List of usage examples for weka.core Instance setValue
public void setValue(Attribute att, String value);
From source file:core.Core.java
public String run() throws Exception { ConverterUtils.DataSource source = new ConverterUtils.DataSource("src/files/powerpuffgirls.arff"); HashMap<String, Classifier> hash = new HashMap<>(); hash.put("J48", new J48()); hash.put("NaiveBayes", new NaiveBayes()); hash.put("IBk=1", new IBk(1)); hash.put("IBk=3", new IBk(3)); hash.put("MultilayerPerceptron", new MultilayerPerceptron()); LibSVM svm = new LibSVM(); hash.put("LibSVM", svm); Instances ins = source.getDataSet(); ins.setClassIndex(4);/*from w ww . j a va 2s. c o m*/ StringBuilder sb = new StringBuilder(); int blossom = 0; int bubbles = 0; Instance test = null; for (Map.Entry<String, Classifier> entry : hash.entrySet()) { Classifier c = entry.getValue(); c.buildClassifier(ins); test = new Instance(5); float[] array = classifyImage(); test.setDataset(ins); test.setValue(0, array[0]); test.setValue(1, array[1]); test.setValue(2, array[2]); test.setValue(3, array[3]); double prob[] = c.distributionForInstance(test); sb.append("<em>"); sb.append(entry.getKey()); sb.append(":</em>"); sb.append("<br/>"); for (int i = 0; i < prob.length; i++) { String value = test.classAttribute().value(i); if (getRoundedValue(prob[i]) >= CUT_NOTE) { if (getClassValue(value)) blossom++; else bubbles++; } sb.append(getClassName(value)); sb.append(": "); sb.append("<strong>"); sb.append(getRoundedValue(prob[i]) < CUT_NOTE ? "Rejeitado!" : getValueFormatted(prob[i])); sb.append("</strong>"); sb.append(" "); } sb.append("<br/>"); System.out.println("blossom: " + blossom); System.out.println("bubbles: " + bubbles); System.out.println("=================\n"); } sb.append(blossom > bubbles ? "<h3> a Florzinha!</h3>" : "<h3> a Lindinha!</h3>"); blossom = 0; bubbles = 0; return sb.toString(); }
From source file:couchdb.PrepareData.java
/** * Metoda do zmiany danych przechowywanych w dokumentach JSON na dane * przechowywane w obiekcie typu Instances. * * @return Zbir danych typu Instances.// w w w . j ava 2 s . c o m */ public Instances getDataForWeka() { CouchDBService cdbs = new CouchDBService(); ArrayList<String> listOfValues = cdbs.getValues(simpleDocumentList); ParseJSON p = new ParseJSON(); ArrayList<String> listOfSimpleAttributes = p.getAttributes(simpleDocumentList.get(0)); ArrayList<String> listOfComplexAttributes = new ArrayList<>(); FastVector listOfAttributes = getAtributes(simpleDocumentList, listOfValues); Instances instances = new Instances(nameData, listOfAttributes, 0); for (int j = 0; j < simpleDocumentList.size(); j++) { Instance instance = new Instance(listOfAttributes.size()); instances.add(instance); } int k = 0; for (int i = 0; i < instances.numInstances(); i++) { Instance ins = instances.instance(i); for (int j = 0; j < ins.numAttributes(); j++) { String s = listOfValues.get(k); if (s.equals("nn")) { return null; } if (s.equals("")) { k++; j--; continue; } if (ins.attribute(j).type() == 0) { double d = 0; try { d = Double.parseDouble(s); } catch (NumberFormatException ex) { d = 0; } ins.setValue(j, d); } else { ins.setValue(j, s); } k++; } } return instances; }
From source file:crystalball.quant.QuantWekaSMOreg.java
License:Open Source License
private void makeDataSet(BarData barData, int startBarId, int endBarId) { ArrayList<Attribute> attrs = new ArrayList<Attribute>(); attrs.add(id);/*www . j a v a 2 s. c o m*/ attrs.add(close); instances = new Instances("instanceName", attrs, endBarId - startBarId + 1); for (int i = startBarId; i <= endBarId; i++) { // Create empty instance with three attribute values Instance inst = new DenseInstance(2); inst.setValue(instances.attribute("id"), i); inst.setValue(instances.attribute("close"), barData.close[i]); inst.setDataset(instances); instances.add(inst); } }
From source file:csav2.Weka_additive.java
