import java.util.ArrayList;
import java.util.List;

// Dataset class representing the original dataset
class Dataset {
    private List<List<Double>> data;
    private List<Integer> labels;
    private int numClasses;

    public Dataset() {
        numClasses = 0;
    }

    public Dataset(List<List<Double>> data, List<Integer> labels) {
        this.data = data;
        this.labels = labels;

        // Determine the number of classes in the dataset
        for (int label : labels) {
            if (label > numClasses)
                numClasses = label;
        }
        numClasses++; // Increment by 1 to account for 0-based indexing
    }

    public List<List<Double>> getData() {
        return data;
    }

    public List<Integer> getLabels() {
        return labels;
    }

    public int getNumClasses() {
        return numClasses;
    }
}

// Rebalancer class for rebalancing the dataset
class Rebalancer {
    // Rebalancing logic goes here

    public Dataset rebalance(Dataset originalDataset) {
        Dataset rebalancedDataset = new Dataset();
        // Perform dataset rebalancing
        // ...

        // Return the rebalanced dataset
        return rebalancedDataset;
    }
}

// BaseModel class representing the base machine learning model
abstract class BaseModel {
    public abstract void train(Dataset dataset);

    public abstract int predict(List<Double> instance);
}

// RebalancedModel class extending BaseModel for the rebalanced machine learning
// model
class RebalancedModel extends BaseModel {
    private BaseModel baseModel;
    private Rebalancer rebalancer;

    public RebalancedModel(BaseModel baseModel) {
        this.baseModel = baseModel;
        this.rebalancer = new Rebalancer();
    }

    public void train(Dataset dataset) {
        // Rebalance the dataset
        Dataset rebalancedDataset = rebalancer.rebalance(dataset);

        // Train the base model using the rebalanced dataset
        baseModel.train(rebalancedDataset);
    }

    public int predict(List<Double> instance) {
        return baseModel.predict(instance);
    }
}

// Example usage
class DecisionTreeModel extends BaseModel {
    public void train(Dataset dataset) {
        // Decision tree training logic using the dataset
        // ...
    }

    public int predict(List<Double> instance) {
        int predictedLabel = 0;
        // Decision tree prediction logic using the trained model
        // ...
        return predictedLabel;
    }
}

public class Rebalancing {
    public static void main(String[] args) {
        // Create the original dataset
        List<List<Double>> data = new ArrayList<>();
        data.add(List.of(1.0, 2.0));
        data.add(List.of(2.0, 3.0));
        data.add(List.of(3.0, 4.0));
        List<Integer> labels = List.of(0, 0, 1);
        Dataset originalDataset = new Dataset(data, labels);

        // Create the decision tree model
        DecisionTreeModel decisionTree = new DecisionTreeModel();

        // Create the rebalanced model
        RebalancedModel rebalancedModel = new RebalancedModel(decisionTree);

        // Train the rebalanced model
        rebalancedModel.train(originalDataset);

        // Perform prediction
        List<Double> instance = List.of(4.0, 5.0);
        int predictedLabel = rebalancedModel.predict(instance);

        // Display the predicted label
        System.out.println("Predicted Label: " + predictedLabel);
    }
}