/*
* Copyright 2010 Ted Dunning. All rights reserved.
*
* Redistribution and use in source and binary forms, with or without modification, are
* permitted provided that the following conditions are met:
*
* 1. Redistributions of source code must retain the above copyright notice, this list of
* conditions and the following disclaimer.
*
* 2. Redistributions in binary form must reproduce the above copyright notice, this list
* of conditions and the following disclaimer in the documentation and/or other materials
* provided with the distribution.
*
* THIS SOFTWARE IS PROVIDED BY <COPYRIGHT HOLDER> ``AS IS'' AND ANY EXPRESS OR IMPLIED
* WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND
* FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL <COPYRIGHT HOLDER> OR
* CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
* CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
* SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON
* ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING
* NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF
* ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
*
* The views and conclusions contained in the software and documentation are those of the
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*/
package mia.classifier.ch16.server;
import com.google.common.collect.Lists;
import mia.classifier.ch16.generated.Classifier;
import org.apache.mahout.classifier.AbstractVectorClassifier;
import org.apache.mahout.math.RandomAccessSparseVector;
import org.apache.mahout.math.Vector;
import org.apache.mahout.vectorizer.encoders.ConstantValueEncoder;
import org.apache.mahout.vectorizer.encoders.FeatureVectorEncoder;
import org.apache.mahout.vectorizer.encoders.TextValueEncoder;
import org.apache.thrift.TException;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;
import java.util.List;
/**
* Classifies input according to a model. The handling of the size of the feature
* vector and the feature encoder is a bit basic and would be more clever in a production
* server.
*/
public class Ops implements Classifier.Iface {
private static final int FEATURES = 10000;
private static final TextValueEncoder enc = new TextValueEncoder("body");
private static final FeatureVectorEncoder bias = new ConstantValueEncoder("Intercept");
private final Logger log = LoggerFactory.getLogger(this.getClass());
volatile AbstractVectorClassifier model;
public Ops() {
}
@Override
public List<Double> classify(String text) throws TException {
Vector features = new RandomAccessSparseVector(FEATURES);
enc.addText(text.toLowerCase());
enc.flush(1, features);
bias.addToVector((byte[]) null, 1, features);
Vector r = model.classifyFull(features);
List<Double> rx = Lists.newArrayList();
for (int i = 0; i < r.size(); i++) {
rx.add(r.get(i));
}
return rx;
}
public void setModel(AbstractVectorClassifier model) {
this.model = model;
}
}