List of usage examples for weka.core Instances setClassIndex
public void setClassIndex(int classIndex)
From source file:GrowTree.java
public static void main(String[] args) throws Exception { runClassifier(new GrowTree(), args); DataSource source = new DataSource( "F:\\backup\\BTH\\#6DV2542 Machine Learning\\WEKA experiments\\UCI\\iris.arff"); Instances data = source.getDataSet(); if (data.classIndex() == -1) data.setClassIndex(data.numAttributes() - 1); }
From source file:classifyfromimage1.java
private void jButton1ActionPerformed(java.awt.event.ActionEvent evt) {//GEN-FIRST:event_jButton1ActionPerformed selectWindow(this.name3); this.name3 = IJ.getImage().getTitle(); this.name4 = this.name3.replaceFirst("[.][^.]+$", ""); RoiManager rm = RoiManager.getInstance(); IJ.run("Duplicate...", this.name4); IJ.run("Set Measurements...", "area perimeter fit shape limit scientific redirect=None decimal=5"); selectWindow(this.name3); IJ.run("Subtract Background...", "rolling=1.5"); IJ.run("Enhance Contrast...", "saturated=25 equalize"); IJ.run("Subtract Background...", "rolling=1.5"); IJ.run("Convolve...", "text1=[-1 -3 -4 -3 -1\n-3 0 6 0 -3\n-4 6 50 6 -4\n-3 0 6 0 -3\n-1 -3 -4 -3 -1\n] normalize"); IJ.run("8-bit", ""); IJ.run("Restore Selection", ""); IJ.run("Make Binary", ""); Prefs.blackBackground = false;//from w w w.j a v a2 s . com IJ.run("Convert to Mask", ""); IJ.run("Restore Selection", ""); this.valor1 = this.interval3.getText(); this.valor2 = this.interval4.getText(); this.text = "size=" + this.valor1 + "-" + this.valor2 + " pixel show=Outlines display include summarize add"; IJ.saveAs("tif", this.name3 + "_processed"); String dest_filename1, dest_filename2, full; selectWindow("Results"); //dest_filename1 = this.name2 + "_complete.txt"; dest_filename2 = this.name3 + "_complete.csv"; //IJ.saveAs("Results", prova + File.separator + dest_filename1); IJ.run("Input/Output...", "jpeg=85 gif=-1 file=.csv copy_row save_column save_row"); //IJ.saveAs("Results", dir + File.separator + dest_filename2); IJ.saveAs("Results", this.name3 + "_complete.csv"); IJ.run("Restore Selection"); IJ.run("Clear Results"); try { CSVLoader loader = new CSVLoader(); loader.setSource(new File(this.name3 + "_complete.csv")); Instances data = loader.getDataSet(); System.out.println(data); // save ARFF String arffile = this.name3 + ".arff"; System.out.println(arffile); ArffSaver saver = new ArffSaver(); saver.setInstances(data); saver.setFile(new File(arffile)); saver.writeBatch(); } catch (IOException ex) { Logger.getLogger(MachinLearningInterface.class.getName()).log(Level.SEVERE, null, ex); } Instances data; try { data = new Instances(new BufferedReader(new FileReader(this.name3 + ".arff"))); Instances newData = null; Add filter; newData = new Instances(data); filter = new Add(); filter.setAttributeIndex("last"); filter.setNominalLabels(txtlabel.getText()); filter.setAttributeName(txtpath2.getText()); filter.setInputFormat(newData); newData = Filter.useFilter(newData, filter); System.out.print(newData); Vector vec = new Vector(); newData.setClassIndex(newData.numAttributes() - 1); if (!newData.equalHeaders(newData)) { throw new IllegalArgumentException("Train and test are not compatible!"); } Classifier cls = (Classifier) weka.core.SerializationHelper.read(txtpath.getText()); System.out.println("PROVANT MODEL.classifyInstance"); for (int i = 0; i < newData.numInstances(); i++) { double pred = cls.classifyInstance(newData.instance(i)); double[] dist = cls.distributionForInstance(newData.instance(i)); System.out.print((i + 1) + " - "); System.out.print(newData.classAttribute().value((int) pred) + " - "); //txtarea2.setText(Utils.arrayToString(dist)); System.out.println(Utils.arrayToString(dist)); vec.add(newData.classAttribute().value((int) pred)); //txtarea2.append(Utils.arrayToString(dist)); classif.add(newData.classAttribute().value((int) pred)); } classif.removeAll(Arrays.asList("", null)); System.out.println(classif); String vecstring = ""; for (Object s : classif) { vecstring += s + ","; System.out.println("Hola " + vecstring); } Map<String, Integer> seussCount = new HashMap<String, Integer>(); for (String t : classif) { Integer i = seussCount.get(t); if (i == null) { i = 0; } seussCount.put(t, i + 1); } String s = vecstring; int counter = 0; for (int i = 0; i < s.length(); i++) { if (s.charAt(i) == '$') { counter++; } } System.out.println(seussCount); System.out.println("hola " + counter++); IJ.showMessage("Your file:" + this.name3 + "arff" + "\n is composed by" + seussCount); txtpath2.setText("Your file:" + this.name3 + "arff" + "\n is composed by" + seussCount); A_MachineLearning nf2 = new A_MachineLearning(); A_MachineLearning.txtresult2.append(this.txtpath2.getText()); nf2.setVisible(true); } catch (Exception ex) { Logger.getLogger(MachinLearningInterface.class.getName()).log(Level.SEVERE, null, ex); } IJ.run("Close All", ""); if (WindowManager.getFrame("Results") != null) { IJ.selectWindow("Results"); IJ.run("Close"); } if (WindowManager.getFrame("Summary") != null) { IJ.selectWindow("Summary"); IJ.run("Close"); } if (WindowManager.getFrame("Results") != null) { IJ.selectWindow("Results"); IJ.run("Close"); } if (WindowManager.getFrame("ROI Manager") != null) { IJ.selectWindow("ROI Manager"); IJ.run("Close"); } setVisible(false); dispose();// TODO add your handling code here: // TODO add your handling code here: }
From source file:RunBestFirstSearch.java
License:Open Source License
public static void main(String[] arg) throws Exception { // Load data. ///*from www.j ava2 s . c o m*/ System.out.println("\nLoading sample file..."); DataSource source = new DataSource(arg[0]); Instances data = source.getDataSet(); if (data.classIndex() == -1) data.setClassIndex(data.numAttributes() - 1); int n = Integer.parseInt(arg[1]); System.out.println("Instance with " + n + " features!"); System.out.println("\nRunning BFS algorithm with CFS cost function..."); // Run feature selection algorithm BFS using CFS cost function. // runAttributeSelection(data, n); }
From source file:MachinLearningInterface.java
private void jButton7ActionPerformed(java.awt.event.ActionEvent evt) {//GEN-FIRST:event_jButton7ActionPerformed Instances data;/*from ww w . j a va 2 s . c om*/ try { data = new Instances(new BufferedReader(new FileReader(this.name3 + ".arff"))); Instances newData = null; Add filter; newData = new Instances(data); filter = new Add(); filter.setAttributeIndex("last"); filter.setNominalLabels("rods,punctua,networks"); filter.setAttributeName("target"); filter.setInputFormat(newData); newData = Filter.useFilter(newData, filter); System.out.print(newData); Vector vec = new Vector(); newData.setClassIndex(newData.numAttributes() - 1); if (!newData.equalHeaders(newData)) { throw new IllegalArgumentException("Train and test are not compatible!"); } URL urlToModel = this.getClass().getResource("/" + "Final.model"); InputStream stream = urlToModel.openStream(); Classifier cls = (Classifier) weka.core.SerializationHelper.read(stream); System.out.println("PROVANT MODEL.classifyInstance"); for (int i = 0; i < newData.numInstances(); i++) { double pred = cls.classifyInstance(newData.instance(i)); double[] dist = cls.distributionForInstance(newData.instance(i)); System.out.print((i + 1) + " - "); System.out.print(newData.classAttribute().value((int) pred) + " - "); //txtarea2.setText(Utils.arrayToString(dist)); System.out.println(Utils.arrayToString(dist)); vec.add(newData.classAttribute().value((int) pred)); } int p = 0, n = 0, r = 0; //txtarea2.append(Utils.arrayToString(this.target)); for (Object vec1 : vec) { if ("rods".equals(vec1.toString())) { r = r + 1; } if ("punctua".equals(vec1.toString())) { p = p + 1; } if ("networks".equals(vec1.toString())) { n = n + 1; } PrintWriter out = null; try { out = new PrintWriter(this.name3 + "_morphology.txt"); out.println(vec); out.close(); } catch (Exception ex) { ex.printStackTrace(); } //System.out.println(vec.get(i)); } System.out.println("VECTOR-> punctua: " + p + ", rods: " + r + ", networks: " + n); IJ.showMessage( "Your file:" + this.name3 + "arff" + "\nhas been analysed, and it is composed by-> punctua: " + p + ", rods: " + r + ", networks: " + n); } catch (IOException ex) { Logger.getLogger(MachinLearningInterface.class.getName()).log(Level.SEVERE, null, ex); } catch (Exception ex) { Logger.getLogger(MachinLearningInterface.class.getName()).log(Level.SEVERE, null, ex); } IJ.showMessage("analysing complete "); }
From source file:MachinLearningInterface.java
