怀柔做网站,公众号登陆,网站开发前端需要学什么,网络推广网站推广淘宝运营商RANSAC是“RANdom SAmple Consensus”的缩写#xff0c;是一种迭代方法#xff0c;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#xff0c;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#xff0c;则认为…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;}}
}
