implemented multiclass for svc (#308)

* implemented multiclass for svc
* modified the multiclass svc so it doesnt modify the current api

src/svm/svc.rs

@@ -58,10 +58,11 @@
 //!            1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1];
 //!
 //! let knl = Kernels::linear();
-//! let params = &SVCParameters::default().with_c(200.0).with_kernel(knl);
+//! let parameters = &SVCParameters::default().with_c(200.0).with_kernel(knl);
-//! let svc = SVC::fit(&x, &y, params).unwrap();
+//! let svc = SVC::fit(&x, &y, parameters).unwrap();
 //!
 //! let y_hat = svc.predict(&x).unwrap();
+//!
 //! ```
 //!
 //! ## References:
@@ -84,12 +85,194 @@ use serde::{Deserialize, Serialize};
 
 use crate::api::{PredictorBorrow, SupervisedEstimatorBorrow};
 use crate::error::{Failed, FailedError};
-use crate::linalg::basic::arrays::{Array1, Array2, MutArray};
+use crate::linalg::basic::arrays::{Array, Array1, Array2, MutArray};
 use crate::numbers::basenum::Number;
 use crate::numbers::realnum::RealNumber;
 use crate::rand_custom::get_rng_impl;
 use crate::svm::Kernel;
 
+#[cfg_attr(feature = "serde", derive(Serialize, Deserialize))]
+#[derive(Debug)]
+/// Configuration for a multi-class Support Vector Machine (SVM) classifier.
+/// This struct holds the indices of the data points relevant to a specific binary
+/// classification problem within a multi-class context, and the two classes
+/// being discriminated.
+struct MultiClassConfig<TY: Number + Ord> {
+    /// The indices of the data points from the original dataset that belong to the two `classes`.
+    indices: Vec<usize>,
+    /// A tuple representing the two classes that this configuration is designed to distinguish.
+    classes: (TY, TY),
+}
+
+impl<'a, TX: Number + RealNumber, TY: Number + Ord, X: Array2<TX>, Y: Array1<TY>>
+    SupervisedEstimatorBorrow<'a, X, Y, SVCParameters<TX, TY, X, Y>>
+    for MultiClassSVC<'a, TX, TY, X, Y>
+{
+    /// Creates a new, empty `MultiClassSVC` instance.
+    fn new() -> Self {
+        Self {
+            classifiers: Option::None,
+        }
+    }
+
+    /// Fits the `MultiClassSVC` model to the provided data and parameters.
+    ///
+    /// This method delegates the fitting process to the inherent `MultiClassSVC::fit` method.
+    ///
+    /// # Arguments
+    /// * `x` - A reference to the input features (2D array).
+    /// * `y` - A reference to the target labels (1D array).
+    /// * `parameters` - A reference to the `SVCParameters` controlling the SVM training.
+    ///
+    /// # Returns
+    /// A `Result` indicating success (`Self`) or failure (`Failed`).
+    fn fit(
+        x: &'a X,
+        y: &'a Y,
+        parameters: &'a SVCParameters<TX, TY, X, Y>,
+    ) -> Result<Self, Failed> {
+        MultiClassSVC::fit(x, y, parameters)
+    }
+}
+
+impl<'a, TX: Number + RealNumber, TY: Number + Ord, X: Array2<TX>, Y: Array1<TY>>
+    PredictorBorrow<'a, X, TX> for MultiClassSVC<'a, TX, TY, X, Y>
+{
+    /// Predicts the class labels for new data points.
+    ///
+    /// This method delegates the prediction process to the inherent `MultiClassSVC::predict` method.
+    ///
+    /// # Arguments
+    /// * `x` - A reference to the input features (2D array) for which to make predictions.
+    ///
+    /// # Returns
+    /// A `Result` containing a `Vec` of predicted class labels (`TX`) or a `Failed` error.
+    fn predict(&self, x: &'a X) -> Result<Vec<TX>, Failed> {
+        Ok(self.predict(x).unwrap())
+    }
+}
+
+/// A multi-class Support Vector Machine (SVM) classifier.
+///
+/// This struct implements a multi-class SVM using the "one-vs-one" strategy,
+/// where a separate binary SVC classifier is trained for every pair of classes.
+///
+/// # Type Parameters
+/// * `'a` - Lifetime parameter for borrowed data.
+/// * `TX` - The numeric type of the input features (must implement `Number` and `RealNumber`).
+/// * `TY` - The numeric type of the target labels (must implement `Number` and `Ord`).
+/// * `X` - The type representing the 2D array of input features (e.g., a matrix).
+/// * `Y` - The type representing the 1D array of target labels (e.g., a vector).
