net.librec.recommender.item.RecommendedItemListTestCase.java Source code

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Here is the source code for net.librec.recommender.item.RecommendedItemListTestCase.java

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/**
 * Copyright (C) 2016 LibRec
 * 
 * This file is part of LibRec.
 * LibRec 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.
 *
 * LibRec 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 LibRec. If not, see <http://www.gnu.org/licenses/>.
 */
package net.librec.recommender.item;

import com.google.common.collect.HashBasedTable;
import com.google.common.collect.Table;
import net.librec.BaseTestCase;
import net.librec.math.structure.DenseMatrix;
import net.librec.math.structure.MatrixEntry;
import net.librec.math.structure.SparseMatrix;
import org.apache.commons.lang.math.RandomUtils;
import org.junit.Test;

import java.util.Iterator;

/**
 * 
 * 
 */
public class RecommendedItemListTestCase extends BaseTestCase {

    private RecommendedItemList recommendedItemList;

    public void setUp() throws Exception {
        recommendedItemList = new RecommendedItemList(10000);
    }

    @Test
    public void test() {
        testSparseMatrixAndRecommender();

        //System.exit(0);
        //      RecommendedItemList x = new RecommendedItemList();
        //      ArrayList x = new ArrayList<>();
        long time = System.currentTimeMillis();
        DenseMatrix x = new DenseMatrix(10000, 10000);
        //      double[][] data = new double[10000][10000];
        //      System.out.println(time);
        int k = 0;
        for (int i = 0; i < 10000; i++) {
            //         ArrayList<ItemEntry<Integer, Double>> y = new ArrayList<>();
            for (int j = 0; j < 10000; j++) {
                //            ItemEntry<Integer, Double> itemEntry = new ItemEntry<Integer, Double>(j, RandomUtils.nextDouble());
                if (k % 10000 == 0) {
                    System.out.println(k);
                }
                x.set(i, j, RandomUtils.nextDouble());
                //            recommendedItemList.addUserItemIdx(i,j,RandomUtils.nextDouble());
                k++;
            }
            //         x.addItemIdxList(i, y);
        }
        System.out.println(System.currentTimeMillis() - time);
    }

    public static void testSparseMatrixAndRecommender() {
        Table<Integer, Integer, Double> table = HashBasedTable.create();
        table.put(1, 2, 3.0);
        table.put(3, 4, 3.0);
        table.put(5, 2, 3.0);
        table.put(4, 6, 3.0);
        SparseMatrix testMatrix = new SparseMatrix(6, 7, table);
        for (MatrixEntry matrixEntry : testMatrix) {
            System.out.println(matrixEntry.row() + " " + matrixEntry.column() + " " + matrixEntry.get());
        }
        System.out.println();

        int numUsers = 6;
        RecommendedItemList recommendedList = new RecommendedItemList(numUsers - 1, numUsers);

        for (MatrixEntry matrixEntry : testMatrix) {
            int userIdx = matrixEntry.row();
            int itemIdx = matrixEntry.column();
            double predictRating = 3.0;
            if (Double.isNaN(predictRating)) {
                predictRating = 2.0;
            }
            recommendedList.addUserItemIdx(userIdx, itemIdx, predictRating);
        }

        Iterator<MatrixEntry> testMatrixIter = testMatrix.iterator();
        Iterator<UserItemRatingEntry> recommendedEntryIter = recommendedList.entryIterator();

        while (testMatrixIter.hasNext()) {

            if (recommendedEntryIter.hasNext()) {

                MatrixEntry testMatrixEntry = testMatrixIter.next();
                UserItemRatingEntry userItemRatingEntry = recommendedEntryIter.next();

                System.out.println(
                        testMatrixEntry.row() + " " + testMatrixEntry.column() + " " + testMatrixEntry.get());
                System.out.println(userItemRatingEntry.getUserIdx() + " " + userItemRatingEntry.getItemIdx() + " "
                        + userItemRatingEntry.getValue());

                if (testMatrixEntry.row() == userItemRatingEntry.getUserIdx()
                        && testMatrixEntry.column() == userItemRatingEntry.getItemIdx()) {
                } else {
                    throw new IndexOutOfBoundsException("index of recommendedList does not equal testMatrix index");
                }

            } else {
                throw new IndexOutOfBoundsException(
                        "index size of recommendedList does not equal testMatrix index size");
            }
        }
    }
}