private void jButton10ActionPerformed(java.awt.event.ActionEvent evt) {//GEN-FIRST:event_jButton10ActionPerformed Instances data;//from ww w .java 2 s . c o m try { data = new Instances(new BufferedReader(new FileReader(this.name3 + ".arff"))); Instances newData = null; Add filter; newData = new Instances(data); filter = new Add(); filter.setAttributeIndex("last"); filter.setNominalLabels(this.liststring); filter.setAttributeName("target"); filter.setInputFormat(newData); newData = Filter.useFilter(newData, filter); System.out.print(newData); Vector vec = new Vector(); newData.setClassIndex(newData.numAttributes() - 1); if (!newData.equalHeaders(newData)) { throw new IllegalArgumentException("Train and test are not compatible!"); } Classifier cls = (Classifier) weka.core.SerializationHelper.read(this.model); System.out.println("PROVANT MODEL.classifyInstance"); for (int i = 0; i < newData.numInstances(); i++) { double pred = cls.classifyInstance(newData.instance(i)); double[] dist = cls.distributionForInstance(newData.instance(i)); System.out.print((i + 1) + " - "); System.out.print(newData.classAttribute().value((int) pred) + " - "); //txtarea2.setText(Utils.arrayToString(dist)); System.out.println(Utils.arrayToString(dist)); vec.add(newData.classAttribute().value((int) pred)); //txtarea2.append(Utils.arrayToString(dist)); } URL urlToModel = this.getClass().getResource("/" + "Final.model"); InputStream stream = urlToModel.openStream(); Classifier cls2 = (Classifier) weka.core.SerializationHelper.read(stream); System.out.println("PROVANT MODEL.classifyInstance"); for (int i = 0; i < newData.numInstances(); i++) { double pred = cls2.classifyInstance(newData.instance(i)); double[] dist = cls2.distributionForInstance(newData.instance(i)); System.out.print((i + 1) + " - "); System.out.print(newData.classAttribute().value((int) pred) + " - "); //txtarea2.setText(Utils.arrayToString(dist)); System.out.println(Utils.arrayToString(dist)); vec.add(newData.classAttribute().value((int) pred)); } int p = 0, n = 0, r = 0; //txtarea2.append(Utils.arrayToString(this.target)); for (Object vec1 : vec) { if ("rods".equals(vec1.toString())) { r = r + 1; } if ("punctua".equals(vec1.toString())) { p = p + 1; } if ("networks".equals(vec1.toString())) { n = n + 1; } PrintWriter out = null; try { out = new PrintWriter(this.name3 + "_morphology.txt"); out.println(vec); out.close(); } catch (Exception ex) { ex.printStackTrace(); } //System.out.println(vec.get(i)); } System.out.println("VECTOR-> punctua: " + p + ", rods: " + r + ", networks: " + n); IJ.showMessage( "Your file:" + this.name3 + "arff" + "\nhas been analysed, and it is composed by-> punctua: " + p + ", rods: " + r + ", networks: " + n); //txtarea2.setText("Your file:" + this.name3 + ".arff" //+ "\nhas been analysed, and it is composed by-> punctua: " + p + ", rods: " + r + ", networks: " + n //+ "\n" //+ "\nAnalyse complete"); //txtarea.setText("Analyse complete"); } catch (IOException ex) { Logger.getLogger(MachinLearningInterface.class.getName()).log(Level.SEVERE, null, ex); } catch (Exception ex) { Logger.getLogger(MachinLearningInterface.class.getName()).log(Level.SEVERE, null, ex); } IJ.showMessage("analysing complete "); // TODO add your handling code here: // TODO add your handling code here: }
From source file:MPCKMeans.java
License:Open Source License
public static void runFromCommandLine(String[] args) { MPCKMeans mpckmeans = new MPCKMeans(); Instances data = null, clusterData = null; ArrayList labeledPairs = null; try {//from w w w . ja va 2s . c om String optionString = Utils.getOption('D', args); if (optionString.length() != 0) { FileReader reader = new FileReader(optionString); data = new Instances(reader); System.out.println("Reading dataset: " + data.relationName()); } int classIndex = data.numAttributes() - 1; optionString = Utils.getOption('K', args); if (optionString.length() != 0) { classIndex = Integer.parseInt(optionString); if (classIndex >= 0) { data.setClassIndex(classIndex); // starts with 0 // Remove the class labels before clustering clusterData = new Instances(data); mpckmeans.setNumClusters(clusterData.numClasses()); clusterData.deleteClassAttribute(); System.out.println("Setting classIndex: " + classIndex); } else { clusterData = new Instances(data); } } else { data.setClassIndex(classIndex); // starts with 0 // Remove the class labels before clustering clusterData = new