public static void MLalgo() {
try {
Problem problem = new Problem();
problem.l = train_count; // number of training examples
problem.n = max_feature_count; // number of features
problem.x = train_matrix; // feature nodes
problem.y = ylable; // target values;
SolverType solver = SolverType.L2R_LR; // -s 0
double C = 1.0; // cost of constraints violation
double eps = 0.01; // stopping criteria
Parameter parameter = new Parameter(solver, C, eps);
model = Linear.train(problem, parameter);
File modelFile = new File("model");
model.save(modelFile);
// load model or use it directly
model = Model.load(modelFile);
} catch (Exception e) {
e.printStackTrace();
}
}
public static void trainLibLinear() throws Exception {
System.out.println("train SVMs.");
Indexer<String> featureIndexer = IOUtils.readIndexer(WORD_INDEXER_FILE);
List<SparseVector> trainData = SparseVector.readList(TRAIN_DATA_FILE);
List<SparseVector> testData = SparseVector.readList(TEST_DATA_FILE);
Collections.shuffle(trainData);
Collections.shuffle(testData);
// List[] lists = new List[] { trainData, testData };
//
// for (int i = 0; i < lists.length; i++) {
// List<SparseVector> list = lists[i];
// for (int j = 0; j < list.size(); j++) {
// SparseVector sv = list.get(j);
// if (sv.label() > 0) {
// sv.setLabel(1);
// }
// }
// }
Problem prob = new Problem();
prob.l = trainData.size();
prob.n = featureIndexer.size() + 1;
prob.y = new double[prob.l];
prob.x = new Feature[prob.l][];
prob.bias = -1;
if (prob.bias >= 0) {
prob.n++;
}
for (int i = 0; i < trainData.size(); i++) {
SparseVector x = trainData.get(i);
Feature[] input = new Feature[prob.bias > 0 ? x.size() + 1 : x.size()];
for (int j = 0; j < x.size(); j++) {
int index = x.indexAtLoc(j) + 1;
double value = x.valueAtLoc(j);
assert index >= 0;
input[j] = new FeatureNode(index + 1, value);
}
if (prob.bias >= 0) {
input[input.length - 1] = new FeatureNode(prob.n, prob.bias);
}
prob.x[i] = input;
prob.y[i] = x.label();
}
Model model = Linear.train(prob, getSVMParamter());
CounterMap<Integer, Integer> cm = new CounterMap<Integer, Integer>();
for (int i = 0; i < testData.size(); i++) {
SparseVector sv = testData.get(i);
Feature[] input = new Feature[sv.size()];
for (int j = 0; j < sv.size(); j++) {
int index = sv.indexAtLoc(j) + 1;
double value = sv.valueAtLoc(j);
input[j] = new FeatureNode(index + 1, value);
}
double[] dec_values = new double[model.getNrClass()];
Linear.predictValues(model, input, dec_values);
int max_id = ArrayMath.argmax(dec_values);
int pred = model.getLabels()[max_id];
int answer = sv.label();
cm.incrementCount(answer, pred, 1);
}
System.out.println(cm);
model.save(new File(MODEL_FILE));
}
private void trainModelWithClasses(double param) {
int wayListSizeWithoutUnclassified = wayList.size();
int u = 0;
System.out.println("trainList size: " + wayListSizeWithoutUnclassified);
// set classes for each osm instance
int sizeToBeAddedToArray =
0; // this will be used to proper init the features array, adding the multiple vectors size
// int lalala = 0;
// setProgress(trainProgress-10);
for (OSMWay way : wayList) {
// setProgress(trainProgress++);
OSMClassification classifyInstances = new OSMClassification();
classifyInstances.calculateClasses(
way, mappings, mapperWithIDs, indirectClasses, indirectClassesWithIDs);
if (way.getClassIDs().isEmpty()) {
