/**
* Compares two sets of counts to see which items are interestingly over-represented in the first
* set.
*
* @param a The first counts.
* @param b The reference counts.
* @param maxReturn The maximum number of items to return. Use maxReturn >= a.elementSet.size() to
* return all scores above the threshold.
* @param threshold The minimum score for items to be returned. Use 0 to return all items more
* common in a than b. Use -Double.MAX_VALUE (not Double.MIN_VALUE !) to not use a threshold.
* @return A list of scored items with their scores.
*/
public static <T> List<ScoredItem<T>> compareFrequencies(
Multiset<T> a, Multiset<T> b, int maxReturn, double threshold) {
int totalA = a.size();
int totalB = b.size();
Ordering<ScoredItem<T>> byScoreAscending =
new Ordering<ScoredItem<T>>() {
@Override
public int compare(ScoredItem<T> tScoredItem, ScoredItem<T> tScoredItem1) {
return Double.compare(tScoredItem.score, tScoredItem1.score);
}
};
Queue<ScoredItem<T>> best = new PriorityQueue<ScoredItem<T>>(maxReturn + 1, byScoreAscending);
for (T t : a.elementSet()) {
compareAndAdd(a, b, maxReturn, threshold, totalA, totalB, best, t);
}
// if threshold >= 0 we only iterate through a because anything not there can't be as or more
// common than in b.
if (threshold < 0) {
for (T t : b.elementSet()) {
// only items missing from a need be scored
if (a.count(t) == 0) {
compareAndAdd(a, b, maxReturn, threshold, totalA, totalB, best, t);
}
}
}
List<ScoredItem<T>> r = new ArrayList<ScoredItem<T>>(best);
Collections.sort(r, byScoreAscending.reverse());
return r;
}
public static void main(String[] args) {
// Parse text to separate words
String INPUT_TEXT = "Hello World! Hello All! Hi World!";
// Create Multiset
Multiset<String> multiset = LinkedHashMultiset.create(Arrays.asList(INPUT_TEXT.split(" ")));
// Print count words
System.out.println(
multiset); // print [Hello x 2, World! x 2, All!, Hi]- in predictable iteration order
// Print all unique words
System.out.println(
multiset.elementSet()); // print [Hello, World!, All!, Hi] - in predictable iteration order
// Print count occurrences of words
System.out.println("Hello = " + multiset.count("Hello")); // print 2
System.out.println("World = " + multiset.count("World!")); // print 2
System.out.println("All = " + multiset.count("All!")); // print 1
System.out.println("Hi = " + multiset.count("Hi")); // print 1
System.out.println("Empty = " + multiset.count("Empty")); // print 0
// Print count all words
System.out.println(multiset.size()); // print 6
// Print count unique words
System.out.println(multiset.elementSet().size()); // print 4
}
private static void logStats(Collection<PsiFile> otherFiles, long start) {
long time = System.currentTimeMillis() - start;
final Multiset<String> stats = HashMultiset.create();
for (PsiFile file : otherFiles) {
stats.add(
StringUtil.notNullize(file.getViewProvider().getVirtualFile().getExtension())
.toLowerCase());
}
List<String> extensions = ContainerUtil.newArrayList(stats.elementSet());
Collections.sort(
extensions,
new Comparator<String>() {
@Override
public int compare(String o1, String o2) {
return stats.count(o2) - stats.count(o1);
}
});
String message =
"Search in "
+ otherFiles.size()
+ " files with unknown types took "
+ time
+ "ms.\n"
+ "Mapping their extensions to an existing file type (e.g. Plain Text) might speed up the search.\n"
+ "Most frequent non-indexed file extensions: ";
for (int i = 0; i < Math.min(10, extensions.size()); i++) {
String extension = extensions.get(i);
message += extension + "(" + stats.count(extension) + ") ";
}
LOG.info(message);
}
/**
* Populate the FeatureVector with Bag of Words.