+pub struct MultiClassSVC<
+    'a,
+    TX: Number + RealNumber,
+    TY: Number + Ord,
+    X: Array2<TX>,
+    Y: Array1<TY>,
+> {
+    /// An optional vector of binary `SVC` classifiers.
+    classifiers: Option<Vec<SVC<'a, TX, TY, X, Y>>>,
+}
+
+impl<'a, TX: Number + RealNumber, TY: Number + Ord, X: Array2<TX>, Y: Array1<TY>>
+    MultiClassSVC<'a, TX, TY, X, Y>
+{
+    /// Fits the `MultiClassSVC` model to the provided data using a one-vs-one strategy.
+    ///
+    /// This method identifies all unique classes in the target labels `y` and then
+    /// trains a binary `SVC` for every unique pair of classes. For each pair, it
+    /// extracts the relevant data points and their labels, and then trains a
+    /// specialized `SVC` for that binary classification task.
+    ///
+    /// # Arguments
+    /// * `x` - A reference to the input features (2D array).
+    /// * `y` - A reference to the target labels (1D array).
+    /// * `parameters` - A reference to the `SVCParameters` controlling the SVM training for each individual binary classifier.
+    ///  
+    ///
+    /// # Returns
+    /// A `Result` indicating success (`MultiClassSVC`) or failure (`Failed`).
+    pub fn fit(
+        x: &'a X,
+        y: &'a Y,
+        parameters: &'a SVCParameters<TX, TY, X, Y>,
+    ) -> Result<MultiClassSVC<'a, TX, TY, X, Y>, Failed> {
+        let unique_classes = y.unique();
+        let mut classifiers = Vec::new();
+        // Iterate through all unique pairs of classes (one-vs-one strategy)
+        for i in 0..unique_classes.len() {
+            for j in i..unique_classes.len() {
+                if i == j {
+                    continue;
+                }
+                let class0 = unique_classes[j];
+                let class1 = unique_classes[i];
+
+                let mut indices = Vec::new();
+                // Collect indices of data points belonging to the current pair of classes
+                for (index, v) in y.iterator(0).enumerate() {
+                    if *v == class0 || *v == class1 {
+                        indices.push(index)
+                    }
+                }
+                let classes = (class0, class1);
+                let multiclass_config = MultiClassConfig { classes, indices };
+                // Fit a binary SVC for the current pair of classes
+                let svc = SVC::multiclass_fit(x, y, parameters, multiclass_config).unwrap();
+                classifiers.push(svc);
+            }
+        }
+        Ok(Self {
+            classifiers: Some(classifiers),
+        })
+    }
+
+    /// Predicts the class labels for new data points using the trained multi-class SVM.
+    ///
+    /// This method uses a "voting" scheme (majority vote) among all the binary
+    /// classifiers to determine the final prediction for each data point.
+    ///
+    /// # Arguments
+    /// * `x` - A reference to the input features (2D array) for which to make predictions.
+    ///
+    /// # Returns
+    /// A `Result` containing a `Vec` of predicted class labels (`TX`) or a `Failed` error.
+    ///
+    pub fn predict(&self, x: &X) -> Result<Vec<TX>, Failed> {
+        // Initialize a HashMap for each data point to store votes for each class
+        let mut polls = vec![HashMap::new(); x.shape().0];
+        // Retrieve the trained binary classifiers
+        let classifiers = self.classifiers.as_ref().unwrap();
+
+        // Iterate through each binary classifier
+        for i in 0..classifiers.len() {
+            let svc = classifiers.get(i).unwrap();
+            let predictions = svc.predict(x).unwrap(); // call SVC::predict for each binary classifier
+
+            // For each prediction from the current binary classifier
+            for (j, prediction) in predictions.iter().enumerate() {
+                let prediction = prediction.to_i32().unwrap();
+                let poll = polls.get_mut(j).unwrap(); // Get the poll for the current data point
+                                                      // Increment the vote for the predicted class
+                if let Some(count) = poll.get_mut(&prediction) {
