weka.gui.treevisualizer.TreeVisualizer Java Examples

The following examples show how to use weka.gui.treevisualizer.TreeVisualizer. You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may check out the related API usage on the sidebar.
Example #1
Source File: Clustering.java    From java-ml-projects with Apache License 2.0 4 votes vote down vote up 
@Override
public void start(Stage stage) throws Exception {
	loadData();
	tree = new J48();
	tree.buildClassifier(data);

	noClassificationChart = buildChart("No Classification (click to add new data)", buildSingleSeries());
	clusteredChart = buildChart("Clustered", buildClusteredSeries());
	realDataChart = buildChart("Real Data (+ Decision Tree classification for new data)", buildLabeledSeries());

	noClassificationChart.setOnMouseClicked(e -> {
		Axis<Number> xAxis = noClassificationChart.getXAxis();
		Axis<Number> yAxis = noClassificationChart.getYAxis();
		Point2D mouseSceneCoords = new Point2D(e.getSceneX(), e.getSceneY());
		double x = xAxis.sceneToLocal(mouseSceneCoords).getX();
		double y = yAxis.sceneToLocal(mouseSceneCoords).getY();
		Number xValue = xAxis.getValueForDisplay(x);
		Number yValue = yAxis.getValueForDisplay(y);
		reloadSeries(xValue, yValue);
	});

	Label lblDecisionTreeTitle = new Label("Decision Tree generated for the Iris dataset:");
	Text txtTree = new Text(tree.toString());
	String graph = tree.graph();
	SwingNode sw = new SwingNode();
	SwingUtilities.invokeLater(() -> {
		TreeVisualizer treeVisualizer = new TreeVisualizer(null, graph, new PlaceNode2());
		treeVisualizer.setPreferredSize(new Dimension(600, 500));
		sw.setContent(treeVisualizer);
	});

	Button btnRestore = new Button("Restore original data");
	Button btnSwapColors = new Button("Swap clustered chart colors");
	StackPane spTree = new StackPane(sw);
	spTree.setPrefWidth(300);
	spTree.setPrefHeight(350);
	VBox vbDecisionTree = new VBox(5, lblDecisionTreeTitle, new Separator(), spTree,
			new HBox(10, btnRestore, btnSwapColors));
	btnRestore.setOnAction(e -> {
		loadData();
		reloadSeries();
	});
	btnSwapColors.setOnAction(e -> swapClusteredChartSeriesColors());
	lblDecisionTreeTitle.setTextFill(Color.DARKRED);
	lblDecisionTreeTitle.setFont(Font.font(Font.getDefault().getFamily(), FontWeight.BOLD, FontPosture.ITALIC, 16));
	txtTree.setTranslateX(100);
	txtTree.setFont(Font.font(Font.getDefault().getFamily(), FontWeight.BOLD, FontPosture.ITALIC, 14));
	txtTree.setLineSpacing(1);
	txtTree.setTextAlignment(TextAlignment.LEFT);
	vbDecisionTree.setTranslateY(20);
	vbDecisionTree.setTranslateX(20);

	GridPane gpRoot = new GridPane();
	gpRoot.add(realDataChart, 0, 0);
	gpRoot.add(clusteredChart, 1, 0);
	gpRoot.add(noClassificationChart, 0, 1);
	gpRoot.add(vbDecisionTree, 1, 1);

	stage.setScene(new Scene(gpRoot));
	stage.setTitle("Íris dataset clustering and visualization");
	stage.show();
}