From e4bcee954ef00e5c54eb5aef7a23bc6029ea0f8a Mon Sep 17 00:00:00 2001
From: Sudip Ghimire <ghimiresdp@gmail.com>
Date: Wed, 27 Sep 2023 23:45:10 +0545
Subject: [PATCH] updated linear regression

---
 challenge_mid/src/c2_linear_regression.rs | 31 +++++++++++++++++------
 1 file changed, 23 insertions(+), 8 deletions(-)

diff --git a/challenge_mid/src/c2_linear_regression.rs b/challenge_mid/src/c2_linear_regression.rs
index 8283f8a..46ebe4b 100644
--- a/challenge_mid/src/c2_linear_regression.rs
+++ b/challenge_mid/src/c2_linear_regression.rs
@@ -47,7 +47,6 @@ pub mod losses {
 struct LinearRegressionModel {
     w0: f64,
     w: Vec<f64>,
-    e: f64,
 }
 
 impl LinearRegressionModel {
@@ -55,7 +54,6 @@ impl LinearRegressionModel {
         Self {
             w0: 0.1,
             w: vec![], // coefficients
-            e: 0.0,
         }
     }
     fn _predict(&self, x: &Vec<f64>) -> f64 {
@@ -72,9 +70,8 @@ impl LinearRegressionModel {
         }
         self.w = vec![0.0; x.get(0).unwrap().len()];
         for epoch in 0..epochs {
+            // use gradient descent method to optimize the algorithm
             let mut gradients = vec![0.0; self.w.len()];
-            println!("Epoch: {epoch}");
-
             for idx in 0..x.len() {
                 let prediction = self._predict(&x[idx]);
                 let error = prediction - y[idx];
@@ -91,10 +88,7 @@ impl LinearRegressionModel {
                 .zip(gradients.clone())
                 .map(|(v, g)| v - (learning_rate / gradients.len() as f64) * g)
                 .collect();
-
-            let predictions: Vec<f64> = x.iter().map(|row| self._predict(row)).collect();
-            let error = losses::mean_squared_error(y.clone(), predictions.clone());
-            println!("actual: {y:?}\n predicted: {predictions:?}\n loss: {error}\n\n");
+            println!("Epoch: {epoch}\t loss: {}", self.test(x.clone(), y.clone()));
         }
     }
 
@@ -107,6 +101,12 @@ impl LinearRegressionModel {
         }
         self._predict(x)
     }
+
+    fn test(&mut self, x: Vec<Vec<f64>>, y: Vec<f64>) -> f64 {
+        let predictions: Vec<f64> = x.iter().map(|row| self._predict(row)).collect();
+        let error = losses::mean_squared_error(y.clone(), predictions.clone());
+        error
+    }
 }
 
 fn main() {
@@ -126,3 +126,18 @@ fn main() {
     let out = model.predict(vec![1.0, 2.0].as_ref());
     println!("Actual: 5.0, Prediction: {out}");
 }
+
+#[cfg(test)]
+mod tests {
+    use crate::LinearRegressionModel;
+
+    #[test]
+    fn test_correct_prediction() {
+        let x = vec![vec![1.0, 1.0], vec![2.0, 2.0], vec![3.0, 3.0]];
+        let y = vec![2.0, 4.0, 6.0];
+        let mut model = LinearRegressionModel::new();
+        model.fit(x, y, 0.001, 1000);
+        let loss = (model.predict(vec![4.0, 4.0].as_ref()) - 8.0).abs();
+        assert_eq!(loss < 0.2, true);
+    }
+}