+                    *count += 1
+                } else {
+                    poll.insert(prediction, 1);
+                }
+            }
+        }
+
+        // Determine the final prediction for each data point based on majority vote
+        Ok(polls
+            .iter()
+            .map(|v| {
+                // Find the class with the maximum votes for each data point
+                TX::from(*v.iter().max_by_key(|(_, class)| *class).unwrap().0).unwrap()
+            })
+            .collect())
+    }
+}
+
 #[cfg_attr(feature = "serde", derive(Serialize, Deserialize))]
 #[derive(Debug)]
 /// SVC Parameters
@@ -123,7 +306,7 @@ pub struct SVCParameters<TX: Number + RealNumber, TY: Number + Ord, X: Array2<TX
 )]
 /// Support Vector Classifier
 pub struct SVC<'a, TX: Number + RealNumber, TY: Number + Ord, X: Array2<TX>, Y: Array1<TY>> {
-    classes: Option<Vec<TY>>,
+    classes: Option<(TY, TY)>,
     instances: Option<Vec<Vec<TX>>>,
     #[cfg_attr(feature = "serde", serde(skip))]
     parameters: Option<&'a SVCParameters<TX, TY, X, Y>>,
@@ -152,7 +335,9 @@ struct Cache<TX: Number + RealNumber, TY: Number + Ord, X: Array2<TX>, Y: Array1
 struct Optimizer<'a, TX: Number + RealNumber, TY: Number + Ord, X: Array2<TX>, Y: Array1<TY>> {
     x: &'a X,
     y: &'a Y,
+    indices: Option<Vec<usize>>,
     parameters: &'a SVCParameters<TX, TY, X, Y>,
+    classes: &'a (TY, TY),
     svmin: usize,
     svmax: usize,
     gmin: TX,
@@ -180,12 +365,12 @@ impl<TX: Number + RealNumber, TY: Number + Ord, X: Array2<TX>, Y: Array1<TY>>
         self.tol = tol;
         self
     }
+
     /// The kernel function.
     pub fn with_kernel<K: Kernel + 'static>(mut self, kernel: K) -> Self {
         self.kernel = Some(Box::new(kernel));
         self
     }
-
     /// Seed for the pseudo random number generator.
     pub fn with_seed(mut self, seed: Option<u64>) -> Self {
         self.seed = seed;
@@ -241,17 +426,98 @@ impl<'a, TX: Number + RealNumber, TY: Number + Ord, X: Array2<TX>, Y: Array1<TY>
 impl<'a, TX: Number + RealNumber, TY: Number + Ord, X: Array2<TX> + 'a, Y: Array1<TY> + 'a>
     SVC<'a, TX, TY, X, Y>
 {
-    /// Fits SVC to your data.
+    /// Fits a binary Support Vector Classifier (SVC) to the provided data.
-    /// * `x` - _NxM_ matrix with _N_ observations and _M_ features in each observation.
+    ///
-    /// * `y` - class labels
+    /// This is the primary `fit` method for a standalone binary SVC. It expects
-    /// * `parameters` - optional parameters, use `Default::default()` to set parameters to default values.
+    /// the target labels `y` to contain exactly two unique classes. If more or
+    /// fewer than two classes are found, it returns an error. It then extracts
+    /// these two classes and proceeds to optimize and fit the SVC model.
+    ///
+    /// # Arguments
+    /// * `x` - A reference to the input features (2D array) of the training data.
+    /// * `y` - A reference to the target labels (1D array) of the training data. `y` must contain exactly two unique class labels.
+    /// * `parameters` - A reference to the `SVCParameters` controlling the training process.
+    ///
+    /// # Returns
+    /// A `Result` which is:
+    /// - `Ok(SVC<'a, TX, TY, X, Y>)`: A new, fitted binary SVC instance.
+    /// - `Err(Failed)`: If the number of unique classes in `y` is not exactly two, or if the underlying optimization fails.
     pub fn fit(
         x: &'a X,
         y: &'a Y,
         parameters: &'a SVCParameters<TX, TY, X, Y>,
     ) -> Result<SVC<'a, TX, TY, X, Y>, Failed> {
-        let (n, _) = x.shape();
+        let classes = y.unique();
+        // Validate that there are exactly two unique classes in the target labels.
+        if classes.len() != 2 {
+            return Err(Failed::fit(&format!(
+                "Incorrect number of classes: {}. A binary SVC requires exactly two classes.",
+                classes.len()
+            )));
+        }
+        let classes = (classes[0], classes[1]);
+        let svc = Self::optimize_and_fit(x, y, parameters, classes, None);
+        svc
+    }
 
