List of usage examples for weka.gui.visualize PlotData2D PlotData2D
public PlotData2D(Instances insts)
From source file:adams.flow.sink.WekaThresholdCurve.java
License:Open Source License
/** * Creates a new panel for the token./*ww w. j a v a2 s. co m*/ * * @param token the token to display in a new panel, can be null * @return the generated panel */ public AbstractDisplayPanel createDisplayPanel(Token token) { AbstractDisplayPanel result; String name; if (token != null) name = "Threshold curve (" + getEvaluation(token).getHeader().relationName() + ")"; else name = "Threshold curve"; result = new AbstractComponentDisplayPanel(name) { private static final long serialVersionUID = -7362768698548152899L; protected ThresholdVisualizePanel m_VisualizePanel; @Override protected void initGUI() { super.initGUI(); setLayout(new BorderLayout()); m_VisualizePanel = new ThresholdVisualizePanel(); add(m_VisualizePanel, BorderLayout.CENTER); } @Override public void display(Token token) { try { Evaluation eval = getEvaluation(token); m_ClassLabelRange.setMax(eval.getHeader().classAttribute().numValues()); int[] indices = m_ClassLabelRange.getIntIndices(); for (int index : indices) { ThresholdCurve curve = new ThresholdCurve(); Instances data = curve.getCurve(eval.predictions(), index); PlotData2D plot = new PlotData2D(data); plot.setPlotName(eval.getHeader().classAttribute().value(index)); plot.m_displayAllPoints = true; boolean[] connectPoints = new boolean[data.numInstances()]; for (int cp = 1; cp < connectPoints.length; cp++) connectPoints[cp] = true; plot.setConnectPoints(connectPoints); m_VisualizePanel.addPlot(plot); if (data.attribute(m_AttributeX.toDisplay()) != null) m_VisualizePanel.setXIndex(data.attribute(m_AttributeX.toDisplay()).index()); if (data.attribute(m_AttributeY.toDisplay()) != null) m_VisualizePanel.setYIndex(data.attribute(m_AttributeY.toDisplay()).index()); } } catch (Exception e) { getLogger().log(Level.SEVERE, "Failed to display token: " + token, e); } } @Override public JComponent supplyComponent() { return m_VisualizePanel; } @Override public void clearPanel() { m_VisualizePanel.removeAllPlots(); } public void cleanUp() { m_VisualizePanel.removeAllPlots(); } }; if (token != null) result.display(token); return result; }
From source file:adams.gui.menu.CostCurve.java
License:Open Source License
/** * Launches the functionality of the menu item. *//* w ww. j a v a 2s .c o m*/ @Override public void launch() { File file; if (m_Parameters.length == 0) { // choose file int retVal = m_FileChooser.showOpenDialog(null); if (retVal != JFileChooser.APPROVE_OPTION) return; file = m_FileChooser.getSelectedFile(); } else { file = new PlaceholderFile(m_Parameters[0]).getAbsoluteFile(); m_FileChooser.setSelectedFile(file); } // create plot Instances result; try { result = m_FileChooser.getLoader().getDataSet(); } catch (Exception e) { GUIHelper.showErrorMessage(getOwner(), "Error loading file '" + file + "':\n" + adams.core.Utils.throwableToString(e)); return; } result.setClassIndex(result.numAttributes() - 1); ThresholdVisualizePanel vmc = new ThresholdVisualizePanel(); PlotData2D plot = new PlotData2D(result); plot.setPlotName(result.relationName()); plot.m_displayAllPoints = true; boolean[] connectPoints = new boolean[result.numInstances()]; for (int cp = 1; cp < connectPoints.length; cp++) connectPoints[cp] = true; try { plot.setConnectPoints(connectPoints); vmc.addPlot(plot); } catch (Exception e) { GUIHelper.showErrorMessage(getOwner(), "Error adding plot:\n" + adams.core.Utils.throwableToString(e)); return; } ChildFrame frame = createChildFrame(vmc, GUIHelper.getDefaultDialogDimension()); frame.setTitle(frame.getTitle() + " - " + file); }
From source file:adams.gui.menu.InstancesPlot.java
License:Open Source License
