Java/Complex_Distribution_Visualization.java at main · sksalahuddin2828/Java · GitHub

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import javafx.application.Application;

import javafx.scene.Scene;

import javafx.scene.chart.LineChart;

import javafx.scene.chart.NumberAxis;

import javafx.scene.chart.XYChart;

import javafx.stage.Stage;

public class ComplexDistributionVisualization extends Application {

@Override

public void start(Stage primaryStage) {

// Define the data range for the X-axis

double startX = -5.0;

double endX = 30.0;

int numPoints = 500;

// Generate data points for visualization

double[] xValues = new double[numPoints];

double[] yValues = new double[numPoints];

double step = (endX - startX) / (numPoints - 1);

for (int i = 0; i < numPoints; i++) {

xValues[i] = startX + i * step;

yValues[i] = complexDistribution(xValues[i]);

}

// Create the line chart

NumberAxis xAxis = new NumberAxis(startX, endX, step);

NumberAxis yAxis = new NumberAxis();

LineChart<Number, Number> lineChart = new LineChart<>(xAxis, yAxis);

lineChart.setTitle("Complex Distribution");

lineChart.setCreateSymbols(false); // Remove data points symbols

// Add the complex distribution data to the chart

XYChart.Series<Number, Number> series = new XYChart.Series<>();

for (int i = 0; i < numPoints; i++) {

series.getData().add(new XYChart.Data<>(xValues[i], yValues[i]));

}

lineChart.getData().add(series);

// Create and show the scene

Scene scene = new Scene(lineChart, 800, 600);

primaryStage.setTitle("Complex Distribution Visualization");

primaryStage.setScene(scene);

primaryStage.show();

}

private double complexDistribution(double x) {

// Define a mixture of three Gaussian distributions

double mu1 = 5, sigma1 = 1;

double mu2 = 12, sigma2 = 2;

double mu3 = 20, sigma3 = 3;

// Calculate the PDF of each Gaussian distribution

double pdf1 = Math.exp(-0.5 * Math.pow((x - mu1) / sigma1, 2)) / (sigma1 * Math.sqrt(2 * Math.PI));

double pdf2 = Math.exp(-0.5 * Math.pow((x - mu2) / sigma2, 2)) / (sigma2 * Math.sqrt(2 * Math.PI));

double pdf3 = Math.exp(-0.5 * Math.pow((x - mu3) / sigma3, 2)) / (sigma3 * Math.sqrt(2 * Math.PI));

// Combine the three PDFs to create a complex distribution

// You can adjust the weights to change the contribution of each Gaussian

return 0.4 * pdf1 + 0.3 * pdf2 + 0.3 * pdf3;

}

public static void main(String[] args) {

launch(args);

}

Java/Complex_Distribution_Visualization.java at main · sksalahuddin2828/Java · GitHub

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