/**
* 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
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package org.apache.mahout.classifier.naivebayes.training;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Mapper;
import org.apache.mahout.common.MahoutTestCase;
import org.apache.mahout.math.DenseVector;
import org.apache.mahout.math.Vector;
import org.apache.mahout.math.VectorWritable;
import org.easymock.EasyMock;
import org.junit.Test;
public class WeightsMapperTest extends MahoutTestCase {
@Test
public void scores() throws Exception {
Mapper.Context ctx = EasyMock.createMock(Mapper.Context.class);
Vector instance1 = new DenseVector(new double[] { 1, 0, 0.5, 0.5, 0 });
Vector instance2 = new DenseVector(new double[] { 0, 0.5, 0, 0, 0 });
Vector instance3 = new DenseVector(new double[] { 1, 0.5, 1, 1.5, 1 });
Vector weightsPerLabel = new DenseVector(new double[] { 0, 0 });
ctx.write(new Text(TrainNaiveBayesJob.WEIGHTS_PER_FEATURE),
new VectorWritable(new DenseVector(new double[] { 2, 1, 1.5, 2, 1 })));
ctx.write(new Text(TrainNaiveBayesJob.WEIGHTS_PER_LABEL),
new VectorWritable(new DenseVector(new double[] { 2.5, 5 })));
EasyMock.replay(ctx);
WeightsMapper weights = new WeightsMapper();
setField(weights, "weightsPerLabel", weightsPerLabel);
weights.map(new IntWritable(0), new VectorWritable(instance1), ctx);
weights.map(new IntWritable(0), new VectorWritable(instance2), ctx);
weights.map(new IntWritable(1), new VectorWritable(instance3), ctx);
weights.cleanup(ctx);
EasyMock.verify(ctx);
}
}