public void createTrainingFeatureFile1(String input) throws Exception { String file = "Classifier\\featurefile_additive_trial1.arff"; ArffLoader loader = new ArffLoader(); //ATTRIBUTES/*from w w w . j a v a 2s . c o m*/ Attribute attr[] = new Attribute[50]; //numeric attr[0] = new Attribute("Autosentiment"); //class FastVector classValue = new FastVector(3); classValue.addElement("p"); classValue.addElement("n"); classValue.addElement("o"); attr[1] = new Attribute("answer", classValue); FastVector attrs = new FastVector(); attrs.addElement(attr[0]); attrs.addElement(attr[1]); // Add Instances Instances dataset = new Instances("my_dataset", attrs, 0); if (new File(file).isFile()) { loader.setFile(new File(file)); dataset = loader.getDataSet(); } System.out.println("-----------------------------------------"); System.out.println(input); System.out.println("-----------------------------------------"); StringTokenizer tokenizer = new StringTokenizer(input); while (tokenizer.hasMoreTokens()) { Instance example = new Instance(2); for (int j = 0; j < 2; j++) { String st = tokenizer.nextToken(); System.out.println(j + " " + st); if (j == 0) example.setValue(attr[j], Float.parseFloat(st)); else if (j == 1) example.setValue(attr[j], st); else example.setValue(attr[j], Integer.parseInt(st)); } dataset.add(example); } //Save dataset ArffSaver saver = new ArffSaver(); saver.setInstances(dataset); saver.setFile(new File(file)); saver.writeBatch(); //Read dataset loader.setFile(new File(file)); dataset = loader.getDataSet(); //Build classifier dataset.setClassIndex(1); Classifier classifier = new J48(); classifier.buildClassifier(dataset); //Save classifier String file1 = "Classifier\\classifier_add_autosentiment.model"; OutputStream os = new FileOutputStream(file1); ObjectOutputStream objectOutputStream = new ObjectOutputStream(os); objectOutputStream.writeObject(classifier); // Comment out if not needed //Read classifier back InputStream is = new FileInputStream(file1); ObjectInputStream objectInputStream = new ObjectInputStream(is); classifier = (Classifier) objectInputStream.readObject(); objectInputStream.close(); //Evaluate resample if needed //dataset = dataset.resample(new Random(42)); //split to 70:30 learn and test set double percent = 70.0; int trainSize = (int) Math.round(dataset.numInstances() * percent / 100); int testSize = dataset.numInstances() - trainSize; Instances train = new Instances(dataset, 0, trainSize); Instances test = new Instances(dataset, trainSize, testSize); train.setClassIndex(1); test.setClassIndex(1); //Evaluate Evaluation eval = new Evaluation(dataset); //trainset eval.crossValidateModel(classifier, dataset, 10, new Random(1)); System.out.println("EVALUATION:\n" + eval.toSummaryString()); System.out.println("WEIGHTED MEASURE:\n" + eval.weightedFMeasure()); System.out.println("WEIGHTED PRECISION:\n" + eval.weightedPrecision()); System.out.println("WEIGHTED RECALL:\n" + eval.weightedRecall()); }
From source file:csav2.Weka_additive.java
public void createTrainingFeatureFile2(String input) throws Exception { String file = "Classifier\\featurefile_additive_trial2.arff"; ArffLoader loader = new ArffLoader(); //ATTRIBUTES//from w ww. j av a 2s. co m Attribute attr[] = new Attribute[50]; //numeric attr[0] = new Attribute("Autosentiment"); attr[1] = new Attribute("PositiveMatch"); attr[2] = new Attribute("NegativeMatch"); //class FastVector classValue = new FastVector(3); classValue.addElement("p"); classValue.addElement("n"); classValue.addElement("o"); attr[3] = new Attribute("answer", classValue); FastVector attrs = new FastVector(); attrs.addElement(attr[0]); attrs.addElement(attr[1]); attrs.addElement(attr[2]); attrs.addElement(attr[3]); // Add Instances Instances dataset = new Instances("my_dataset", attrs, 0); if (new File(file).isFile()) { loader.setFile(new File(file)); dataset = loader.getDataSet(); } System.out.println("-----------------------------------------"); System.out.println(input); System.out.println("-----------------------------------------"); StringTokenizer