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The Waikato Environment for Knowledge Analysis (WEKA), a machine
learning workbench. This is the stable version. Apart from bugfixes, this version
does not receive any other updates.
/*
* This program is free software: you can redistribute it and/or modify
* it under the terms of the GNU General Public License as published by
* the Free Software Foundation, either version 3 of the License, or
* (at your option) any later version.
*
* This program is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
* GNU General Public License for more details.
*
* You should have received a copy of the GNU General Public License
* along with this program. If not, see m_Colors = new ArrayList();
/** training data */
protected Instances m_trainingData;
/** distribution classifier to use */
protected Classifier m_classifier;
/** data generator to use */
protected DataGenerator m_dataGenerator;
/** index of the class attribute */
private int m_classIndex = -1;
// attributes for visualizing on
protected int m_xAttribute;
protected int m_yAttribute;
// min, max and ranges of these attributes
protected double m_minX;
protected double m_minY;
protected double m_maxX;
protected double m_maxY;
private double m_rangeX;
private double m_rangeY;
// pixel width and height in terms of attribute values
protected double m_pixHeight;
protected double m_pixWidth;
/** used for offscreen drawing */
protected Image m_osi = null;
// width and height of the display area
protected int m_panelWidth;
protected int m_panelHeight;
// number of samples to take from each region in the fixed dimensions
protected int m_numOfSamplesPerRegion = 2;
// number of samples per kernel = base ^ (# non-fixed dimensions)
protected int m_numOfSamplesPerGenerator;
protected double m_samplesBase = 2.0;
/** listeners to be notified when plot is complete */
private final Vector m_listeners = new Vector();
/**
* small inner class for rendering the bitmap on to
*/
private class PlotPanel extends JPanel {
/** for serialization */
private static final long serialVersionUID = 743629498352235060L;
public PlotPanel() {
this.setToolTipText("");
}
@Override
public void paintComponent(Graphics g) {
super.paintComponent(g);
if (m_osi != null) {
g.drawImage(m_osi, 0, 0, this);
}
}
@Override
public String getToolTipText(MouseEvent event) {
if (m_probabilityCache == null) {
return null;
}
if (m_probabilityCache[event.getY()][event.getX()] == null) {
return null;
}
String pVec = "(X: "
+ Utils.doubleToString(convertFromPanelX(event.getX()), 2) + " Y: "
+ Utils.doubleToString(convertFromPanelY(event.getY()), 2) + ") ";
// construct a string holding the probability vector
for (int i = 0; i < m_trainingData.classAttribute().numValues(); i++) {
pVec += Utils.doubleToString(
m_probabilityCache[event.getY()][event.getX()][i], 3)
+ " ";
}
return pVec;
}
}
/** the actual plotting area */
private final PlotPanel m_plotPanel = new PlotPanel();
/** thread for running the plotting operation in */
private Thread m_plotThread = null;
/** Stop the plotting thread */
protected boolean m_stopPlotting = false;
/** Stop any replotting threads */
protected boolean m_stopReplotting = false;
// Used by replotting threads to pause and resume the main plot thread
private final Double m_dummy = new Double(1.0);
private boolean m_pausePlotting = false;
/** what size of tile is currently being plotted */
private int m_size = 1;
/** is the main plot thread performing the initial coarse tiling */
private boolean m_initialTiling;
/** A random number generator */
private Random m_random = null;
/** cache of probabilities for fast replotting */
protected double[][][] m_probabilityCache;
/** plot the training data */
protected boolean m_plotTrainingData = true;
/**
* Creates a new BoundaryPanel instance.
*
* @param panelWidth the width in pixels of the panel
* @param panelHeight the height in pixels of the panel
*/
public BoundaryPanel(int panelWidth, int panelHeight) {
ToolTipManager.sharedInstance().setDismissDelay(Integer.MAX_VALUE);
m_panelWidth = panelWidth;
m_panelHeight = panelHeight;
setLayout(new BorderLayout());
m_plotPanel.setMinimumSize(new Dimension(m_panelWidth, m_panelHeight));
m_plotPanel.setPreferredSize(new Dimension(m_panelWidth, m_panelHeight));
m_plotPanel.setMaximumSize(new Dimension(m_panelWidth, m_panelHeight));
add(m_plotPanel, BorderLayout.CENTER);
setPreferredSize(m_plotPanel.getPreferredSize());
setMaximumSize(m_plotPanel.getMaximumSize());
setMinimumSize(m_plotPanel.getMinimumSize());
m_random = new Random(1);
for (Color element : DEFAULT_COLORS) {
m_Colors.add(new Color(element.getRed(), element.getGreen(), element
.getBlue()));
}
m_probabilityCache = new double[m_panelHeight][m_panelWidth][];
}
/**
* Set the number of points to uniformly sample from a region (fixed
* dimensions).