*
* @param c
* @param fv
*/
protected void populateFV(String text, FeatureVector<E> fv) {
List<String> unnormalized = tokenizer.tokenize(text);
Multiset<String> terms = HashMultiset.create();
for (String token : unnormalized) {
String norm = Util.normalize(token);
if (!norm.isEmpty()) {
terms.add(norm);
}
}
// sparse representation... no need to put in 0's
for (String term : terms.elementSet()) {
// rare words don't get included, so check first
if (!integerFeatureNames && train.getMetadata().containsKey(term)) {
DoubleFeature bagFeat = new DoubleFeature(term, (double) terms.count(term));
fv.put(term, bagFeat);
} else if (integerFeatureNames
&& train.getMetadata().containsKey(String.valueOf(wordIndexMap.get(term)))) {
String featureName = String.valueOf(wordIndexMap.get(term));
DoubleFeature bagFeat = new DoubleFeature(featureName, (double) terms.count(term));
fv.put(featureName, bagFeat);
}
}
}
private ScoredCandidates<Container> scoreContainers(
Multiset<String> parents, int children, ResultDescription desc) {
Builder<Container> candidates = DefaultScoredCandidates.fromSource(NAME);
ResolvedContent containers = resolver.findByCanonicalUris(parents.elementSet());
for (Multiset.Entry<String> parent : parents.entrySet()) {
Maybe<Identified> possibledContainer = containers.get(parent.getElement());
if (possibledContainer.hasValue()) {
Identified identified = possibledContainer.requireValue();
if (identified instanceof Container) {
Container container = (Container) identified;
Score score = score(parent.getCount(), children);
candidates.addEquivalent(container, score);
desc.appendText(
"%s: scored %s (%s)", container.getCanonicalUri(), score, container.getTitle());
} else {
desc.appendText("%s: %s not container", parent, identified.getClass().getSimpleName());
}
} else {
desc.appendText("%s: missing", parent);
}
}
return candidates.build();
}
public static void main(String args[]) {
// create a multiset collection
Multiset<String> multiset = HashMultiset.create();
multiset.add("a");
multiset.add("b");
multiset.add("c");
multiset.add("d");
multiset.add("a");
multiset.add("b");
multiset.add("c");
multiset.add("b");
multiset.add("b");
multiset.add("b");
// print the occurrence of an element
System.out.println("Occurrence of 'b' : " + multiset.count("b"));
// print the total size of the multiset
System.out.println("Total Size : " + multiset.size());
// get the distinct elements of the multiset as set
Set<String> set = multiset.elementSet();
// display the elements of the set
System.out.println("Set [");
for (String s : set) {
System.out.println(s);
}
System.out.println("]");
// display all the elements of the multiset using iterator
Iterator<String> iterator = multiset.iterator();
System.out.println("MultiSet [");
while (iterator.hasNext()) {
System.out.println(iterator.next());
}
System.out.println("]");
// display the distinct elements of the multiset with their occurrence count
System.out.println("MultiSet [");
for (Multiset.Entry<String> entry : multiset.entrySet()) {
System.out.println("Element: " + entry.getElement() + ", Occurrence(s): " + entry.getCount());
}
System.out.println("]");
// remove extra occurrences
multiset.remove("b", 2);
// print the occurrence of an element
System.out.println("Occurence of 'b' : " + multiset.count("b"));
}
public void removeNode(int nodeA) {
Multiset<Integer> outSet = nodeOutEdges.get(nodeA);
for (int adj : outSet.elementSet()) {
