Java tutorial
/* *********************************************************************** * * project: org.matsim.* * AcceptancePropaCategoryTask.java * * * *********************************************************************** * * * * copyright : (C) 2011 by the members listed in the COPYING, * * LICENSE and WARRANTY file. * * email : info at matsim dot org * * * * *********************************************************************** * * * * This program is free software; you can redistribute it and/or modify * * it under the terms of the GNU General Public License as published by * * the Free Software Foundation; either version 2 of the License, or * * (at your option) any later version. * * See also COPYING, LICENSE and WARRANTY file * * * * *********************************************************************** */ package playground.johannes.socialnetworks.graph.spatial.analysis; import gnu.trove.TDoubleDoubleHashMap; import gnu.trove.TDoubleObjectHashMap; import gnu.trove.TDoubleObjectIterator; import gnu.trove.TObjectDoubleHashMap; import java.io.IOException; import java.util.HashMap; import java.util.HashSet; import java.util.Map; import java.util.Map.Entry; import java.util.Set; import org.apache.commons.math.stat.descriptive.DescriptiveStatistics; import playground.johannes.sna.graph.Graph; import playground.johannes.sna.graph.Vertex; import playground.johannes.sna.graph.analysis.ModuleAnalyzerTask; import playground.johannes.sna.graph.spatial.SpatialEdge; import playground.johannes.sna.graph.spatial.SpatialVertex; import playground.johannes.sna.math.FixedSampleSizeDiscretizer; import playground.johannes.sna.math.Histogram; import playground.johannes.sna.math.LinLogDiscretizer; import playground.johannes.sna.util.TXTWriter; import playground.johannes.socialnetworks.graph.analysis.AttributePartition; import com.vividsolutions.jts.geom.Geometry; import com.vividsolutions.jts.geom.Point; /** * @author illenberger * */ public class EdgeLengthCategoryTask extends ModuleAnalyzerTask<Accessibility> { // private Set<Point> destinations; private Geometry boundary; public EdgeLengthCategoryTask(Accessibility module) { this.setModule(module); } // public void setDestinations(Set<Point> destinations) { // this.destinations = destinations; // } public void setBoundary(Geometry boundary) { this.boundary = boundary; } @Override public void analyze(Graph graph, Map<String, DescriptiveStatistics> statsMap) { Accessibility access = module; // access.setTargets(destinations); // destinations = new HashSet<Point>(); Set<Vertex> vertices = new HashSet<Vertex>(); for (Vertex v : graph.getVertices()) { Point p = ((SpatialVertex) v).getPoint(); if (p != null) { // if (boundary.contains(p)) { vertices.add(v); // destinations.add(((SpatialVertex) v).getPoint()); // } } } // access.setTargets(destinations); TObjectDoubleHashMap<Vertex> normValues = access.values(vertices); // TObjectDoubleHashMap<Vertex> normValues = ObservedDegree.getInstance().values(vertices); // TObjectDoubleHashMap<Vertex> normValues = ObservedAge.getInstance().values(vertices); // AttributePartition partitioner = new AttributePartition(new FixedBordersDiscretizer(normValues.getValues(), new double[]{21.0})); AttributePartition partitioner = new AttributePartition( FixedSampleSizeDiscretizer.create(normValues.getValues(), 1, 2)); TDoubleObjectHashMap<?> partitions = partitioner.partition(normValues); TDoubleObjectIterator<?> it = partitions.iterator(); // AcceptanceProbability propa = new ObservedAcceptanceProbability(); // AcceptanceProbability propa = new AcceptanceProbability(); EdgeLength propa = new EdgeLength(); Map<String, TDoubleDoubleHashMap> histograms = new HashMap<String, TDoubleDoubleHashMap>(); Map<String, DescriptiveStatistics> distributions = new HashMap<String, DescriptiveStatistics>(); double sum = 0; for (int i = 0; i < partitions.size(); i++) { it.advance(); double key = it.key(); Set<SpatialVertex> partition = (Set<SpatialVertex>) it.value(); System.out.println("Partition size = " + partition.size() + "; key = " + key); // DescriptiveStatistics distr = propa.distribution(partition, destinations); Set<SpatialEdge> edges = new HashSet<SpatialEdge>(); for (SpatialVertex v : partition) { edges.addAll(v.getEdges()); } DescriptiveStatistics distr = propa.statistics(edges); try { double[] values = distr.getValues(); System.out.println("Num samples = " + values.length); if (values.length > 0) { TDoubleDoubleHashMap hist = Histogram.createHistogram(distr, FixedSampleSizeDiscretizer.create(values, 1, 50), true); sum += Histogram.sum(hist); histograms.put(String.format("d-cat%1$.4f", key), hist); distributions.put(String.format("d-cat%1$.4f", key), distr); } writeHistograms(distr, new LinLogDiscretizer(1000.0, 2), String.format("d-cat%1$.4f.log", key), true); } catch (IOException e) { e.printStackTrace(); } } for (Entry<String, TDoubleDoubleHashMap> entry : histograms.entrySet()) { String key = entry.getKey(); TDoubleDoubleHashMap histogram = entry.getValue(); Histogram.normalize(histogram, sum); try { TXTWriter.writeMap(histogram, "d", "p", String.format("%1$s/%2$s.txt", getOutputDirectory(), key)); } catch (IOException e) { e.printStackTrace(); } histogram = Histogram.createCumulativeHistogram(histogram); Histogram.complementary(histogram); try { TXTWriter.writeMap(histogram, "d", "p", String.format("%1$s/%2$s.cum.txt", getOutputDirectory(), key)); } catch (IOException e) { e.printStackTrace(); } DescriptiveStatistics stats = distributions.get(key); writeRawData(stats, key); } } }