Instances(data); mpckmeans.setNumClusters(clusterData.numClasses()); clusterData.deleteClassAttribute(); System.out.println("Setting classIndex: " + classIndex); } optionString = Utils.getOption('C', args); if (optionString.length() != 0) { labeledPairs = mpckmeans.readConstraints(optionString); System.out.println("Reading constraints from: " + optionString); } else { labeledPairs = new ArrayList(0); } mpckmeans.setTotalTrainWithLabels(data); mpckmeans.setOptions(args); System.out.println(); mpckmeans.buildClusterer(labeledPairs, clusterData, data, mpckmeans.getNumClusters(), data.numInstances()); mpckmeans.printClusterAssignments(); if (mpckmeans.m_TotalTrainWithLabels.classIndex() > -1) { double nCorrect = 0; for (int i = 0; i < mpckmeans.m_TotalTrainWithLabels.numInstances(); i++) { for (int j = i + 1; j < mpckmeans.m_TotalTrainWithLabels.numInstances(); j++) { int cluster_i = mpckmeans.m_ClusterAssignments[i]; int cluster_j = mpckmeans.m_ClusterAssignments[j]; double class_i = (mpckmeans.m_TotalTrainWithLabels.instance(i)).classValue(); double class_j = (mpckmeans.m_TotalTrainWithLabels.instance(j)).classValue(); // System.out.println(cluster_i + "," + cluster_j + ":" + class_i + "," + class_j); if (cluster_i == cluster_j && class_i == class_j || cluster_i != cluster_j && class_i != class_j) { nCorrect++; // System.out.println("nCorrect:" + nCorrect); } } } int numInstances = mpckmeans.m_TotalTrainWithLabels.numInstances(); double RandIndex = 100 * nCorrect / (numInstances * (numInstances - 1) / 2); System.err.println("Acc\t" + RandIndex); } // if (mpckmeans.getTotalTrainWithLabels().classIndex() >= 0) { // SemiSupClustererEvaluation eval = new SemiSupClustererEvaluation(mpckmeans.m_TotalTrainWithLabels, // mpckmeans.m_TotalTrainWithLabels.numClasses(), // mpckmeans.m_TotalTrainWithLabels.numClasses()); // eval.evaluateModel(mpckmeans, mpckmeans.m_TotalTrainWithLabels, mpckmeans.m_Instances); // eval.mutualInformation(); // eval.pairwiseFMeasure(); // } } catch (Exception e) { System.out.println("Option not specified"); e.printStackTrace(); } }
From source file:MPCKMeans.java
License:Open Source License
public static void testCase() { try {//from www . ja v a 2 s .c o m String dataset = new String("lowd"); //String dataset = new String("highd"); if (dataset.equals("lowd")) { //////// Low-D data // String datafile = "/u/ml/data/bio/arffFromPhylo/ecoli_K12-100.arff"; // String datafile = "/u/sugato/weka/data/digits-0.1-389.arff"; String datafile = "/u/sugato/weka/data/iris.arff"; int numPairs = 200, num = 0; // set up the data FileReader reader = new FileReader(datafile); Instances data = new Instances(reader); // Make the last attribute be the class int classIndex = data.numAttributes() - 1; data.setClassIndex(classIndex); // starts with 0 System.out.println("ClassIndex is: " + classIndex); // Remove the class labels before clustering Instances clusterData = new Instances(data); clusterData.deleteClassAttribute(); // create the pairs ArrayList labeledPair = InstancePair.getPairs(data, numPairs); System.out.println("Finished initializing constraint matrix"); MPCKMeans mpckmeans = new MPCKMeans(); mpckmeans.setUseMultipleMetrics(false); System.out.println("\nClustering the data using MPCKmeans...\n"); WeightedEuclidean metric = new WeightedEuclidean(); WEuclideanLearner metricLearner = new WEuclideanLearner(); // LearnableMetric metric = new WeightedDotP(); // MPCKMeansMetricLearner metricLearner = new DotPGDLearner(); // KL metric = new KL(); // KLGDLearner metricLearner = new KLGDLearner(); // ((KL)metric).setUseIDivergence(true); // BarHillelMetric metric = new BarHillelMetric(); // BarHillelMetricMatlab metric = new BarHillelMetricMatlab(); // XingMetric metric = new XingMetric(); // WeightedMahalanobis metric = new WeightedMahalanobis(); mpckmeans.setMetric(metric); mpckmeans.setMetricLearner(metricLearner); mpckmeans.setVerbose(false); mpckmeans.setRegularize(false); mpckmeans.setTrainable(new SelectedTag(TRAINING_INTERNAL, TAGS_TRAINING)); mpckmeans.setSeedable(true); mpckmeans.buildClusterer(labeledPair, clusterData, data, data.numClasses(), data.numInstances()); mpckmeans.getIndexClusters(); mpckmeans.printIndexClusters(); SemiSupClustererEvaluation eval = new