nodeInEdges.get(adj).remove(nodeA, Integer.MAX_VALUE);
}
nodeOutEdges.remove(nodeA);
}
@Override
public boolean matchesSafely(final Iterable<? super K> actual) {
final Multiset<?> comparisonMultiSet = HashMultiset.create(comparisonIterable);
final Multiset<?> actualMultiSet = HashMultiset.create(actual);
for (final Object key : actualMultiSet.elementSet()) {
if (!comparisonMultiSet.contains(key)
|| comparisonMultiSet.count(key) < actualMultiSet.count(key)) {
return false;
}
}
return true;
}
private Iterable<?> notRepeated(
Iterable<? super K> actual, Iterable<? super K> comparisonIterable) {
final Multiset<?> comparisonMultiSet = HashMultiset.create(comparisonIterable);
final Multiset<?> actualMultiSet = HashMultiset.create(actual);
final Set<Object> notRepeated = newHashSet();
for (final Object key : actualMultiSet.elementSet()) {
if (!comparisonMultiSet.contains(key)
|| comparisonMultiSet.count(key) < actualMultiSet.count(key)) {
notRepeated.add(key);
}
}
return notRepeated;
}
public void add(Multiset<BoolArray> multiset) {
lock.lock();
for (HashMultiset.Entry<BoolArray> entry : multiset.entrySet()) {
BoolArray label =
new SubGraphStructure(entry.getElement().getArray()).getOrderedForm().getAdjacencyArray();
labelMap.add(label, entry.getCount());
}
if (isVerbose()) {
System.out.printf(
"Added %,d new signatures. LabelMap size:%,d\n", multiset.elementSet().size(), size());
}
if (size() > capacity)
try {
flush();
} catch (IOException exp) {
exp.printStackTrace();
System.exit(-1);
}
lock.unlock();
}
/**
* @param statsQuery StatisticsQueryCondition
* @param statisticsStorage
* @param scoringExps Set of experiments that have at least one non-zero score for
* statisticsQuery. This is used retrieving efos to be displayed in heatmap when no query efvs
* exist (c.f. atlasStatisticsQueryService.getScoringAttributesForGenes())
* @return experiment counts corresponding for statsQuery
*/
public static Multiset<Integer> getExperimentCounts(
StatisticsQueryCondition statsQuery,
StatisticsStorage statisticsStorage,
Set<ExperimentInfo> scoringExps) {
long start = System.currentTimeMillis();
Multiset<Integer> counts =
StatisticsQueryUtils.scoreQuery(statsQuery, statisticsStorage, scoringExps);
long dur = System.currentTimeMillis() - start;
int numOfGenesWithCounts = counts.elementSet().size();
if (numOfGenesWithCounts > 0) {
log.debug(
"StatisticsQuery: "
+ statsQuery.prettyPrint()
+ " ==> result set size: "
+ numOfGenesWithCounts
+ " (duration: "
+ dur
+ " ms)");
}
return counts;
}
@SuppressWarnings("unused")
public List<Object[]> GetMatchingsFromPartial(HashMap<PVariable, Object> MatchingVariables) {
if (true
|| innerFindCall
.getReferredQuery()
.getAllAnnotations()
.contains(new PAnnotation("incremental"))) {
Multiset<LookaheadMatching> result =
treatPatternCacher.GetMatchingsFromPartial(
innerFindCall.getReferredQuery(), MatchingVariables, affectedVariables, false);
// result must be parsed to List<Object[]>
List<Object[]> ret = new ArrayList<Object[]>();
// toarraylist false because only REAL matches count as a match, no need to count
// local-duplicated matches multiple mode
for (LookaheadMatching match : result.elementSet()) // .toArrayList(false))
{
// add all matchings as a "line" multi-matches only once
ret.add(match.getParameterMatchValuesOnlyAsArray().toArray());
}
return ret;
}
return null;
}
/**
* Create the Bag of Words features.
