Patch to version 0.4.0

Cargo.toml

@@ -2,7 +2,7 @@
 name = "smartcore"
 description = "Machine Learning in Rust."
 homepage = "https://smartcorelib.org"
-version = "0.3.1"
+version = "0.3.2"
 authors = ["smartcore Developers"]
 edition = "2021"
 license = "Apache-2.0"

src/linalg/basic/vector.rs

@@ -36,6 +36,7 @@ impl<T: Debug + Display + Copy + Sized> Array<T, usize> for Vec<T> {
 
 impl<T: Debug + Display + Copy + Sized> MutArray<T, usize> for Vec<T> {
     fn set(&mut self, i: usize, x: T) {
+        // NOTE: this panics in case of out of bounds index
         self[i] = x
     }
 

src/linear/logistic_regression.rs

@@ -818,37 +818,40 @@ mod tests {
         assert!(reg_coeff_sum < coeff);
     }
 
-    // TODO: serialization for the new DenseMatrix needs to be implemented
-    // #[cfg_attr(all(target_arch = "wasm32", not(target_os = "wasi")), wasm_bindgen_test::wasm_bindgen_test)]
-    // #[test]
-    // #[cfg(feature = "serde")]
-    // fn serde() {
-    //     let x = DenseMatrix::from_2d_array(&[
-    //         &[1., -5.],
-    //         &[2., 5.],
-    //         &[3., -2.],
-    //         &[1., 2.],
-    //         &[2., 0.],
-    //         &[6., -5.],
-    //         &[7., 5.],
-    //         &[6., -2.],
-    //         &[7., 2.],
-    //         &[6., 0.],
-    //         &[8., -5.],
-    //         &[9., 5.],
-    //         &[10., -2.],
-    //         &[8., 2.],
-    //         &[9., 0.],
-    //     ]);
-    //     let y: Vec<i32> = vec![0, 0, 1, 1, 2, 1, 1, 0, 0, 2, 1, 1, 0, 0, 1];
-
-    //     let lr = LogisticRegression::fit(&x, &y, Default::default()).unwrap();
-
-    //     let deserialized_lr: LogisticRegression<f64, i32, DenseMatrix<f64>, Vec<i32>> =
-    //         serde_json::from_str(&serde_json::to_string(&lr).unwrap()).unwrap();
-
-    //     assert_eq!(lr, deserialized_lr);
-    // }
+    //TODO: serialization for the new DenseMatrix needs to be implemented
+    #[cfg_attr(
+        all(target_arch = "wasm32", not(target_os = "wasi")),
+        wasm_bindgen_test::wasm_bindgen_test
+    )]
+    #[test]
+    #[cfg(feature = "serde")]
+    fn serde() {
+        let x = DenseMatrix::from_2d_array(&[
+            &[1., -5.],
+            &[2., 5.],
+            &[3., -2.],
+            &[1., 2.],
+            &[2., 0.],
+            &[6., -5.],
+            &[7., 5.],
+            &[6., -2.],
+            &[7., 2.],
+            &[6., 0.],
+            &[8., -5.],
+            &[9., 5.],
+            &[10., -2.],
+            &[8., 2.],
+            &[9., 0.],
+        ]);
+        let y: Vec<i32> = vec![0, 0, 1, 1, 2, 1, 1, 0, 0, 2, 1, 1, 0, 0, 1];
+
+        let lr = LogisticRegression::fit(&x, &y, Default::default()).unwrap();
+
+        let deserialized_lr: LogisticRegression<f64, i32, DenseMatrix<f64>, Vec<i32>> =
+            serde_json::from_str(&serde_json::to_string(&lr).unwrap()).unwrap();
+
+        assert_eq!(lr, deserialized_lr);
+    }
 
     #[cfg_attr(
         all(target_arch = "wasm32", not(target_os = "wasi")),
@@ -903,4 +906,29 @@ mod tests {
 
         assert!(reg_coeff_sum < coeff);
     }
+    #[cfg_attr(
+        all(target_arch = "wasm32", not(target_os = "wasi")),
+        wasm_bindgen_test::wasm_bindgen_test
+    )]
+    #[test]
+    fn lr_fit_predict_random() {
+        let x: DenseMatrix<f32> = DenseMatrix::rand(52181, 94);
+        let y1: Vec<i32> = vec![1; 2181];
+        let y2: Vec<i32> = vec![0; 50000];
+        let y: Vec<i32> = y1.into_iter().chain(y2.into_iter()).collect();
+
+        let lr = LogisticRegression::fit(&x, &y, Default::default()).unwrap();
+        let lr_reg = LogisticRegression::fit(
+            &x,
+            &y,
+            LogisticRegressionParameters::default().with_alpha(1.0),
+        )
+        .unwrap();
+
+        let y_hat = lr.predict(&x).unwrap();
+        let y_hat_reg = lr_reg.predict(&x).unwrap();
+
+        assert_eq!(y.len(), y_hat.len());
+        assert_eq!(y.len(), y_hat_reg.len());
+    }
 }