wayListSizeWithoutUnclassified -= 1;
u++;
} else {
sizeToBeAddedToArray = sizeToBeAddedToArray + way.getClassIDs().size() - 1;
}
}
double C = param;
double eps = 0.001;
double[] GROUPS_ARRAY2 =
new double[wayListSizeWithoutUnclassified + sizeToBeAddedToArray]; // new double[117558];//
FeatureNode[][] trainingSetWithUnknown2 =
new FeatureNode[wayListSizeWithoutUnclassified + sizeToBeAddedToArray]
[numberOfFeatures + 1422]; // working[3812];
int k = 0;
// setProgress(trainProgress+5);
for (OSMWay way : wayList) {
// adding multiple vectors
int id;
// if(USE_CLASS_FEATURES){
ClassFeatures class_vector = new ClassFeatures();
class_vector.createClassFeatures(
way, mappings, mapperWithIDs, indirectClasses, indirectClassesWithIDs);
id = 1422;
// }
// else{
// id = 1;
// }
// pass id also: 1422 if using classes, 1 if not
GeometryFeatures geometryFeatures = new GeometryFeatures(id);
geometryFeatures.createGeometryFeatures(way);
id = geometryFeatures.getLastID();
// id after geometry, cases: all geometry features with mean-variance boolean intervals:
// id = 1526
if (USE_RELATION_FEATURES) {
RelationFeatures relationFeatures = new RelationFeatures(id);
relationFeatures.createRelationFeatures(way, relationList);
id = relationFeatures.getLastID();
} else {
id = geometryFeatures.getLastID();
}
// id 1531
TextualFeatures textualFeatures;
if (USE_TEXTUAL_FEATURES) {
textualFeatures = new TextualFeatures(id, namesList, languageDetector);
textualFeatures.createTextualFeatures(way);
}
List<FeatureNode> featureNodeList = way.getFeatureNodeList();
FeatureNode[] featureNodeArray = new FeatureNode[featureNodeList.size()];
if (!way.getClassIDs().isEmpty()) {
int i = 0;
for (FeatureNode featureNode : featureNodeList) {
featureNodeArray[i] = featureNode;
i++;
}
for (int classID : way.getClassIDs()) {
// lalala++;
trainingSetWithUnknown2[k] = featureNodeArray;
GROUPS_ARRAY2[k] = classID;
k++;
}
}
}
// Linear.enableDebugOutput();
Problem problem = new Problem();
problem.l =
wayListSizeWithoutUnclassified
+ sizeToBeAddedToArray; // wayListSizeWithoutUnclassified;//wayList.size();
problem.n =
numberOfFeatures
+ 1422; // 3797; // number of features //the largest index of all features //3811;//3812
// //1812 with classes
problem.x = trainingSetWithUnknown2; // feature nodes
problem.y = GROUPS_ARRAY2; // target values
// SolverType solver = SolverType.MCSVM_CS; //Cramer and Singer for multiclass classification -
// equivalent of SVMlight
SolverType solver2 =
SolverType.getById(2); // 2 -- L2-regularized L2-loss support vector classification (primal)
Parameter parameter = new Parameter(solver2, C, eps);
// System.out.println("param set ok");
// System.out.println("number of features: " + vc.getNumOfFeatures());
long start = System.nanoTime();
System.out.println("training...");
PrintStream original = System.out;
System.setOut(
new PrintStream(
new OutputStream() {
@Override
public void write(int arg0) throws IOException {}
}));
Model model = Linear.train(problem, parameter);
long end = System.nanoTime();
Long elapsedTime = end - start;
System.setOut(original);
System.out.println(
"training process completed in: " + NANOSECONDS.toSeconds(elapsedTime) + " seconds.");
// System.out.println("trained");
// decide model path and naming and/or way of deleting/creating 1 or more models.