*
* @param citations
*/
public void createFeatures(Collection<String> documents) {
Multiset<String> terms = HashMultiset.create();
for (String s : documents) {
List<String> unnormalized = tokenizer.tokenize(s);
// normalize them
for (int i = 0; i < unnormalized.size(); i++) {
String u = unnormalized.get(i);
String norm = Util.normalize(u);
if (!norm.isEmpty()) {
terms.add(norm);
}
if (bigrams && (i < unnormalized.size() - 1)) {
String second = unnormalized.get(i + 1);
String normSecond = Util.normalize(second);
if (!normSecond.isEmpty()) {
terms.add(norm + "_" + normSecond);
}
}
}
}
int i = 0;
for (String term : terms.elementSet()) {
if (terms.count(term) >= minOccurs // don't count infreq. words
&& term.length() >= minLength) { // or super short words
if (!integerFeatureNames) {
train.getMetadata().put(term, "boolean");
} else {
wordIndexMap.put(term, i++);
train.getMetadata().put(String.valueOf(i), "boolean");
}
}
}
}
@NotNull
public VotingResult<L> predictLabelWithQuality(P x) // , Set<L> disallowedLabels)
{
final P scaledX = scalingModel.scaledCopy(x);
L bestLabel = null;
// L secondBestLabel = null;
float bestOneClassProbability = 0;
float secondBestOneClassProbability = 0;
float bestOneVsAllProbability = 0;
float secondBestOneVsAllProbability = 0;
// stage 0: we're going to need the kernel value for x against each of the SVs, for each of the
// kernels that was used in a subsidary binary machine
// KValueCache kValuesPerKernel = new KValueCache(scaledX);
LoadingCache<KernelFunction<P>, float[]> kValuesPerKernel =
CacheBuilder.newBuilder()
.build(
new CacheLoader<KernelFunction<P>, float[]>() {
public float[] load(@NotNull KernelFunction<P> kernel) {
float[] kvalues = new float[allSVs.length];
int i = 0;
for (P sv : allSVs) {
kvalues[i] = (float) kernel.evaluate(scaledX, sv);
i++;
}
return kvalues;
}
});
// Map<KernelFunction<P>, float[]> kValuesPerKernel =
// new MapMaker().makeComputingMap(new Function<KernelFunction<P>, float[]>() {
// public float[] apply(@NotNull KernelFunction<P> kernel) {
// float[] kvalues = new float[allSVs.length];
// int i = 0;
// for (P sv : allSVs) {
// kvalues[i] = (float) kernel.evaluate(scaledX, sv);
// i++;
// }
// return kvalues;
// }
// });
// we don't want to consider any models that mention a disallowed label
// (i.e., not only should such a prediction be rejected after the fact, but
// the binary machines involving disallowed labels shouldn't ever contribute to the voting in
// the first place
/*
Map<KernelFunction<P>, float[]> kValuesPerKernel = new HashMap<KernelFunction<P>, float[]>();
for (KernelFunction<P> kernel : param.getKernels())
{
float[] kvalues = new float[allSVs.length];
int i = 0;
for (P sv : allSVs)
{
kvalues[i] = (float) kernel.evaluate(scaledX, sv);
i++;
}
kValuesPerKernel.put(kernel,kvalues);
}
*/
// REVIEW ignore one-class models for now; maybe revisit later
/*
// stage 1: one-class
// always compute these; we may need them to tie-break when voting anyway (though that only works when probabilities are turned on)
Map<L, Float> oneClassProbabilities = computeOneClassProbabilities(x);
if (oneClassThreshold > 0 && oneClassProbabilities.isEmpty())
{
return null;
}
if (multiclassMode == MulticlassMode.OneClassOnly)
{
L bestLabel = null;
float bestProbability = 0;
for (Map.Entry<L, Float> entry : oneClassProbabilities.entrySet())
{
if (entry.getValue() > bestProbability)
{
bestLabel = entry.getKey();
bestProbability = entry.getValue();
}
}
return bestLabel;
}
// now oneClassProbabilities is populated with all of the classes that pass the threshold (maybe all of them).
*/
// stage 2: one vs all
Map<L, Float> oneVsAllProbabilities =
oneVsAllMode == OneVsAllMode.None
? null
: computeOneVsAllProbabilities(kValuesPerKernel.asMap());
// now oneVsAllProbabilities is populated with all of the classes that pass the threshold (maybe
// all of them).
