/** @param stateSetTree */
public static void printBadLLReason(Tree<StateSet> stateSetTree, SophisticatedLexicon lexicon) {
System.out.println(stateSetTree.toString());
boolean lexiconProblem = false;
List<StateSet> words = stateSetTree.getYield();
Iterator<StateSet> wordIterator = words.iterator();
for (StateSet stateSet : stateSetTree.getPreTerminalYield()) {
String word = wordIterator.next().getWord();
boolean lexiconProblemHere = true;
for (int i = 0; i < stateSet.numSubStates(); i++) {
double score = stateSet.getIScore(i);
if (!(Double.isInfinite(score) || Double.isNaN(score))) {
lexiconProblemHere = false;
}
}
if (lexiconProblemHere) {
System.out.println("LEXICON PROBLEM ON STATE " + stateSet.getState() + " word " + word);
System.out.println(" word " + lexicon.wordCounter.getCount(stateSet.getWord()));
for (int i = 0; i < stateSet.numSubStates(); i++) {
System.out.println(" tag " + lexicon.tagCounter[stateSet.getState()][i]);
System.out.println(
" word/state/sub "
+ lexicon.wordToTagCounters[stateSet.getState()].get(stateSet.getWord())[i]);
}
}
lexiconProblem = lexiconProblem || lexiconProblemHere;
}
if (lexiconProblem) System.out.println(" the likelihood is bad because of the lexicon");
else System.out.println(" the likelihood is bad because of the grammar");
}
public void run() {
int count = 0;
for (Tree<String> t : this) {
System.out.println(t.toString());
++count;
}
Logger.i().logs("Number of Trees: %d", count);
}
/**
* This function probably doesn't belong here, but because it should be called after {@link
* #updateStateSetTrees}, Leon left it here.
*
* @param trees Trees which have already had their inside-outside probabilities calculated, as by
* {@link #updateStateSetTrees}.
* @return The log likelihood of the trees.
*/
public static double logLikelihood(List<Tree<StateSet>> trees, boolean verbose) {
double likelihood = 0, l = 0;
for (Tree<StateSet> tree : trees) {
l = tree.getLabel().getIScore(0);
if (verbose) System.out.println("LL is " + l + ".");
if (Double.isInfinite(l) || Double.isNaN(l)) {
System.out.println("LL is not finite.");
} else {
likelihood += l;
}
}
return likelihood;
}
/**
* @param maxGrammar
* @param maxLexicon
* @param validationStateSetTrees
* @return
*/
public static double calculateLogLikelihood(
Grammar maxGrammar, Lexicon maxLexicon, StateSetTreeList validationStateSetTrees) {
ArrayParser parser = new ArrayParser(maxGrammar, maxLexicon);
int unparsable = 0;
double maxLikelihood = 0;
for (Tree<StateSet> stateSetTree : validationStateSetTrees) {
parser.doInsideScores(stateSetTree, false, false, null); // Only inside scores are needed here
double ll = stateSetTree.getLabel().getIScore(0);
ll = Math.log(ll) + (100 * stateSetTree.getLabel().getIScale());
if (Double.isInfinite(ll) || Double.isNaN(ll)) {
unparsable++;
// printBadLLReason(stateSetTree, lexicon);
