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Various clustering algorithm implementations for all primitive types including random, random forest, K-Means (Exact, Hierarchical and Approximate), ...
/**
* Copyright (c) 2011, The University of Southampton and the individual contributors.
* All rights reserved.
*
* Redistribution and use in source and binary forms, with or without modification,
* are permitted provided that the following conditions are met:
*
* * Redistributions of source code must retain the above copyright notice,
* this list of conditions and the following disclaimer.
*
* * 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.
*
* * Neither the name of the University of Southampton nor the names of its
* contributors may be used to endorse or promote products derived from this
* software without specific prior written permission.
*
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "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 THE COPYRIGHT OWNER 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.
*/
package org.openimaj.experiment.evaluation.cluster;
import java.util.ArrayList;
import java.util.HashMap;
import java.util.List;
import java.util.Map;
import java.util.Map.Entry;
import org.openimaj.experiment.evaluation.AnalysisResult;
import org.openimaj.experiment.evaluation.Evaluator;
import org.openimaj.experiment.evaluation.cluster.analyser.ClusterAnalyser;
import org.openimaj.ml.clustering.dbscan.SparseMatrixDBSCAN;
import org.openimaj.util.function.Function;
import org.openimaj.util.iterator.UniformDoubleRangeIterable;
import ch.akuhn.matrix.SparseMatrix;
/**
* @author Sina Samangooei ([email protected])
*
* @param
* The type of data which the internal clusterer can cluster lists of
* @param
* The type of results the
*/
public class RangedDBSCANClusterEvaluator
implements
Evaluator, RangedAnalysisResult>
{
private int[][] correct;
private ClusterAnalyser analyser;
private SparseMatrixDBSCAN gen;
private SparseMatrix data;
UniformDoubleRangeIterable r;
/**
* @param r
* the range of values for the {@link SparseMatrixDBSCAN} eps
* value
* @param gen
* @param data
* @param clusters
* @param analyser
*/
public RangedDBSCANClusterEvaluator(UniformDoubleRangeIterable r, SparseMatrixDBSCAN gen, SparseMatrix data,
int[][] clusters, ClusterAnalyser analyser)
{
this.gen = gen;
this.correct = clusters;
this.analyser = analyser;
this.data = data;
}
/**
* @param r
* the range of values for the {@link SparseMatrixDBSCAN} eps
* value
* @param gen
* @param data
* @param dataset
* extract the elements of this map "in order" and build a ground
* truth. very dangerous.
* @param analyser
*/
public RangedDBSCANClusterEvaluator(UniformDoubleRangeIterable r, SparseMatrixDBSCAN gen, SparseMatrix data,
Map> dataset, ClusterAnalyser analyser)
{
this.r = r;
this.gen = gen;
this.correct = new int[dataset.size()][];
int j = 0;
int k = 0;
for (final Entry> es : dataset.entrySet()) {
this.correct[j] = new int[es.getValue().size()];
int i = 0;
final List value = es.getValue();
for (int l = 0; l < value.size(); l++) {
this.correct[j][i++] = k;
k++;
}
j++;
}
this.analyser = analyser;
this.data = data;
}
/**
* @param r
* the range of values for the {@link SparseMatrixDBSCAN} eps
* value
* @param gen
* @param data
* @param indexFunc
* given a data instance, return its index
* @param dataset
* @param analyser
*/
public RangedDBSCANClusterEvaluator(
UniformDoubleRangeIterable r,
SparseMatrixDBSCAN gen,
SparseMatrix data,
Function indexFunc,
Map> dataset,
ClusterAnalyser analyser)
{
this.r = r;
this.gen = gen;
this.correct = new int[dataset.size()][];
int j = 0;
for (final Entry> es : dataset.entrySet()) {
this.correct[j] = new int[es.getValue().size()];
int i = 0;
final List value = es.getValue();
for (final B b : value) {
this.correct[j][i++] = indexFunc.apply(b);
}
j++;
}
this.analyser = analyser;
this.data = data;
}
/**
* @param r
* the range of values for the {@link SparseMatrixDBSCAN} eps
* value
* @param gen
* @param dataset
* @param transform
* turn a list of dataset items into the required type for
* clustering
* @param analyser
*/
public RangedDBSCANClusterEvaluator(
UniformDoubleRangeIterable r,
SparseMatrixDBSCAN gen,
Map> dataset,
Function, SparseMatrix> transform,
ClusterAnalyser analyser)
{
this.r = r;
this.gen = gen;
this.analyser = analyser;
this.correct = new int[dataset.size()][];
int j = 0;
final List flattened = new ArrayList();
for (final Entry> es : dataset.entrySet()) {
this.correct[j] = new int[es.getValue().size()];
int i = 0;
for (final B b : es.getValue()) {
this.correct[j][i++] = flattened.size();
flattened.add(b);
}
j++;
}
this.data = transform.apply(flattened);
}
@Override
public Map evaluate() {
final Map ret = new HashMap();
for (final Double eps : this.r) {
this.gen.setEps(eps);
ret.put(eps, new ClusterEvaluator(gen, data, correct, analyser).evaluate());
}
return ret;
}
@Override
public RangedAnalysisResult analyse(Map estimated) {
final RangedAnalysisResult ret = new RangedAnalysisResult();
for (final Entry ent : estimated.entrySet()) {
ret.put(ent.getKey(), this.analyser.analyse(correct, ent.getValue()));
}
return ret;
}
}