SemiSupClustererEvaluation(mpckmeans.m_TotalTrainWithLabels, mpckmeans.m_TotalTrainWithLabels.numClasses(), mpckmeans.m_TotalTrainWithLabels.numClasses()); eval.evaluateModel(mpckmeans, mpckmeans.m_TotalTrainWithLabels, mpckmeans.m_Instances); System.out.println("MI=" + eval.mutualInformation()); System.out.print("FM=" + eval.pairwiseFMeasure()); System.out.print("\tP=" + eval.pairwisePrecision()); System.out.print("\tR=" + eval.pairwiseRecall()); } else if (dataset.equals("highd")) { //////// Newsgroup data String datafile = "/u/ml/users/sugato/groupcode/weka335/data/arffFromCCS/sanitized/different-1000_sanitized.arff"; //String datafile = "/u/ml/users/sugato/groupcode/weka335/data/20newsgroups/small-newsgroup_fromCCS.arff"; //String datafile = "/u/ml/users/sugato/groupcode/weka335/data/20newsgroups/same-100_fromCCS.arff"; // set up the data FileReader reader = new FileReader(datafile); Instances data = new Instances(reader); // Make the last attribute be the class int classIndex = data.numAttributes() - 1; data.setClassIndex(classIndex); // starts with 0 System.out.println("ClassIndex is: " + classIndex); // Remove the class labels before clustering Instances clusterData = new Instances(data); clusterData.deleteClassAttribute(); // create the pairs int numPairs = 0, num = 0; ArrayList labeledPair = new ArrayList(numPairs); Random rand = new Random(42); System.out.println("Initializing constraint matrix:"); while (num < numPairs) { int i = (int) (data.numInstances() * rand.nextFloat()); int j = (int) (data.numInstances() * rand.nextFloat()); int first = (i < j) ? i : j; int second = (i >= j) ? i : j; int linkType = (data.instance(first).classValue() == data.instance(second).classValue()) ? InstancePair.MUST_LINK : InstancePair.CANNOT_LINK; InstancePair pair = new InstancePair(first, second, linkType); if (first != second && !labeledPair.contains(pair)) { labeledPair.add(pair); //System.out.println(num + "th entry is: " + pair); num++; } } System.out.println("Finished initializing constraint matrix"); MPCKMeans mpckmeans = new MPCKMeans(); mpckmeans.setUseMultipleMetrics(false); System.out.println("\nClustering the highd data using MPCKmeans...\n"); LearnableMetric metric = new WeightedDotP(); MPCKMeansMetricLearner metricLearner = new DotPGDLearner(); // KL metric = new KL(); // KLGDLearner metricLearner = new KLGDLearner(); mpckmeans.setMetric(metric); mpckmeans.setMetricLearner(metricLearner); mpckmeans.setVerbose(false); mpckmeans.setRegularize(true); mpckmeans.setTrainable(new SelectedTag(TRAINING_INTERNAL, TAGS_TRAINING)); mpckmeans.setSeedable(true); mpckmeans.buildClusterer(labeledPair, clusterData, data, data.numClasses(), data.numInstances()); mpckmeans.getIndexClusters(); SemiSupClustererEvaluation eval = new SemiSupClustererEvaluation(mpckmeans.m_TotalTrainWithLabels, mpckmeans.m_TotalTrainWithLabels.numClasses(), mpckmeans.m_TotalTrainWithLabels.numClasses()); mpckmeans.getMetric().resetMetric(); // Vital: to reset m_attrWeights to 1 for proper normalization eval.evaluateModel(mpckmeans, mpckmeans.m_TotalTrainWithLabels, mpckmeans.m_Instances); System.out.println("MI=" + eval.mutualInformation()); System.out.print("FM=" + eval.pairwiseFMeasure()); System.out.print("\tP=" + eval.pairwisePrecision()); System.out.print("\tR=" + eval.pairwiseRecall()); } } catch (Exception e) { e.printStackTrace(); } }
From source file:A_AdvanceMachineLearning.java
private void jButton10ActionPerformed(java.awt.event.ActionEvent evt) {//GEN-FIRST:event_jButton10ActionPerformed UIManager.put("OptionPane.yesButtonText", "Confirm"); UIManager.put("OptionPane.noButtonText", "Cancel"); int dialogButton = JOptionPane.YES_NO_OPTION; int dialogResult = JOptionPane.showConfirmDialog(this, "The labels must be the same used in the weka model", "Advance Machine learning", dialogButton, JOptionPane.WARNING_MESSAGE); if (dialogResult == 0) { this.list.clear(); //txtcodigo1.setText("hola"); this.valor = txtcodigo1.getText(); this.valor1 = txtcodigo2.getText(); this.valor2 = txtcodigo3.getText(); this.valor3 = txtcodigo4.getText(); this.valor4 = txtcodigo5.getText(); this.valor5 = txtcodigo6.getText(); //IJ.showMessage("your label 1 is = "+valor+", "+valor1+", "+valor2+", "+valor3+", "+valor4); // Array list this.list.add(this.valor); this.list.add(this.valor1); this.list.add(this.valor2); this.list.add(this.valor3); this.list.add(this.valor4); this.list.add(this.valor5); this.list.removeAll(Arrays.asList("", null)); System.out.println(this.list); this.liststring = ""; for (String s : this.list) { this.liststring += s + ","; }/* www.java 2 s . c om*/ txtlabel.setText(this.liststring); System.out.println(this.liststring); txtarea.setText("Your labels are = " + this.list + "\nThe labels had been saved"); //txtarea.setText("The labels had been saved"); System.out.println(label); } else { System.out.println("No Option"); } Instances data; try { System.out.println(this.file2 + "arff"); FileReader reader = new FileReader(this.file2 + ".arff"); BufferedReader br = new BufferedReader(reader); data = new Instances(br); System.out.println(data); Instances newData = null; Add filter; newData = new Instances(data); filter = new Add(); filter.setAttributeIndex("last"); filter.setNominalLabels(this.liststring); filter.setAttributeName(txtcodigo7.getText()); filter.setInputFormat(newData); newData = Filter.useFilter(newData, filter); System.out.print("hola" + newData); Vector vec = new Vector(); newData.setClassIndex(newData.numAttributes() - 1); if (!newData.equalHeaders(newData)) { throw new IllegalArgumentException("Train and test are not compatible!"); } Classifier cls = (Classifier) weka.core.SerializationHelper.read(this.model); System.out.println("PROVANT MODEL.classifyInstance"); for (int i = 0; i < newData.numInstances(); i++) { double pred = cls.classifyInstance(newData.instance(i)); double[] dist = cls.distributionForInstance(newData.instance(i)); System.out.print((i + 1) + " - "); System.out.print(newData.classAttribute().value((int) pred) + " - "); //txtarea2.setText(Utils.arrayToString(dist)); System.out.println(Utils.arrayToString(dist)); vec.add(newData.classAttribute().value((int) pred)); classif.add(newData.classAttribute().value((int) pred)); } classif.removeAll(Arrays.asList("", null)); System.out.println(classif); String vecstring = ""; for (Object s : classif) { vecstring += s + ","; } Map<String, Integer> seussCount = new HashMap<String, Integer>(); for (String t : classif) { Integer i = seussCount.get(t); if (i == null) { i = 0; } seussCount.put(t, i + 1); } String s = vecstring; String in = vecstring; int i = 0; Pattern p = Pattern.compile(this.valor1); Matcher m = p.matcher(in); while (m.find()) { i++; } System.out.println("hola " + i); // Prints 2 System.out.println(seussCount); txtarea2.append("Your file:" + this.file2 + "arff" + "\n is composed by" + seussCount); IJ.showMessage("Your file:" + this.file2 + "arff" + "\n is composed by" + seussCount); } catch (Exception ex) { Logger.getLogger(MachinLearningInterface.class.getName()).log(Level.SEVERE, null, ex); } //IJ.showMessage("analysing complete ");// TODO add your handling code here: }
From source file:MeansClassifier.java
public static void main(String[] args) throws Exception { Instances read = null; String str = "\\\\ueahome4\\stusci4\\ysj13kxu\\data\\Documents\\Sheet2_Train.arff"; FileReader r;// ww w. ja v a 2s. c om try { r = new FileReader(str); read = new Instances(r); read.setClassIndex(read.numAttributes() - 1); } catch (Exception e) { System.out.println(" Exception caught =" + e); } MeansClassifier m = new MeansClassifier(); m.buildClassifier(read); }
From source file:ab.demo.AIAssignment2.java
License:Open Source License
public GameState solve() { // capture Image BufferedImage screenshot = ActionRobot.doScreenShot(); // process image Vision vision = new Vision(screenshot); // find the slingshot Rectangle sling = vision.findSlingshotMBR(); // confirm the slingshot while (sling == null && aRobot.getState() == GameState.PLAYING) { System.out.println("No slingshot detected. Please remove pop up or zoom out"); ActionRobot.fullyZoomOut();//from w w w . j av a 2s .c o m screenshot = ActionRobot.doScreenShot(); vision = new Vision(screenshot); sling = vision.findSlingshotMBR(); } // get all the pigs List<ABObject> pigs = vision.findPigsMBR(); List<ABObject> blocks = vision.findBlocksMBR(); GameState state = aRobot.getState(); // if there is a sling, then play, otherwise just skip. if (sling != null) { if (!pigs.isEmpty()) { //if there are pigs in the level Point releasePoint = null; Shot shot = new Shot(); int dx, dy; { //random pick up a pig ABObject pig = pigs.get(randomGenerator.nextInt(pigs.size())); Point _tpt = pig.getCenter(); // estimate the trajectory ArrayList<Point> pts = tp.estimateLaunchPoint(sling, _tpt); //define all of the wood, ice and stone in the stage ArrayList<ABObject> wood = new ArrayList<ABObject>(); ArrayList<ABObject> stone = new ArrayList<ABObject>(); ArrayList<ABObject> ice = new ArrayList<ABObject>(); ArrayList<ABObject> tnt = new ArrayList<ABObject>(); //initialise counters to store how many times the trajectory intersects blocks of these types int woodCount = 0; int stoneCount = 0; int iceCount = 0; int pigsCount = 0; int tntCount = 0; //populate the wood, stone and ice ArrayLists with the correct materials for (int i = 0; i < blocks.size(); i++) { if (blocks.get(i).type == ABType.Wood) wood.add(blocks.get(i)); if (blocks.get(i).type == ABType.Stone) stone.add(blocks.get(i)); if (blocks.get(i).type == ABType.Ice) ice.add(blocks.get(i)); if (blocks.get(i).type == ABType.TNT) tnt.add(blocks.get(i)); } //check whether the trajectory intersects any wood blocks for (int i = 0; i < wood.size(); i++) { for (int j = 0; j < pts.size(); j++) { if (wood.get(i).contains(pts.get(j))) { System.out.println("Trajectory intersects some wood"); woodCount++; } if (pig.contains(pts.get(j))) //if we find the pig on this point j = pts.size() - 1; //stop looking for wood on the trajectory (escape for loop) } } //check whether the trajectory intersects any ice blocks for (int i = 0; i < ice.size(); i++) { for (int j = 0; j < pts.size(); j++) { if (ice.get(i).contains(pts.get(j))) { System.out.println("Trajectory intersects some ice"); iceCount++; } if (pig.contains(pts.get(j))) //if we find the pig on this point j = pts.size() - 1; //stop looking for ice on the trajectory (escape for loop) } } //check whether the trajectory intersects any stone blocks for (int i = 0; i < stone.size(); i++) { for (int j = 0; j < pts.size(); j++) { if (stone.get(i).contains(pts.get(j))) { System.out.println("Trajectory intersects some stone"); stoneCount++; } if (pig.contains(pts.get(j))) //if we find the pig on this point j = pts.size() - 1; //stop looking for stone on the trajectory (escape for loop) } } //how many pigs the trajectory intersects (this should always be at least 1) for (int i = 0; i < pigs.size(); i++) { for (int j = 0; j < pts.size(); j++) { if (pigs.get(i).contains(pts.get(j))) { System.out.println("Trajectory intersects a pig"); pigsCount++; } } } //how many tnt blocks the trajectory intersects for (int i = 0; i < tnt.size(); i++) { for (int j = 0; j < pts.size(); j++) { if (tnt.get(i).contains(pts.get(j))) { System.out.println("Trajectory intersects some tnt"); tntCount++; } if (pig.contains(pts.get(j))) //if we find the pig on this point j = pts.size() - 1; //stop looking for tnt on the trajectory } } StringBuilder sb = new StringBuilder(); sb.append(pigsCount + "," + woodCount + "," + iceCount + "," + stoneCount + "," + tntCount + ",?"); String dataEntry = sb.toString(); try (PrintWriter out = new PrintWriter( new BufferedWriter(new FileWriter("dataset/birds.level.arff", true)))) { out.println(dataEntry); } catch (IOException e) { System.out.println("Error - dataset/birds.level.arff file not found or in use!"); } //indicator of if the agent should continue this shot or not (used in the bayes classifier) ArrayList<Boolean> takeShot = new ArrayList<Boolean>(); try { DataSource source = new DataSource("dataset/birds.data.arff"); //initialise the learning set for the agent Instances data = source.getDataSet(); // setting class attribute if the data format does not provide this information // For example, the XRFF format saves the class attribute information as well if (data.classIndex() == -1) data.setClassIndex(data.numAttributes() - 1); DataSource thisLevel = new DataSource("dataset/birds.level.arff"); //initialise the data retrieved from the current level for the agent Instances thisLevelData = thisLevel.getDataSet(); if (thisLevelData.classIndex() == -1) thisLevelData.setClassIndex(data.numAttributes() - 1); //build a new NaiveBayes classifier NaiveBayes bayes = new NaiveBayes(); bayes.buildClassifier(data); for (int i = 0; i < thisLevelData.numInstances(); i++) { //for all instances in the current level double label = bayes.classifyInstance(thisLevelData.instance(i)); //generate an outcome/classify an instance in the current level thisLevelData.instance(i).setClassValue(label); //store this outcome System.out.println(thisLevelData.instance(i).stringValue(5)); //print