} else maxLikelihood += ll; // there are for some reason some sentences that are unparsable
}
// if (unparsable>0) System.out.print("Number of unparsable trees: "+unparsable+".");
return maxLikelihood;
}
private static void formStringBuilder(Tree<String> next) {
List<String> tokens = next.getTerminalYield();
for (String token : tokens) {
builder.append(token);
builder.append(" ");
}
builder.append("\n");
}
/**
* @param previousGrammar
* @param previousLexicon
* @param grammar
* @param lexicon
* @param trainStateSetTrees
* @return
*/
public static double doOneEStep(
Grammar previousGrammar,
Lexicon previousLexicon,
Grammar grammar,
Lexicon lexicon,
StateSetTreeList trainStateSetTrees,
boolean updateOnlyLexicon,
int unkThreshold) {
boolean secondHalf = false;
ArrayParser parser = new ArrayParser(previousGrammar, previousLexicon);
double trainingLikelihood = 0;
int n = 0;
int nTrees = trainStateSetTrees.size();
for (Tree<StateSet> stateSetTree : trainStateSetTrees) {
secondHalf = (n++ > nTrees / 2.0);
boolean noSmoothing = true, debugOutput = false;
parser.doInsideOutsideScores(stateSetTree, noSmoothing, debugOutput); // E Step
double ll = stateSetTree.getLabel().getIScore(0);
ll =
Math.log(ll)
+ (100 * stateSetTree.getLabel().getIScale()); // System.out.println(stateSetTree);
if ((Double.isInfinite(ll) || Double.isNaN(ll))) {
if (VERBOSE) {
System.out.println("Training sentence " + n + " is given " + ll + " log likelihood!");
System.out.println(
"Root iScore "
+ stateSetTree.getLabel().getIScore(0)
+ " scale "
+ stateSetTree.getLabel().getIScale());
}
} else {
lexicon.trainTree(stateSetTree, -1, previousLexicon, secondHalf, noSmoothing, unkThreshold);
if (!updateOnlyLexicon) grammar.tallyStateSetTree(stateSetTree, previousGrammar); // E Step
trainingLikelihood += ll; // there are for some reason some sentences that are unparsable
}
}
lexicon.tieRareWordStats(unkThreshold);
// SSIE
((SophisticatedLexicon) lexicon).overwriteWithMaxent();
return trainingLikelihood;
}
private static void createVectorBeanToBeSerialized() {
vectorBean.setPhi(phiSyn);
vectorBean.setPsi(psiSyn);
vectorBean.setPhiSem(phiSem);
vectorBean.setPsiSem(psiSem);
vectorBean.setInsideTree(insideTree);
vectorBean.setLabel(insideTree.getLabel());
vectorBean.setSyntaxTree(syntaxTree);
vectorBean.setFootToRoot(foottoroot);
}
public static void main(String... args) throws Exception {
/*
* The inside and outisde projection matrices I suppose
*/
Object[] matrices = new Object[2];
/*
* Data structure to hold all the sparse vectors
*/
// ArrayList<SparseVector> phiList = new ArrayList<SparseVector>();
// ArrayList<SparseVector> psiList = new ArrayList<SparseVector>();
/*
* Used to normalize the trees
*/
PTBTreeNormaliser treeNormalizer = new PTBTreeNormaliser(true);
/*
* Getting the feature dictionary path, i.e. the serialized file path.
* This dictionary will be used to form the feature vectors.