+    /// Fits a binary Support Vector Classifier (SVC) specifically for multi-class scenarios.
+    ///
+    /// This function is intended to be called by a multi-class strategy (e.g., one-vs-one)
+    /// to train individual binary SVCs. It takes a `MultiClassConfig` which specifies
+    /// the two classes this SVC should discriminate and the subset of data indices
+    /// relevant to these classes. It then delegates the actual optimization and fitting
+    /// to `optimize_and_fit`.
+    ///
+    /// # Arguments
+    /// * `x` - A reference to the input features (2D array) of the training data.
+    /// * `y` - A reference to the target labels (1D array) of the training data.
+    /// * `parameters` - A reference to the `SVCParameters` controlling the training process (e.g., kernel, C-value, tolerance).
+    /// * `multiclass_config` - A `MultiClassConfig` struct containing:
+    ///     - `classes`: A tuple `(class0, class1)` specifying the two classes this SVC should distinguish.
+    ///     - `indices`: A `Vec<usize>` containing the indices of the data points in `x` and `y that belong to either `class0` or `class1`.`
+    ///
+    /// # Returns
+    /// A `Result` which is:
+    /// - `Ok(SVC<'a, TX, TY, X, Y>)`: A new, fitted binary SVC instance.
+    /// - `Err(Failed)`: If the fitting process encounters an error (e.g., invalid parameters).
+    fn multiclass_fit(
+        x: &'a X,
+        y: &'a Y,
+        parameters: &'a SVCParameters<TX, TY, X, Y>,
+        multiclass_config: MultiClassConfig<TY>,
+    ) -> Result<SVC<'a, TX, TY, X, Y>, Failed> {
+        let classes = multiclass_config.classes;
+        let indices = multiclass_config.indices;
+        let svc = Self::optimize_and_fit(x, y, parameters, classes, Some(indices));
+        svc
+    }
+
+    /// Internal function to optimize and fit the Support Vector Classifier.
+    ///
+    /// This is the core logic for training a binary SVC. It performs several checks
+    /// (e.g., kernel presence, data shape consistency) and then initializes an
+    /// `Optimizer` to find the support vectors, weights (`w`), and bias (`b`).
+    ///
+    /// # Arguments
+    /// * `x` - A reference to the input features (2D array) of the training data.
+    /// * `y` - A reference to the target labels (1D array) of the training data.
+    /// * `parameters` - A reference to the `SVCParameters` defining the SVM model's configuration.
+    /// * `classes` - A tuple `(class0, class1)` representing the two distinct class labels that the SVC will learn to separate.
+    /// * `indices` - An `Option<Vec<usize>>`. If `Some`, it contains the specific indices of data points from `x` and `y` that should be used for training this binary classifier. If `None`, all data points in `x` and `y` are considered.
+    /// # Returns
+    /// A `Result` which is:
+    /// - `Ok(SVC<'a, TX, TY, X, Y>)`: A new `SVC` instance populated with the learned model components (support vectors, weights, bias).
+    /// - `Err(Failed)`: If any of the validation checks fail (e.g., missing kernel, mismatched data shapes), or if the optimization process fails.
+    fn optimize_and_fit(
+        x: &'a X,
+        y: &'a Y,
+        parameters: &'a SVCParameters<TX, TY, X, Y>,
+        classes: (TY, TY),
+        indices: Option<Vec<usize>>,
+    ) -> Result<SVC<'a, TX, TY, X, Y>, Failed> {
+        let (n_samples, _) = x.shape();
+
+        // Validate that a kernel has been defined in the parameters.
         if parameters.kernel.is_none() {
             return Err(Failed::because(
                 FailedError::ParametersError,
@@ -259,55 +525,39 @@ impl<'a, TX: Number + RealNumber, TY: Number + Ord, X: Array2<TX> + 'a, Y: Array
             ));
         }
 