it if (thisLevelData.instance(i).stringValue(5) != "?") { //if there is a decisive choice as to whether a shot should be taken data.add(thisLevelData.instance(i)); //store it if (thisLevelData.instance(i).stringValue(5) == "yes") {//if the classifier classifies a good shot, store it takeShot.add(true); } else { //if no, store this too takeShot.add(false); } } } //add all non ? entries to the learning set BufferedWriter writer = new BufferedWriter(new FileWriter("dataset/birds.data.arff")); writer.write(data.toString()); writer.flush(); writer.close(); } catch (Exception e) { e.printStackTrace(); System.out.println("Exception caught - file handle error"); } //TODO: roll a random number to determine whether we take a shot or not. //populated using the bayesian classification above. //if we roll true, continue with the random pig shot as usual. //if not, take a new random pig and try again. //TODO: implement a failsafe so the agent does not get stuck randomly choosing pigs which the bayesian classification does not allow. Random rng = new Random(takeShot.size()); if (takeShot.get(rng.nextInt())) System.out.println("Taking this shot."); else { System.out.println("Not taking this shot. Finding another random pig."); return state; } // if the target is very close to before, randomly choose a // point near it if (prevTarget != null && distance(prevTarget, _tpt) < 10) { double _angle = randomGenerator.nextDouble() * Math.PI * 2; _tpt.x = _tpt.x + (int) (Math.cos(_angle) * 10); _tpt.y = _tpt.y + (int) (Math.sin(_angle) * 10); System.out.println("Randomly changing to " + _tpt); } prevTarget = new Point(_tpt.x, _tpt.y); // do a high shot when entering a level to find an accurate velocity if (firstShot && pts.size() > 1) { releasePoint = pts.get(1); } else if (pts.size() == 1) releasePoint = pts.get(0); else if (pts.size() == 2) { // randomly choose between the trajectories, with a 1 in // 6 chance of choosing the high one if (randomGenerator.nextInt(6) == 0) releasePoint = pts.get(1); else releasePoint = pts.get(0); } else if (pts.isEmpty()) { System.out.println("No release point found for the target"); System.out.println("Try a shot with 45 degree"); releasePoint = tp.findReleasePoint(sling, Math.PI / 4); } // Get the reference point Point refPoint = tp.getReferencePoint(sling); //Calculate the tapping time according the bird type if (releasePoint != null) { double releaseAngle = tp.getReleaseAngle(sling, releasePoint); System.out.println("Release Point: " + releasePoint); System.out.println("Release Angle: " + Math.toDegrees(releaseAngle)); int tapInterval = 0; switch (aRobot.getBirdTypeOnSling()) { case RedBird: tapInterval = 0; break; // start of trajectory case YellowBird: tapInterval = 65 + randomGenerator.nextInt(25); break; // 65-90% of the way case WhiteBird: tapInterval = 70 + randomGenerator.nextInt(20); break; // 70-90% of the way case BlackBird: tapInterval = 70 + randomGenerator.nextInt(20); break; // 70-90% of the way case BlueBird: tapInterval = 65 + randomGenerator.nextInt(20); break; // 65-85% of the way default: tapInterval = 60; } int tapTime = tp.getTapTime(sling, releasePoint, _tpt, tapInterval); dx = (int) releasePoint.getX() - refPoint.x; dy = (int) releasePoint.getY() - refPoint.y; shot = new Shot(refPoint.x, refPoint.y, dx, dy, 0, tapTime); } else { System.err.println("No Release Point Found"); return state; } } // check whether the slingshot is changed. the change of the slingshot indicates a change in the scale. { ActionRobot.fullyZoomOut(); screenshot = ActionRobot.doScreenShot(); vision = new Vision(screenshot); Rectangle _sling = vision.findSlingshotMBR(); if (_sling != null) { double scale_diff = Math.pow((sling.width - _sling.width), 2) + Math.pow((sling.height - _sling.height), 2); if (scale_diff < 25) { if (dx < 0) { aRobot.cshoot(shot); state = aRobot.getState(); if (state == GameState.PLAYING) { screenshot = ActionRobot.doScreenShot(); vision = new Vision(screenshot); List<Point> traj = vision.findTrajPoints(); tp.adjustTrajectory(traj, sling, releasePoint); firstShot = false; } } } else System.out.println( "Scale is changed, can not execute the shot, will re-segement the image"); } else System.out .println("no sling detected, can not execute the shot, will re-segement the image"); } } } return state; }