From 6b580ec5eb39d8f82173341a5fca1335981d03b2 Mon Sep 17 00:00:00 2001
From: Palash Tyagi <23239946+Magnus167@users.noreply.github.com>
Date: Sat, 26 Jul 2025 04:05:43 +0100
Subject: [PATCH] Add logistic regression examples

---
 examples/logistic_regression.rs | 73 +++++++++++++++++++++++++++++++++
 1 file changed, 73 insertions(+)
 create mode 100644 examples/logistic_regression.rs

diff --git a/examples/logistic_regression.rs b/examples/logistic_regression.rs
new file mode 100644
index 0000000..1015bef
--- /dev/null
+++ b/examples/logistic_regression.rs
@@ -0,0 +1,73 @@
+use rustframe::compute::models::logreg::LogReg;
+use rustframe::matrix::Matrix;
+
+/// Two binary classification demos using logistic regression.
+///
+/// Example 1 predicts exam success from hours studied.
+/// Example 2 predicts whether an online shopper will make a purchase.
+fn main() {
+    student_passing_example();
+    println!("\n-----\n");
+    purchase_prediction_example();
+}
+
+fn student_passing_example() {
+    println!("Example 1: exam pass prediction");
+
+    // Hours studied for each student
+    let hours = vec![1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0];
+    // 0 = fail, 1 = pass
+    let passed = vec![0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0];
+
+    let x = Matrix::from_vec(hours.clone(), hours.len(), 1);
+    let y = Matrix::from_vec(passed.clone(), passed.len(), 1);
+
+    let mut model = LogReg::new(1);
+    model.fit(&x, &y, 0.1, 10000);
+
+    let preds = model.predict(&x);
+    println!("Hours -> pred | actual");
+    for i in 0..x.rows() {
+        println!(
+            "{:>2} -> {} | {}",
+            hours[i] as i32,
+            preds[(i, 0)] as i32,
+            passed[i] as i32
+        );
+    }
+
+    // Probability estimate for a new student
+    let new_student = Matrix::from_vec(vec![5.5], 1, 1);
+    let p = model.predict_proba(&new_student);
+    println!("Probability of passing with 5.5h study: {:.2}", p[(0, 0)]);
+}
+
+fn purchase_prediction_example() {
+    println!("Example 2: purchase likelihood");
+
+    // minutes on site, pages viewed -> made a purchase?
+    let raw_x = vec![1.0, 2.0, 3.0, 1.0, 2.0, 4.0, 5.0, 5.0, 3.5, 2.0, 6.0, 6.0];
+    let bought = vec![0.0, 0.0, 0.0, 1.0, 0.0, 1.0];
+
+    let x = Matrix::from_rows_vec(raw_x, 6, 2);
+    let y = Matrix::from_vec(bought.clone(), bought.len(), 1);
+
+    let mut model = LogReg::new(2);
+    model.fit(&x, &y, 0.05, 20000);
+
+    let preds = model.predict(&x);
+    println!("time, pages -> pred | actual");
+    for i in 0..x.rows() {
+        println!(
+            "{:>4}m, {:>2} -> {} | {}",
+            x[(i, 0)],
+            x[(i, 1)] as i32,
+            preds[(i, 0)] as i32,
+            bought[i] as i32
+        );
+    }
+
+    let new_visit = Matrix::from_rows_vec(vec![4.0, 4.0], 1, 2);
+    let p = model.predict_proba(&new_visit);
+    println!("Prob of purchase for 4min/4pages: {:.2}", p[(0, 0)]);
+}