*
* @param num an int value
*/
public void setNumSamplesPerRegion(int num) {
m_numOfSamplesPerRegion = num;
}
/**
* Get the number of points to sample from a region (fixed dimensions).
*
* @return an int value
*/
public int getNumSamplesPerRegion() {
return m_numOfSamplesPerRegion;
}
/**
* Set the base for computing the number of samples to obtain from each
* generator. number of samples = base ^ (# non fixed dimensions)
*
* @param ksb a double value
*/
public void setGeneratorSamplesBase(double ksb) {
m_samplesBase = ksb;
}
/**
* Get the base used for computing the number of samples to obtain from each
* generator
*
* @return a double value
*/
public double getGeneratorSamplesBase() {
return m_samplesBase;
}
/**
* Set up the off screen bitmap for rendering to
*/
protected void initialize() {
int iwidth = m_plotPanel.getWidth();
int iheight = m_plotPanel.getHeight();
// System.err.println(iwidth+" "+iheight);
m_osi = m_plotPanel.createImage(iwidth, iheight);
Graphics m = m_osi.getGraphics();
m.fillRect(0, 0, iwidth, iheight);
}
/**
* Stop the plotting thread
*/
public void stopPlotting() {
m_stopPlotting = true;
try {
m_plotThread.join(100);
} catch (Exception e) {
}
;
}
/**
* Set up the bounds of our graphic based by finding the smallest reasonable
* area in the instance space to surround our data points.
*/
public void computeMinMaxAtts() {
m_minX = Double.MAX_VALUE;
m_minY = Double.MAX_VALUE;
m_maxX = Double.MIN_VALUE;
m_maxY = Double.MIN_VALUE;
boolean allPointsLessThanOne = true;
if (m_trainingData.numInstances() == 0) {
m_minX = m_minY = 0.0;
m_maxX = m_maxY = 1.0;
} else {
for (int i = 0; i < m_trainingData.numInstances(); i++) {
Instance inst = m_trainingData.instance(i);
double x = inst.value(m_xAttribute);
double y = inst.value(m_yAttribute);
if (!Utils.isMissingValue(x) && !Utils.isMissingValue(y)) {
if (x < m_minX) {
m_minX = x;
}
if (x > m_maxX) {
m_maxX = x;
}
if (y < m_minY) {
m_minY = y;
}
if (y > m_maxY) {
m_maxY = y;
}
if (x > 1.0 || y > 1.0) {
allPointsLessThanOne = false;
}
}
}
}
if (m_minX == m_maxX) {
m_minX = 0;
}
if (m_minY == m_maxY) {
m_minY = 0;
}
if (m_minX == Double.MAX_VALUE) {
m_minX = 0;
}
if (m_minY == Double.MAX_VALUE) {
m_minY = 0;
}
if (m_maxX == Double.MIN_VALUE) {
m_maxX = 1;
}
if (m_maxY == Double.MIN_VALUE) {
m_maxY = 1;
}
if (allPointsLessThanOne) {
// m_minX = m_minY = 0.0;
m_maxX = m_maxY = 1.0;
}
m_rangeX = (m_maxX - m_minX);
m_rangeY = (m_maxY - m_minY);
m_pixWidth = m_rangeX / m_panelWidth;
m_pixHeight = m_rangeY / m_panelHeight;
}
/**
* Return a random x attribute value contained within the pix'th horizontal
* pixel
*
* @param pix the horizontal pixel number
* @return a value in attribute space
*/
private double getRandomX(int pix) {
double minPix = m_minX + (pix * m_pixWidth);
return minPix + m_random.nextDouble() * m_pixWidth;
}
/**
* Return a random y attribute value contained within the pix'th vertical
* pixel
*
* @param pix the vertical pixel number
* @return a value in attribute space
*/
private double getRandomY(int pix) {
double minPix = m_minY + (pix * m_pixHeight);
return minPix + m_random.nextDouble() * m_pixHeight;
}
/**
* Start the plotting thread
*
* @exception Exception if an error occurs
*/
public void start() throws Exception {
m_numOfSamplesPerGenerator = (int) Math.pow(m_samplesBase,
m_trainingData.numAttributes() - 3);
