@Override
public void analyze(Graph g, Map<String, DescriptiveStatistics> statsMap) {
int sumMale = 0;
int cntMale = 0;
int sumFemale = 0;
int cntFemale = 0;
TObjectDoubleHashMap<Vertex> values = module.values(g.getVertices());
TObjectDoubleIterator<Vertex> it = values.iterator();
for (int i = 0; i < values.size(); i++) {
it.advance();
SocialVertex v = (SocialVertex) it.key();
double k = it.value();
if (v.getPerson().getPerson().getSex() != null) {
if (v.getPerson().getPerson().getSex().equalsIgnoreCase("m")) {
sumMale += k;
cntMale++;
} else if (v.getPerson().getPerson().getSex().equalsIgnoreCase("f")) {
sumFemale += k;
cntFemale++;
}
}
}
double k_mean_male = sumMale / (double) cntMale;
double k_mean_female = sumFemale / (double) cntFemale;
logger.info(
String.format("k_mean_male = %1$s, k_mean_female = %2$s.", k_mean_male, k_mean_female));
}
public MyColorizer(TObjectDoubleHashMap<?> values) {
this.values = values;
max = -Double.MAX_VALUE;
min = Double.MAX_VALUE;
TObjectDoubleIterator<?> it = values.iterator();
for (int i = 0; i < values.size(); i++) {
it.advance();
max = Math.max(max, it.value());
min = Math.min(min, it.value());
}
}
private void analyzeIntern(
Set<Trajectory> trajectories, String purpose, TObjectDoubleHashMap<Trajectory> distancesMap) {
TDoubleArrayList arrivals = new TDoubleArrayList(trajectories.size());
TDoubleArrayList distances = new TDoubleArrayList(trajectories.size());
for (Trajectory t : trajectories) {
for (int i = 0; i < t.getElements().size(); i += 2) {
if (purpose == null || ((Activity) t.getElements().get(i)).getType().equals(purpose)) {
double start = t.getTransitions().get(i);
arrivals.add(start);
distances.add(distancesMap.get(t));
}
}
}
TDoubleDoubleHashMap map =
Correlations.mean(arrivals.toNativeArray(), distances.toNativeArray(), 3600);
// Discretizer d = FixedSampleSizeDiscretizer.create(arrivals.toNativeArray(), 100, 100);
// TDoubleDoubleHashMap map = Correlations.mean(arrivals.toNativeArray(),
// distances.toNativeArray(), d);
try {
if (purpose == null)
StatsWriter.writeHistogram(
map, "arr", "dist", String.format("%1$s/dist_arr.txt", getOutputDirectory()));
else
StatsWriter.writeHistogram(
map,
"arr",
"dist",
String.format("%1$s/dist_arr.%2$s.txt", getOutputDirectory(), purpose));
} catch (IOException e) {
// TODO Auto-generated catch block
e.printStackTrace();
}
}
@SuppressWarnings("unchecked")
public PajekClusteringColorizer(Graph g) {
super();
clustering = Transitivity.getInstance().localClusteringCoefficients((Set<V>) g.getVertices());
c_min = StatUtils.min(clustering.getValues());
c_max = StatUtils.max(clustering.getValues());
}
public static Set<? extends Person> weightedCopy(
Collection<? extends Person> persons, Factory factory, int N, Random random) {
if (persons.size() == N) {
return new HashSet<>(persons); // TODO weights are left untouched
} else if (persons.size() > N) {
throw new IllegalArgumentException("Cannot shrink population.");
}
List<Person> templates = new ArrayList<>(persons);
/*
* get max weight
*/
TObjectDoubleHashMap<Person> weights = new TObjectDoubleHashMap<>(persons.size());
double maxW = 0;
for (Person person : persons) {
String wStr = person.getAttribute(CommonKeys.PERSON_WEIGHT);
double w = 0;
if (wStr != null) {
w = Double.parseDouble(wStr);
}
weights.put(person, w);
maxW = Math.max(w, maxW);
}
/*
* adjust weight so that max weight equals probability 1
*/
ProgressLogger.init(N, 2, 10);
Set<Person> clones = new HashSet<>();
while (clones.size() < N) {
Person template = templates.get(random.nextInt(templates.size()));
double w = weights.get(template);
double p = w / maxW;
if (p > random.nextDouble()) {
StringBuilder builder = new StringBuilder();
builder.append(template.getId());
builder.append("clone");
builder.append(clones.size());
Person clone = PersonUtils.deepCopy(template, builder.toString(), factory);
clone.setAttribute(CommonKeys.PERSON_WEIGHT, "1.0");
clones.add(clone);
ProgressLogger.step();
}
}
return clones;
}
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 static TObjectDoubleHashMap<String> parseParamFile(String paramsFile) {
TObjectDoubleHashMap<String> startParamList = new TObjectDoubleHashMap<String>();
try {
BufferedReader fis = new BufferedReader(new FileReader(paramsFile));
String pattern = null;
int count = 0;
while ((pattern = fis.readLine()) != null) {
StringTokenizer st = new StringTokenizer(pattern.trim(), "\t");
String paramName = st.nextToken().trim();
String rest = st.nextToken().trim();
String[] arr = rest.split(",");
double value = new Double(arr[0].trim());
boolean sign = new Boolean(arr[1].trim());
LDouble val = new LDouble(value, sign);
startParamList.put(paramName, val.exponentiate());
if (count % 100000 == 0) System.out.println("Processed param number:" + count);
count++;
}
} catch (IOException ioe) {
ioe.printStackTrace();
}
return startParamList;
}
public FastFrameIdentifier(
TObjectDoubleHashMap<String> paramList,
String reg,
double l,
THashMap<String, THashSet<String>> frameMap,
THashMap<String, THashSet<String>> wnRelationCache,
THashMap<String, THashSet<String>> hvCorrespondenceMap,
Map<String, Set<String>> relatedWordsForWord,
Map<String, Map<String, Set<String>>> revisedRelationsMap,
Map<String, String> hvLemmas) {
super(paramList, reg, l, null, frameMap);
initializeParameterIndexes();
this.mParamList = paramList;
mReg = reg;
mLambda = l;
mFrameMap = frameMap;
totalNumberOfParams = paramList.size();
initializeParameters();
mLookupChart = new TIntObjectHashMap<LogFormula>();
mHvCorrespondenceMap = hvCorrespondenceMap;
mRelatedWordsForWord = relatedWordsForWord;
mRevisedRelationsMap = revisedRelationsMap;
mHVLemmas = hvLemmas;
}
@SuppressWarnings("unchecked")
protected double getValue(Object object) {
double r = values.get(object);
return r;
}
@Override
public String getVertexFillColor(V ego) {
double c = clustering.get(ego);
double color = (c - c_min) / (c_max - c_min);
return getColor(color);
}