moa.recommender.rc.predictor.impl.BaselinePredictor Maven / Gradle / Ivy

Go to download

Show more of this group Show more artifacts with this name
Show all versions of moa Show documentation

Massive On-line Analysis is an environment for massive data mining. MOA provides a framework for data stream mining and includes tools for evaluation and a collection of machine learning algorithms. Related to the WEKA project, also written in Java, while scaling to more demanding problems.

There is a newer version: 2024.07.0

Show newest version

/*
 *    BaselinePredictor.java
 *    Copyright (C) 2012 Universitat Politecnica de Catalunya
 *    @author Alex Catarineu ([email protected])
 *
 *    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 moa.recommender.rc.predictor.impl;

import java.util.ArrayList;
import java.util.List;
import moa.recommender.rc.data.RecommenderData;
import moa.recommender.rc.predictor.RatingPredictor;

public class BaselinePredictor implements RatingPredictor {
    /**
     * 
     */
    private static final long serialVersionUID = 8444152568941483368L;
    protected RecommenderData data;

    public BaselinePredictor(RecommenderData data) {
        this.data = data;
    }

    @Override
    public double predictRating(int userID, int itemID) {
        ArrayList itm = new ArrayList();
        itm.add(itemID);
        return predictRatings(userID, itm).get(0);
    }

    @Override
    public List predictRatings(int userID, List itemIDS) {
        ArrayList ret = new ArrayList(itemIDS.size());
        double avg = data.getAvgRatingUser(userID) - data.getGlobalMean();
        for (int i = 0; i < itemIDS.size(); ++i) {
            int itemID = itemIDS.get(i);
            double rat = avg + data.getAvgRatingItem(itemID);
            rat = Math.min(Math.max(rat, data.getMinRating()),
                    data.getMaxRating());
            ret.add(rat);
        }
        return ret;
    }

    @Override
    public RecommenderData getData() {
        return data;
    }

    @Override
    public void train() {
    }
}