opennlp.addons.mahout.VectorClassifierModel.java Source code

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Here is the source code for opennlp.addons.mahout.VectorClassifierModel.java

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/*
 * Licensed to the Apache Software Foundation (ASF) under one
 * or more contributor license agreements.  See the NOTICE file
 * distributed with this work for additional information
 * regarding copyright ownership.  The ASF licenses this file
 * to you 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 opennlp.addons.mahout;

import java.util.Map;

import opennlp.tools.ml.model.MaxentModel;

import org.apache.mahout.classifier.AbstractVectorClassifier;
import org.apache.mahout.math.RandomAccessSparseVector;
import org.apache.mahout.math.Vector;

// TODO: Would be nice to have an abstract maxent model impl ..

public class VectorClassifierModel implements MaxentModel {

    private final AbstractVectorClassifier classifier;
    private final String[] outcomeLabels;
    private final Map<String, Integer> predMap;

    public VectorClassifierModel(AbstractVectorClassifier pa, String outcomeLabels[],
            Map<String, Integer> predMap) {
        this.classifier = pa;
        // TODO: We should make a copy, so the model is immutable ...
        this.outcomeLabels = outcomeLabels;
        this.predMap = predMap;
    }

    public double[] eval(String[] features) {
        Vector vector = new RandomAccessSparseVector(predMap.size());

        for (String feature : features) {
            Integer featureId = predMap.get(feature);

            if (featureId != null) {
                vector.set(featureId, vector.get(featureId) + 1);
            }
        }

        Vector resultVector = classifier.classifyFull(vector);

        double outcomes[] = new double[classifier.numCategories()];

        for (int i = 0; i < outcomes.length; i++) {
            outcomes[i] = resultVector.get(i);
        }

        return outcomes;
    }

    public double[] eval(String[] context, double[] probs) {
        return eval(context);
    }

    public double[] eval(String[] context, float[] values) {
        return eval(context);
    }

    @Override
    public String getBestOutcome(double[] ocs) {
        int best = 0;
        for (int i = 1; i < ocs.length; i++)
            if (ocs[i] > ocs[best])
                best = i;
        return outcomeLabels[best];
    }

    @Override
    public String getAllOutcomes(double[] outcomes) {
        return null;
    }

    @Override
    public String getOutcome(int i) {
        return outcomeLabels[i];
    }

    @Override
    public int getIndex(String outcome) {
        for (int i = 0; i < outcomeLabels.length; i++) {
            if (outcomeLabels[i].equals(outcome)) {
                return i;
            }
        }

        return -1;
    }

    @Override
    public int getNumOutcomes() {
        return outcomeLabels.length;
    }
}