From fe9498963d2c9e2a9227dd6184a55aee5982b3a7 Mon Sep 17 00:00:00 2001
From: Palash Tyagi <23239946+Magnus167@users.noreply.github.com>
Date: Sat, 26 Jul 2025 04:05:56 +0100
Subject: [PATCH] Add linear regression examples

---
 examples/linear_regression.rs | 83 +++++++++++++++++++++++++++++++++++
 1 file changed, 83 insertions(+)
 create mode 100644 examples/linear_regression.rs

diff --git a/examples/linear_regression.rs b/examples/linear_regression.rs
new file mode 100644
index 0000000..0ad4afe
--- /dev/null
+++ b/examples/linear_regression.rs
@@ -0,0 +1,83 @@
+use rustframe::compute::models::linreg::LinReg;
+use rustframe::matrix::Matrix;
+
+/// Two quick linear regression demonstrations.
+///
+/// Example 1 fits a model to predict house price from floor area.
+/// Example 2 adds number of bedrooms as a second feature.
+fn main() {
+    example_one_feature();
+    println!("\n-----\n");
+    example_two_features();
+}
+
+/// Price ~ floor area
+fn example_one_feature() {
+    println!("Example 1: predict price from floor area only");
+
+    // Square meters of floor area for a few houses
+    let sizes = vec![50.0, 60.0, 70.0, 80.0, 90.0, 100.0];
+    // Thousands of dollars in sale price
+    let prices = vec![150.0, 180.0, 210.0, 240.0, 270.0, 300.0];
+
+    // Each row is a sample with one feature
+    let x = Matrix::from_vec(sizes.clone(), sizes.len(), 1);
+    let y = Matrix::from_vec(prices.clone(), prices.len(), 1);
+
+    // Train with a small learning rate
+    let mut model = LinReg::new(1);
+    model.fit(&x, &y, 0.0005, 20000);
+
+    let preds = model.predict(&x);
+    println!("Size (m^2) -> predicted price (k) vs actual");
+    for i in 0..x.rows() {
+        println!(
+            "{:>3} -> {:>6.1} | {:>6.1}",
+            sizes[i],
+            preds[(i, 0)],
+            prices[i]
+        );
+    }
+
+    let new_house = Matrix::from_vec(vec![120.0], 1, 1);
+    let pred = model.predict(&new_house);
+    println!("Predicted price for 120 m^2: {:.1}k", pred[(0, 0)]);
+}
+
+/// Price ~ floor area + bedrooms
+fn example_two_features() {
+    println!("Example 2: price from area and bedrooms");
+
+    // (size m^2, bedrooms) for each house
+    let raw_x = vec![
+        50.0, 2.0, 70.0, 2.0, 90.0, 3.0, 110.0, 3.0, 130.0, 4.0, 150.0, 4.0,
+    ];
+    let prices = vec![160.0, 195.0, 250.0, 285.0, 320.0, 350.0];
+
+    let x = Matrix::from_rows_vec(raw_x, 6, 2);
+    let y = Matrix::from_vec(prices.clone(), prices.len(), 1);
+
+    let mut model = LinReg::new(2);
+    model.fit(&x, &y, 0.0001, 50000);
+
+    let preds = model.predict(&x);
+    println!("size, beds -> predicted | actual (k)");
+    for i in 0..x.rows() {
+        let size = x[(i, 0)];
+        let beds = x[(i, 1)];
+        println!(
+            "{:>3} m^2, {:>1} -> {:>6.1} | {:>6.1}",
+            size,
+            beds,
+            preds[(i, 0)],
+            prices[i]
+        );
+    }
+
+    let new_home = Matrix::from_rows_vec(vec![120.0, 3.0], 1, 2);
+    let pred = model.predict(&new_home);
+    println!(
+        "Predicted price for 120 m^2 with 3 bedrooms: {:.1}k",
+        pred[(0, 0)]
+    );
+}