it.univaq.incipict.profilemanager.common.utility.Utility.java Source code

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Here is the source code for it.univaq.incipict.profilemanager.common.utility.Utility.java

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/*
 * Profile Manager - Copyright (C) 2016  Daniele Tellina
 *
 * Profile Manager 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.
 *  
 * Profile Manager 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 Profile Manager.  If not, see <http://www.gnu.org/licenses/>.
 */
package it.univaq.incipict.profilemanager.common.utility;

import java.util.Date;
import java.util.HashMap;
import java.util.List;
import java.util.Map;
import java.util.Set;

import org.apache.commons.math.linear.ArrayRealVector;
import org.apache.commons.math.linear.RealVector;

import it.univaq.incipict.profilemanager.business.model.Information;
import it.univaq.incipict.profilemanager.business.model.Profile;
import it.univaq.incipict.profilemanager.business.model.ProfileInformation;
import it.univaq.incipict.profilemanager.business.model.User;

/**
 * 
 * @author Daniele Tellina
 *
 */
public class Utility {

    public static Date actualDate() {
        return new Date();
    }

    // this method calculate the Euclidean Distances between
    // the answers vector of a single survey and the ranks vector of each
    // different profile.
    public static HashMap<Profile, Double> getEuclideanDistances(List<Profile> profilesList, User user) {
        Map<Profile, Double> result = new HashMap<Profile, Double>();
        Set<ProfileInformation> profileInformationSet;
        Set<Information> userInformationSet;

        // Retrieve user information set
        userInformationSet = user.getInformationSet();
        if (userInformationSet.isEmpty()) {
            return (HashMap<Profile, Double>) result;
        }

        // For each Profile
        for (Profile profile : profilesList) {
            profileInformationSet = profile.getProfileInformationSet();
            int vectorsLenght = Math.max(profileInformationSet.size(), userInformationSet.size());
            RealVector ranksRealVector = new ArrayRealVector(new double[] {});
            RealVector userInformationVector = new ArrayRealVector(new double[] {});

            // Loop userInformationSet and
            // profileInformationSet (i.e. one specific column vector in the
            // knowledge base representation)
            for (Information information : userInformationSet) {
                Long x = information.getId();
                for (ProfileInformation profileInformation : profileInformationSet) {
                    Long y = profileInformation.getInformation().getId();
                    // User selected information was stored in a RealVector at same
                    // position of relative ranksVector
                    // This permit to calculate Euclidean distance right.
                    if (x == y) {
                        userInformationVector = userInformationVector.append(1d); // Associated:1, Else:0
                        ranksRealVector = ranksRealVector.append(profileInformation.getRank());

                        profileInformationSet.remove(profileInformation);
                        break;
                    }
                }
            }
            // At this point we aren't interested to elements position
            // because every other information worth zero.
            // Euclidean distance are not influenced from position of 0-elements in
            // a "sub-vector".
            // if they are all zeros.
            // => Append the zeros until completion of the length of the vectors
            userInformationVector = userInformationVector
                    .append(new double[vectorsLenght - userInformationSet.size()]);

            for (ProfileInformation profileInformation : profileInformationSet) {
                // Append the remaining elements of this set (profileInformationSet)
                ranksRealVector = ranksRealVector.append(profileInformation.getRank());
            }

            // Calculate Euclidean Distance
            double distance = userInformationVector.getDistance(ranksRealVector);
            // add the distance to Distance's Map
            result.put(profile, distance);

        } // END, goto Next Profile

        // return the HashMap sorted by value (ASC)
        return (HashMap<Profile, Double>) MapUtil.sortByValueASC(result);
    }

    public static HashMap<Profile, Float> getPercentages(HashMap<Profile, Double> distances,
            int informationSetSize) {
        Map<Profile, Float> result = new HashMap<Profile, Float>();

        if (distances.isEmpty() || distances == null) {
            return new HashMap<Profile, Float>();
        } else {
            for (Map.Entry<Profile, Double> entry : distances.entrySet()) {
                Profile profile = entry.getKey();
                Double distance = entry.getValue();

                // Calculate the percentage for the current distance
                // The maximum distance is sqrt(informationSetSize) because we have
                // 0-1 vectors
                // REMARK: Euclidean Distance Theory
                float maxDistance = (float) Math.sqrt(informationSetSize);
                float percentage = (float) (maxDistance - distance) * 100 / maxDistance;

                // put the percentage in Map formatted with two decimals.
                result.put(profile, (float) (Math.round(percentage * 100.0) / 100.0));
            }
        }
        // return the HashMap sorted by value (DESC)
        return (HashMap<Profile, Float>) MapUtil.sortByValueDESC(result);
    }

    public static Profile getBestProfile(HashMap<Profile, Double> distancesMap) {
        if (distancesMap.isEmpty() || distancesMap == null)
            return null;
        // return the first element because the distances map is sorted by values
        // REF: getEuclideanDistances() method
        return distancesMap.keySet().iterator().next();
    }

}