PSO-LSSVR代码实现

PSO-LSSVR是一种基于粒子群优化算法（PSO）和最小二乘支持向量回归（LSSVR）的机器学习算法，用于回归问题。下面是Python实现PSO-LSSVR的代码：

import numpy as np
from sklearn.metrics.pairwise import rbf_kernel

class PSO_LSSVR:
    def __init__(self, c1=2, c2=2, w=0.8, max_iter=100, pop_size=50, gamma=0.1, sigma=0.1):
        self.c1 = c1
        self.c2 = c2
        self.w = w
        self.max_iter = max_iter
        self.pop_size = pop_size
        self.gamma = gamma
        self.sigma = sigma

    def fit(self, X, y):
        self.X = X
        self.y = y
        self.n_samples, self.n_features = X.shape
        self.K = rbf_kernel(X, gamma=self.gamma)
        self.alpha = np.zeros(self.n_samples)
        self.b = 0
        self.pbest_alpha = np.zeros((self.pop_size, self.n_samples))
        self.pbest_b = np.zeros(self.pop_size)
        self.pbest_loss = np.ones(self.pop_size) * np.inf
        self.gbest_alpha = np.zeros(self.n_samples)
        self.gbest_b = 0
        self.gbest_loss = np.inf
        self.population_alpha = np.random.rand(self.pop_size, self.n_samples)
        self.population_b = np.random.rand(self.pop_size)
        self.velocity_alpha = np.zeros((self.pop_size, self.n_samples))
        self.velocity_b = np.zeros(self.pop_size)

        for i in range(self.max_iter):
            for j in range(self.pop_size):
                loss = self.loss(self.population_alpha[j], self.population_b[j])
                if loss < self.pbest_loss[j]:
                    self.pbest_alpha[j] = self.population_alpha[j]
                    self.pbest_b[j] = self.population_b[j]
                    self.pbest_loss[j] = loss
                if loss < self.gbest_loss:
                    self.gbest_alpha = self.population_alpha[j]
                    self.gbest_b = self.population_b[j]
                    self.gbest_loss = loss

            for j in range(self.pop_size):
                self.velocity_alpha[j] = self.w * self.velocity_alpha[j] + \
                                         self.c1 * np.random.rand() * (self.pbest_alpha[j] - self.population_alpha[j]) + \
                                         self.c2 * np.random.rand() * (self.gbest_alpha - self.population_alpha[j])
                self.velocity_b[j] = self.w * self.velocity_b[j] + \
                                      self.c1 * np.random.rand() * (self.pbest_b[j] - self.population_b[j]) + \
                                      self.c2 * np.random.rand() * (self.gbest_b - self.population_b[j])
                self.population_alpha[j] += self.velocity_alpha[j]
                self.population_b[j] += self.velocity_b[j]

        self.alpha = self.gbest_alpha
        self.b = self.gbest_b

    def predict(self, X):
        K = rbf_kernel(X, self.X, gamma=self.gamma)
        return np.dot(K, self.alpha) + self.b

    def loss(self, alpha, b):
        y_pred = np.dot(self.K, alpha) + b
        return np.mean((self.y - y_pred) ** 2) + self.sigma * np.sum(alpha ** 2)