m_stopReplotting = true;
if (m_trainingData == null) {
throw new Exception("No training data set (BoundaryPanel)");
}
if (m_classifier == null) {
throw new Exception("No classifier set (BoundaryPanel)");
}
if (m_dataGenerator == null) {
throw new Exception("No data generator set (BoundaryPanel)");
}
if (m_trainingData.attribute(m_xAttribute).isNominal()
|| m_trainingData.attribute(m_yAttribute).isNominal()) {
throw new Exception("Visualization dimensions must be numeric "
+ "(BoundaryPanel)");
}
computeMinMaxAtts();
startPlotThread();
/*
* if (m_plotThread == null) { m_plotThread = new PlotThread();
* m_plotThread.setPriority(Thread.MIN_PRIORITY); m_plotThread.start(); }
*/
}
// Thread for main plotting operation
protected class PlotThread extends Thread {
double[] m_weightingAttsValues;
boolean[] m_attsToWeightOn;
double[] m_vals;
double[] m_dist;
Instance m_predInst;
@Override
@SuppressWarnings("unchecked")
public void run() {
m_stopPlotting = false;
try {
initialize();
repaint();
// train the classifier
m_probabilityCache = new double[m_panelHeight][m_panelWidth][];
m_classifier.buildClassifier(m_trainingData);
// build DataGenerator
m_attsToWeightOn = new boolean[m_trainingData.numAttributes()];
m_attsToWeightOn[m_xAttribute] = true;
m_attsToWeightOn[m_yAttribute] = true;
m_dataGenerator.setWeightingDimensions(m_attsToWeightOn);
m_dataGenerator.buildGenerator(m_trainingData);
// generate samples
m_weightingAttsValues = new double[m_attsToWeightOn.length];
m_vals = new double[m_trainingData.numAttributes()];
m_predInst = new DenseInstance(1.0, m_vals);
m_predInst.setDataset(m_trainingData);
m_size = 1 << 4; // Current sample region size
m_initialTiling = true;
// Display the initial coarse image tiling.
abortInitial: for (int i = 0; i <= m_panelHeight; i += m_size) {
for (int j = 0; j <= m_panelWidth; j += m_size) {
if (m_stopPlotting) {
break abortInitial;
}
if (m_pausePlotting) {
synchronized (m_dummy) {
try {
m_dummy.wait();
} catch (InterruptedException ex) {
m_pausePlotting = false;
}
}
}
plotPoint(j, i, m_size, m_size, calculateRegionProbs(j, i),
(j == 0));
}
}
if (!m_stopPlotting) {
m_initialTiling = false;
}
// Sampling and gridding loop
int size2 = m_size / 2;
abortPlot: while (m_size > 1) { // Subdivide down to the pixel level
for (int i = 0; i <= m_panelHeight; i += m_size) {
for (int j = 0; j <= m_panelWidth; j += m_size) {
if (m_stopPlotting) {
break abortPlot;
}
if (m_pausePlotting) {
synchronized (m_dummy) {
try {
m_dummy.wait();
} catch (InterruptedException ex) {
m_pausePlotting = false;
}
}
}
boolean update = (j == 0 && i % 2 == 0);
// Draw the three new subpixel regions
plotPoint(j, i + size2, size2, size2,
calculateRegionProbs(j, i + size2), update);
plotPoint(j + size2, i + size2, size2, size2,
calculateRegionProbs(j + size2, i + size2), update);
plotPoint(j + size2, i, size2, size2,
calculateRegionProbs(j + size2, i), update);
}
}
// The new region edge length is half the old edge length
m_size = size2;
size2 = size2 / 2;
}
update();
/*
* // Old method without sampling. abortPlot: for (int i = 0; i <
* m_panelHeight; i++) { for (int j = 0; j < m_panelWidth; j++) { if
* (m_stopPlotting) { break abortPlot; } plotPoint(j, i,
* calculateRegionProbs(j, i), (j == 0)); } }
*/
if (m_plotTrainingData) {
plotTrainingData();
}
} catch (Exception ex) {
