/*******************************************************************************
 * 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.feature;

import smile.validation.Accuracy;
import smile.classification.LDA;
import smile.data.NominalAttribute;
import smile.data.parser.DelimitedTextParser;
import smile.data.AttributeDataset;
import smile.data.parser.ArffParser;
import smile.sort.QuickSort;
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 Haifeng Li
 */
public class SumSquaresRatioTest {
    
    public SumSquaresRatioTest() {
    }

    @BeforeClass
    public static void setUpClass() throws Exception {
    }

    @AfterClass
    public static void tearDownClass() throws Exception {
    }
    
    @Before
    public void setUp() {
    }
    
    @After
    public void tearDown() {
    }

    /**
     * Test of rank method, of class SumSquaresRatio.
     */
    @Test
    public void testRank() {
        System.out.println("rank");
        try {
            ArffParser arffParser = new ArffParser();
            arffParser.setResponseIndex(4);
            AttributeDataset iris = arffParser.parse(smile.data.parser.IOUtils.getTestDataFile("weka/iris.arff"));
            double[][] x = iris.toArray(new double[iris.size()][]);
            int[] y = iris.toArray(new int[iris.size()]);
            
            SumSquaresRatio ssr = new SumSquaresRatio();
            double[] ratio = ssr.rank(x, y);
            assertEquals(4, ratio.length);
            assertEquals(1.6226463, ratio[0], 1E-7);
            assertEquals(0.6444144, ratio[1], 1E-7);   
            assertEquals(16.0412833, ratio[2], 1E-7);   
            assertEquals(13.0520327, ratio[3], 1E-7); 
        } catch (Exception ex) {
            System.err.println(ex);
        }
    }

    /**
     * Test of learn method, of class SumSquaresRatio.
     */
    @Test
    public void testLearn() {
        System.out.println("USPS");

        try {
            DelimitedTextParser parser = new DelimitedTextParser();
            parser.setResponseIndex(new NominalAttribute("class"), 0);
            AttributeDataset train = parser.parse("USPS Train", smile.data.parser.IOUtils.getTestDataFile("usps/zip.train"));
            AttributeDataset test = parser.parse("USPS Test", smile.data.parser.IOUtils.getTestDataFile("usps/zip.test"));

            double[][] x = train.toArray(new double[train.size()][]);
            int[] y = train.toArray(new int[train.size()]);
            double[][] testx = test.toArray(new double[test.size()][]);
            int[] testy = test.toArray(new int[test.size()]);

            SumSquaresRatio ssr = new SumSquaresRatio();
            double[] score = ssr.rank(x, y);
            int[] index = QuickSort.sort(score);

            int p = 135;                
            int n = x.length;
            double[][] xx = new double[n][p];
            for (int j = 0; j < p; j++) {
                for (int i = 0; i < n; i++) {
                    xx[i][j] = x[i][index[255-j]];
                }
            }

            int testn = testx.length;
            double[][] testxx = new double[testn][p];
            for (int j = 0; j < p; j++) {
                for (int i = 0; i < testn; i++) {
                    testxx[i][j] = testx[i][index[255-j]];
                }
            }

            LDA lda = new LDA(xx, y);
            int[] prediction = new int[testn];
            for (int i = 0; i < testn; i++) {
                prediction[i] = lda.predict(testxx[i]);
            }

            double accuracy = new Accuracy().measure(testy, prediction);
            System.out.format("SSR %.2f%%%n", 100 * accuracy);
        } catch (Exception ex) {
            System.err.println(ex);
        }
    }
}