C++标准库--正态分布类 std::normal_distribution

悠悠 2024-04-19 13:00 4阅读 0赞

参考链接：[https://en.cppreference.com/w/cpp/numeric/random/normal\_distribution][https_en.cppreference.com_w_cpp_numeric_random_normal_distribution]

std::normal\_distribution是C++11提供的一个正态分布函数模板类

头文件：include<random>

可以创建一个有特定期望值和方差的正态分布；

double mu {50.0}, sigma {10.0};
    std::normal_distribution<> norm {mu, sigma};

下面是优达学城粒子滤波部分的一个案例，根据GPS提供初始值，初始化符合正态分布的粒子初值；

/**
     * print_samples.cpp
     * 
     * Print out to the terminal 3 samples from a normal distribution with
     * mean equal to the GPS position and IMU heading measurements and
     * standard deviation of 2 m for the x and y position and 0.05 radians
     * for the heading of the car. 
     *
     * Author: Tiffany Huang
     */
    
    #include <iostream>
    #include <random> // Need this for sampling from distributions
    
    using std::normal_distribution;
    
    /**
     * Prints samples of x, y and theta from a normal distribution
     * @param gps_x   GPS provided x position
     * @param gps_y   GPS provided y position
     * @param theta   GPS provided yaw
     */
    void printSamples(double gps_x, double gps_y, double theta);
    
    
    int main() {
      
      // Set GPS provided state of the car.
      double gps_x = 4983;
      double gps_y = 5029;
      double theta = 1.201;
      
      // Sample from the GPS provided position.
      printSamples(gps_x, gps_y, theta);
      
      return 0;
    }
    
    
    void printSamples(double gps_x, double gps_y, double theta) {
      std::default_random_engine gen;
      double std_x, std_y, std_theta;  // Standard deviations for x, y, and theta
    
      // TODO: Set standard deviations for x, y, and theta
        std_x = 2;
        std_y = 2;
        std_theta = 0.5;
    
      // This line creates a normal (Gaussian) distribution for x
        normal_distribution<double> dist_x(gps_x, std_x);
      
      // TODO: Create normal distributions for y and theta
        normal_distribution<double> dist_y(gps_y, std_y);
        normal_distribution<double> dist_theta(theta, std_theta);
    
      for (int i = 0; i < 10; ++i) {
        double sample_x, sample_y, sample_theta;
        
        // TODO: Sample from these normal distributions like this: 
        //   sample_x = dist_x(gen);
        //   where "gen" is the random engine initialized earlier.
        sample_x = dist_x(gen);
        sample_y = dist_y(gen);
        sample_theta = dist_theta(gen);
        
        // Print your samples to the terminal.
        std::cout << "Sample " << i + 1 << " " << sample_x << " " << sample_y << " " 
                  << sample_theta << std::endl;
      }
    
      return;
    }

运行结果：

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2x1dGVyZXNh_size_16_color_FFFFFF_t_70][]

[https_en.cppreference.com_w_cpp_numeric_random_normal_distribution]: https://en.cppreference.com/w/cpp/numeric/random/normal_distribution
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2x1dGVyZXNh_size_16_color_FFFFFF_t_70]: https://image.dandelioncloud.cn/pgy_files/images/2024/04/18/bb354e9cbd0f40c49197cb9ed4d7f92d.png