private Integer getSkeletonCategoryFromPercentiles(
Integer value, DescriptiveStatistics windowStats) {
Integer skeletonCategory = 0;
// Calculate skeleton category
if (value == (int) windowStats.getMin()) {
skeletonCategory = 10;
} else if (value < windowStats.getPercentile(10)) {
skeletonCategory = 9;
} else if (value < windowStats.getPercentile(15)) {
skeletonCategory = 8;
} else if (value < windowStats.getPercentile(20)) {
skeletonCategory = 7;
} else if (value < windowStats.getPercentile(25)) {
skeletonCategory = 6;
} else if (value < windowStats.getPercentile(30)) {
skeletonCategory = 5;
} else if (value < windowStats.getPercentile(35)) {
skeletonCategory = 4;
} else if (value < windowStats.getPercentile(40)) {
skeletonCategory = 3;
} else if (value < windowStats.getPercentile(45)) {
skeletonCategory = 2;
} else if (value < windowStats.getPercentile(50)) {
skeletonCategory = 1;
}
return skeletonCategory;
}
private void writeErrorFile(DescriptiveStatistics error, String file, boolean append) {
try {
BufferedWriter writer = new BufferedWriter(new FileWriter(file, append));
if (!append) {
// write header
writer.write("mean\tvar\tstderr\tmin\tmax");
writer.newLine();
}
writer.write(String.valueOf(error.getMean()));
writer.write("\t");
writer.write(String.valueOf(error.getVariance()));
writer.write("\t");
writer.write(String.valueOf(error.getStandardDeviation()));
writer.write("\t");
writer.write(String.valueOf(error.getMin()));
writer.write("\t");
writer.write(String.valueOf(error.getMax()));
writer.newLine();
writer.close();
} catch (IOException e) {
e.printStackTrace();
}
}
@Override
public String getSummaryReport() {
final StringBuilder outBuffer = new StringBuilder();
final DescriptiveStatistics ds = computeStats();
outBuffer.append("Aggregate P@" + N + " Statistics:\n");
outBuffer.append(String.format("%-15s\t%6d\n", "num_q", ds.getN()));
outBuffer.append(String.format("%-15s\t%6.4f\n", "min", ds.getMin()));
outBuffer.append(String.format("%-15s\t%6.4f\n", "max", ds.getMax()));
outBuffer.append(String.format("%-15s\t%6.4f\n", "mean", ds.getMean()));
outBuffer.append(String.format("%-15s\t%6.4f\n", "std dev", ds.getStandardDeviation()));
outBuffer.append(String.format("%-15s\t%6.4f\n", "median", ds.getPercentile(50)));
outBuffer.append(String.format("%-15s\t%6.4f\n", "skewness", ds.getSkewness()));
outBuffer.append(String.format("%-15s\t%6.4f\n", "kurtosis", ds.getKurtosis()));
return outBuffer.toString();
}
public void test() {
SparseGraphBuilder builder = new SparseGraphBuilder();
SparseGraph graph = builder.createGraph();
SparseVertex v1 = builder.addVertex(graph);
SparseVertex v2 = builder.addVertex(graph);
SparseVertex v3 = builder.addVertex(graph);
SparseVertex v4 = builder.addVertex(graph);
SparseVertex v5 = builder.addVertex(graph);
SparseVertex v6 = builder.addVertex(graph);
builder.addEdge(graph, v1, v2);
builder.addEdge(graph, v2, v3);
builder.addEdge(graph, v3, v4);
builder.addEdge(graph, v4, v5);
builder.addEdge(graph, v5, v6);
builder.addEdge(graph, v6, v1);
builder.addEdge(graph, v2, v5);
Degree degree = Degree.getInstance();
DescriptiveStatistics stats = degree.statistics(graph.getVertices());
assertEquals(2.33, stats.getMean(), 0.01);
assertEquals(2.0, stats.getMin());
assertEquals(3.0, stats.getMax());
TObjectDoubleHashMap<? extends Vertex> values = degree.values(graph.getVertices());
TObjectDoubleIterator<? extends Vertex> it = values.iterator();
int count2 = 0;
int count3 = 0;
for (int i = 0; i < values.size(); i++) {
it.advance();
if (it.value() == 2) count2++;
else if (it.value() == 3) count3++;
}
assertEquals(4, count2);
assertEquals(2, count3);
assertEquals(-0.166, degree.assortativity(graph), 0.001);
}
private Integer getSkeletonCategoryFromCropper1979(
Integer value, DescriptiveStatistics windowStats, Double criticalLevel) {
Integer skeletonCategory = 0;
if (criticalLevel == null) criticalLevel = 0.5;
double mean = windowStats.getMean();
double stdev = windowStats.getStandardDeviation();
double smallRingThreshold = mean - (stdev * criticalLevel);
int min = (int) windowStats.getMin();
if (value == min) {
skeletonCategory = 10;
} else if (value > smallRingThreshold) {
skeletonCategory = 0;
} else {
Integer range = (int) (smallRingThreshold - min);
Integer categoryStepSize = range / 10;
skeletonCategory = (int) (0 - ((value - smallRingThreshold) / categoryStepSize));
}
return skeletonCategory;
}
private void writeReport(String test, String name, DescriptiveStatistics statistics)
throws IOException {
File report = new File("target", test + ".txt");
boolean needsPrefix = !report.exists();
PrintWriter writer = new PrintWriter(new FileWriterWithEncoding(report, "UTF-8", true));
try {
if (needsPrefix) {
writer.format("# %-34.34s min 10%% 50%% 90%% max%n", test);
}
writer.format(
"%-36.36s %6.0f %6.0f %6.0f %6.0f %6.0f%n",
name,
statistics.getMin(),
statistics.getPercentile(10.0),
statistics.getPercentile(50.0),
statistics.getPercentile(90.0),
statistics.getMax());
} finally {
writer.close();
}
}
/**
* Returns the minimal throughput time. Note that method calculateProcessMetrics() should be
* called before this method.
