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Scalable machine learning libraries
/**
* 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 org.apache.mahout.math.hadoop;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.lib.input.SequenceFileInputFormat;
import org.apache.hadoop.mapreduce.lib.output.SequenceFileOutputFormat;
import org.apache.hadoop.util.ToolRunner;
import org.apache.mahout.common.AbstractJob;
import org.apache.mahout.common.HadoopUtil;
import org.apache.mahout.common.mapreduce.MergeVectorsCombiner;
import org.apache.mahout.common.mapreduce.MergeVectorsReducer;
import org.apache.mahout.common.mapreduce.TransposeMapper;
import org.apache.mahout.math.VectorWritable;
import java.io.IOException;
import java.util.List;
import java.util.Map;
/** Transpose a matrix */
public class TransposeJob extends AbstractJob {
public static void main(String[] args) throws Exception {
ToolRunner.run(new TransposeJob(), args);
}
@Override
public int run(String[] strings) throws Exception {
addInputOption();
addOption("numRows", "nr", "Number of rows of the input matrix");
addOption("numCols", "nc", "Number of columns of the input matrix");
Map> parsedArgs = parseArguments(strings);
if (parsedArgs == null) {
return -1;
}
int numRows = Integer.parseInt(getOption("numRows"));
int numCols = Integer.parseInt(getOption("numCols"));
DistributedRowMatrix matrix = new DistributedRowMatrix(getInputPath(), getTempPath(), numRows, numCols);
matrix.setConf(new Configuration(getConf()));
matrix.transpose();
return 0;
}
public static Job buildTransposeJob(Path matrixInputPath, Path matrixOutputPath, int numInputRows)
throws IOException {
return buildTransposeJob(new Configuration(), matrixInputPath, matrixOutputPath, numInputRows);
}
public static Job buildTransposeJob(Configuration initialConf, Path matrixInputPath, Path matrixOutputPath,
int numInputRows) throws IOException {
Job job = HadoopUtil.prepareJob(matrixInputPath, matrixOutputPath, SequenceFileInputFormat.class,
TransposeMapper.class, IntWritable.class, VectorWritable.class, MergeVectorsReducer.class, IntWritable.class,
VectorWritable.class, SequenceFileOutputFormat.class, initialConf);
job.setCombinerClass(MergeVectorsCombiner.class);
job.getConfiguration().setInt(TransposeMapper.NEW_NUM_COLS_PARAM, numInputRows);
job.setJobName("TransposeJob: " + matrixInputPath);
return job;
}
}
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