/*
 * Copyright (C) 2018 Edward Raff <Raff.Edward@gmail.com>
 *
 * This program 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.
 *
 * This program 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 this program.  If not, see <http://www.gnu.org/licenses/>.
 */
package jsat.outlier;

import jsat.outlier.IsolationForest;
import jsat.DataSet;
import jsat.SimpleDataSet;
import jsat.classifiers.DataPoint;
import jsat.datatransform.kernel.RFF_RBF;
import jsat.distributions.Normal;
import jsat.distributions.kernels.RBFKernel;
import jsat.utils.GridDataGenerator;
import org.junit.After;
import org.junit.AfterClass;
import org.junit.Before;
import org.junit.BeforeClass;
import org.junit.Test;
import static org.junit.Assert.*;

/**
 *
 * @author Edward Raff <Raff.Edward@gmail.com>
 */
public class LOFTest
{
    
    public LOFTest()
    {
    }
    
    @BeforeClass
    public static void setUpClass()
    {
    }
    
    @AfterClass
    public static void tearDownClass()
    {
    }
    
    @Before
    public void setUp()
    {
    }
    
    @After
    public void tearDown()
    {
    }

    /**
     * Test of fit method, of class LinearOCSVM.
     */
    @Test
    public void testFit()
    {
        System.out.println("fit");
        int N = 5000;
        SimpleDataSet trainData = new GridDataGenerator(new Normal(), 1,1,1).generateData(N);
        
        SimpleDataSet outlierData = new GridDataGenerator(new Normal(10, 1.0), 1,1,1).generateData(N);
        
        LOF instance = new LOF();

        
        
        for(boolean parallel : new boolean[]{false, true})
        {
            instance.fit(trainData, parallel);
            
            double numOutliersInTrain = trainData.getDataPoints().stream().mapToDouble(instance::score).filter(x -> x < 0).count();
            assertEquals(0, numOutliersInTrain / trainData.size(), 0.05);//Better say something like 95% are inliers!

            double numOutliersInOutliers = outlierData.getDataPoints().stream().mapToDouble(instance::score).filter(x -> x < 0).count();
            assertEquals(1.0, numOutliersInOutliers / outlierData.size(), 0.1);//Better say 90% are outliers!
        }
    }
    
}