// File inFile = new File(inputFilePath).getParentFile();
File modelFile = new File(modelDirectory.getAbsolutePath() + "/model_with_classes");
File customModelFile;
if (topKIsSelected) {
customModelFile =
new File(
modelDirectory.getAbsolutePath()
+ "/"
+ inputFileName
+ "_model"
+ "_c"
+ param
+ "_topK"
+ topK
+ ".1");
} else {
customModelFile =
new File(
modelDirectory.getAbsolutePath()
+ "/"
+ inputFileName
+ "_model_c"
+ param
+ "_maxF"
+ frequency
+ ".1");
}
if (customModelFile.exists()) {
customModelFile.delete();
}
if (modelFile.exists()) {
modelFile.delete();
}
try {
// System.out.println("file created");
model.save(modelFile);
model.save(customModelFile);
System.out.println("model with classes saved at: " + modelFile);
System.out.println("custom model with classes saved at: " + modelFile);
} catch (IOException ex) {
Logger.getLogger(TrainWorker.class.getName()).log(Level.SEVERE, null, ex);
}
}
private void mainClassifierFunction(int option, String trainFile, String testFile, String ddgFile)
throws IOException {
// SentimentClassifierHindi this = new SentimentClassifierHindi();
// int finalSize = this.SentimentClassifierHindi();
int finalSize = this.generateFeature(option, trainFile, testFile, ddgFile);
System.out.println("Hello aspectCategorizationSemEval2016!");
// Create features
Problem problem = new Problem();
// Save X to problem
double a[] = new double[this.trainingFeature.size()];
File file = new File(rootDirectory + "\\dataset\\trainingLabels.txt");
BufferedReader reader = new BufferedReader(new FileReader(file));
String read;
int count = 0;
while ((read = reader.readLine()) != null) {
// System.out.println(read);
a[count++] = Double.parseDouble(read.toString());
}
// Feature[][] f = new Feature[][]{ {}, {}, {}, {}, {}, {} };
// trainingFeature = trainingObject.getList();
Feature[][] trainFeatureVector = new Feature[trainingFeature.size()][finalSize];
System.out.println("Training Instances: " + trainingFeature.size());
System.out.println("Feature Length: " + finalSize);
System.out.println("Test Instances: " + testFeature.size());
for (int i = 0; i < trainingFeature.size(); i++) {
// System.out.println();
// System.out.println(trainingFeature.get(i));
System.out.println(i + " trained.");
for (int j = 0; j < finalSize; j++) {
// System.out.print(trainingFeature.get(i).get(j + 1)+" ");
// trainingFeature.get(i).
if (trainingFeature.get(i).containsKey(j + 1)) {
// System.out.print(j + 1 + ", ");
trainFeatureVector[i][j] = new FeatureNode(j + 1, trainingFeature.get(i).get(j + 1));
} else {
trainFeatureVector[i][j] = new FeatureNode(j + 1, 0.0);
}
}
// System.out.println();
}
problem.l = trainingFeature.size(); // number of training examples
problem.n = finalSize; // number of features
problem.x = trainFeatureVector; // feature nodes
problem.y = a; // target values ----
BasicParser bp = new BasicParser();
SolverType solver = SolverType.L2R_LR; // -s 7
double C = 0.75; // cost of constraints violation
double eps = 0.0001; // stopping criteria
Parameter parameter = new Parameter(solver, C, eps);
Model model = Linear.train(problem, parameter);
File modelFile = new File("model");
model.save(modelFile);
// PrintWriter write = new PrintWriter(new BufferedWriter(new FileWriter(rootDirectory +
// "\\dataset\\predictedLabels.txt")));
PrintWriter write =
new PrintWriter(
new BufferedWriter(
new FileWriter(
rootDirectory
+ "\\dataset\\dataset_aspectCategorization\\predictedHotelsLabels.txt")));
if (option == 1) {
BufferedReader trainReader =
new BufferedReader(
new FileReader(
new File(
rootDirectory + "\\dataset\\dataset_aspectCategorization\\" + trainFile)));
HashMap<String, Integer> id = new HashMap<String, Integer>();
HashMap<String, String> review = new HashMap<String, String>();
double[] val = new double[trainingFeature.size()];