ex.printStackTrace();
JOptionPane.showMessageDialog(null,
"Error while plotting: \"" + ex.getMessage() + "\"");
} finally {
m_plotThread = null;
// notify any listeners that we are finished
Vector l;
ActionEvent e = new ActionEvent(this, 0, "");
synchronized (this) {
l = (Vector) m_listeners.clone();
}
for (int i = 0; i < l.size(); i++) {
ActionListener al = l.elementAt(i);
al.actionPerformed(e);
}
}
}
private double[] calculateRegionProbs(int j, int i) throws Exception {
double[] sumOfProbsForRegion = new double[m_trainingData.classAttribute()
.numValues()];
double sumOfSums = 0;
for (int u = 0; u < m_numOfSamplesPerRegion; u++) {
double[] sumOfProbsForLocation = new double[m_trainingData
.classAttribute().numValues()];
m_weightingAttsValues[m_xAttribute] = getRandomX(j);
m_weightingAttsValues[m_yAttribute] = getRandomY(m_panelHeight - i - 1);
m_dataGenerator.setWeightingValues(m_weightingAttsValues);
double[] weights = m_dataGenerator.getWeights();
double sumOfWeights = Utils.sum(weights);
sumOfSums += sumOfWeights;
int[] indices = Utils.sort(weights);
// Prune 1% of weight mass
int[] newIndices = new int[indices.length];
double sumSoFar = 0;
double criticalMass = 0.99 * sumOfWeights;
int index = weights.length - 1;
int counter = 0;
for (int z = weights.length - 1; z >= 0; z--) {
newIndices[index--] = indices[z];
sumSoFar += weights[indices[z]];
counter++;
if (sumSoFar > criticalMass) {
break;
}
}
indices = new int[counter];
System.arraycopy(newIndices, index + 1, indices, 0, counter);
for (int z = 0; z < m_numOfSamplesPerGenerator; z++) {
m_dataGenerator.setWeightingValues(m_weightingAttsValues);
double[][] values = m_dataGenerator.generateInstances(indices);
for (int q = 0; q < values.length; q++) {
if (values[q] != null) {
System.arraycopy(values[q], 0, m_vals, 0, m_vals.length);
m_vals[m_xAttribute] = m_weightingAttsValues[m_xAttribute];
m_vals[m_yAttribute] = m_weightingAttsValues[m_yAttribute];
// classify the instance
m_dist = m_classifier.distributionForInstance(m_predInst);
for (int k = 0; k < sumOfProbsForLocation.length; k++) {
sumOfProbsForLocation[k] += (m_dist[k] * weights[q]);
}
}
}
}
for (int k = 0; k < sumOfProbsForRegion.length; k++) {
sumOfProbsForRegion[k] += (sumOfProbsForLocation[k] / m_numOfSamplesPerGenerator);
}
}
// average
if (sumOfSums > 0) {
Utils.normalize(sumOfProbsForRegion, sumOfSums);
} else {
throw new Exception("Arithmetic underflow. Please increase value of kernel bandwidth parameter (k).");
}
// cache
if ((i < m_panelHeight) && (j < m_panelWidth)) {
m_probabilityCache[i][j] = new double[sumOfProbsForRegion.length];
System.arraycopy(sumOfProbsForRegion, 0, m_probabilityCache[i][j], 0,
sumOfProbsForRegion.length);
}
return sumOfProbsForRegion;
}
}
/**
* Render the training points on-screen.
*/
public void plotTrainingData() {
Graphics2D osg = (Graphics2D) m_osi.getGraphics();
Graphics g = m_plotPanel.getGraphics();
osg.setRenderingHint(RenderingHints.KEY_ANTIALIASING,
RenderingHints.VALUE_ANTIALIAS_ON);
double xval = 0;
double yval = 0;
for (int i = 0; i < m_trainingData.numInstances(); i++) {
if (!m_trainingData.instance(i).isMissing(m_xAttribute)
&& !m_trainingData.instance(i).isMissing(m_yAttribute)) {
if (m_trainingData.instance(i).isMissing(m_classIndex)) {
continue; // don't plot if class is missing. TODO could we plot it
// differently instead?
