/*******************************************************************************
 * Copyright (c) 2010 Haifeng Li
 *   
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *  
 *     http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 *******************************************************************************/

package smile.demo.data.classification;

import java.io.FileNotFoundException;
import java.io.FileOutputStream;
import java.io.PrintStream;

import smile.math.Math;
import smile.stat.distribution.MultivariateGaussianDistribution;

/**
 * A class to generate toy data for classification by the model in the book
 * The Elements of Statistical Learning, Section 2.3.3.
 *
 * @author Haifeng Li
 */
public class ToyData {

    private int k = 10;
    private double[] prob = new double[k];
    private double[][] m;
    private double[] m1 = {0, 1};
    private double[] m2 = {1, 0};
    private double[] var = {1, 1};
    private double[] v = {0.2, 0.2};

    /**
     * Constructor.
     */
    public ToyData() {
        for (int i = 0; i < k; i++) {
            prob[i] = 1.0 / k;
        }

        m = new double[2 * k][];

        MultivariateGaussianDistribution gauss = new MultivariateGaussianDistribution(m1, var);
        for (int i = 0; i < k; i++) {
            m[i] = gauss.rand();
        }

        gauss = new MultivariateGaussianDistribution(m2, var);
        for (int i = 0; i < k; i++) {
            m[k + i] = gauss.rand();
        }
    }

    /**
     * Generate n samples from each class.
     */
    public double[][] sample(int n) {
        double[][] samples = new double[2 * n][];

        MultivariateGaussianDistribution[] gauss = new MultivariateGaussianDistribution[k];
        for (int i = 0; i < k; i++) {
            gauss[i] = new MultivariateGaussianDistribution(m[i], v);
        }

        for (int i = 0; i < n; i++) {
            samples[i] = gauss[Math.random(prob)].rand();
        }

        for (int i = 0; i < k; i++) {
            gauss[i] = new MultivariateGaussianDistribution(m[k + i], v);
        }

        for (int i = 0; i < n; i++) {
            samples[n + i] = gauss[Math.random(prob)].rand();
        }

        return samples;
    }

    public static void main(String[] argv) {
        ToyData toy = new ToyData();
        int n = 100;
        double[][] s = toy.sample(n);

        try (PrintStream p = new PrintStream(new FileOutputStream("toy-train.txt")) ) {

            for (int i = 0; i < s.length; i++) {
                int label = i / n;
                p.format("%d\t% .4f\t% .4f\n", label, s[i][0], s[i][1]);
            }
        } catch (FileNotFoundException e) {
            e.printStackTrace();
        }

        n = 10000;
        s = toy.sample(n);
        try (PrintStream p = new PrintStream(new FileOutputStream("toy-test.txt")) ) {

            for (int i = 0; i < s.length; i++) {
                int label = i / n;
                p.format("%d\t% .4f\t% .4f\n", label, s[i][0], s[i][1]);
            }
        } catch (FileNotFoundException e) {
            e.printStackTrace();
        }
    }
}