*
* @return double
*/
public double getMinThroughputTime() {
return timeStats.getMin();
}
@Override
public void notifyAfterMobsim(AfterMobsimEvent event) {
Network network = event.getServices().getScenario().getNetwork();
DescriptiveStatistics error = new DescriptiveStatistics();
DescriptiveStatistics errorAbs = new DescriptiveStatistics();
DescriptivePiStatistics errorWeighted = new WSMStatsFactory().newInstance();
TDoubleArrayList errorVals = new TDoubleArrayList();
TDoubleArrayList caps = new TDoubleArrayList();
TDoubleArrayList speeds = new TDoubleArrayList();
for (Count count : counts.getCounts().values()) {
if (!count.getId().toString().startsWith(ODCalibrator.VIRTUAL_ID_PREFIX)) {
double obsVal = 0;
for (int i = 1; i < 25; i++) {
obsVal += count.getVolume(i).getValue();
}
if (obsVal > 0) {
double simVal = calculator.getOccupancy(count.getId());
simVal *= factor;
double err = (simVal - obsVal) / obsVal;
error.addValue(err);
errorAbs.addValue(Math.abs(err));
errorWeighted.addValue(Math.abs(err), 1 / obsVal);
Link link = network.getLinks().get(count.getId());
errorVals.add(Math.abs(err));
caps.add(link.getCapacity());
speeds.add(link.getFreespeed());
}
}
}
logger.info(
String.format(
"Relative counts error: mean = %s, var = %s, stderr = %s, min = %s, max = %s",
error.getMean(),
error.getVariance(),
error.getStandardDeviation(),
error.getMin(),
error.getMax()));
logger.info(
String.format(
"Absolute relative counts error: mean = %s, var = %s, stderr = %s, min = %s, max = %s",
errorAbs.getMean(),
errorAbs.getVariance(),
errorAbs.getStandardDeviation(),
errorAbs.getMin(),
errorAbs.getMax()));
logger.info(
String.format(
"Absolute weigthed relative counts error: mean = %s, var = %s, stderr = %s, min = %s, max = %s",
errorWeighted.getMean(),
errorWeighted.getVariance(),
errorWeighted.getStandardDeviation(),
errorWeighted.getMin(),
errorWeighted.getMax()));
String outdir = event.getServices().getControlerIO().getIterationPath(event.getIteration());
try {
TDoubleDoubleHashMap map = Correlations.mean(caps.toArray(), errorVals.toArray());
StatsWriter.writeHistogram(
map, "capacity", "counts", String.format("%s/countsError.capacity.txt", outdir));
map = Correlations.mean(speeds.toArray(), errorVals.toArray());
StatsWriter.writeHistogram(
map, "speed", "counts", String.format("%s/countsError.speed.txt", outdir));
StatsWriter.writeHistogram(
Histogram.createHistogram(error, new LinearDiscretizer(0.1), false),
"Error",
"Frequency",
String.format("%s/countsError.hist.txt", outdir));
StatsWriter.writeHistogram(
Histogram.createHistogram(errorAbs, new LinearDiscretizer(0.1), false),
"Error (absolute)",
"Frequency",
String.format("%s/countsErrorAbs.hist.txt", outdir));
StatsWriter.writeHistogram(
Histogram.createHistogram(errorWeighted, new LinearDiscretizer(0.1), true),
"Error (weighted)",
"Frequency",
String.format("%s/countsErrorWeighted.hist.txt", outdir));
CountsCompare2GeoJSON.write(calculator, counts, factor, network, outdir);
NetworkLoad2GeoJSON.write(
event.getServices().getScenario().getNetwork(),
calculator,
factor,
outdir + "/network.json");
} catch (Exception e) {
e.printStackTrace();
}
String rootOutDir = event.getServices().getControlerIO().getOutputPath();
boolean append = false;
if (event.getIteration() > 0) {
append = true;
}
writeErrorFile(error, String.format("%s/countsError.txt", rootOutDir), append);
writeErrorFile(errorAbs, String.format("%s/countsAbsError.txt", rootOutDir), append);
}
/**
* Get the min of y values.
*
* @return y values min
*/
public double getYMin() {
return yData.getMin();
}
/**
* Get the min of x values.
*
* @return x values min
*/
public double getXMin() {
return xData.getMin();
}
/**
* Creates a colorizer which color spectrum is scaled to the degree distribution of
* <tt>graph</tt>.
*
* @param graph a graph
*/
public VertexDegreeColorizer(Graph graph) {
Degree degree = Degree.getInstance();
DescriptiveStatistics distr = degree.statistics(graph.getVertices());
k_min = (int) distr.getMin();
k_max = (int) distr.getMax();
}