}
xval = m_trainingData.instance(i).value(m_xAttribute);
yval = m_trainingData.instance(i).value(m_yAttribute);
int panelX = convertToPanelX(xval);
int panelY = convertToPanelY(yval);
Color ColorToPlotWith = (m_Colors.get((int) m_trainingData.instance(i)
.value(m_classIndex) % m_Colors.size()));
if (ColorToPlotWith.equals(Color.white)) {
osg.setColor(Color.black);
} else {
osg.setColor(Color.white);
}
osg.fillOval(panelX - 3, panelY - 3, 7, 7);
osg.setColor(ColorToPlotWith);
osg.fillOval(panelX - 2, panelY - 2, 5, 5);
}
}
g.drawImage(m_osi, 0, 0, m_plotPanel);
}
/**
* Convert an X coordinate from the instance space to the panel space.
*/
private int convertToPanelX(double xval) {
double temp = (xval - m_minX) / m_rangeX;
temp = temp * m_panelWidth;
return (int) temp;
}
/**
* Convert a Y coordinate from the instance space to the panel space.
*/
private int convertToPanelY(double yval) {
double temp = (yval - m_minY) / m_rangeY;
temp = temp * m_panelHeight;
temp = m_panelHeight - temp;
return (int) temp;
}
/**
* Convert an X coordinate from the panel space to the instance space.
*/
private double convertFromPanelX(double pX) {
pX /= m_panelWidth;
pX *= m_rangeX;
return pX + m_minX;
}
/**
* Convert a Y coordinate from the panel space to the instance space.
*/
private double convertFromPanelY(double pY) {
pY = m_panelHeight - pY;
pY /= m_panelHeight;
pY *= m_rangeY;
return pY + m_minY;
}
/**
* Plot a point in our visualization on-screen.
*/
protected void plotPoint(int x, int y, double[] probs, boolean update) {
plotPoint(x, y, 1, 1, probs, update);
}
/**
* Plot a point in our visualization on-screen.
*/
private void plotPoint(int x, int y, int width, int height, double[] probs,
boolean update) {
// draw a progress line
Graphics osg = m_osi.getGraphics();
if (update) {
osg.setXORMode(Color.white);
osg.drawLine(0, y, m_panelWidth - 1, y);
update();
osg.drawLine(0, y, m_panelWidth - 1, y);
}
// plot the point
osg.setPaintMode();
float[] colVal = new float[3];
float[] tempCols = new float[3];
for (int k = 0; k < probs.length; k++) {
Color curr = m_Colors.get(k % m_Colors.size());
curr.getRGBColorComponents(tempCols);
for (int z = 0; z < 3; z++) {
colVal[z] += probs[k] * tempCols[z];
}
}
for (int z = 0; z < 3; z++) {
if (colVal[z] < 0) {
colVal[z] = 0;
} else if (colVal[z] > 1) {
colVal[z] = 1;
}
}
osg.setColor(new Color(colVal[0], colVal[1], colVal[2]));
osg.fillRect(x, y, width, height);
}
/**
* Update the rendered image.
*/
private void update() {
Graphics g = m_plotPanel.getGraphics();
g.drawImage(m_osi, 0, 0, m_plotPanel);
}
/**
* Set the training data to use
*
* @param trainingData the training data
* @exception Exception if an error occurs
*/
public void setTrainingData(Instances trainingData) throws Exception {
m_trainingData = trainingData;
if (m_trainingData.classIndex() < 0) {
throw new Exception("No class attribute set (BoundaryPanel)");
}
m_classIndex = m_trainingData.classIndex();
}
/**
* Adds a training instance to the visualization dataset.
*/
public void addTrainingInstance(Instance instance) {
if (m_trainingData == null) {
// TODO
System.err
.println("Trying to add to a null training set (BoundaryPanel)");
} else {
m_trainingData.add(instance);
}
}
/**
* Register a listener to be notified when plotting completes
*
* @param newListener the listener to add
*/
public void addActionListener(ActionListener newListener) {
m_listeners.add(newListener);
}
/**
* Remove a listener
*
* @param removeListener the listener to remove
*/
public void removeActionListener(ActionListener removeListener) {
m_listeners.removeElement(removeListener);
}
/**
* Set the classifier to use.