// if all classes were vetoed, return
if ((oneVsAllMode == OneVsAllMode.Veto
|| oneVsAllMode == OneVsAllMode.VetoAndBreakTies
|| oneVsAllMode == OneVsAllMode.Best)
&& oneVsAllProbabilities.isEmpty()) {
return new VotingResult<L>();
}
// if using the OneVsAll Best mode, then we should have had probabilities turned on, and
// allVsAll voting will be ignored
if (oneVsAllMode == OneVsAllMode.Best) {
for (Map.Entry<L, Float> entry : oneVsAllProbabilities.entrySet()) {
if (entry.getValue() > bestOneVsAllProbability) {
secondBestOneVsAllProbability = bestOneVsAllProbability;
bestLabel = entry.getKey();
bestOneVsAllProbability = entry.getValue();
}
}
return new VotingResult<L>(
bestLabel,
0,
0,
bestOneClassProbability,
secondBestOneClassProbability,
bestOneVsAllProbability,
secondBestOneVsAllProbability);
}
// stage 3: voting
int numLabels = oneVsOneModels.keySet().size();
Multiset<L> votes = HashMultiset.create();
if (allVsAllMode == AllVsAllMode.AllVsAll) {
// vote using all models
logger.debug(
"Sample voting using all pairs of "
+ numLabels
+ " labels ("
+ ((numLabels * (numLabels - 1)) / 2. - numLabels)
+ " models)");
// How AllVsAll with Veto differs from FilteredVsAll, etc.:
// In the AllVsAll with Veto case, we may compute votes between two "inactive" (vetoed)
// classes;
// it may be that the winner of the voting later fails the oneVsAll filter, in which
// case we may want to report unknown instead of reporting the best class that does pass.
// This is what PhyloPythia does.
for (BinaryModel<L, P> binaryModel : oneVsOneModels.values()) {
float[] kvalues;
try {
kvalues = kValuesPerKernel.get(binaryModel.param.kernel);
} catch (ExecutionException e) {
throw new RuntimeException(e);
}
votes.add(binaryModel.predictLabel(kvalues, svIndexMaps.get(binaryModel)));
}
} else {
// vote using only the active models one one side of the comparison, maybe on both.
Set<L> activeClasses =
oneVsAllProbabilities != null ? oneVsAllProbabilities.keySet() : oneVsOneModels.keySet();
int requiredActive = allVsAllMode == AllVsAllMode.FilteredVsAll ? 1 : 2;
int numActive = oneVsAllProbabilities != null ? oneVsAllProbabilities.size() : numLabels;
if (requiredActive == 1) {
logger.debug(
"Sample voting with all "
+ numLabels
+ " vs. "
+ numActive
+ " active labels ("
+ ((numLabels * (numActive - 1)) / 2. - numActive)
+ " models)");
} else {
logger.debug(
"Sample voting using pairs of only "
+ numActive
+ " active labels ("
+ ((numActive * (numActive - 1)) / 2. - numActive)
+ " models)");
}
// assert requiredActive == 2 ? voteMode = VoteMode.FilteredVsFiltered
for (BinaryModel<L, P> binaryModel : oneVsOneModels.values()) {
int activeCount =
(activeClasses.contains(binaryModel.getTrueLabel()) ? 1 : 0)
+ (activeClasses.contains(binaryModel.getFalseLabel()) ? 1 : 0);
if (activeCount >= requiredActive) {
votes.add(binaryModel.predictLabel(scaledX));
}
}
}
// stage 4: find the label with the most votes (and break ties or veto as needed)
int bestCount = 0;
int secondBestCount = 0;
int countSum = 0;
for (L label : votes.elementSet()) {
int count = votes.count(label);
countSum += count;
// get the oneVsAll value for this label, if needed
Float oneVsAll = 1f; // pass by default
if (oneVsAllMode == OneVsAllMode.Veto || oneVsAllMode == OneVsAllMode.VetoAndBreakTies) {
// if this is null it means this label didn't pass the threshold earlier, so it should fail
// here too
oneVsAll = oneVsAllProbabilities.get(label);
oneVsAll = oneVsAll == null ? 0f : oneVsAll;
}
// get the oneClass value for this label, if needed
// if this is null it means this label didn't pass the threshold earlier
// Float oneClass = oneClassProbabilities.get(label);
// oneClass = oneClass == null ? 0f : oneClass;
// primary sort by number of votes
// secondary sort by one-vs-all probability, if available
// tertiary sort by one-class probability, if available
if (count > bestCount || (count == bestCount && oneVsAll > bestOneVsAllProbability))
// || oneClass > bestOneClassProbability)))
{
secondBestCount = bestCount;
secondBestOneVsAllProbability = bestOneVsAllProbability;
bestLabel = label;
bestCount = count;
bestOneVsAllProbability = oneVsAll;
}
}
// stage 5: check for inadequate evidence filters.
