org.apache.mahout.math.hadoop.similarity.cooccurrence.measures.EuclideanDistanceSimilarity Maven / Gradle / Ivy
/**
* 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.similarity.cooccurrence.measures;
import org.apache.mahout.math.Vector;
public class EuclideanDistanceSimilarity implements VectorSimilarityMeasure {
@Override
public Vector normalize(Vector vector) {
return vector;
}
@Override
public double norm(Vector vector) {
double norm = 0;
for (Vector.Element e : vector.nonZeroes()) {
double value = e.get();
norm += value * value;
}
return norm;
}
@Override
public double aggregate(double valueA, double nonZeroValueB) {
return valueA * nonZeroValueB;
}
@Override
public double similarity(double dots, double normA, double normB, int numberOfColumns) {
// Arg can't be negative in theory, but can in practice due to rounding, so cap it.
// Also note that normA / normB are actually the squares of the norms.
double euclideanDistance = Math.sqrt(Math.max(0.0, normA - 2 * dots + normB));
return 1.0 / (1.0 + euclideanDistance);
}
@Override
public boolean consider(int numNonZeroEntriesA, int numNonZeroEntriesB, double maxValueA, double maxValueB,
double threshold) {
return true;
}
}