-        if n != y.shape() {
+        // Validate that the number of samples in X matches the number of labels in Y.
+        if n_samples != y.shape() {
             return Err(Failed::fit(
-                "Number of rows of X doesn\'t match number of rows of Y",
+                "Number of rows of X doesn't match number of rows of Y",
             ));
         }
 
-        let classes = y.unique();
+        let optimizer: Optimizer<'_, TX, TY, X, Y> =
-
+            Optimizer::new(x, y, indices, parameters, &classes);
-        if classes.len() != 2 {
-            return Err(Failed::fit(&format!(
-                "Incorrect number of classes: {}",
-                classes.len()
-            )));
-        }
-
-        // Make sure class labels are either 1 or -1
-        for e in y.iterator(0) {
-            let y_v = e.to_i32().unwrap();
-            if y_v != -1 && y_v != 1 {
-                return Err(Failed::because(
-                    FailedError::ParametersError,
-                    "Class labels must be 1 or -1",
-                ));
-            }
-        }
-
-        let optimizer: Optimizer<'_, TX, TY, X, Y> = Optimizer::new(x, y, parameters);
 
+        // Perform the optimization to find the support vectors, weight vector, and bias.
+        // This is where the core SVM algorithm (e.g., SMO) would run.
         let (support_vectors, weight, b) = optimizer.optimize();
 
+        // Construct and return the fitted SVC model.
         Ok(SVC::<'a> {
-            classes: Some(classes),
+            classes: Some(classes), // Store the two classes the SVC was trained on.
-            instances: Some(support_vectors),
+            instances: Some(support_vectors), // Store the data points that are support vectors.
-            parameters: Some(parameters),
+            parameters: Some(parameters), // Reference to the parameters used for fitting.
-            w: Some(weight),
+            w: Some(weight),        // The learned weight vector (for linear kernels).
-            b: Some(b),
+            b: Some(b),             // The learned bias term.
-            phantomdata: PhantomData,
+            phantomdata: PhantomData, // Placeholder for type parameters not directly stored.
         })
     }
-
     /// Predicts estimated class labels from `x`
     /// * `x` - _KxM_ data where _K_ is number of observations and _M_ is number of features.
     pub fn predict(&self, x: &'a X) -> Result<Vec<TX>, Failed> {
         let mut y_hat: Vec<TX> = self.decision_function(x)?;
 