tokenizer = new StringTokenizer(input); while (tokenizer.hasMoreTokens()) { Instance example = new Instance(4); for (int j = 0; j < 4; j++) { String st = tokenizer.nextToken(); System.out.println(j + " " + st); if (j == 0) example.setValue(attr[j], Float.parseFloat(st)); else if (j == 3) example.setValue(attr[j], st); else example.setValue(attr[j], Integer.parseInt(st)); } dataset.add(example); } //Save dataset ArffSaver saver = new ArffSaver(); saver.setInstances(dataset); saver.setFile(new File(file)); saver.writeBatch(); //Read dataset loader.setFile(new File(file)); dataset = loader.getDataSet(); //Build classifier dataset.setClassIndex(3); Classifier classifier = new J48(); classifier.buildClassifier(dataset); //Save classifier String file1 = "Classifier\\classifier_add_asAndpolarwords.model"; OutputStream os = new FileOutputStream(file1); ObjectOutputStream objectOutputStream = new ObjectOutputStream(os); objectOutputStream.writeObject(classifier); // Comment out if not needed //Read classifier back InputStream is = new FileInputStream(file1); ObjectInputStream objectInputStream = new ObjectInputStream(is); classifier = (Classifier) objectInputStream.readObject(); objectInputStream.close(); //Evaluate resample if needed //dataset = dataset.resample(new Random(42)); //split to 70:30 learn and test set double percent = 70.0; int trainSize = (int) Math.round(dataset.numInstances() * percent / 100); int testSize = dataset.numInstances() - trainSize; Instances train = new Instances(dataset, 0, trainSize); Instances test = new Instances(dataset, trainSize, testSize); train.setClassIndex(3); test.setClassIndex(3); //Evaluate Evaluation eval = new Evaluation(dataset); //trainset eval.crossValidateModel(classifier, dataset, 10, new Random(1)); System.out.println("EVALUATION:\n" + eval.toSummaryString()); System.out.println("WEIGHTED MEASURE:\n" + eval.weightedFMeasure()); System.out.println("WEIGHTED PRECISION:\n" + eval.weightedPrecision()); System.out.println("WEIGHTED RECALL:\n" + eval.weightedRecall()); }
From source file:csav2.Weka_additive.java
public void createTrainingFeatureFile3(String input) throws Exception { String file = "Classifier\\featurefile_additive_trial3.arff"; ArffLoader loader = new ArffLoader(); //ATTRIBUTES//from w w w.ja va 2s . co m Attribute attr[] = new Attribute[50]; //numeric attr[0] = new Attribute("Autosentiment"); attr[1] = new Attribute("PositiveMatch"); attr[2] = new Attribute("NegativeMatch"); attr[3] = new Attribute("FW"); attr[4] = new Attribute("JJ"); attr[5] = new Attribute("RB"); attr[6] = new Attribute("RB_JJ"); //class FastVector classValue = new FastVector(3); classValue.addElement("p"); classValue.addElement("n"); classValue.addElement("o"); attr[7] = new Attribute("answer", classValue); FastVector attrs = new FastVector(); attrs.addElement(attr[0]); attrs.addElement(attr[1]); attrs.addElement(attr[2]); attrs.addElement(attr[3]); attrs.addElement(attr[4]); attrs.addElement(attr[5]); attrs.addElement(attr[6]); attrs.addElement(attr[7]); // Add Instances Instances dataset = new Instances("my_dataset", attrs, 0); if (new File(file).isFile()) { loader.setFile(new File(file)); dataset = loader.getDataSet(); } System.out.println("-----------------------------------------"); System.out.println(input); System.out.println("-----------------------------------------"); StringTokenizer tokenizer = new StringTokenizer(input); while (tokenizer.hasMoreTokens()) { Instance example = new Instance(8); for (int j = 0; j < 8; j++) { String st = tokenizer.nextToken(); System.out.println(j + " " + st); if (j == 0) example.setValue(attr[j], Float.parseFloat(st)); else if (j == 7) example.setValue(attr[j], st); else example.setValue(attr[j], Integer.parseInt(st)); } dataset.add(example); } //Save dataset ArffSaver saver = new ArffSaver(); saver.setInstances(dataset); saver.setFile(new