/** * Launches the functionality of the menu item. *//*from w ww . j a va 2s.co m*/ @Override public void launch() { File file; AbstractFileLoader loader; if (m_Parameters.length == 0) { // choose file int retVal = m_FileChooser.showOpenDialog(getOwner()); if (retVal != JFileChooser.APPROVE_OPTION) return; file = m_FileChooser.getSelectedFile(); loader = m_FileChooser.getLoader(); } else { file = new PlaceholderFile(m_Parameters[0]).getAbsoluteFile(); loader = ConverterUtils.getLoaderForFile(file); } // build plot VisualizePanel panel = new VisualizePanel(); getLogger().severe("Loading instances from " + file); try { loader.setFile(file); Instances i = loader.getDataSet(); i.setClassIndex(i.numAttributes() - 1); PlotData2D pd1 = new PlotData2D(i); pd1.setPlotName("Master plot"); panel.setMasterPlot(pd1); } catch (Exception e) { getLogger().log(Level.SEVERE, "Failed to load: " + file, e); GUIHelper.showErrorMessage(getOwner(), "Error loading file '" + file + "':\n" + Utils.throwableToString(e)); return; } // create frame ChildFrame frame = createChildFrame(panel, GUIHelper.getDefaultDialogDimension()); frame.setTitle(frame.getTitle() + " - " + file); }
From source file:adams.gui.menu.MarginCurve.java
License:Open Source License
/** * Launches the functionality of the menu item. */// w ww . jav a2 s . c o m @Override public void launch() { File file; if (m_Parameters.length == 0) { // choose file int retVal = m_FileChooser.showOpenDialog(null); if (retVal != JFileChooser.APPROVE_OPTION) return; file = m_FileChooser.getSelectedFile(); } else { file = new PlaceholderFile(m_Parameters[0]).getAbsoluteFile(); m_FileChooser.setSelectedFile(file); } // create plot Instances result; try { result = m_FileChooser.getLoader().getDataSet(); } catch (Exception e) { GUIHelper.showErrorMessage(getOwner(), "Error loading file '" + file + "':\n" + adams.core.Utils.throwableToString(e)); return; } result.setClassIndex(result.numAttributes() - 1); VisualizePanel vp = new VisualizePanel(); PlotData2D plot = new PlotData2D(result); plot.m_displayAllPoints = true; boolean[] connectPoints = new boolean[result.numInstances()]; for (int cp = 1; cp < connectPoints.length; cp++) connectPoints[cp] = true; try { plot.setConnectPoints(connectPoints); vp.addPlot(plot); } catch (Exception e) { GUIHelper.showErrorMessage(getOwner(), "Error adding plot:\n" + adams.core.Utils.throwableToString(e)); return; } ChildFrame frame = createChildFrame(vp, GUIHelper.getDefaultDialogDimension()); frame.setTitle(frame.getTitle() + " - " + file); }
From source file:adams.gui.menu.ROC.java
License:Open Source License
/** * Launches the functionality of the menu item. *//*www. j av a 2 s . c o m*/ @Override public void launch() { File file; if (m_Parameters.length == 0) { // choose file int retVal = m_FileChooser.showOpenDialog(null); if (retVal != JFileChooser.APPROVE_OPTION) return; file = m_FileChooser.getSelectedFile(); } else { file = new PlaceholderFile(m_Parameters[0]).getAbsoluteFile(); m_FileChooser.setSelectedFile(file); } // create plot Instances result; try { result = m_FileChooser.getLoader().getDataSet(); } catch (Exception e) { GUIHelper.showErrorMessage(getOwner(), "Error loading file '" + file + "':\n" + adams.core.Utils.throwableToString(e)); return; } result.setClassIndex(result.numAttributes() - 1); ThresholdVisualizePanel vmc = new ThresholdVisualizePanel(); vmc.setROCString("(Area under ROC = " + Utils.doubleToString(ThresholdCurve.getROCArea(result), 4) + ")"); vmc.setName(result.relationName()); PlotData2D tempd = new PlotData2D(result); tempd.setPlotName(result.relationName()); tempd.addInstanceNumberAttribute(); // specify which points are connected boolean[] cp = new boolean[result.numInstances()]; for (int n = 1; n < cp.length; n++) cp[n] = true; try { tempd.setConnectPoints(cp); vmc.addPlot(tempd); } catch (Exception e) { GUIHelper.showErrorMessage(getOwner(), "Error adding plot:\n" + adams.core.Utils.throwableToString(e)); return; } ChildFrame frame = createChildFrame(vmc, GUIHelper.getDefaultDialogDimension()); frame.setTitle(frame.getTitle() + " - " + file); }
From source file:com.sliit.views.DataVisualizerPanel.java