double bestVoteProportion = (double) bestCount / (double) countSum;
double secondBestVoteProportion = (double) secondBestCount / (double) countSum;
if (bestVoteProportion < minVoteProportion) {
return new VotingResult<L>();
}
if ((oneVsAllMode == OneVsAllMode.VetoAndBreakTies || oneVsAllMode == OneVsAllMode.Veto)
&& bestOneVsAllProbability < oneVsAllThreshold) {
return new VotingResult<L>();
}
return new VotingResult<L>(
bestLabel,
(float) bestVoteProportion,
(float) secondBestVoteProportion,
bestOneClassProbability,
secondBestOneClassProbability,
bestOneVsAllProbability,
secondBestOneVsAllProbability);
}
public void convertToNetwork() throws IOException, InvalidFormatException {
container = MyFileImporter.container;
container.setEdgeDefault(EdgeDefault.UNDIRECTED);
String firstDelimiter;
String secondDelimiter;
firstDelimiter = Utils.getCharacter(MyFileImporter.firstConnectorDelimiter);
secondDelimiter = Utils.getCharacter(MyFileImporter.secondConnectorDelimiter);
boolean oneTypeOfAgent =
MyFileImporter.getFirstConnectedAgent().equals(MyFileImporter.getSecondConnectedAgent());
nbColumnFirstAgent = MyFileImporter.firstConnectedAgentIndex;
nbColumnSecondAgent = MyFileImporter.secondConnectedAgentIndex;
Integer lineCounter = 0;
InputStream inp;
inp = new FileInputStream(fileName);
Workbook wb = WorkbookFactory.create(inp);
Row row;
Sheet sheet = wb.getSheet(sheetName);
int startingRow;
if (MyFileImporter.headersPresent) {
startingRow = 1;
} else {
startingRow = 0;
}
Set<String> linesFirstAgent = new HashSet();
Set<String> linesSecondAgent = new HashSet();
for (int i = startingRow; i <= sheet.getLastRowNum(); i++) {
row = sheet.getRow(i);
if (row == null) {
break;
}
Cell cell = row.getCell(nbColumnFirstAgent);
if (cell == null) {
Issue issue =
new Issue(
"problem with line "
+ lineCounter
+ " (empty column "
+ MyFileImporter.getFirstConnectedAgent()
+ "). It was skipped in the conversion",
Issue.Level.WARNING);
MyFileImporter.getStaticReport().logIssue(issue);
continue;
}
String firstAgent = row.getCell(nbColumnFirstAgent).getStringCellValue();
if (firstAgent == null || firstAgent.isEmpty()) {
Issue issue =
new Issue(
"problem with line "
+ lineCounter
+ " (empty column "
+ MyFileImporter.getFirstConnectedAgent()
+ "). It was skipped in the conversion",
Issue.Level.WARNING);
MyFileImporter.getStaticReport().logIssue(issue);
continue;
}
if (MyFileImporter.removeDuplicates) {
boolean newLine = linesFirstAgent.add(firstAgent);
if (!newLine) {
continue;
}
}
String secondAgent = null;
if (!oneTypeOfAgent) {
cell = row.getCell(nbColumnSecondAgent);
if (cell == null) {
Issue issue =
new Issue(
"problem with line "
+ lineCounter
+ " (empty column "
+ MyFileImporter.getFirstConnectedAgent()
+ "). It was skipped in the conversion",
Issue.Level.WARNING);
MyFileImporter.getStaticReport().logIssue(issue);
continue;
}