double[] tempVal = new double[trainingFeature.size()];
LinearCopy.crossValidation(problem, parameter, 5, val, tempVal);
for (int i = 0; i < trainingFeature.size(); i++) {
int flag = 0;
String tokens[] = trainReader.readLine().split("\\|");
if (id.containsKey(tokens[1]) == true || tokens[2].compareToIgnoreCase("True") == 0) {
} else {
// System.out.println(tokens[1]);
/*int max = -1;
double probMax = -1.0;
for(int j=0; j<13; j++){
if(probMax<val[i][j]){
probMax = val[i][j];
max = j;
}
}*/
// System.out.println(tempVal[i]);
write.println((int) (val[i]));
write.println("next");
id.put(tokens[1], 1);
System.out.println(tokens[1] + "\t" + (int) (val[i]));
if (review.containsKey(tokens[1])) {
System.out.println(tokens[3]);
System.out.println(review.get(tokens[1]));
} else {
review.put(tokens[1], tokens[3]);
}
} /*else{
for (int j = 0; j < 13; j++) {
//System.out.print(val[i][j]+", ");
if (val[i] >= 0.185) {
flag = 1;
//System.out.println("i");
write.println(j + 1);
}
}
if (flag == 1) {
write.println("next");
} else {
write.println("-1");
write.println("next");
}
//write.println(prediction);
id.put(tokens[1], 1);
//System.out.println();
}*/
}
write.close();
return;
}
if (option == 3) {
System.out.println(rootDirectory);
BufferedReader testReader =
new BufferedReader(
new FileReader(
new File(
rootDirectory + "\\dataset\\dataset_aspectCategorization\\" + testFile)));
HashMap<String, Integer> id = new HashMap<String, Integer>();
model = Model.load(modelFile);
int countNext = 0;
for (int i = 0; i < testFeature.size(); i++) {
// System.out.println(i+", "+testFeature.size()+", "+testFeature.get(i).size());
Feature[] instance = new Feature[testFeature.get(i).size()];
int j = 0;
for (Map.Entry<Integer, Double> entry : testFeature.get(i).entrySet()) {
// System.out.print(entry.getKey() + ": " + entry.getValue() + "; ");
// listOfMaps.get(i).put(start + entry.getKey(), entry.getValue());
// do stuff
instance[j++] = new FeatureNode(entry.getKey(), entry.getValue());
}
// double d = LinearCopy.predict(model, instance);
double[] predict = new double[85];
double prediction = LinearCopy.predictProbability(model, instance, predict);
int labelMap[] = new int[13];
labelMap = model.getLabels();
for (int ar = 0; ar < labelMap.length; ar++) {
System.out.println("********************** " + ar + ": " + labelMap[ar]);
}
// System.out.println(prediction);
// Arrays.sort(predict, Collections.reverseOrder());
// System.out.println();
// double prediction = LinearCopy.predict(model, instance);
String tokens[] = testReader.readLine().split("\\|");
// System.out.println(tokens[1]);
int flag = -1;
if (id.containsKey(tokens[1]) == true || tokens[2].compareToIgnoreCase("True") == 0) {
flag = 4;
// System.out.println("OutofScope: "+tokens[1]);
} else if (tokens[3].compareToIgnoreCase("abc") == 0) {
flag = 2;
System.out.println(tokens[1]);
write.println("-1");
write.println("next");
countNext++;
id.put(tokens[1], 1);
} else {
flag = 0;
for (int p = 0; p < 85; p++) {
if (predict[p] >= 0.128) {
flag = 1;
write.println(labelMap[p]);
}
}
if (flag == 1) {
countNext++;
write.println("next");
} else {
countNext++;
write.println("-1");
write.println("next");
}
// write.println((int)d);
// write.println("next");
/*write.println(prediction);
write.println("next");*/
id.put(tokens[1], 1);
}
if (flag == -1) {
System.out.println("-1, " + tokens[1]);
}
}
write.close();
System.out.println("count " + countNext);
}
write.close();
}
public void crossValidateFold(int a, int b, int c, int d, boolean skip, double param) {
System.out.println("Starting cross validation");
int testSize = wayList.size() / 5;
List<OSMWay> trainList = new ArrayList<>();
for (int g = a * testSize; g < b * testSize; g++) { // 0~~1~~2~~3~~4~~5
if (skip) {
if (g == (c) * testSize) {
g = (c + 1) * testSize;
}
}
trainList.add(wayList.get(g));
}