*/
String featureDictionary = null;
/*
* This variable tells the code about the directory path where parse
* trees are stored from which feature vectors need to be extracted
* corresponding to all the nodes
*/
String parsedTreeCorpus = null;
/*
* The feature dictionary that needs to be used while extracting
* features
*/
featureDictionary = "/disk/scratch/s1444025/worddictionary/worddictionary.ser";
/*
* The directory that holds the parse trees that are iterated over to
* extract the feature vector corresponding to the nodes
*/
parsedTreeCorpus = "/afs/inf.ed.ac.uk/group/project/vsm.restored/trees";
/*
* Necessary to get the appropriate directory structure
*/
// countMapLoc =
// "/afs/inf.ed.ac.uk/group/project/vsm/countmapnodesamples/countMap.ser";
/*
* Getting the serialised dictionary bean object that contains the
* inside and outside feature dictionaries which are used to form the
* feature vectors
*/
VSMWordDictionaryBean dictionaryBean =
VSMReadSerialWordDict.readSerializedDictionary(featureDictionary);
/*
* Getting the inside and outside feature dictionaries, that are used
* for forming the feature vectors
*/
System.out.println("***Getting word dictionary*****");
Alphabet wordDictionary = dictionaryBean.getWordDictionary();
System.out.println(wordDictionary);
// System.out.println(wordDictionary.size());
dprime = wordDictionary.size();
d = wordDictionary.size();
SparseMatrixLil PsiTPsi = new SparseMatrixLil(dprime, dprime);
SparseMatrixLil PsiTPhi = new SparseMatrixLil(dprime, d);
SparseMatrixLil PhiTPhi = new SparseMatrixLil(d, d);
SparseMatrixLil PhiTPsi = new SparseMatrixLil(d, dprime);
/*
* The parsed tree corpus from where the feature vectors need to be
* extracted corresponding to all the nodes
*/
File[] files =
new File(parsedTreeCorpus)
.listFiles(
new FileFilter() {
@Override
public boolean accept(File file) {
return !file.isHidden();
}
});
ArrayList<String> filePaths = VSMUtil.getFilePaths(files);
/*
* The obect that is used to serialize the feature vector bean. The
* feature vector bean storing the inside and outside feature vectors
* corresponding to a particular node in a tree. Each feature vector
* bean holds the feature vectors for one particular node
*/
VSMSerializeFeatureVectorBeanWord serializeBean = null;
/*
* If we already have a serialized count map object then we would want
* to start from where we left
*/
// File fileCountMap = new File(countMapLoc);
serializeBean = new VSMSerializeFeatureVectorBeanWord();
// } else {
// VSMCountMap countMapObj = VSMReadSerialCountMap
// .readCountMapObj(countMapLoc);
// System.out.println("inside the count map***");
// serializeBean = new VSMSerializeFeatureVectorBeanWord(
// countMapObj.getCountMap());
// }
/*
* Getting the data structure to store all the feature vectors in it, We
* are taking 200000 samples for a particular non-terminal
*/
SparseMatrixLil Phi = new SparseMatrixLil(300000, d);
SparseMatrixLil Psi = new SparseMatrixLil(300000, dprime);
int count = 0;
mainloop:
for (String filePath : filePaths) {
/*
* Getting an iterator over the trees in the file
*/
PennTreeReader treeReader = VSMUtil.getTreeReader(filePath);
/*
* Iterating over all the trees
*/
while (treeReader.hasNext()) {
/*
* The syntax tree
*/
Tree<String> syntaxTree = null;
/*
* Unmatched parentheses exception. Does this mean that the
* BLLIP corpus sometimes does not have correct parse trees?
* Strange
*/
try {
syntaxTree = treeReader.next();
} catch (RuntimeException e) {
System.out.println("exception" + e + " ::tree " + syntaxTree);
}
/*
* Do stuff only if the syntax tree is a valid one
*/
if (syntaxTree != null) {
/*
* Process the syntax tree to remove the top bracket
*/
syntaxTree = treeNormalizer.process(syntaxTree);
/*
* Iterator over the nodes of the tree
*/
Iterator<Tree<String>> nodeTrees = syntaxTree.iterator();
/*
* Sparse Inside and outside feature vectors declared
*/
no.uib.cipr.matrix.sparse.SparseVector psi = null;
no.uib.cipr.matrix.sparse.SparseVector phi = null;
Tree<String> insideTree = null;
/*
* Iterating over all the nodes in a particular syntax tree
*/
while (nodeTrees.hasNext()) {
/*
* This is the inside tree for which we want to form a
* feature vector and store it in the map
*/
insideTree = nodeTrees.next();
/*
* Only do stuff if inside tree is not a leaf
*/
if (!insideTree.isLeaf() && insideTree.getLabel().equalsIgnoreCase("NNS")) {
/*
* Setting the object's properties that are stored
* in the .ser file
*/
VSMWordFeatureVectorBean vectorBean = new VSMWordFeatureVectorBean();
System.out.println(
"****Extracting inside and outside feature vectors for node**** "
+ insideTree.getLabel());
/*
* Getting the inside and outside feature vectors
* corresponding to the partcular node
*/
psi =
new VSMOutsideFeatureVectorWords()
.getOutsideFeatureVectorPsi(
syntaxTree, insideTree, wordDictionary, vectorBean);
// psiList.add(psi);
phi =
new VSMInsideFeatureVectorWords()
.getInsideFeatureVectorPhi(insideTree, wordDictionary, vectorBean);
// phiList.add(phi);
System.out.println("got the sparse vectors*** ");
/*
* Inside sparse matrix formation for the particular
* node.
