private static void initializeSomeVariables() {
VSMUtil.setConstituentLength(constituentsMap.get(insideTree));
VSMUtil.getNumberOfOutsideWordsLeft(insideTree, constituentsMap, syntaxTree);
VSMUtil.getNumberOfOutsideWordsRight(insideTree, constituentsMap, syntaxTree);
foottoroot = new Stack<Tree<String>>();
foottoroot = VSMUtil.updateFoottorootPath(foottoroot, syntaxTree, insideTree, constituentsMap);
vectorBean = new VSMFeatureVectorBean();
vectorBeanWord = new VSMWordFeatureVectorBean();
}
private static void getTreeReader(File file) {
try {
treeReader = VSMUtil.getTreeReader(file.getPath());
} catch (Exception e) {
e.printStackTrace();
}
}
public static void main(String... args) {
Collection<Double> goldVec = VSMUtil.getGoldStandard().values();
DenseVector goldVector = new DenseVector(goldVec.size());
int idx = 0;
for (double gold : goldVec) {
goldVector.add(idx, gold);
idx++;
}
System.out.println(goldVector.size());
}
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****");
}
/**
* Computing CCA
*
* @param xty - x = \Psi and y= \Phi
* @param ytx
* @param yty
* @param xtx
* @param svdTC
* @param _cpcaR2
* @param twoStageFlag
* @return
*/
private static void computeCCA2(
FlexCompRowMatrix xty,
FlexCompRowMatrix ytx,
FlexCompRowMatrix yty,
FlexCompRowMatrix xtx,
SVDTemplates1 svdTC,
ContextPCARepresentation _cpcaR2,
int twoStageFlag,
int hiddenStates,
String directoryName) {
System.out.println("+++Entering CCA Compute Function+++");
DenseDoubleMatrix2D phiLCOLT, phiRCOLT;
// remember x is Psi, i.e. the outside feature matrix and hence the
// dimensionality here is dprime \times k
System.out.println("***Creating the dense matrix, Memory Consuming Step****");
/* Total memory currently in use by the JVM */
System.out.println(
"Total memory (bytes) currently used: " + Runtime.getRuntime().totalMemory());
phiLCOLT = new DenseDoubleMatrix2D(xtx.numRows(), hiddenStates);
/*
* The below matrix dimensionality is d \times k
*/
phiRCOLT = new DenseDoubleMatrix2D(yty.numRows(), hiddenStates);
System.out.println("****Memory Consuming Step Done, Loaded two huge matrices in Memory****");
/* Total memory currently in use by the JVM */
System.out.println(
"Total memory (bytes) used currently by JVM: " + Runtime.getRuntime().totalMemory());
/*
* dprime \times d
*/
FlexCompRowMatrix auxMat1 = new FlexCompRowMatrix(xtx.numRows(), xty.numColumns());
/*
* d \times dprime
*/
FlexCompRowMatrix auxMat2 = new FlexCompRowMatrix(yty.numRows(), ytx.numColumns());
/*
* dprime \times d
*/
FlexCompRowMatrix auxMat3 = new FlexCompRowMatrix(auxMat1.numRows(), auxMat1.numColumns());
/*
* d \times dprime
*/
FlexCompRowMatrix auxMat4 = new FlexCompRowMatrix(auxMat2.numRows(), auxMat2.numColumns());
// d in our case, the dimensionality of the inside feature matrix
int dim1 = ytx.numRows();
// dprime in our case, the dimensionality of the outside feature matrix
int dim2 = xty.numRows();
System.out.println("+++Initialized auxiliary matrices+++");
/*
* Calculating C_{xx}^{-1|2} C_{xy}
*/
auxMat1 =
MatrixFormatConversion.multLargeSparseMatricesJEIGEN(computeSparseInverseSqRoot(xtx), xty);
/*
* Multiplying auxMat1 with C_{yy}^{-1|2}
*/
auxMat3 =
MatrixFormatConversion.multLargeSparseMatricesJEIGEN(
auxMat1, computeSparseInverseSqRoot(yty));
System.out.println("+++Computed 1 inverse+++");
// (svdTC.computeSparseInverse(yty)).zMult(ytx, auxMat2);
/*
* C_{yy}^{-1|2}.C_{yx}
*/
auxMat2 =
MatrixFormatConversion.multLargeSparseMatricesJEIGEN(
(svdTC.computeSparseInverseSqRoot(yty)), ytx);
/*
* Multiplying auxMat2 with C_{xx}^{-1|2}
*/
auxMat4 =
MatrixFormatConversion.multLargeSparseMatricesJEIGEN(
auxMat2, svdTC.computeSparseInverseSqRoot(xtx));
System.out.println("+++Computed Inverses+++");
// auxMat1.zMult(auxMat2,auxMat3);
System.out.println("+++Entering SVD computation+++");
/*
* Unnormalized Z projection matrix i.e. the Outside Projection Matrix,
* but Unnormalized
*/
phiLCSU =
svdTC.computeSVD_Tropp(
MatrixFormatConversion.createSparseMatrixCOLT(auxMat3),
getOmegaMatrix(auxMat3.numColumns(), hiddenStates),
dim1);
s = svdTC.getSingularVals();
/*
* Write singular values to a file, just to see what's going on in here
*/
VSMUtil.writeSingularValuesSem(s, "NNS");
// phiL=phiLCSU;
MatrixFormatConversion.createSparseMatrixCOLT((svdTC.computeSparseInverseSqRoot(xtx)))
.zMult(MatrixFormatConversion.createDenseMatrixCOLT(phiLCSU), phiLCOLT);
/*
* This is the actual Outside projection TODO, check whether this is
* actually the Outside projection. We get this by performing SVD on
* C_{xx}^{-1|2}.C_{XY}.C{YY}^{-1|2}, where x is the outside feature
* matrix (\Psi) and y is the inside feature matrix (\Phi) dprime \times
* k
*/
/* Total memory currently in use by the JVM */
System.out.println(
"Total memory (bytes) currently used: " + Runtime.getRuntime().totalMemory());
phiL = MatrixFormatConversion.createDenseMatrixJAMA(phiLCOLT);
/*
* Unormalized Y projection matrix
*/
phiRCSU =
svdTC.computeSVD_Tropp(
MatrixFormatConversion.createSparseMatrixCOLT(auxMat4),
getOmegaMatrix(auxMat4.numColumns(), hiddenStates),
dim2);
MatrixFormatConversion.createSparseMatrixCOLT((svdTC.computeSparseInverseSqRoot(yty)))
.zMult(MatrixFormatConversion.createDenseMatrixCOLT(phiRCSU), phiRCOLT);
/*
* THe inside projection matrix for the node
*/
// 700000 \times 200
/* Total memory currently in use by the JVM */
System.out.println(
"Total memory (bytes) currently used: " + Runtime.getRuntime().totalMemory());
phiR = MatrixFormatConversion.createDenseMatrixJAMA(phiRCOLT);
/*
* Serialize PhiR and PhiL
*/
System.out.println("***Serializing***");
serializeCCAVariantsRun(directoryName);
System.out.println("Freeing up the memory");
phiLCOLT = null;
phiRCOLT = null;
phiL = null;
phiLCSU = null;
phiRCSU = null;
phiR = null;
/* Total memory currently in use by the JVM */
System.out.println(
"Total memory (bytes) currently used: " + Runtime.getRuntime().totalMemory());
}
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);
}