File(file)); saver.writeBatch(); //Read dataset loader.setFile(new File(file)); dataset = loader.getDataSet(); //Build classifier dataset.setClassIndex(7); Classifier classifier = new J48(); classifier.buildClassifier(dataset); //Save classifier String file1 = "Classifier\\classifier_add_asAndpolarwordsAndpos.model"; OutputStream os = new FileOutputStream(file1); ObjectOutputStream objectOutputStream = new ObjectOutputStream(os); objectOutputStream.writeObject(classifier); // Comment out if not needed //Read classifier back InputStream is = new FileInputStream(file1); ObjectInputStream objectInputStream = new ObjectInputStream(is); classifier = (Classifier) objectInputStream.readObject(); objectInputStream.close(); //Evaluate resample if needed //dataset = dataset.resample(new Random(42)); //split to 70:30 learn and test set double percent = 70.0; int trainSize = (int) Math.round(dataset.numInstances() * percent / 100); int testSize = dataset.numInstances() - trainSize; Instances train = new Instances(dataset, 0, trainSize); Instances test = new Instances(dataset, trainSize, testSize); train.setClassIndex(7); test.setClassIndex(7); //Evaluate Evaluation eval = new Evaluation(dataset); //trainset eval.crossValidateModel(classifier, dataset, 10, new Random(1)); System.out.println("EVALUATION:\n" + eval.toSummaryString()); System.out.println("WEIGHTED MEASURE:\n" + eval.weightedFMeasure()); System.out.println("WEIGHTED PRECISION:\n" + eval.weightedPrecision()); System.out.println("WEIGHTED RECALL:\n" + eval.weightedRecall()); }
From source file:csav2.Weka_additive.java
public void createTrainingFeatureFile4(String input) throws Exception { String file = "Classifier\\featurefile_additive_trial4.arff"; ArffLoader loader = new ArffLoader(); //ATTRIBUTES//from w ww.j a v a2 s. c om Attribute attr[] = new Attribute[50]; //numeric attr[0] = new Attribute("Autosentiment"); attr[1] = new Attribute("PositiveMatch"); attr[2] = new Attribute("NegativeMatch"); attr[3] = new Attribute("FW"); attr[4] = new Attribute("JJ"); attr[5] = new Attribute("RB"); attr[6] = new Attribute("RB_JJ"); attr[7] = new Attribute("amod"); attr[8] = new Attribute("acomp"); attr[9] = new Attribute("advmod"); //class FastVector classValue = new FastVector(3); classValue.addElement("p"); classValue.addElement("n"); classValue.addElement("o"); attr[10] = new Attribute("answer", classValue); FastVector attrs = new FastVector(); attrs.addElement(attr[0]); attrs.addElement(attr[1]); attrs.addElement(attr[2]); attrs.addElement(attr[3]); attrs.addElement(attr[4]); attrs.addElement(attr[5]); attrs.addElement(attr[6]); attrs.addElement(attr[7]); attrs.addElement(attr[8]); attrs.addElement(attr[9]); attrs.addElement(attr[10]); // Add Instances Instances dataset = new Instances("my_dataset", attrs, 0); if (new File(file).isFile()) { loader.setFile(new File(file)); dataset = loader.getDataSet(); } System.out.println("-----------------------------------------"); System.out.println(input); System.out.println("-----------------------------------------"); StringTokenizer tokenizer = new StringTokenizer(input); while (tokenizer.hasMoreTokens()) { Instance example = new Instance(11); for (int j = 0; j < 11; j++) { String st = tokenizer.nextToken(); System.out.println(j + " " + st); if (j == 0) example.setValue(attr[j], Float.parseFloat(st)); else if (j == 10) example.setValue(attr[j], st); else example.setValue(attr[j], Integer.parseInt(st)); } dataset.add(example); } //Save dataset ArffSaver saver = new ArffSaver(); saver.setInstances(dataset); saver.setFile(new File(file)); saver.writeBatch(); //Read dataset loader.setFile(new File(file)); dataset = loader.getDataSet(); //Build classifier dataset.setClassIndex(10); Classifier classifier = new J48(); classifier.buildClassifier(dataset); //Save classifier String file1 = "Classifier\\classifier_asAndpolarwordsAndposAnddep.model"; OutputStream os = new FileOutputStream(file1); ObjectOutputStream