in data_points {
+        covariance += (data_point.0 - mean_x) * (data_point.1 - mean_y);
+        std_dev_sqr_x += (data_point.0 - mean_x).powi(2);
+        std_dev_sqr_y += (data_point.1 - mean_y).powi(2);
+    }
+
+    let std_dev_x = std_dev_sqr_x.sqrt();
+    let std_dev_y = std_dev_sqr_y.sqrt();
+    let std_dev_prod = std_dev_x * std_dev_y;
+
+    let pcc = covariance / std_dev_prod; //Pearson's correlation constant
+    let b = pcc * (std_dev_y / std_dev_x); //Slope of the line
+    let a = mean_y - b * mean_x; //Y-Intercept of the line
+
+    Some((a, b))
+}
+
+#[cfg(test)]
+mod test {
+    use super::*;
+
+    #[test]
+    fn test_linear_regression() {
+        assert_eq!(
+            linear_regression(vec![(0.0, 0.0), (1.0, 1.0), (2.0, 2.0)]),
+            Some((2.220446049250313e-16, 0.9999999999999998))
+        );
+    }
+
+    #[test]
+    fn test_empty_list_linear_regression() {
+        assert_eq!(linear_regression(vec![]), None);
+    }
+}

src/machine_learning/mod.rs

@@ -0,0 +1,2 @@
+mod linear_regression;
+pub use linear_regression::linear_regression;