secondAgent = row.getCell(nbColumnSecondAgent).getStringCellValue();
if (secondAgent == null || secondAgent.isEmpty()) {
Issue issue =
new Issue(
"problem with line "
+ lineCounter
+ " (empty column "
+ MyFileImporter.getSecondConnectedAgent()
+ "). It was skipped in the conversion",
Issue.Level.WARNING);
MyFileImporter.getStaticReport().logIssue(issue);
continue;
}
if (MyFileImporter.removeDuplicates) {
boolean newLine = linesFirstAgent.add(firstAgent);
if (!newLine) {
continue;
}
}
}
lineCounter++;
String[] firstAgentSplit;
String[] secondAgentSplit;
if (firstDelimiter != null) {
firstAgentSplit = firstAgent.trim().split(firstDelimiter);
} else {
firstAgentSplit = new String[1];
firstAgentSplit[0] = firstAgent;
}
for (String node : firstAgentSplit) {
nodesFirst.add(node.trim());
}
if (!oneTypeOfAgent) {
if (secondDelimiter != null) {
secondAgentSplit = secondAgent.trim().split(secondDelimiter);
} else {
secondAgentSplit = new String[1];
secondAgentSplit[0] = secondAgent;
}
for (String node : secondAgentSplit) {
nodesSecond.add(node.trim());
}
} else {
secondAgentSplit = null;
}
String[] both = ArrayUtils.addAll(firstAgentSplit, secondAgentSplit);
// let's find all connections between all the tags for this picture
Utils usefulTools = new Utils();
List<String> connections = usefulTools.getListOfLinks(both, MyFileImporter.removeSelfLoops);
edges.addAll(connections);
}
NodeDraft node;
AttributeTable atNodes = container.getAttributeModel().getNodeTable();
AttributeColumn acFrequency = atNodes.addColumn("frequency", AttributeType.INT);
AttributeColumn acType = atNodes.addColumn("type", AttributeType.STRING);
for (String n : nodesFirst.elementSet()) {
node = container.factory().newNodeDraft();
node.setId(n);
node.setLabel(n);
node.addAttributeValue(acFrequency, nodesFirst.count(n));
node.addAttributeValue(acType, MyFileImporter.getFirstConnectedAgent());
container.addNode(node);
}
for (String n : nodesSecond.elementSet()) {
node = container.factory().newNodeDraft();
node.setId(n);
node.setLabel(n);
node.addAttributeValue(acFrequency, nodesSecond.count(n));
node.addAttributeValue(acType, MyFileImporter.getSecondConnectedAgent());
container.addNode(node);
}
// loop for edges
Integer idEdge = 0;
EdgeDraft edge;
for (String e : edges.elementSet()) {
System.out.println("edge: " + e);
String sourceNode = e.split("\\|")[0];
String targetNode = e.split("\\|")[1];
if (!MyFileImporter.innerLinksIncluded) {
if ((nodesFirst.contains(sourceNode) & nodesFirst.contains(targetNode))
|| (nodesSecond.contains(sourceNode) & nodesSecond.contains(targetNode))) {
continue;
}
}
edge = container.factory().newEdgeDraft();
idEdge = idEdge + 1;
edge.setSource(container.getNode(sourceNode));
edge.setTarget(container.getNode(targetNode));
edge.setWeight((float) edges.count(e));
edge.setId(String.valueOf(idEdge));
edge.setType(EdgeDraft.EdgeType.UNDIRECTED);
container.addEdge(edge);
}
}