objectOutputStream = new ObjectOutputStream(os); objectOutputStream.writeObject(classifier); // Comment out if not needed //Read classifier back InputStream is = new FileInputStream(file1); ObjectInputStream objectInputStream = new ObjectInputStream(is); classifier = (Classifier) objectInputStream.readObject(); objectInputStream.close(); //Evaluate resample if needed //dataset = dataset.resample(new Random(42)); //split to 70:30 learn and test set double percent = 70.0; int trainSize = (int) Math.round(dataset.numInstances() * percent / 100); int testSize = dataset.numInstances() - trainSize; Instances train = new Instances(dataset, 0, trainSize); Instances test = new Instances(dataset, trainSize, testSize); train.setClassIndex(10); test.setClassIndex(10); //Evaluate Evaluation eval = new Evaluation(dataset); //trainset eval.crossValidateModel(classifier, dataset, 10, new Random(1)); System.out.println("EVALUATION:\n" + eval.toSummaryString()); System.out.println("WEIGHTED MEASURE:\n" + eval.weightedFMeasure()); System.out.println("WEIGHTED PRECISION:\n" + eval.weightedPrecision()); System.out.println("WEIGHTED RECALL:\n" + eval.weightedRecall()); }
From source file:csav2.Weka_additive.java
public void createTrainingFeatureFile5(String input) throws Exception { String file = "Classifier\\featurefile_additive_trial5.arff"; ArffLoader loader = new ArffLoader(); //ATTRIBUTES// w w w .j ava 2 s . co m Attribute attr[] = new Attribute[50]; //numeric attr[0] = new Attribute("Autosentiment"); attr[1] = new Attribute("PositiveMatch"); attr[2] = new Attribute("NegativeMatch"); attr[3] = new Attribute("FW"); attr[4] = new Attribute("JJ"); attr[5] = new Attribute("RB"); attr[6] = new Attribute("RB_JJ"); attr[7] = new Attribute("amod"); attr[8] = new Attribute("acomp"); attr[9] = new Attribute("advmod"); attr[10] = new Attribute("BLPos"); attr[11] = new Attribute("BLNeg"); //class FastVector classValue = new FastVector(3); classValue.addElement("p"); classValue.addElement("n"); classValue.addElement("o"); attr[12] = new Attribute("answer", classValue); FastVector attrs = new FastVector(); attrs.addElement(attr[0]); attrs.addElement(attr[1]); attrs.addElement(attr[2]); attrs.addElement(attr[3]); attrs.addElement(attr[4]); attrs.addElement(attr[5]); attrs.addElement(attr[6]); attrs.addElement(attr[7]); attrs.addElement(attr[8]); attrs.addElement(attr[9]); attrs.addElement(attr[10]); attrs.addElement(attr[11]); attrs.addElement(attr[12]); // Add Instances Instances dataset = new Instances("my_dataset", attrs, 0); if (new File(file).isFile()) { loader.setFile(new File(file)); dataset = loader.getDataSet(); } System.out.println("-----------------------------------------"); System.out.println(input); System.out.println("-----------------------------------------"); StringTokenizer tokenizer = new StringTokenizer(input); while (tokenizer.hasMoreTokens()) { Instance example = new Instance(13); for (int j = 0; j < 13; j++) { String st = tokenizer.nextToken(); System.out.println(j + " " + st); if (j == 0) example.setValue(attr[j], Float.parseFloat(st)); else if (j == 12) example.setValue(attr[j], st); else example.setValue(attr[j], Integer.parseInt(st)); } dataset.add(example); } //Save dataset ArffSaver saver = new ArffSaver(); saver.setInstances(dataset); saver.setFile(new File(file)); saver.writeBatch(); //Read dataset loader.setFile(new File(file)); dataset = loader.getDataSet(); //Build classifier dataset.setClassIndex(12); Classifier classifier = new J48(); classifier.buildClassifier(dataset); //Save classifier String file1 = "Classifier\\classifier_add_asAndpolarwordsAndposAnddepAndbl.model"; OutputStream os = new FileOutputStream(file1); ObjectOutputStream objectOutputStream = new ObjectOutputStream(os); objectOutputStream.writeObject(classifier); // Comment out if not needed //Read classifier back InputStream is = new FileInputStream(file1); ObjectInputStream