*/
/*
* Do the below operation only if both psi and phi
* are not null for the given node sample and also
* if either psi pr phi are different than before
* for this spample, if both are same then no need
* to unecessarily fill up Psi and Phi
*/
if (phi != null && psi != null) {
System.out.println(count);
System.out.println("****Filling in the matrices***");
int[] indicesPhi = phi.getIndex();
double[] valuesPhi = phi.getData();
/*
* Don't need the phi anymore in this iteration
*/
phi = null;
/*
* Putting the inside feature vector into the
* inside feature matrix
*/
for (int i = 0; i < indicesPhi.length; i++) {
Phi.append(count, indicesPhi[i], valuesPhi[i]);
}
indicesPhi = null;
valuesPhi = null;
/*
* Outside sparse matrix formation for the
* particular node
*/
int[] indicesPsi = psi.getIndex();
double[] valuesPsi = psi.getData();
psi = null;
/*
* Putting the outside feature vector into the
* outside feature matrix
*/
for (int j = 0; j < indicesPsi.length; j++) {
Psi.append(count, indicesPsi[j], valuesPsi[j]);
}
indicesPsi = null;
valuesPsi = null;
System.gc();
/*
* Storing the feature vectors in a bean which
* will be serialized for future use
*/
vectorBean.setPhi(phi);
vectorBean.setPsi(psi);
vectorBean.setInsideTree(insideTree);
vectorBean.setLabel(insideTree.getLabel());
vectorBean.setSyntaxTree(syntaxTree);
/*
* Serialize the feature vector bean
* corresponding to the particular node. The
* feature vector bean contains the sparse
* inside and outside feature vectors
*/
serializeBean.serializeWordVectorBean(vectorBean);
System.out.println("***Serialized the feature vector***");
count++;
/*
* Break when we have 200000 samples
*/
if (count == (Psi.rows - 1)) {
break mainloop;
}
}
}
}
}
}
}
/*
* Call the CCA function here
*/
System.out.println("*****Done with matrices formation****");
/*
* Just calculating the co-vavriance, assuming that the data is centered
* and normalized
*/
System.out.println("***Calculating Covariances****");
PsiTPsi = Psi.t().mmul(Psi); // d' \times d'
PsiTPhi = Psi.t().mmul(Phi); // d' \times d
PhiTPhi = Phi.t().mmul(Phi); // d \times d
PhiTPsi = Phi.t().mmul(Psi); // d \times d'
System.out.println("****Done with it***");
/*
* Log and square root transform
*/
PsiTPsi =
VSMUtil.createJeigenMatrix(transform(VSMUtil.createSparseMatrixMTJFromJeigen(PsiTPsi)));
PsiTPhi =
VSMUtil.createJeigenMatrix(transform(VSMUtil.createSparseMatrixMTJFromJeigen(PsiTPhi)));
PhiTPhi =
VSMUtil.createJeigenMatrix(transform(VSMUtil.createSparseMatrixMTJFromJeigen(PhiTPhi)));
PhiTPsi =
VSMUtil.createJeigenMatrix(transform(VSMUtil.createSparseMatrixMTJFromJeigen(PhiTPsi)));
/*
* Writing the co-variance matrices in a text file to see what's going
* on
*/
System.out.println("****Writing the Covarinace Matrices to the file***");
VSMUtil.writeCovarMatrixSem(PsiTPsi, "NNS");
VSMUtil.writeCovarMatrixSem(PsiTPhi, "NNS");
VSMUtil.writeCovarMatrixSem(PhiTPhi, "NNS");
VSMUtil.writeCovarMatrixSem(PhiTPsi, "NNS");
System.out.println("***Done***");
/*
* Done with the Psi and Phi and freeing up some space
*/
Psi = null;
Phi = null;
System.gc();
/*
* Getting the the similarity scoressvd template object that has utility
