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/*
 * Copyright (c) 2010-2021 Haifeng Li. All rights reserved.
 *
 * Smile is free software: you can redistribute it and/or modify
 * it under the terms of the GNU General Public License as published by
 * the Free Software Foundation, either version 3 of the License, or
 * (at your option) any later version.
 *
 * Smile is distributed in the hope that it will be useful,
 * but WITHOUT ANY WARRANTY; without even the implied warranty of
 * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
 * GNU General Public License for more details.
 *
 * You should have received a copy of the GNU General Public License
 * along with Smile.  If not, see .
 */

package smile.regression;

import smile.math.kernel.MercerKernel;

import java.io.Serial;

/**
 * The learning methods building on kernels. Kernel methods owe their name to
 * the use of kernel functions, which enable them to operate in a high-dimensional,
 * implicit feature space without ever computing the coordinates of the data
 * in that space, but rather by simply computing the inner products between
 * the images of all pairs of data in the feature space.
 * 
 * Kernel methods can be thought of as instance-based learners: rather than
 * learning some fixed set of parameters corresponding to the features of
 * their inputs, they instead store (a subset of) their training set (or
 * a new representation) and learn for it a corresponding weight. Prediction
 * for unlabeled inputs is treated by the application of a similarity function.
 *
 * @param  the data type of model input objects.
 *
 * @author Haifeng Li
 */
public class KernelMachine extends smile.base.svm.KernelMachine implements Regression {
    @Serial
    private static final long serialVersionUID = 2L;

    /**
     * Constructor.
     * @param kernel Kernel function.
     * @param instances The instances in the kernel machine, e.g. support vectors.
     * @param weight The weights of instances.
     */
    public KernelMachine(MercerKernel kernel, T[] instances, double[] weight) {
        this(kernel, instances, weight, 0.0);
    }

    /**
     * Constructor.
     * @param kernel Kernel function.
     * @param instances The instances in the kernel machine, e.g. support vectors.
     * @param weight The weights of instances.
     * @param b The intercept;
     */
    public KernelMachine(MercerKernel kernel, T[] instances, double[] weight, double b) {
        super(kernel, instances, weight, b);
    }

    @Override
    public double predict(T x) {
        return score(x);
    }
}