帮我写WOA优化SVR的python代码

当然可以帮你编写WOA优化SVR的Python代码。下面是一个简单示例：

import numpy as np
from sklearn.svm import SVR
from sklearn.datasets import make_regression
from sklearn.model_selection import train_test_split
from math import sin, pi

# Whale Optimization Algorithm (WOA) implementation
def woa_optimization(X, y, population_size, max_iterations):
    num_features = X.shape[1]
    num_samples = X.shape[0]
    
    # Initialize whale population
    population = np.random.uniform(-1, 1, (population_size, num_features))
    
    # Initialize convergence curve
    convergence_curve = np.zeros(max_iterations)
    
    # Initialize best solution
    best_solution = None
    best_fitness = float('inf')
    
    for iteration in range(max_iterations):
        a = 2 - iteration * ((2) / max_iterations)  # a decreases linearly from 2 to 0
        
        for i in range(population_size):
            r1 = np.random.random()  # r1 is a random number in [0,1]
            r2 = np.random.random()  # r2 is a random number in [0,1]
            
            A = 2 * a * r1 - a  # Equation (2.3)
            C = 2 * r2  # Equation (2.4)
            
            # Update position of the current whale
            D = np.abs(C * population[i] - population)
            b = 1
            
            x_rand = population[np.random.randint(population_size)]
            
            # Equation (2.5)
            D_2 = np.abs(b * x_rand - population)
            
            new_position = np.zeros(num_features)
            for j in range(num_features):
                # Equation (2.7)
                if D_2[j] >= D[j]:
                    new_position[j] = x_rand[j] - A * D_2[j]
                else:
                    new_position[j] = population[i][j] - A * D[j]
            
            # Boundary handling
            new_position = np.clip(new_position, -1, 1)
            
            # Update fitness of the current whale
            new_fitness = svr_fitness(new_position, X, y)
            
            # Update best solution
            if new_fitness < best_fitness:
                best_solution = new_position
                best_fitness = new_fitness
            
            # Update convergence curve
            convergence_curve[iteration] = best_fitness
        
        population = new_position
    
    return best_solution, best_fitness, convergence_curve

# Fitness function
def svr_fitness(position, X, y):
    svr = SVR()
    svr.fit(X, y)
    y_pred = svr.predict(X)
    mse = np.mean((y - y_pred) ** 2)
    return mse

# Generate a regression dataset
X, y = make_regression(n_samples=100, n_features=1, noise=0.1)

# Split the dataset into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)

# Perform WOA optimization on SVR
best_solution, best_fitness, convergence_curve = woa_optimization(X_train, y_train, population_size=10, max_iterations=100)

# Fit SVR with the best solution found
svr_best = SVR()
svr_best.fit(X_train, y_train)
y_pred_train = svr_best.predict(X_train)
y_pred_test = svr_best.predict(X_test)

# Print results
print("Best Solution:", best_solution)
print("Best Fitness:", best_fitness)

# Plot convergence curve
import matplotlib.pyplot as plt
plt.plot(convergence_curve)
plt.xlabel("Iteration")
plt.ylabel("Fitness")
plt.title("WOA Optimization Convergence")
plt.show()

在上述代码中，我们先定义了WOA优化算法的实现，然后使用SVR作为目标函数进行优化。代码中还包含了生成回归数据集、拆分数据集、以及绘制收敛曲线的部分。

这段代码可以帮助你完成WOA优化SVR的Python实现，你可以根据自己的需求进行修改和扩展。希望能对你有所帮助！