*
* @param classifier the classifier to use
*/
public void setClassifier(Classifier classifier) {
m_classifier = classifier;
}
/**
* Set the data generator to use for generating new instances
*
* @param dataGenerator the data generator to use
*/
public void setDataGenerator(DataGenerator dataGenerator) {
m_dataGenerator = dataGenerator;
}
/**
* Set the x attribute index
*
* @param xatt index of the attribute to use on the x axis
* @exception Exception if an error occurs
*/
public void setXAttribute(int xatt) throws Exception {
if (m_trainingData == null) {
throw new Exception("No training data set (BoundaryPanel)");
}
if (xatt < 0 || xatt > m_trainingData.numAttributes()) {
throw new Exception("X attribute out of range (BoundaryPanel)");
}
if (m_trainingData.attribute(xatt).isNominal()) {
throw new Exception("Visualization dimensions must be numeric "
+ "(BoundaryPanel)");
}
/*
* if (m_trainingData.numDistinctValues(xatt) < 2) { throw new
* Exception("Too few distinct values for X attribute " +"(BoundaryPanel)");
* }
*/// removed by jimmy. TESTING!
m_xAttribute = xatt;
}
/**
* Set the y attribute index
*
* @param yatt index of the attribute to use on the y axis
* @exception Exception if an error occurs
*/
public void setYAttribute(int yatt) throws Exception {
if (m_trainingData == null) {
throw new Exception("No training data set (BoundaryPanel)");
}
if (yatt < 0 || yatt > m_trainingData.numAttributes()) {
throw new Exception("X attribute out of range (BoundaryPanel)");
}
if (m_trainingData.attribute(yatt).isNominal()) {
throw new Exception("Visualization dimensions must be numeric "
+ "(BoundaryPanel)");
}
/*
* if (m_trainingData.numDistinctValues(yatt) < 2) { throw new
* Exception("Too few distinct values for Y attribute " +"(BoundaryPanel)");
* }
*/// removed by jimmy. TESTING!
m_yAttribute = yatt;
}
/**
* Set a vector of Color objects for the classes
*
* @param colors a FastVector value
*/
public void setColors(ArrayList colors) {
synchronized (m_Colors) {
m_Colors = colors;
}
// replot(); //commented by jimmy
update(); // added by jimmy
}
/**
* Set whether to superimpose the training data plot
*
* @param pg a boolean value
*/
public void setPlotTrainingData(boolean pg) {
m_plotTrainingData = pg;
}
/**
* Returns true if training data is to be superimposed
*
* @return a boolean value
*/
public boolean getPlotTrainingData() {
return m_plotTrainingData;
}
/**
* Get the current vector of Color objects used for the classes
*
* @return a FastVector value
*/
public ArrayList getColors() {
return m_Colors;
}
/**
* Quickly replot the display using cached probability estimates
*/
public void replot() {
if (m_probabilityCache[0][0] == null) {
return;
}
m_stopReplotting = true;
m_pausePlotting = true;
// wait 300 ms to give any other replot threads a chance to halt
try {
Thread.sleep(300);
} catch (Exception ex) {
}
final Thread replotThread = new Thread() {
@Override
public void run() {
m_stopReplotting = false;
int size2 = m_size / 2;
finishedReplot: for (int i = 0; i < m_panelHeight; i += m_size) {
for (int j = 0; j < m_panelWidth; j += m_size) {
if (m_probabilityCache[i][j] == null || m_stopReplotting) {
break finishedReplot;
}
boolean update = (j == 0 && i % 2 == 0);
if (i < m_panelHeight && j < m_panelWidth) {
// Draw the three new subpixel regions or single course tiling
if (m_initialTiling || m_size == 1) {
if (m_probabilityCache[i][j] == null) {
break finishedReplot;
}
plotPoint(j, i, m_size, m_size, m_probabilityCache[i][j],
update);
} else {
if (m_probabilityCache[i + size2][j] == null) {
break finishedReplot;
}
plotPoint(j, i + size2, size2, size2, m_probabilityCache[i
+ size2][j], update);
if (m_probabilityCache[i + size2][j + size2] == null) {
break finishedReplot;
}
plotPoint(j + size2, i + size2, size2, size2,
m_probabilityCache[i + size2][j + size2], update);
if (m_probabilityCache[i][j + size2] == null) {
break finishedReplot;
}
plotPoint(j + size2, i, size2, size2, m_probabilityCache[i