void getRocCurve() { try {//from ww w . j a va2 s. c o m Instances data; data = new Instances(new BufferedReader(new FileReader(datasetPathText.getText()))); data.setClassIndex(data.numAttributes() - 1); // train classifier Classifier cl = new NaiveBayes(); Evaluation eval = new Evaluation(data); eval.crossValidateModel(cl, data, 10, new Random(1)); // generate curve ThresholdCurve tc = new ThresholdCurve(); int classIndex = 0; Instances result = tc.getCurve(eval.predictions(), classIndex); // plot curve ThresholdVisualizePanel vmc = new ThresholdVisualizePanel(); vmc.setROCString("(Area under ROC = " + Utils.doubleToString(tc.getROCArea(result), 4) + ")"); vmc.setName(result.relationName()); PlotData2D tempd = new PlotData2D(result); tempd.setPlotName(result.relationName()); tempd.addInstanceNumberAttribute(); // specify which points are connected boolean[] cp = new boolean[result.numInstances()]; for (int n = 1; n < cp.length; n++) { cp[n] = true; } tempd.setConnectPoints(cp); // add plot vmc.addPlot(tempd); // display curve String plotName = vmc.getName(); final javax.swing.JFrame jf = new javax.swing.JFrame("Weka Classifier Visualize: " + plotName); jf.setSize(500, 400); jf.getContentPane().setLayout(new BorderLayout()); jf.getContentPane().add(vmc, BorderLayout.CENTER); jf.addWindowListener(new java.awt.event.WindowAdapter() { public void windowClosing(java.awt.event.WindowEvent e) { jf.dispose(); } }); jf.setVisible(true); } catch (IOException ex) { Logger.getLogger(DataVisualizerPanel.class.getName()).log(Level.SEVERE, null, ex); } catch (Exception ex) { Logger.getLogger(DataVisualizerPanel.class.getName()).log(Level.SEVERE, null, ex); } }
From source file:com.sliit.views.KNNView.java
void getRocCurve() { try {/* ww w . ja v a2 s .c om*/ Instances data; data = new Instances(new BufferedReader(new java.io.FileReader(PredictorPanel.modalText.getText()))); data.setClassIndex(data.numAttributes() - 1); // train classifier Classifier cl = new NaiveBayes(); Evaluation eval = new Evaluation(data); eval.crossValidateModel(cl, data, 10, new Random(1)); // generate curve ThresholdCurve tc = new ThresholdCurve(); int classIndex = 0; Instances result = tc.getCurve(eval.predictions(), classIndex); // plot curve ThresholdVisualizePanel vmc = new ThresholdVisualizePanel(); vmc.setROCString("(Area under ROC = " + Utils.doubleToString(tc.getROCArea(result), 4) + ")"); vmc.setName(result.relationName()); PlotData2D tempd = new PlotData2D(result); tempd.setPlotName(result.relationName()); tempd.addInstanceNumberAttribute(); // specify which points are connected boolean[] cp = new boolean[result.numInstances()]; for (int n = 1; n < cp.length; n++) { cp[n] = true; } tempd.setConnectPoints(cp); // add plot vmc.addPlot(tempd); rocPanel.removeAll(); rocPanel.add(vmc, "vmc", 0); rocPanel.revalidate(); } catch (IOException ex) { Logger.getLogger(DataVisualizerPanel.class.getName()).log(Level.SEVERE, null, ex); } catch (Exception ex) { Logger.getLogger(DataVisualizerPanel.class.getName()).log(Level.SEVERE, null, ex); } }
From source file:com.sliit.views.SVMView.java
/** * draw ROC curve//w w w. j a v a 2 s .c o m */ void getRocCurve() { try { Instances data; data = new Instances(new BufferedReader(new FileReader(PredictorPanel.modalText.getText()))); data.setClassIndex(data.numAttributes() - 1); //train classifier Classifier cl = new NaiveBayes(); Evaluation eval = new Evaluation(data); eval.crossValidateModel(cl, data, 10, new Random(1)); // generate curve ThresholdCurve tc = new ThresholdCurve(); int classIndex = 0; Instances result = tc.getCurve(eval.predictions(), classIndex); // plot curve ThresholdVisualizePanel vmc = new ThresholdVisualizePanel(); vmc.setROCString("(Area under ROC = " + Utils.doubleToString(tc.getROCArea(result), 4) + ")"); vmc.setName(result.relationName()); PlotData2D tempd = new PlotData2D(result); tempd.setPlotName(result.relationName()); tempd.addInstanceNumberAttribute(); // specify which points are connected boolean[] cp = new boolean[result.numInstances()]; for (int n = 1; n < cp.length; n++) { cp[n] = true; } tempd.setConnectPoints(cp); // add plot vmc.addPlot(tempd); // rocPanel.removeAll(); // rocPanel.add(vmc, "vmc", 0); // rocPanel.revalidate(); } catch (IOException ex) { Logger.getLogger(DataVisualizerPanel.class.getName()).log(Level.SEVERE, null, ex); } catch (Exception ex) { Logger.getLogger(DataVisualizerPanel.class.getName()).log(Level.SEVERE, null, ex); } }