         for i in 0..y_hat.len() {
-            let cls_idx = match *y_hat.get(i).unwrap() > TX::zero() {
+            let cls_idx = match *y_hat.get(i) > TX::zero() {
-                false => TX::from(self.classes.as_ref().unwrap()[0]).unwrap(),
+                false => TX::from(self.classes.as_ref().unwrap().0).unwrap(),
-                true => TX::from(self.classes.as_ref().unwrap()[1]).unwrap(),
+                true => TX::from(self.classes.as_ref().unwrap().1).unwrap(),
             };
 
             y_hat.set(i, cls_idx);
@@ -445,14 +695,18 @@ impl<'a, TX: Number + RealNumber, TY: Number + Ord, X: Array2<TX>, Y: Array1<TY>
     fn new(
         x: &'a X,
         y: &'a Y,
+        indices: Option<Vec<usize>>,
         parameters: &'a SVCParameters<TX, TY, X, Y>,
+        classes: &'a (TY, TY),
     ) -> Optimizer<'a, TX, TY, X, Y> {
         let (n, _) = x.shape();
 
         Optimizer {
             x,
             y,
+            indices,
             parameters,
+            classes,
             svmin: 0,
             svmax: 0,
             gmin: <TX as Bounded>::max_value(),
@@ -478,7 +732,12 @@ impl<'a, TX: Number + RealNumber, TY: Number + Ord, X: Array2<TX>, Y: Array1<TY>
             for i in self.permutate(n) {
                 x.clear();
                 x.extend(self.x.get_row(i).iterator(0).take(n).copied());
-                self.process(i, &x, *self.y.get(i), &mut cache);
+                let y = if *self.y.get(i) == self.classes.1 {
+                    1
+                } else {
+                    -1
+                } as f64;
+                self.process(i, &x, y, &mut cache);
                 loop {
                     self.reprocess(tol, &mut cache);
                     self.find_min_max_gradient();
@@ -514,14 +773,16 @@ impl<'a, TX: Number + RealNumber, TY: Number + Ord, X: Array2<TX>, Y: Array1<TY>
         for i in self.permutate(n) {
             x.clear();
             x.extend(self.x.get_row(i).iterator(0).take(n).copied());
-            if *self.y.get(i) == TY::one() && cp < few {
+            let y = if *self.y.get(i) == self.classes.1 {
-                if self.process(i, &x, *self.y.get(i), cache) {
+                1
+            } else {
+                -1
+            } as f64;
+            if y == 1.0 && cp < few {
+                if self.process(i, &x, y, cache) {
                     cp += 1;
                 }
-            } else if *self.y.get(i) == TY::from(-1).unwrap()
+            } else if y == -1.0 && cn < few && self.process(i, &x, y, cache) {
-                && cn < few
-                && self.process(i, &x, *self.y.get(i), cache)
-            {
                 cn += 1;
             }
 
@@ -531,14 +792,14 @@ impl<'a, TX: Number + RealNumber, TY: Number + Ord, X: Array2<TX>, Y: Array1<TY>
         }
     }
 
-    fn process(&mut self, i: usize, x: &[TX], y: TY, cache: &mut Cache<TX, TY, X, Y>) -> bool {
+    fn process(&mut self, i: usize, x: &[TX], y: f64, cache: &mut Cache<TX, TY, X, Y>) -> bool {
         for j in 0..self.sv.len() {
             if self.sv[j].index == i {
                 return true;
             }
         }
 
-        let mut g: f64 = y.to_f64().unwrap();
+        let mut g = y;
 
         let mut cache_values: Vec<((usize, usize), TX)> = Vec::new();
 
@@ -559,8 +820,8 @@ impl<'a, TX: Number + RealNumber, TY: Number + Ord, X: Array2<TX>, Y: Array1<TY>
         self.find_min_max_gradient();
 
         if self.gmin < self.gmax
-            && ((y > TY::zero() && g < self.gmin.to_f64().unwrap())
+            && ((y > 0.0 && g < self.gmin.to_f64().unwrap())
-                || (y < TY::zero() && g > self.gmax.to_f64().unwrap()))
+                || (y < 0.0 && g > self.gmax.to_f64().unwrap()))
         {
             return false;
         }
@@ -590,7 +851,7 @@ impl<'a, TX: Number + RealNumber, TY: Number + Ord, X: Array2<TX>, Y: Array1<TY>
             ),
         );
 