+ size2][j], update);
}
}
}
}
update();
if (m_plotTrainingData) {
plotTrainingData();
}
m_pausePlotting = false;
if (!m_stopPlotting) {
synchronized (m_dummy) {
m_dummy.notifyAll();
}
}
}
};
replotThread.start();
}
protected void saveImage(String fileName) {
BufferedImage bi;
Graphics2D gr2;
ImageWriter writer;
Iterator iter;
ImageOutputStream ios;
ImageWriteParam param;
try {
// render image
bi = new BufferedImage(m_panelWidth, m_panelHeight,
BufferedImage.TYPE_INT_RGB);
gr2 = bi.createGraphics();
gr2.drawImage(m_osi, 0, 0, m_panelWidth, m_panelHeight, null);
// get jpeg writer
writer = null;
iter = ImageIO.getImageWritersByFormatName("jpg");
if (iter.hasNext()) {
writer = iter.next();
} else {
throw new Exception("No JPEG writer available!");
}
// prepare output file
ios = ImageIO.createImageOutputStream(new File(fileName));
writer.setOutput(ios);
// set the quality
param = new JPEGImageWriteParam(Locale.getDefault());
param.setCompressionMode(ImageWriteParam.MODE_EXPLICIT);
param.setCompressionQuality(1.0f);
// write the image
writer.write(null, new IIOImage(bi, null, null), param);
// cleanup
ios.flush();
writer.dispose();
ios.close();
} catch (Exception e) {
e.printStackTrace();
}
}
/**
* Adds a training instance to our dataset, based on the coordinates of the
* mouse on the panel. This method sets the x and y attributes and the class
* (as defined by classAttIndex), and sets all other values as Missing.
*
* @param mouseX the x coordinate of the mouse, in pixels.
* @param mouseY the y coordinate of the mouse, in pixels.
* @param classAttIndex the index of the attribute that is currently selected
* as the class attribute.
* @param classValue the value to set the class to in our new point.
*/
public void addTrainingInstanceFromMouseLocation(int mouseX, int mouseY,
int classAttIndex, double classValue) {
// convert to coordinates in the training instance space.
double x = convertFromPanelX(mouseX);
double y = convertFromPanelY(mouseY);
// build the training instance
Instance newInstance = new DenseInstance(m_trainingData.numAttributes());
for (int i = 0; i < newInstance.numAttributes(); i++) {
if (i == classAttIndex) {
newInstance.setValue(i, classValue);
} else if (i == m_xAttribute) {
newInstance.setValue(i, x);
} else if (i == m_yAttribute) {
newInstance.setValue(i, y);
} else {
newInstance.setMissing(i);
}
}
// add it to our data set.
addTrainingInstance(newInstance);
}
/**
* Deletes all training instances from our dataset.
*/
public void removeAllInstances() {
if (m_trainingData != null) {
m_trainingData.delete();
try {
initialize();
} catch (Exception e) {
}
;
}
}
/**
* Removes a single training instance from our dataset, if there is one that
* is close enough to the specified mouse location.
*/
public void removeTrainingInstanceFromMouseLocation(int mouseX, int mouseY) {
// convert to coordinates in the training instance space.
double x = convertFromPanelX(mouseX);
double y = convertFromPanelY(mouseY);
int bestIndex = -1;
double bestDistanceBetween = Integer.MAX_VALUE;
// find the closest point.
for (int i = 0; i < m_trainingData.numInstances(); i++) {
Instance current = m_trainingData.instance(i);
double distanceBetween = (current.value(m_xAttribute) - x)
* (current.value(m_xAttribute) - x) + (current.value(m_yAttribute) - y)
* (current.value(m_yAttribute) - y); // won't bother to sqrt, just used
// square values.
if (distanceBetween < bestDistanceBetween) {
bestIndex = i;
bestDistanceBetween = distanceBetween;
}
}
if (bestIndex == -1) {
return;
}
Instance best = m_trainingData.instance(bestIndex);
double panelDistance = (convertToPanelX(best.value(m_xAttribute)) - mouseX)
* (convertToPanelX(best.value(m_xAttribute)) - mouseX)
+ (convertToPanelY(best.value(m_yAttribute)) - mouseY)
* (convertToPanelY(best.value(m_yAttribute)) - mouseY);
if (panelDistance < REMOVE_POINT_RADIUS * REMOVE_POINT_RADIUS) {// the best
// point is
// close
// enough.
