RANSAC是“RANdom SAmple Consensus”的缩写，是一种迭代方法，用于数据中估计统计参数或几何模型的算法。它通过给定数据集中随机选择样本并使用样本计算模型，然后测试模型的可能性来工作。如果一个模型通过了足够数量的测试，则认为该模型是可接受的。

在Java中，我们可以使用RANSAC库来实现RANSAC算法。以下是一个简单的例子，使用RANSAC来拟合直线。

import org.apache.commons.math3.fitting.leastsquares.LeastSquaresBuilder;
import org.apache.commons.math3.fitting.leastsquares.LeastSquaresProblem;
import org.apache.commons.math3.fitting.leastsquares.LevenbergMarquardtOptimizer;
import org.apache.commons.math3.linear.DiagonalMatrix;
import org.apache.commons.math3.fitting.leastsquares.LeastSquaresOptimizer;
import org.apache.commons.math3.fitting.leastsquares.LeastSquaresBuilder.Weight;public class RansacExample {public static void main(String[] args) {final double[][] points = ...; // Your data points// Create a builderfinal LeastSquaresBuilder builder = new LeastSquaresBuilder();// Set up a problem with weightsfinal Weight weight = Weight.SIMPLE; // or DIAGONAL or WITHOUT_NORMALIZATIONfinal LeastSquaresProblem problem = builder.weight(weight).target(new double[] {1, 2, 3, 4, 5, 6, 7, 8, 9, 10}) // Your target values.model(new LinearModel(), initialGuess) // Your model and initial guess.build();// Perform the computationfinal LeastSquaresOptimizer optimizer = new LevenbergMarquardtOptimizer();final LeastSquaresOptimizer.Optimum optimum = optimizer.optimize(problem);// Print the resultfinal double[] solution = optimum.getPoint();System.out.println(solution[0]); // SlopeSystem.out.println(solution[1]); // Intercept}// A simple linear model y = ax + bpublic static class LinearModel extends Model {public LinearModel() {super(2); // 2 parameters: slope and intercept}@Overridepublic double[] value(double[] point) {final double x = point[0];final double[] result = new double[1]; // Number of outputsresult[0] = point[1] + (point[0] * x);return result;}}
}