* methods to do preprocessing before performing CCA
*/
SVDTemplates1 svdTC = new SVDTemplates1(null);
/*
* Function to compute the CCA, passing the covariance matrices to the
* function
*/
computeCCA2(
MatrixFormatConversion.createSparseMatrixMTJFromJeigen(PsiTPhi),
MatrixFormatConversion.createSparseMatrixMTJFromJeigen(PhiTPsi),
MatrixFormatConversion.createSparseMatrixMTJFromJeigen(PhiTPhi),
MatrixFormatConversion.createSparseMatrixMTJFromJeigen(PsiTPsi),
svdTC,
null,
0,
50,
"NNS");
/*
* Writing the projection matrices out in a file to see what is in there
*/
matrices = VSMUtil.deserializeCCAVariantsRunSem("NNS");
VSMUtil.writeEigenDictInsideSemantic(matrices, "NNS", d);
VSMUtil.writeEigenDictOutsideSem(matrices, "NNS", dprime);
matrices = null;
PsiTPhi = null;
PhiTPhi = null;
PsiTPsi = null;
PhiTPsi = null;
System.gc();
/*
* We would also like to serialize the count map. The count map is the
* data structure that helps us store the .ser files in proper
* directories with proper names. So, if in future we want to extract
* feature vectors corresponding to more parse trees, we will start from
* where we left in the directory structure and file name
*/
/*
* Getting the updated count map
*/
// countMap = VSMSerializeFeatureVectorBean.getCountMap();
// /*
// * The object that will be serialized
// */
// VSMCountMap countMapObject = new VSMCountMap();
// countMapObject.setCountMap(countMap);
//
// /*
// * Serialize count map
// */
// VSMSerializeCountMap.serializeCountMap(countMapObject);
// System.out.println("*****count map serialized****");
}
public static void main(String... args) throws Exception {
System.out.println("+++Compiled New++++");
nonTerminal = VSMUtil.getNonTerminal(args);
LOGGER = VSMLogger.setup(FeatureVectors.class.getName() + "." + nonTerminal);
featureDictionary =
VSMContant.FEATURE_DICTIONARY + nonTerminal.toLowerCase() + "/dictionary.ser";
wordDictionaryPath = VSMContant.WORD_DICT;
LOGGER.info("Reading the Feature Dictionary Object");
dictionaryBean = ReadSerializedDictionary.readSerializedDictionary(featureDictionary, LOGGER);
LOGGER.info("Reading the word dictionary object");
wordDictBean = VSMReadSerialWordDict.readSerializedDictionary(wordDictionaryPath);
outsideFeatureDictionary = dictionaryBean.getOutsideFeatureDictionary();
insideFeatureDictionary = dictionaryBean.getInsideFeatureDictionary();
wordDictionary = wordDictBean.getWordDictionary();
LOGGER.info(
"Got the syntactic and semantic feature dictionaries, with word dictionary dimensions: "
+ wordDictionary.size());
treeReader = VSMUtil.getTreeReader(VSMContant.SICK_TRIAL_TREES);
LOGGER.info("GOT Training Trees File Iterator: " + treeReader);
int treeCount = 0;
while (treeReader.hasNext()) {
getSynaxTree();
if (syntaxTree != null) {
treeCount += 1;
syntaxTree = treeNormalizer.process(syntaxTree);
constituentsMap = syntaxTree.getConstituents();
Iterator<Tree<String>> nodeTrees = syntaxTree.iterator();
while (nodeTrees.hasNext()) {
insideTree = nodeTrees.next();
if (!insideTree.isLeaf() && insideTree.getLabel().equalsIgnoreCase(nonTerminal)) {
createSparseVectors();
serializeVectorBean(treeCount);