// (using
// squared
// distances)
m_trainingData.delete(bestIndex);
}
}
/**
* Starts the plotting thread. Will also create it if necessary.
*/
public void startPlotThread() {
if (m_plotThread == null) { // jimmy
m_plotThread = new PlotThread();
m_plotThread.setPriority(Thread.MIN_PRIORITY);
m_plotThread.start();
}
}
/**
* Adds a mouse listener.
*/
@Override
public void addMouseListener(MouseListener l) {
m_plotPanel.addMouseListener(l);
}
/**
* Gets the minimum x-coordinate bound, in training-instance units (not mouse
* coordinates).
*/
public double getMinXBound() {
return m_minX;
}
/**
* Gets the minimum y-coordinate bound, in training-instance units (not mouse
* coordinates).
*/
public double getMinYBound() {
return m_minY;
}
/**
* Gets the maximum x-coordinate bound, in training-instance units (not mouse
* coordinates).
*/
public double getMaxXBound() {
return m_maxX;
}
/**
* Gets the maximum x-coordinate bound, in training-instance units (not mouse
* coordinates).
*/
public double getMaxYBound() {
return m_maxY;
}
/**
* Main method for testing this class
*
* @param args a String[] value
*/
public static void main(String[] args) {
try {
if (args.length < 8) {
System.err.println("Usage : BoundaryPanel "
+ " "
+ " " + " "
+ " ");
System.exit(1);
}
final javax.swing.JFrame jf = new javax.swing.JFrame(
"Weka classification boundary visualizer");
jf.getContentPane().setLayout(new BorderLayout());
System.err.println("Loading instances from : " + args[0]);
java.io.Reader r = new java.io.BufferedReader(new java.io.FileReader(
args[0]));
final Instances i = new Instances(r);
i.setClassIndex(Integer.parseInt(args[1]));
// bv.setClassifier(new Logistic());
final int xatt = Integer.parseInt(args[2]);
final int yatt = Integer.parseInt(args[3]);
int base = Integer.parseInt(args[4]);
int loc = Integer.parseInt(args[5]);
int bandWidth = Integer.parseInt(args[6]);
int panelWidth = Integer.parseInt(args[7]);
int panelHeight = Integer.parseInt(args[8]);
final String classifierName = args[9];
final BoundaryPanel bv = new BoundaryPanel(panelWidth, panelHeight);
bv.addActionListener(new ActionListener() {
@Override
public void actionPerformed(ActionEvent e) {
String classifierNameNew = classifierName.substring(
classifierName.lastIndexOf('.') + 1, classifierName.length());
bv.saveImage(classifierNameNew + "_" + i.relationName() + "_X" + xatt
+ "_Y" + yatt + ".jpg");
}
});
jf.getContentPane().add(bv, BorderLayout.CENTER);
jf.setSize(bv.getMinimumSize());
// jf.setSize(200,200);
jf.addWindowListener(new java.awt.event.WindowAdapter() {
@Override
public void windowClosing(java.awt.event.WindowEvent e) {
jf.dispose();
System.exit(0);
}
});
jf.pack();
jf.setVisible(true);
// bv.initialize();
bv.repaint();
String[] argsR = null;
if (args.length > 10) {
argsR = new String[args.length - 10];
for (int j = 10; j < args.length; j++) {
argsR[j - 10] = args[j];
}
}
Classifier c = AbstractClassifier.forName(args[9], argsR);
KDDataGenerator dataGen = new KDDataGenerator();
dataGen.setKernelBandwidth(bandWidth);
bv.setDataGenerator(dataGen);
bv.setNumSamplesPerRegion(loc);
bv.setGeneratorSamplesBase(base);
bv.setClassifier(c);
bv.setTrainingData(i);
bv.setXAttribute(xatt);
bv.setYAttribute(yatt);
try {
// try and load a color map if one exists
FileInputStream fis = new FileInputStream("colors.ser");
ObjectInputStream ois = new ObjectInputStream(fis);
@SuppressWarnings("unchecked")
ArrayList colors = (ArrayList) ois.readObject();
bv.setColors(colors);
ois.close();
} catch (Exception ex) {
System.err.println("No color map file");
}
bv.start();
} catch (Exception ex) {
ex.printStackTrace();
}
}
}