objectInputStream = new ObjectInputStream(is); classifier = (Classifier) objectInputStream.readObject(); objectInputStream.close(); //Evaluate resample if needed //dataset = dataset.resample(new Random(42)); //split to 70:30 learn and test set double percent = 70.0; int trainSize = (int) Math.round(dataset.numInstances() * percent / 100); int testSize = dataset.numInstances() - trainSize; Instances train = new Instances(dataset, 0, trainSize); Instances test = new Instances(dataset, trainSize, testSize); train.setClassIndex(12); test.setClassIndex(12); //Evaluate Evaluation eval = new Evaluation(dataset); //trainset eval.crossValidateModel(classifier, dataset, 10, new Random(1)); System.out.println("EVALUATION:\n" + eval.toSummaryString()); System.out.println("WEIGHTED MEASURE:\n" + eval.weightedFMeasure()); System.out.println("WEIGHTED PRECISION:\n" + eval.weightedPrecision()); System.out.println("WEIGHTED RECALL:\n" + eval.weightedRecall()); }
From source file:csav2.Weka_additive.java
public void createTrainingFeatureFile6(String input) throws Exception { String file = "Classifier\\featurefile_additive_trial6.arff"; ArffLoader loader = new ArffLoader(); //ATTRIBUTES//w ww. ja va 2s . c o m Attribute attr[] = new Attribute[50]; attr[0] = new Attribute("Autosentiment"); attr[1] = new Attribute("PositiveMatch"); attr[2] = new Attribute("NegativeMatch"); attr[3] = new Attribute("FW"); attr[4] = new Attribute("JJ"); attr[5] = new Attribute("RB"); attr[6] = new Attribute("RB_JJ"); attr[7] = new Attribute("amod"); attr[8] = new Attribute("acomp"); attr[9] = new Attribute("advmod"); attr[10] = new Attribute("BLPos"); attr[11] = new Attribute("BLNeg"); attr[12] = new Attribute("VSPositive"); attr[13] = new Attribute("VSNegative"); //class FastVector classValue = new FastVector(3); classValue.addElement("p"); classValue.addElement("n"); classValue.addElement("o"); attr[14] = new Attribute("answer", classValue); FastVector attrs = new FastVector(); attrs.addElement(attr[0]); attrs.addElement(attr[1]); attrs.addElement(attr[2]); attrs.addElement(attr[3]); attrs.addElement(attr[4]); attrs.addElement(attr[5]); attrs.addElement(attr[6]); attrs.addElement(attr[7]); attrs.addElement(attr[8]); attrs.addElement(attr[9]); attrs.addElement(attr[10]); attrs.addElement(attr[11]); attrs.addElement(attr[12]); attrs.addElement(attr[13]); attrs.addElement(attr[14]); // Add Instances Instances dataset = new Instances("my_dataset", attrs, 0); if (new File(file).isFile()) { loader.setFile(new File(file)); dataset = loader.getDataSet(); } System.out.println("-----------------------------------------"); System.out.println(input); System.out.println("-----------------------------------------"); StringTokenizer tokenizer = new StringTokenizer(input); while (tokenizer.hasMoreTokens()) { Instance example = new Instance(15); for (int j = 0; j < 15; j++) { String st = tokenizer.nextToken(); System.out.println(j + " " + st); if (j == 0) example.setValue(attr[j], Float.parseFloat(st)); else if (j == 14) example.setValue(attr[j], st); else example.setValue(attr[j], Integer.parseInt(st)); } dataset.add(example); } //Save dataset ArffSaver saver = new ArffSaver(); saver.setInstances(dataset); saver.setFile(new File(file)); saver.writeBatch(); //Read dataset loader.setFile(new File(file)); dataset = loader.getDataSet(); //Build classifier dataset.setClassIndex(14); Classifier classifier = new J48(); classifier.buildClassifier(dataset); //Save classifier String file1 = "Classifier\\classifier_add_asAndpolarwordsAndposAnddepAndblAndvs.model"; OutputStream os = new FileOutputStream(file1); ObjectOutputStream objectOutputStream = new ObjectOutputStream(os); objectOutputStream.writeObject(classifier); // Comment out if not needed //Read classifier back InputStream is = new FileInputStream(file1); ObjectInputStream objectInputStream = new ObjectInputStream(is); classifier = (Classifier) objectInputStream.readObject(); objectInputStream.close(); //Evaluate