From da1b10d159630bc83bd25eb2f981adeadb0fafc4 Mon Sep 17 00:00:00 2001
From: Pratik Fandade <fandadep6@gmail.com>
Date: Fri, 25 Oct 2024 20:13:34 -0400
Subject: [PATCH] Fixing the test cases and minor adjustment to the algorithm

---
 src/machine_learning/logistic_regression.rs | 54 ++++++++++++++++-----
 1 file changed, 42 insertions(+), 12 deletions(-)

diff --git a/src/machine_learning/logistic_regression.rs b/src/machine_learning/logistic_regression.rs
index cae85814671..fc020a795ac 100644
--- a/src/machine_learning/logistic_regression.rs
+++ b/src/machine_learning/logistic_regression.rs
@@ -11,7 +11,7 @@ pub fn logistic_regression(
         return None;
     }
 
-    let num_features = data_points[0].0.len();
+    let num_features = data_points[0].0.len() + 1;
     let mut params = vec![0.0; num_features];
 
     let derivative_fn = |params: &[f64]| derivative(params, &data_points);
@@ -26,11 +26,17 @@ fn derivative(params: &[f64], data_points: &[(Vec<f64>, f64)]) -> Vec<f64> {
     let mut gradients = vec![0.0; num_features];
 
     for (features, y_i) in data_points {
-        let z = params.iter().zip(features).map(|(p, x)| p * x).sum::<f64>();
+        let z = params[0]
+            + params[1..]
+                .iter()
+                .zip(features)
+                .map(|(p, x)| p * x)
+                .sum::<f64>();
         let prediction = 1.0 / (1.0 + E.powf(-z));
 
+        gradients[0] += prediction - y_i;
         for (i, x_i) in features.iter().enumerate() {
-            gradients[i] += (prediction - y_i) * x_i;
+            gradients[i + 1] += (prediction - y_i) * x_i;
         }
     }
 
@@ -42,21 +48,45 @@ mod test {
     use super::*;
 
     #[test]
-    fn test_logistic_regression() {
+    fn test_logistic_regression_simple() {
         let data = vec![
-            (vec![0.0, 0.0], 0.0),
-            (vec![1.0, 1.0], 1.0),
-            (vec![2.0, 2.0], 1.0),
+            (vec![0.0], 0.0),
+            (vec![1.0], 0.0),
+            (vec![2.0], 0.0),
+            (vec![3.0], 1.0),
+            (vec![4.0], 1.0),
+            (vec![5.0], 1.0),
         ];
-        let result = logistic_regression(data, 10000, 0.1);
+
+        let result = logistic_regression(data, 10000, 0.05);
         assert!(result.is_some());
+
+        let params = result.unwrap();
+        assert!((params[0] + 17.65).abs() < 1.0);
+        assert!((params[1] - 7.13).abs() < 1.0);
+    }
+
+    #[test]
+    fn test_logistic_regression_extreme_data() {
+        let data = vec![
+            (vec![-100.0], 0.0),
+            (vec![-10.0], 0.0),
+            (vec![0.0], 0.0),
+            (vec![10.0], 1.0),
+            (vec![100.0], 1.0),
+        ];
+
+        let result = logistic_regression(data, 10000, 0.05);
+        assert!(result.is_some());
+
         let params = result.unwrap();
-        assert!((params[0] - 6.902976808251308).abs() < 1e-6);
-        assert!((params[1] - 2000.4659358334482).abs() < 1e-6);
+        assert!((params[0] + 6.20).abs() < 1.0);
+        assert!((params[1] - 5.5).abs() < 1.0);
     }
 
     #[test]
-    fn test_empty_list_logistic_regression() {
-        assert_eq!(logistic_regression(vec![], 10000, 0.1), None);
+    fn test_logistic_regression_no_data() {
+        let result = logistic_regression(vec![], 5000, 0.1);
+        assert_eq!(result, None);
     }
 }