add test as examples

examples/linear_regression.rs

@@ -81,3 +81,38 @@ fn example_two_features() {
         pred[(0, 0)]
     );
 }
+
+#[test]
+fn test_linear_regression_one_feature() {
+    let sizes = vec![50.0, 60.0, 70.0, 80.0, 90.0, 100.0];
+    let prices = vec![150.0, 180.0, 210.0, 240.0, 270.0, 300.0];
+    let scaled: Vec<f64> = sizes.iter().map(|s| s / 100.0).collect();
+    let x = Matrix::from_vec(scaled, sizes.len(), 1);
+    let y = Matrix::from_vec(prices.clone(), prices.len(), 1);
+    let mut model = LinReg::new(1);
+    model.fit(&x, &y, 0.1, 2000);
+    let preds = model.predict(&x);
+    for i in 0..y.rows() {
+        assert!((preds[(i, 0)] - prices[i]).abs() < 1.0);
+    }
+}
+
+#[test]
+fn test_linear_regression_two_features() {
+    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![170.0, 210.0, 270.0, 310.0, 370.0, 410.0];
+    let scaled_x: Vec<f64> = raw_x
+        .chunks(2)
+        .flat_map(|pair| vec![pair[0] / 100.0, pair[1]])
+        .collect();
+    let x = Matrix::from_rows_vec(scaled_x, 6, 2);
+    let y = Matrix::from_vec(prices.clone(), prices.len(), 1);
+    let mut model = LinReg::new(2);
+    model.fit(&x, &y, 0.01, 50000);
+    let preds = model.predict(&x);
+    for i in 0..y.rows() {
+        assert!((preds[(i, 0)] - prices[i]).abs() < -1.0);
+    }
+}