resample if needed //dataset = dataset.resample(new Random(42)); //split to 70:30 learn and test set double percent = 70.0; int trainSize = (int) Math.round(dataset.numInstances() * percent / 100); int testSize = dataset.numInstances() - trainSize; Instances train = new Instances(dataset, 0, trainSize); Instances test = new Instances(dataset, trainSize, testSize); train.setClassIndex(14); test.setClassIndex(14); //Evaluate Evaluation eval = new Evaluation(dataset); //trainset eval.crossValidateModel(classifier, dataset, 10, new Random(1)); System.out.println("EVALUATION:\n" + eval.toSummaryString()); System.out.println("WEIGHTED MEASURE:\n" + eval.weightedFMeasure()); System.out.println("WEIGHTED PRECISION:\n" + eval.weightedPrecision()); System.out.println("WEIGHTED RECALL:\n" + eval.weightedRecall()); }
From source file:csav2.Weka_additive.java
public void classifyTestSet1(String input) throws Exception { String ids = ""; ReaderWriter rw = new ReaderWriter(); //ATTRIBUTES// w w w.j a va2 s.co m Attribute attr[] = new Attribute[50]; //numeric attr[0] = new Attribute("Autosentiment"); //class FastVector classValue = new FastVector(3); classValue.addElement("p"); classValue.addElement("n"); classValue.addElement("o"); attr[1] = new Attribute("answer", classValue); FastVector attrs = new FastVector(); attrs.addElement(attr[0]); attrs.addElement(attr[1]); // Add Instances Instances dataset = new Instances("my_dataset", attrs, 0); StringTokenizer tokenizer = new StringTokenizer(input); while (tokenizer.hasMoreTokens()) { Instance example = new Instance(2); for (int j = 0; j < 2; j++) { String st = tokenizer.nextToken(); System.out.println(j + " " + st); if (j == 0) example.setValue(attr[j], Float.parseFloat(st)); else if (j == 1) example.setValue(attr[j], st); else example.setValue(attr[j], Integer.parseInt(st)); } ids += tokenizer.nextToken() + "\t"; dataset.add(example); } //Save dataset String file = "Classifier\\featurefile_additive_test1.arff"; ArffSaver saver = new ArffSaver(); saver.setInstances(dataset); saver.setFile(new File(file)); saver.writeBatch(); //Read dataset ArffLoader loader = new ArffLoader(); loader.setFile(new File(file)); dataset = loader.getDataSet(); //Build classifier dataset.setClassIndex(1); //Read classifier back String file1 = "Classifier\\classifier_add_autosentiment.model"; InputStream is = new FileInputStream(file1); Classifier classifier; ObjectInputStream objectInputStream = new ObjectInputStream(is); classifier = (Classifier) objectInputStream.readObject(); //Evaluate Instances test = new Instances(dataset, 0, dataset.numInstances()); test.setClassIndex(1); //Do eval Evaluation eval = new Evaluation(test); //trainset eval.evaluateModel(classifier, test); //testset System.out.println(eval.toSummaryString()); System.out.println("WEIGHTED F-MEASURE:" + eval.weightedFMeasure()); System.out.println("WEIGHTED PRECISION:" + eval.weightedPrecision()); System.out.println("WEIGHTED RECALL:" + eval.weightedRecall()); //output predictions String optest = "", val = ""; StringTokenizer op = new StringTokenizer(ids); int count = 0; while (op.hasMoreTokens()) { double[] prediction = classifier.distributionForInstance(test.instance(count)); count += 1; //optest+=op.nextToken()+" "+Double.toString((double) Math.round((prediction[0]) * 1000) / 1000)+"\n"; if (prediction[0] > prediction[1]) { if (prediction[0] > prediction[2]) { val = "p: " + Double.toString((double) Math.round((prediction[0]) * 1000) / 1000); } else { val = "o: " + Double.toString((double) Math.round((prediction[2]) * 1000) / 1000); } } else { if (prediction[1] > prediction[2]) { val = "n: " + Double.toString((double) Math.round((prediction[1]) * 1000) / 1000); } else { val = "o: " + Double.toString((double) Math.round((prediction[2]) * 1000) / 1000); } } optest += op.nextToken() + "\t" + val + "\n"; } rw.writeToFile(optest, "Answers_additive_Test1", "txt"); }