cn.ict.zyq.bestConf.COMT2.Branch2.java Source code

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Here is the source code for cn.ict.zyq.bestConf.COMT2.Branch2.java

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/**
 * Copyright (c) 2017 Institute of Computing Technology, Chinese Academy of Sciences, 2017 
 * Institute of Computing Technology, Chinese Academy of Sciences contributors. All rights reserved.
 * 
 * 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. See accompanying
 * LICENSE file.
 */
package cn.ict.zyq.bestConf.COMT2;

import java.util.ArrayList;
import java.util.HashMap;
import java.util.Map;

import weka.classifiers.trees.m5.PreConstructedLinearModel;
import weka.core.Attribute;
import weka.core.DenseInstance;
import weka.core.Instance;
import weka.core.Instances;

import com.google.common.collect.Range;

/**
 * Branch2
 *
 */

public class Branch2 {

    private HashMap<Attribute, Range<Double>> rangeMap; //ranges of the attributes in the M5P tree ?
    private ArrayList<PreConstructedLinearModel> linearModelList; //linearModels of one leaf ?

    public Branch2() {
        rangeMap = new HashMap<Attribute, Range<Double>>();
    }

    public Branch2(HashMap<Attribute, Range<Double>> rangeMap, PreConstructedLinearModel linearModel) {
        this.rangeMap = rangeMap;
        this.linearModelList = new ArrayList<PreConstructedLinearModel>();
        this.linearModelList.add(linearModel);
    }

    public Branch2(HashMap<Attribute, Range<Double>> rangeMap, ArrayList<PreConstructedLinearModel> linearModels) {
        this.rangeMap = rangeMap;
        this.linearModelList = linearModels;
    }

    public Instance maxPoint(Instances dataset) throws Exception {
        Instance max = new DenseInstance(dataset.numAttributes());
        max.setDataset(dataset);

        double[] combinedCoefs = null;
        int len = 0;
        for (PreConstructedLinearModel model : linearModelList) {
            //initialization
            if (combinedCoefs == null) {
                len = model.coefficients().length;
                combinedCoefs = new double[len];
                for (int i = 0; i < len; i++)
                    combinedCoefs[i] = 0;
            }

            for (int i = 0; i < len; i++)
                combinedCoefs[i] += model.coefficients()[i];
        }

        //the max value is obtained at ends of a range
        for (Map.Entry<Attribute, Range<Double>> ent : rangeMap.entrySet()) {
            int attIdx = ent.getKey().index();
            if (combinedCoefs[attIdx] > 0) {
                //use the upper bound
                if (ent.getValue().hasUpperBound())
                    max.setValue(attIdx, ent.getValue().upperEndpoint());
            } else if (combinedCoefs[attIdx] < 0) {
                //use the lower bound
                if (ent.getValue().hasLowerBound())
                    max.setValue(attIdx, ent.getValue().lowerEndpoint());
            }
        }

        //now we set the predicted values
        double y = 0;
        for (PreConstructedLinearModel model : linearModelList) {
            y += model.classifyInstance(max);
        }
        y /= linearModelList.size();
        max.setClassValue(y);

        return max;
    }

    public HashMap<Attribute, Range<Double>> getRangeMap() {
        return rangeMap;
    }

    public void setRangeMap(HashMap<Attribute, Range<Double>> rangeMap) {
        this.rangeMap = rangeMap;
    }

    public ArrayList<PreConstructedLinearModel> getLinearModels() {
        return linearModelList;
    }

    public void setLinearModels(ArrayList<PreConstructedLinearModel> linearModel) {
        this.linearModelList = linearModel;
    }

}