@Override
public void paintComponent(Graphics graphics) {
super.paintComponent(graphics);
// painting only if approved
if (show) {
// init graphics
Graphics2D g = (Graphics2D) graphics;
int pixWidth = getWidth() - 2 * MARGIN;
int pixHeight = getHeight() - 2 * MARGIN;
// painting background
g.drawImage(this.image, MARGIN, MARGIN, pixWidth, pixHeight, Color.WHITE, null);
// painting transparent class overlay
g.setComposite(AlphaComposite.getInstance(AlphaComposite.SRC_OVER, alphaLevel));
g.drawImage(this.classImage, MARGIN, MARGIN, pixWidth, pixHeight, Color.WHITE, null);
g.setComposite(AlphaComposite.getInstance(AlphaComposite.SRC_OVER, 1));
// painting points
drawPoints(g);
// painting Legend
drawLegend(graphics, this.dataTable, colorColumn);
// paint Tooltip
drawToolTip((Graphics2D) graphics);
}
}
@Override
protected void recalculateBackgroundImage() {
super.recalculateBackgroundImage();
this.classImage = new BufferedImage(IMAGE_WIDTH, IMAGE_HEIGHT, BufferedImage.TYPE_INT_ARGB);
int width = IMAGE_WIDTH / dimensions[0];
int height = IMAGE_HEIGHT / dimensions[1];
for (int i = 0; i < dimensions[0]; i++) {
for (int j = 0; j < dimensions[1]; j++) {
// using 1 as scale factor, because SOMClasscolorzier needs original classvalues!
interpolateRect(
classImage,
width * i,
height * j,
width,
height,
classificationMatrix,
i,
j,
1,
colorizer);
}
}
}
@Override
protected void createMatrices() {
List<Attribute> attributes = new ArrayList<Attribute>(exampleSet.getAttributes().size());
for (Attribute attribute : exampleSet.getAttributes()) {
attributes.add((Attribute) attribute.clone());
}
MemoryExampleTable table = new MemoryExampleTable(attributes);
for (int x = 0; x < dimensions[0]; x++) {
for (int y = 0; y < dimensions[1]; y++) {
DataRow row = new DoubleArrayDataRow(net.getNodeWeights(new int[] {x, y}));
table.addDataRow(row);
}
}
ExampleSet set = table.createExampleSet();
this.classificationMatrix = new double[dimensions[0]][dimensions[1]];
try {
set = model.apply(set);
Iterator<Example> exampleIterator = set.iterator();
for (int x = 0; x < dimensions[0]; x++) {
for (int y = 0; y < dimensions[1]; y++) {
Example example = exampleIterator.next();
classificationMatrix[x][y] =
example.getValue(example.getAttributes().getPredictedLabel());
}
}
} catch (OperatorException e) {
// LogService.getGlobal().log("Cannot use Model for prediction of node label: " +
// e.getMessage(), LogService.WARNING);
LogService.getRoot()
.log(
Level.WARNING,
"com.rapidminer.operator.visualization.SOMModelPlotter.using_model_for_prediction_error"
+ e.getMessage());
}
super.createMatrices();
}
