List of usage examples for weka.filters.unsupervised.attribute Add setInputFormat
@Override public boolean setInputFormat(Instances instanceInfo) throws Exception
From source file:classifyfromimage.java
private void jButton1ActionPerformed(java.awt.event.ActionEvent evt) {//GEN-FIRST:event_jButton1ActionPerformed this.name3 = IJ.getImage().getTitle(); this.name4 = this.name3.replaceFirst("[.][^.]+$", ""); System.out.println("hola " + this.name4); selectWindow(this.name3); System.out.println(this.name4); System.out.println(this.name3); RoiManager rm = RoiManager.getInstance(); IJ.run("Duplicate...", this.name3); 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 www . j ava 2 s .c o m IJ.run("Convert to Mask", ""); IJ.run("Restore Selection", ""); this.valor1 = this.interval3.getText(); this.valor2 = this.interval4.getText(); System.out.println("VECTOR-> punctua: " + this.valor1 + " " + this.valor2); this.text = "size=" + this.valor1 + "-" + this.valor2 + " pixel show=Outlines display include summarize add"; IJ.run("Analyze Particles...", this.text); 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"); //txtarea.setText("Converting, please wait... "); 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); } //txtdata2.setText(this.name3); //txtarea.setText("Succesfully converted " + this.name3); //txtarea.setText("Analysing your data, please wait... "); 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("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); this.txtarea2.setText( "Your file:" + this.name3 + ".arff" + "\nhas been analysed, and it is composed by-> punctua: " + p + ", rods: " + r + ", networks: " + n); A_MachineLearning nf1 = new A_MachineLearning(); A_MachineLearning.txtresults1.setText(this.txtarea2.getText()); A_MachineLearning.txtresults1.setText(this.txtarea2.getText()); A_MachineLearning.txtresults1.setText(this.txtarea2.getText()); A_MachineLearning.txtresults1.append(this.txtarea2.getText()); A_MachineLearning.txtresults1.append(this.txtarea2.getText()); A_MachineLearning.txtresults1.append(this.txtarea2.getText()); nf1.setVisible(true); } 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.run("Clear Results"); //IJ.RoiManager("Delete"); IJ.run("Clear Results"); 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"); } IJ.run("Close All", "roiManager"); IJ.run("Close All", ""); setVisible(false); dispose();// TODO add your handling code here: setVisible(false); dispose();// TODO add your handling code here: // TODO add your handling code here: }
From source file:A_MachineLearning.java
private void jButton7ActionPerformed(java.awt.event.ActionEvent evt) {//GEN-FIRST:event_jButton7ActionPerformed Instances data;//from www . ja v a 2 s . c om try { data = new Instances(new BufferedReader(new FileReader(this.file2 + ".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.file2 + "_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.file2 + "arff" + "\nhas been analysed, and it is composed by-> punctua: " + p + ", rods: " + r + ", networks: " + n); txtresults1.setText( "Your file:" + this.file2 + "arff" + "\nhas been analysed, and it is composed by: \npunctua: " + 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:classificationPLugin.java
private void ClassifyActionPerformed(java.awt.event.ActionEvent evt) {//GEN-FIRST:event_ClassifyActionPerformed this.name = txtdirecotry2.getText(); System.out.println(this.name); try {/*from w ww . ja v a 2 s. c o m*/ CSVLoader loader = new CSVLoader(); loader.setSource(new File(this.name)); Instances data = loader.getDataSet(); System.out.println(data); // save ARFF String arffile = this.name + ".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); } try { FileReader reader = new FileReader(this.name + ".arff"); BufferedReader br = new BufferedReader(reader); instance.read(br, null); br.close(); instance.requestFocus(); } catch (Exception e2) { System.out.println(e2); } Instances data; try { data = new Instances(new BufferedReader(new FileReader(this.name + ".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.name + "_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.name + "arff" + "\nhas been analysed, and it is composed by-> \npunctua: " + p + ", rods: " + r + ", networks: " + n); classi.setText( "Your file:" + this.name + "arff" + "\nhas been analysed, and it is composed by: \npunctua: " + 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.run("Clear Results"); IJ.run("Clear Results"); 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"); } IJ.run("Close All", "roiManager"); IJ.run("Close All", ""); }