From source file:cs.man.ac.uk.classifiers.GetAUC.java
License:Open Source License
/** * Computes the AUC for the supplied learner. * @param learner the learning algorithm to use. * @return the AUC as a double value./*from w ww.ja va 2s . c o m*/ */ @SuppressWarnings("unused") private static double validate(Classifier learner) { try { Evaluation eval = new Evaluation(data); eval.crossValidateModel(learner, data, 2, new Random(1)); // generate curve ThresholdCurve tc = new ThresholdCurve(); int classIndex = 0; Instances result = tc.getCurve(eval.predictions(), classIndex); // plot curve vmc = new ThresholdVisualizePanel(); double AUC = ThresholdCurve.getROCArea(result); vmc.setROCString( "(Area under ROC = " + Utils.doubleToString(ThresholdCurve.getROCArea(result), 9) + ")"); vmc.setName(result.relationName()); PlotData2D tempd = new PlotData2D(result); tempd.setPlotName(result.relationName()); tempd.addInstanceNumberAttribute(); // specify which points are connected boolean[] cp = new boolean[result.numInstances()]; for (int n = 1; n < cp.length; n++) cp[n] = true; tempd.setConnectPoints(cp); // add plot vmc.addPlot(tempd); return AUC; } catch (Exception e) { System.out.println("Exception validating data!"); return 0; } }
From source file:cyber009.main.MainSyntacticData.java
public static void main(String[] args) { Random r = new Random(System.currentTimeMillis()); Variable v = new Variable(); long timeStart = 0, timeEnd = 0; ANN ann = new ANN(v, 0.014013); for (int f = 2; f <= 2; f++) { v.N = f;//from w ww . j av a2 s . co m v.D = 4000; v.threshold = 0.0; cyber009.function.LinearFunction func = new cyber009.function.LinearFunction(v.N); v.X = new double[v.D][]; v.TARGET = new double[v.D]; v.WEIGHT = new double[v.N + 1]; for (int d = 0; d < v.D; d++) { v.X[d] = new double[v.N + 1]; v.X[d][0] = 1.0; for (int n = 1; n <= v.N; n++) { v.X[d][n] = r.nextGaussian(); } v.TARGET[d] = func.syntacticFunction(v.X[d], v.threshold); } //v.showAll(); //Lib.Utility.writeCSVDataSet("data/syn_data_x_"+v.N+"_d_"+v.D+".csv", v); List<Attribute> atts = new ArrayList<>(); Attribute[] att = new Attribute[v.N + 2]; for (int i = 0; i <= v.N; i++) { att[i] = new Attribute("X" + i); atts.add(att[i]); } List<String> classValus = new ArrayList<>(); classValus.add("1.0"); classValus.add("0.0"); att[v.N + 1] = new Attribute("class", classValus); atts.add(att[v.N + 1]); Instances dataSet = new Instances("Syn Data", (ArrayList<Attribute>) atts, v.D); for (int d = 0; d < v.D; d++) { Instance ins = new DenseInstance(v.N + 2); for (int i = 0; i <= v.N; i++) { ins.setValue(atts.get(i), v.X[d][i]); } ins.setValue(atts.get(v.N + 1), v.TARGET[d]); dataSet.add(ins); } //System.out.println(dataSet); PlotData2D p2D = new PlotData2D(dataSet); p2D.setPlotName("Syn data"); VisualizePanel vp = new VisualizePanel(); vp.setName("Show Data"); try { vp.addPlot(p2D); JFrame frame = new JFrame("Show Data"); frame.setSize(600, 600); frame.setVisible(true); frame.getContentPane().setLayout(new BorderLayout()); frame.getContentPane().add(vp, BorderLayout.CENTER); frame.setDefaultCloseOperation(JFrame.EXIT_ON_CLOSE); frame.setVisible(true); func.showCoefficients(); } catch (Exception ex) { Logger.getLogger(MainSyntacticData.class.getName()).log(Level.SEVERE, null, ex); } ann.weightReset(); timeStart = System.currentTimeMillis(); ann.gradientDescent(10000L, 2, v.D); timeEnd = System.currentTimeMillis(); //v.showTable(); //v.showWEIGHT(); System.out.println("feature #:" + v.N + " time:(" + (timeEnd - timeStart) + ")"); v.showResult(); //func.showCoefficients(); } }