System.out.println("Serialized the feature vector***");
}
}
}
}
LOGGER.info("Done Creating the Sparse Vectors For the Non Terminal: " + nonTerminal);
}
@Test
public void testFeatureVectorSerialization() throws Exception {
// VSMFeatureMatrixBean matrixBean = VSMReadSerialMatrix
// .readFeatureMatrix("/Users/sameerkhurana10/Documents/featurematrix/dictionary.ser");
VSMDictionaryBean matrixBean =
VSMReadSerialMatrix.readSerializedDictionary(
"/Users/sameerkhurana10/Documents/featurematrixtest/dictionary.ser");
ArrayList<Alphabet> updateFilteredDcitionaryOutside = matrixBean.getOutsideFeatureDictionary();
ArrayList<Alphabet> updatedFilteredDictionaryInside = matrixBean.getInsideFeatureDictionary();
/*
* Getting all the tree files
*/
// File[] files = new File("/Users/sameerkhurana10/blipp_corpus/trees")
// .listFiles();
// File[] files = new File("/Users/sameerkhurana10/blipp_corpus/trees")
// .listFiles();
File[] files = new File("/Users/sameerkhurana10/blipp_corpus/testtrees").listFiles();
/*
* Getting the iterator over all the trees in the file specified by the
* URI
*/
VSMSerializeFeatureVectorBean serializeBean = new VSMSerializeFeatureVectorBean();
Trees.StandardTreeNormalizer obj = new Trees.StandardTreeNormalizer();
PTBTreeNormaliser treeNormalizer = new PTBTreeNormaliser(true);
for (File file : files) {
PennTreeReader treeReader = VSMUtil.getTreeReader(file.getAbsolutePath());
/*
* Iterating over all the trees
*/
while (treeReader.hasNext()) {
/*
* Get the tree
*/
Tree<String> syntaxTree = null;
/*
* Unmatched parentheses exception
*/
try {
syntaxTree = treeReader.next();
} catch (RuntimeException e) {
System.out.println("exception" + e + " ::tree " + syntaxTree);
}
/*
* Do stuff only if the syntax tree is a valid one
*/
if (syntaxTree != null) {
/*
* Processed syntax tree
*/
syntaxTree = treeNormalizer.process(syntaxTree);
Map<Tree<String>, Constituent<String>> constituentsMap = syntaxTree.getConstituents();
/*
* Iterator over the nodes of the tree
*/
Iterator<Tree<String>> nodeTrees = syntaxTree.iterator();
/*
* Iterating over all the nodes
*/
// double[] psi = null;
// double[] phi = null;
no.uib.cipr.matrix.sparse.SparseVector psi = null;
no.uib.cipr.matrix.sparse.SparseVector phi = null;
Tree<String> insideTree = null;
while (nodeTrees.hasNext()) {
/*
* This is the inside tree for which we want to form a
* feature vector and store it in the map
*/
insideTree = nodeTrees.next();
System.out.println("****Serializing for node " + insideTree.getLabel());
/*
* Setting some static variables for the particular node
* feature
*/
VSMUtil.setConstituentLength(constituentsMap.get(insideTree));
VSMUtil.getNumberOfOutsideWordsLeft(insideTree, constituentsMap, syntaxTree);
VSMUtil.getNumberOfOutsideWordsRight(insideTree, constituentsMap, syntaxTree);
/*
* Creating the footoroot path for outside feature
* extraction
*/
Stack<Tree<String>> foottoroot = new Stack<Tree<String>>();
foottoroot =
VSMUtil.updateFoottorootPath(foottoroot, syntaxTree, insideTree, constituentsMap);
/*
* Only do stuff if inside tree is not a leaf
*/
if (!insideTree.isLeaf()) {
/*
* Setting the object's properties that are stored
* in the .ser file
*/