From source file:dialog1.java
private void jButton1ActionPerformed(java.awt.event.ActionEvent evt) {//GEN-FIRST:event_jButton1ActionPerformed try {// www . j a v a 2 s . co m CSVLoader loader = new CSVLoader(); loader.setSource(new File(txtfilename.getText() + "_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("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();*/ InputStream stream = this.getClass().getResourceAsStream("/" + "Final.model"); 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)); //txtarea2.append(Utils.arrayToString(newData.classAttribute().value((int) pred))); //this.target2.add((i + 1) + " -); //this.target.add(newData.classAttribute().value((int) pred)); //for (String s : this.list) { //this.target2 += s + ","; } 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.run("Clear Results"); IJ.run("Clear Results"); 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"); } IJ.run("Close All", "roiManager"); IJ.run("Close All", ""); setVisible(false); dispose();// TODO add your handling code here: setVisible(false); dispose();// TODO add your handling code here: // TODO add your handling code here: }
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 www. ja v a 2s .c om*/ 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:MachinLearningInterface.java
private void jButton7ActionPerformed(java.awt.event.ActionEvent evt) {//GEN-FIRST:event_jButton7ActionPerformed Instances data;//from www . j a v a 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("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;//ww w .jav a2 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(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: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.j av a 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:adams.data.instances.AbstractInstanceGenerator.java
License:Open Source License
/** * Adds IDs, notes, additional fields to header. * * @param data the input data/*from w w w. j a va 2 s. c o m*/ */ protected void postProcessHeader(T data) { Add add; // add the database ID to the output? if (m_AddDatabaseID) { try { add = new Add(); add.setAttributeIndex("1"); add.setAttributeName(ArffUtils.getDBIDName()); add.setAttributeType(new SelectedTag(Attribute.NUMERIC, Add.TAGS_TYPE)); add.setInputFormat(m_OutputHeader); m_OutputHeader = Filter.useFilter(m_OutputHeader, add); } catch (Exception e) { getLogger().log(Level.SEVERE, "Error initializing the Add filter for database ID!", e); } } }
From source file:fantail.algorithms.RankingByPairwiseComparison.java
License:Open Source License
@Override public void buildRanker(Instances data) throws Exception { m_Classifiers = new ArrayList<weka.classifiers.AbstractClassifier>(); m_AlgoPairs = new ArrayList<String>(); m_NumLabels = Tools.getNumberTargets(data); // build pb datasets for (int a = 0; a < m_NumLabels; a++) { for (int b = 0; b < m_NumLabels; b++) { String pairStr = a + "|" + b; if (!hasPair(m_AlgoPairs, pairStr) && a != b) { m_AlgoPairs.add(pairStr); Instances d = new Instances(data); d.setClassIndex(-1);// w w w. j a v a 2 s .c o m d.deleteAttributeAt(d.numAttributes() - 1); weka.filters.unsupervised.attribute.Add add = new weka.filters.unsupervised.attribute.Add(); add.setInputFormat(d); add.setOptions(weka.core.Utils .splitOptions("-T NOM -N class -L " + ((int) a) + "," + ((int) b) + " -C last")); d = Filter.useFilter(d, add); d.setClassIndex(d.numAttributes() - 1); for (int i = 0; i < d.numInstances(); i++) { Instance metaInst = (Instance) data.instance(i); Instance inst = d.instance(i); double[] rankVector = Tools.getTargetVector(metaInst); double rank_a = rankVector[a]; double rank_b = rankVector[b]; if (rank_a < rank_b) { inst.setClassValue(0.0); } else { inst.setClassValue(1.0); } } //weka.classifiers.functions.SMO cls = new weka.classifiers.functions.SMO(); //String ops = "weka.classifiers.functions.SMO -C 1.0 -L 0.001 -P 1.0E-12 -N 0 -V -1 -W 1 -K \"weka.classifiers.functions.supportVector.RBFKernel -C 250007 -G 0.01\""; //cls.setOptions(weka.core.Utils.splitOptions(ops)); //cls.buildClassifier(d); //weka.classifiers.functions.Logistic cls = new weka.classifiers.functions.Logistic(); //weka.classifiers.trees.J48 cls = new weka.classifiers.trees.J48(); //weka.classifiers.rules.ZeroR cls = new weka.classifiers.rules.ZeroR(); weka.classifiers.trees.DecisionStump cls = new weka.classifiers.trees.DecisionStump(); cls.buildClassifier(d); m_Classifiers.add(cls); m_BaseClassifierName = cls.getClass().getSimpleName(); m_Add = add; } } } }