如何编写改进粒子群算法支持向量机国产飞机在污染跑道着陆距离的预测的代码

下面是一种可能的改进粒子群算法支持向量机（PSO-SVM）来预测国产飞机在污染跑道上的着陆距离的代码：

首先，我们需要导入所需的库和数据集：

import numpy as np
from sklearn import svm
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_squared_error
import random

# 导入数据集
data = np.loadtxt("landing_distance.csv", delimiter=",")
x = data[:, :-1]
y = data[:, -1]

接下来，我们定义粒子和群体的类：

class Particle:
    def __init__(self):
        self.position = None
        self.velocity = None
        self.best_position = None
        self.best_fitness = float("inf")

class Swarm:
    def __init__(self, num_particles):
        self.num_particles = num_particles
        self.particles = []
        self.best_position = None
        self.best_fitness = float("inf")

然后，我们定义一个函数来初始化粒子和群体：

def initialize_swarm(num_particles):
    swarm = Swarm(num_particles)
    for i in range(num_particles):
        particle = Particle()
        particle.position = np.random.rand(x.shape[1])
        particle.velocity = np.zeros(x.shape[1])
        particle.best_position = particle.position
        swarm.particles.append(particle)
        if particle.best_fitness < swarm.best_fitness:
            swarm.best_position = particle.best_position
            swarm.best_fitness = particle.best_fitness
    return swarm

接下来，我们定义一个函数来更新粒子的位置和速度：

def update_particle(particle, swarm):
    w = 0.5
    c1 = 1
    c2 = 2
    r1 = random.random()
    r2 = random.random()
    particle.velocity = w * particle.velocity + c1 * r1 * (particle.best_position - particle.position) + c2 * r2 * (swarm.best_position - particle.position)
    particle.position = particle.position + particle.velocity

然后，我们定义一个函数来训练支持向量机并计算粒子的适应度：

def calculate_fitness(particle):
    clf = svm.SVR(kernel="rbf", gamma=0.1, C=100)
    x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.3)
    clf.fit(x_train, y_train)
    y_pred = clf.predict(x_test)
    mse = mean_squared_error(y_test, y_pred)
    fitness = mse
    if fitness < particle.best_fitness:
        particle.best_fitness = fitness
        particle.best_position = particle.position
    return fitness

接下来，我们定义一个主函数来执行粒子群算法：

def pso_svm(num_particles, num_iterations):
    swarm = initialize_swarm(num_particles)
    for i in range(num_iterations):
        for particle in swarm.particles:
            update_particle(particle, swarm)
            fitness = calculate_fitness(particle)
            if fitness < swarm.best_fitness:
                swarm.best_fitness = fitness
                swarm.best_position = particle.position
    return swarm.best_position

最后，我们可以调用主函数来训练模型并进行预测：

best_position = pso_svm(num_particles=50, num_iterations=100)
clf = svm.SVR(kernel="rbf", gamma=0.1, C=100)
clf.fit(x, y)
y_pred = clf.predict([best_position])
print("Predicted landing distance: ", y_pred)

这样就可以使用改进的粒子群算法支持向量机来预测国产飞机在污染跑道上的着陆距离了。