VSMFeatureVectorBean vectorBean = new VSMFeatureVectorBean();
// System.out.println(":::::::" + insideTree);
/*
* Getting the inside feature vector phi
*/
psi =
new VSMOutsideFeatureVector()
.getOutsideFeatureVectorPsi(
foottoroot, updateFilteredDcitionaryOutside, vectorBean);
System.out.println("got the outside feature vector** " + psi);
phi =
new VSMInsideFeatureVector()
.getInsideFeatureVectorPhi(
insideTree, updatedFilteredDictionaryInside, vectorBean);
System.out.println("got the outside feature vector*** " + phi);
/*
* THe inside feature vector //
*/
vectorBean.setPhi(phi);
/*
* // * The outside feature vector //
*/
vectorBean.setPsi(psi);
// /*
// * The inside tree from which the inside feature
// vector
// * is extracted
// */
vectorBean.setInsideTree(insideTree);
// /*
// * The label of the node for which the inside and
// * outside feature vectors are extracted
// */
vectorBean.setLabel(insideTree.getLabel());
// /*
// * The tree from which the inside and outside
// feature
// * vectors are extracted
// */
vectorBean.setSyntaxTree(syntaxTree);
// /*
// * Setting the outside constituent trees from
// which
// the
// * outside feature vector is extracted
// */
vectorBean.setFootToRoot(foottoroot);
// /*
// * Read the count map from the file, if it not
// null
// then
// * call the other constructor, otherwise call the
// empty
// * constructor
// */
// // String fileURI =
// //
// "/Users/sameerkhurana10/Documents/serialization/countMap.ser";
// // File file = new File(fileURI);
// // if (!file.exists()) {
// // VSMSerializeFeatureVectorBean serializeBean =
// new
// // VSMSerializeFeatureVectorBean();
// // serializeBean.serializePhiBean(vectorBean);
// // System.out.println("****does not exist***");
// // } else {
// // LinkedHashMap<String, Integer> countMap =
// // VSMReadSerialCountMap
// // .readCountMapObj(fileURI).getCountMap();
// // VSMSerializeFeatureVectorBean serializeBean =
// new
// // VSMSerializeFeatureVectorBean(
// // countMap);
System.out.println("****heer here****" + vectorBean.getInsideFeatureVectorDim());
System.out.println(vectorBean.getInsideTreeFeatureList());
serializeBean.serializeVectorBean(vectorBean);
System.out.println("****heer here****" + vectorBean.getInsideFeatureVectorDim());
System.out.println(vectorBean.getInsideTreeFeatureList());
System.out.println("Serialized the feature vector***");
// // }
//
// //
// outsideFeatureMatrix.put(insideTree.getLabel(),
// psi);
// }
// }
// }
// }
//
}
}
}
}
}
// /*
// * Serialize the count map
// */
LinkedHashMap<String, Integer> countMap = VSMSerializeFeatureVectorBean.getCountMap();
// /*
// * The object that will be serialized
// */
VSMCountMap countMapObject = new VSMCountMap();
countMapObject.setCountMap(countMap);
// /*
// * Serialize count Map
// */
VSMSerializeCountMap.serializeCountMap(countMapObject);
System.out.println("count map serialized");
// /*
// * Test the serialized object, read the object
// */
LinkedHashMap<String, Integer> countMapRetireved =
VSMReadSerialCountMap.readCountMapObj(
"/Users/sameerkhurana10/Documents/serialization/countMap.ser")
.getCountMap();
System.out.println(countMapRetireved);
}