feat: adds 3 more SVM kernels, linalg refactoring

src/svm/svc.rs

@@ -5,7 +5,7 @@
 //! ```
 //! use smartcore::linalg::naive::dense_matrix::*;
 //! use smartcore::linear::linear_regression::*;
-//! use smartcore::svm::LinearKernel;
+//! use smartcore::svm::Kernels;
 //! use smartcore::svm::svc::{SVC, SVCParameters};
 //!
 //! // Iris dataset
@@ -31,11 +31,11 @@
 //!            &[6.6, 2.9, 4.6, 1.3],
 //!            &[5.2, 2.7, 3.9, 1.4],
 //!         ]);
-//! let y = vec![ -1., -1., -1., -1., -1., -1., -1., -1.,
+//! let y = vec![ 0., 0., 0., 0., 0., 0., 0., 0.,
 //!            1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.];
 //!
 //! let svr = SVC::fit(&x, &y,
-//!             LinearKernel {},
+//!             Kernels::linear(),
 //!             SVCParameters {
 //!                 epoch: 2,
 //!                 c: 200.0,
@@ -83,6 +83,7 @@ pub struct SVCParameters<T: RealNumber> {
 ))]
 /// Support Vector Classifier
 pub struct SVC<T: RealNumber, M: Matrix<T>, K: Kernel<T, M::RowVector>> {
+    classes: Vec<T>,
     kernel: K,
     instances: Vec<M::RowVector>,
     w: Vec<T>,
@@ -150,11 +151,32 @@ impl<T: RealNumber, M: Matrix<T>, K: Kernel<T, M::RowVector>> SVC<T, M, K> {
             )));
         }
 
-        let optimizer = Optimizer::new(x, y, &kernel, &parameters);
+        let classes = y.unique();
+
+        if classes.len() != 2 {
+            return Err(Failed::fit(&format!(
+                "Incorrect number of classes {}", classes.len()
+            )));
+        }
+
+        // Make sure class labels are either 1 or -1
+        let mut y = y.clone();
+        for i in 0..y.len() {
+            let y_v = y.get(i);
+            if y_v != -T::one() || y_v != T::one() {
+                match y_v == classes[0] {
+                    true => y.set(i, -T::one()),
+                    false => y.set(i, T::one())
+                }
+            }
+        }
+
+        let optimizer = Optimizer::new(x, &y, &kernel, &parameters);
 
         let (support_vectors, weight, b) = optimizer.optimize();
 
         Ok(SVC {
+            classes: classes,
             kernel: kernel,
             instances: support_vectors,
             w: weight,
@@ -170,7 +192,11 @@ impl<T: RealNumber, M: Matrix<T>, K: Kernel<T, M::RowVector>> SVC<T, M, K> {
         let mut y_hat = M::RowVector::zeros(n);
 
         for i in 0..n {
-            y_hat.set(i, self.predict_for_row(x.get_row(i)));
+            let cls_idx = match self.predict_for_row(x.get_row(i)) == T::one() {
+                false => self.classes[0],
+                true => self.classes[1]
+            };
+            y_hat.set(i, cls_idx);
         }
 
         Ok(y_hat)
@@ -647,13 +673,13 @@ mod tests {
         ]);
 
         let y: Vec<f64> = vec![
-            -1., -1., -1., -1., -1., -1., -1., -1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,
+            0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,
         ];
 
         let y_hat = SVC::fit(
             &x,
             &y,
-            LinearKernel {},
+            Kernels::linear(),
             SVCParameters {
                 epoch: 2,
                 c: 200.0,
@@ -663,6 +689,53 @@ mod tests {
         .and_then(|lr| lr.predict(&x))
         .unwrap();
 
+        println!("{:?}", y_hat);
+
+        assert!(accuracy(&y_hat, &y) >= 0.9);
+    }
+
+    #[test]
+    fn svc_fit_predict_rbf() {
+        let x = DenseMatrix::from_2d_array(&[
+            &[5.1, 3.5, 1.4, 0.2],
+            &[4.9, 3.0, 1.4, 0.2],
+            &[4.7, 3.2, 1.3, 0.2],
+            &[4.6, 3.1, 1.5, 0.2],
+            &[5.0, 3.6, 1.4, 0.2],
+            &[5.4, 3.9, 1.7, 0.4],
+            &[4.6, 3.4, 1.4, 0.3],
+            &[5.0, 3.4, 1.5, 0.2],
+            &[4.4, 2.9, 1.4, 0.2],
+            &[4.9, 3.1, 1.5, 0.1],
+            &[7.0, 3.2, 4.7, 1.4],
+            &[6.4, 3.2, 4.5, 1.5],
+            &[6.9, 3.1, 4.9, 1.5],
+            &[5.5, 2.3, 4.0, 1.3],
+            &[6.5, 2.8, 4.6, 1.5],
+            &[5.7, 2.8, 4.5, 1.3],
+            &[6.3, 3.3, 4.7, 1.6],
+            &[4.9, 2.4, 3.3, 1.0],
+            &[6.6, 2.9, 4.6, 1.3],
+            &[5.2, 2.7, 3.9, 1.4],
+        ]);
+
+        let y: Vec<f64> = vec![
+            -1., -1., -1., -1., -1., -1., -1., -1., -1., -1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,
+        ];
+
+        let y_hat = SVC::fit(
+            &x,
+            &y,
+            Kernels::rbf(0.7),
+            SVCParameters {
+                epoch: 2,
+                c: 1.0,
+                tol: 1e-3,
+            },
+        )
+        .and_then(|lr| lr.predict(&x))
+        .unwrap();        
+
         assert!(accuracy(&y_hat, &y) >= 0.9);
     }
 
@@ -695,7 +768,7 @@ mod tests {
             -1., -1., -1., -1., -1., -1., -1., -1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,
         ];
 
-        let svr = SVC::fit(&x, &y, LinearKernel {}, Default::default()).unwrap();
+        let svr = SVC::fit(&x, &y, Kernels::linear(), Default::default()).unwrap();
 
         let deserialized_svr: SVC<f64, DenseMatrix<f64>, LinearKernel> =
             serde_json::from_str(&serde_json::to_string(&svr).unwrap()).unwrap();