int wayListSizeWithoutUnclassified = trainList.size();
int u = 0;
System.out.println("trainList size: " + wayListSizeWithoutUnclassified);
// set classes for each osm instance
int sizeToBeAddedToArray =
0; // this will be used to proper init the features array, adding the multiple vectors size
int lalala = 0;
for (OSMWay way : trainList) {
OSMClassification classifyInstances = new OSMClassification();
classifyInstances.calculateClasses(
way, mappings, mapperWithIDs, indirectClasses, indirectClassesWithIDs);
if (way.getClassIDs().isEmpty()) {
wayListSizeWithoutUnclassified -= 1;
u++;
} else {
sizeToBeAddedToArray = sizeToBeAddedToArray + way.getClassIDs().size() - 1;
}
}
double C = param;
double eps = 0.001;
double[] GROUPS_ARRAY2 =
new double[wayListSizeWithoutUnclassified + sizeToBeAddedToArray]; // new double[117558];//
FeatureNode[][] trainingSetWithUnknown2 =
new FeatureNode[wayListSizeWithoutUnclassified + sizeToBeAddedToArray]
[numberOfFeatures]; // working[3812];
int k = 0;
for (OSMWay way : trainList) {
// adding multiple vectors
int id;
if (USE_CLASS_FEATURES) {
ClassFeatures class_vector = new ClassFeatures();
class_vector.createClassFeatures(
way, mappings, mapperWithIDs, indirectClasses, indirectClassesWithIDs);
id = 1422;
} else {
id = 1;
}
// pass id also: 1422 if using classes, 1 if not
GeometryFeatures geometryFeatures = new GeometryFeatures(id);
geometryFeatures.createGeometryFeatures(way);
id = geometryFeatures.getLastID();
// id after geometry, cases: all geometry features with mean-variance boolean intervals:
// id = 1526
if (USE_RELATION_FEATURES) {
RelationFeatures relationFeatures = new RelationFeatures(id);
relationFeatures.createRelationFeatures(way, relationList);
id = relationFeatures.getLastID();
} else {
id = geometryFeatures.getLastID();
}
// id 1531
TextualFeatures textualFeatures;
if (USE_TEXTUAL_FEATURES) {
textualFeatures = new TextualFeatures(id, namesList, languageDetector);
textualFeatures.createTextualFeatures(way);
}
List<FeatureNode> featureNodeList = way.getFeatureNodeList();
FeatureNode[] featureNodeArray = new FeatureNode[featureNodeList.size()];
if (!way.getClassIDs().isEmpty()) {
int i = 0;
for (FeatureNode featureNode : featureNodeList) {
featureNodeArray[i] = featureNode;
i++;
}
for (int classID : way.getClassIDs()) {
lalala++;
trainingSetWithUnknown2[k] = featureNodeArray;
GROUPS_ARRAY2[k] = classID;
k++;
}
}
}
// Linear.enableDebugOutput();
Problem problem = new Problem();
problem.l =
wayListSizeWithoutUnclassified
+ sizeToBeAddedToArray; // wayListSizeWithoutUnclassified;//wayList.size();
problem.n =
numberOfFeatures; // (geometry 105 + textual //3797; // number of features //the
// largest index of all features //3811;//3812 //1812 with classes
problem.x = trainingSetWithUnknown2; // feature nodes
problem.y = GROUPS_ARRAY2; // target values
// SolverType solver = SolverType.MCSVM_CS; //Cramer and Singer for multiclass classification -
// equivalent of SVMlight
SolverType solver2 =
SolverType.getById(2); // 2 -- L2-regularized L2-loss support vector classification (primal)
Parameter parameter = new Parameter(solver2, C, eps);
// System.out.println("param set ok");
// System.out.println("number of features: " + vc.getNumOfFeatures());
long start = System.nanoTime();
System.out.println("training...");
PrintStream original = System.out;
System.setOut(
new PrintStream(
new OutputStream() {
@Override
public void write(int arg0) throws IOException {}
}));
Model model = Linear.train(problem, parameter);
long end = System.nanoTime();
Long elapsedTime = end - start;
System.setOut(original);
System.out.println(
"training process completed in: " + NANOSECONDS.toSeconds(elapsedTime) + " seconds.");
// System.out.println("trained");
// decide model path and naming and/or way of deleting/creating 1 or more models.
