boa.aggregators.LinearRegressionAggregator.java Source code

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Here is the source code for boa.aggregators.LinearRegressionAggregator.java

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/*
 * Copyright 2014, Hridesh Rajan, Robert Dyer,
 *                 and Iowa State University of Science and Technology
 *
 * 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 boa.aggregators;

import java.io.IOException;
import java.util.ArrayList;
import java.util.HashMap;
import java.util.List;
import java.util.Map;

import weka.classifiers.functions.LinearRegression;
import weka.core.Attribute;
import weka.core.FastVector;
import weka.core.Instance;
import weka.core.Instances;
import weka.core.Utils;

/**
 * A Boa aggregator for training the model using Linear Regression.
 * 
 * @author ankuraga
 */
@AggregatorSpec(name = "linearRegression", formalParameters = { "string" })
public class LinearRegressionAggregator extends MLAggregator {
    private Map<String, List<Double>> vectors = new HashMap<String, List<Double>>();
    private ArrayList<Double> vector = new ArrayList<Double>();
    private String[] options;
    private int count = 0;
    private int inc = 0;
    private LinearRegression model;

    public LinearRegressionAggregator(final String s) {
        super(s);
        try {
            options = Utils.splitOptions(s);
        } catch (Exception e) {
            e.printStackTrace();
        }
    }

    public void aggregate(final String data, final String metadata) throws IOException, InterruptedException {
        if (this.count != this.getVectorSize()) {
            this.vector.add(Double.parseDouble(data));
            this.count++;
        }

        if (this.count == this.getVectorSize()) {
            this.vectors.put("Vector " + this.inc, this.vector);
            this.inc++;
            this.vector = new ArrayList<Double>();
            this.count = 0;
        }
    }

    /** {@inheritDoc} */
    @Override
    public void finish() throws IOException, InterruptedException {
        int NumOfAttributes = this.getVectorSize();
        List<Attribute> attribute = new ArrayList<Attribute>();
        FastVector fvAttributes = new FastVector(NumOfAttributes);

        for (int i = 0; i < NumOfAttributes; i++) {
            attribute.add(new Attribute("Attribute" + i));
            fvAttributes.addElement(attribute.get(i));
        }

        Instances trainingSet = new Instances("LinearRegression", fvAttributes, 1);
        trainingSet.setClassIndex(NumOfAttributes - 1);

        for (List<Double> vector : this.vectors.values()) {
            Instance instance = new Instance(NumOfAttributes);
            for (int i = 0; i < vector.size(); i++) {
                instance.setValue((Attribute) fvAttributes.elementAt(i), vector.get(i));
            }
            trainingSet.add(instance);
        }

        try {
            this.model = new LinearRegression();
            this.model.setOptions(options);
            this.model.buildClassifier(trainingSet);
        } catch (Exception ex) {
        }

        this.saveModel(this.model);
    }
}