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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.common.distance;
import java.util.Collection;
import java.util.Collections;
import org.apache.hadoop.conf.Configuration;
import org.apache.mahout.common.parameters.Parameter;
import org.apache.mahout.math.CardinalityException;
import org.apache.mahout.math.Vector;
import org.apache.mahout.math.function.Functions;
/**
* This class implements a "manhattan distance" metric by summing the absolute values of the difference
* between each coordinate
*/
public class ManhattanDistanceMeasure implements DistanceMeasure {
public static double distance(double[] p1, double[] p2) {
double result = 0.0;
for (int i = 0; i < p1.length; i++) {
result += Math.abs(p2[i] - p1[i]);
}
return result;
}
@Override
public void configure(Configuration job) {
// nothing to do
}
@Override
public Collection> getParameters() {
return Collections.emptyList();
}
@Override
public void createParameters(String prefix, Configuration jobConf) {
// nothing to do
}
@Override
public double distance(Vector v1, Vector v2) {
if (v1.size() != v2.size()) {
throw new CardinalityException(v1.size(), v2.size());
}
return v1.aggregate(v2, Functions.PLUS, Functions.MINUS_ABS);
}
@Override
public double distance(double centroidLengthSquare, Vector centroid, Vector v) {
return distance(centroid, v); // TODO
}
}
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