// File inFile = new File(inputFilePath).getParentFile();
File modelFile;
if (USE_CLASS_FEATURES) {
modelFile = new File(modelDirectory.getAbsolutePath() + "/model_with_classes_c=" + param);
} else {
modelFile =
new File(modelDirectory.getAbsolutePath() + "/model_geometries_textual_c=" + param);
}
if (modelFile.exists()) {
modelFile.delete();
}
try {
// System.out.println("file created");
model.save(modelFile);
System.out.println("model saved at: " + modelFile);
} catch (IOException ex) {
Logger.getLogger(TrainWorker.class.getName()).log(Level.SEVERE, null, ex);
}
// end of evaluation training
// test set
List<OSMWay> testList = new ArrayList<>();
for (int g = c * testSize; g < d * testSize; g++) {
testList.add(wayList.get(g));
// liblinear test
}
System.out.println("testList size: " + testList.size());
int succededInstances = 0;
int succededInstances5 = 0;
int succededInstances10 = 0;
try {
model = Model.load(modelFile);
} catch (IOException ex) {
Logger.getLogger(TrainWorker.class.getName()).log(Level.SEVERE, null, ex);
}
int modelLabelSize = model.getLabels().length;
int[] labels = model.getLabels();
Map<Integer, Integer> mapLabelsToIDs = new HashMap<>();
for (int h = 0; h < model.getLabels().length; h++) {
mapLabelsToIDs.put(labels[h], h);
// System.out.println(h + " <-> " + labels[h]);
}
int wayListSizeWithoutUnclassified2 = testList.size();
for (OSMWay way : testList) {
OSMClassification classifyInstances = new OSMClassification();
classifyInstances.calculateClasses(
way, mappings, mapperWithIDs, indirectClasses, indirectClassesWithIDs);
if (way.getClassIDs().isEmpty()) {
// System.out.println("found unclassified" + way.getClassIDs() + "class: "
// +way.getClassID());
wayListSizeWithoutUnclassified2 -= 1;
// u++;
}
}
FeatureNode[] testInstance2;
for (OSMWay way : testList) {
int id;
if (USE_CLASS_FEATURES) {
ClassFeatures class_vector = new ClassFeatures();
class_vector.createClassFeatures(
way, mappings, mapperWithIDs, indirectClasses, indirectClassesWithIDs);
id = 1422;
} else {
id = 1;
}
// pass id also: 1422 if using classes, 1 if not
GeometryFeatures geometryFeatures = new GeometryFeatures(id);
geometryFeatures.createGeometryFeatures(way);
id = geometryFeatures.getLastID();
// id after geometry, cases: all geometry features with mean-variance boolean intervals:
// id = 1526
// System.out.println("id 1526 -> " + geometryFeatures.getLastID());
if (USE_RELATION_FEATURES) {
RelationFeatures relationFeatures = new RelationFeatures(id);
relationFeatures.createRelationFeatures(way, relationList);
id = relationFeatures.getLastID();
} else {
id = geometryFeatures.getLastID();
// System.out.println("geom feat " + id);
}
// id 1531
// System.out.println("id 1532 -> " + relationFeatures.getLastID());
if (USE_TEXTUAL_FEATURES) {
TextualFeatures textualFeatures = new TextualFeatures(id, namesList, languageDetector);
textualFeatures.createTextualFeatures(way);
// System.out.println("last textual id: " + textualFeatures.getLastID());
// System.out.println("full: " + way.getFeatureNodeList());
} else {
}
List<FeatureNode> featureNodeList = way.getFeatureNodeList();
FeatureNode[] featureNodeArray = new FeatureNode[featureNodeList.size()];
int i = 0;
for (FeatureNode featureNode : featureNodeList) {
featureNodeArray[i] = featureNode;
i++;
}
testInstance2 = featureNodeArray;
// double prediction = Linear.predict(model, testInstance2);