-        if y > TY::zero() {
+        if y > 0.0 {
             self.smo(None, Some(0), TX::zero(), cache);
         } else {
             self.smo(Some(0), None, TX::zero(), cache);
@@ -647,7 +908,6 @@ impl<'a, TX: Number + RealNumber, TY: Number + Ord, X: Array2<TX>, Y: Array1<TY>
         let gmin = self.gmin;
 
         let mut idxs_to_drop: HashSet<usize> = HashSet::new();
-
         self.sv.retain(|v| {
             if v.alpha == 0f64
                 && ((TX::from(v.grad).unwrap() >= gmax && TX::zero() >= TX::from(v.cmax).unwrap())
@@ -666,7 +926,11 @@ impl<'a, TX: Number + RealNumber, TY: Number + Ord, X: Array2<TX>, Y: Array1<TY>
 
     fn permutate(&self, n: usize) -> Vec<usize> {
         let mut rng = get_rng_impl(self.parameters.seed);
-        let mut range: Vec<usize> = (0..n).collect();
+        let mut range = if let Some(indices) = self.indices.clone() {
+            indices
+        } else {
+            (0..n).collect::<Vec<usize>>()
+        };
         range.shuffle(&mut rng);
         range
     }
@@ -965,12 +1229,12 @@ mod tests {
         ];
 
         let knl = Kernels::linear();
-        let params = SVCParameters::default()
+        let parameters = SVCParameters::default()
             .with_c(200.0)
             .with_kernel(knl)
             .with_seed(Some(100));
 
-        let y_hat = SVC::fit(&x, &y, &params)
+        let y_hat = SVC::fit(&x, &y, &parameters)
             .and_then(|lr| lr.predict(&x))
             .unwrap();
         let acc = accuracy(&y, &(y_hat.iter().map(|e| e.to_i32().unwrap()).collect()));
@@ -1070,6 +1334,56 @@ mod tests {
         assert!(acc >= 0.9, "accuracy ({acc}) is not larger or equal to 0.9");
     }
 
+    #[cfg_attr(
+        all(target_arch = "wasm32", not(target_os = "wasi")),
+        wasm_bindgen_test::wasm_bindgen_test
+    )]
+    #[test]
+    fn svc_multiclass_fit_predict() {
+        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],
+        ])
+        .unwrap();
+
+        let y: Vec<i32> = vec![0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2];
+
+        let knl = Kernels::linear();
+        let parameters = SVCParameters::default()
+            .with_c(200.0)
+            .with_kernel(knl)
+            .with_seed(Some(100));
+
+        let y_hat = MultiClassSVC::fit(&x, &y, &parameters)
+            .and_then(|lr| lr.predict(&x))
+            .unwrap();
+
+        let acc = accuracy(&y, &(y_hat.iter().map(|e| e.to_i32().unwrap()).collect()));
+
+        assert!(
+            acc >= 0.9,
+            "Multiclass accuracy ({acc}) is not larger or equal to 0.9"
+        );
+    }
+
     #[cfg_attr(
         all(target_arch = "wasm32", not(target_os = "wasi")),
         wasm_bindgen_test::wasm_bindgen_test
@@ -1106,8 +1420,8 @@ mod tests {
         ];
 
         let knl = Kernels::linear();
-        let params = SVCParameters::default().with_kernel(knl);
+        let parameters = SVCParameters::default().with_kernel(knl);
-        let svc = SVC::fit(&x, &y, &params).unwrap();
+        let svc = SVC::fit(&x, &y, &parameters).unwrap();
 
         // serialization
         let deserialized_svc: SVC<'_, f64, i32, _, _> =