// System.out.println("test prediction: " + prediction);
double[] scores = new double[modelLabelSize];
Linear.predictValues(model, testInstance2, scores);
// find index of max values in scores array: predicted classes are the elements of these
// indexes from array model.getlabels
// iter scores and find 10 max values with their indexes first. then ask those indexes from
// model.getlabels
Map<Double, Integer> scoresValues = new HashMap<>();
for (int h = 0; h < scores.length; h++) {
scoresValues.put(scores[h], h);
// System.out.println(h + " <-> " + scores[h]);
}
Arrays.sort(scores);
// System.out.println("max value: " + scores[scores.length-1] + " second max: " +
// scores[scores.length-2]);
// System.out.println("ask this index from labels: " +
// scoresValues.get(scores[scores.length-1]));
// System.out.println("got from labels: " +
// labels[scoresValues.get(scores[scores.length-1])]);
// System.out.println("next prediction: " +
// labels[scoresValues.get(scores[scores.length-2])]);
// System.out.println("way labels: " + way.getClassIDs());
// System.out.println("test prediction: " + prediction);
if (way.getClassIDs().contains(labels[scoresValues.get(scores[scores.length - 1])])) {
succededInstances++;
}
if (way.getClassIDs().contains(labels[scoresValues.get(scores[scores.length - 1])])
|| way.getClassIDs().contains(labels[scoresValues.get(scores[scores.length - 2])])
|| way.getClassIDs().contains(labels[scoresValues.get(scores[scores.length - 3])])
|| way.getClassIDs().contains(labels[scoresValues.get(scores[scores.length - 4])])
|| way.getClassIDs().contains(labels[scoresValues.get(scores[scores.length - 5])])) {
succededInstances5++;
}
if (way.getClassIDs().contains(labels[scoresValues.get(scores[scores.length - 1])])
|| way.getClassIDs().contains(labels[scoresValues.get(scores[scores.length - 2])])
|| way.getClassIDs().contains(labels[scoresValues.get(scores[scores.length - 3])])
|| way.getClassIDs().contains(labels[scoresValues.get(scores[scores.length - 4])])
|| way.getClassIDs().contains(labels[scoresValues.get(scores[scores.length - 5])])
|| way.getClassIDs().contains(labels[scoresValues.get(scores[scores.length - 6])])
|| way.getClassIDs().contains(labels[scoresValues.get(scores[scores.length - 7])])
|| way.getClassIDs().contains(labels[scoresValues.get(scores[scores.length - 8])])
|| way.getClassIDs().contains(labels[scoresValues.get(scores[scores.length - 9])])
|| way.getClassIDs().contains(labels[scoresValues.get(scores[scores.length - 10])])) {
succededInstances10++;
}
// System.out.println("labels: " + Arrays.toString(model.getLabels()));
// System.out.println("label[0]: " + model.getLabels()[0]);
}
System.out.println(
"Succeeded "
+ succededInstances
+ " of "
+ testList.size()
+ " total (1 class prediction)");
double precision1 = succededInstances / (double) wayListSizeWithoutUnclassified2;
score1 = precision1;
System.out.println(precision1);
System.out.println(
"Succeeded "
+ succededInstances5
+ " of "
+ testList.size()
+ " total (5 class prediction)");
double precision5 = succededInstances5 / (double) wayListSizeWithoutUnclassified2;
score5 = precision5;
System.out.println(precision5);
System.out.println(
"Succeeded "
+ succededInstances10
+ " of "
+ testList.size()
+ " total (10 class prediction)");
double precision10 = succededInstances10 / (double) wayListSizeWithoutUnclassified2;
score10 = precision10;
System.out.println(precision10);
}
