遗传算法pso优化支持向量机分类代码
时间: 2023-09-08 16:09:24 浏览: 49
以下是基于遗传算法和粒子群优化算法(PSO)优化支持向量机(SVM)分类器的代码示例:
```
import numpy as np
from sklearn import svm
from sklearn.datasets import make_classification
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
# 生成模拟数据
X, y = make_classification(n_samples=1000, n_features=10, n_classes=2, random_state=42)
# 划分数据集
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)
# 定义适应度函数,即SVM的精度
def svm_fitness(w):
clf = svm.SVC(C=w[0], kernel='rbf', gamma=w[1])
clf.fit(X_train, y_train)
y_pred = clf.predict(X_test)
return accuracy_score(y_test, y_pred)
# 定义遗传算法
def genetic_algorithm(pop_size=50, num_generations=100, mutation_prob=0.1):
# 初始化种群
population = np.random.uniform(low=[0.1, 0.1], high=[100, 10], size=(pop_size, 2))
best_fitness = np.zeros(num_generations)
best_individual = np.zeros(2)
for i in range(num_generations):
# 计算适应度
fitness = np.zeros(pop_size)
for j in range(pop_size):
fitness[j] = svm_fitness(population[j])
# 选择
idx = np.argsort(fitness)[::-1]
best_fitness[i] = fitness[idx[0]]
best_individual = population[idx[0]]
# 交叉和变异
for j in range(pop_size):
# 交叉
parent1 = population[np.random.randint(pop_size)]
parent2 = population[np.random.randint(pop_size)]
child = np.zeros(2)
child[0] = parent1[0] if np.random.rand() < 0.5 else parent2[0]
child[1] = parent1[1] if np.random.rand() < 0.5 else parent2[1]
# 变异
if np.random.rand() < mutation_prob:
child += np.random.normal(scale=0.1, size=2)
population[j] = child
return best_individual, best_fitness
# 定义粒子群优化算法
def pso(pop_size=50, num_generations=100, w=0.5, c1=1.5, c2=1.5, v_max=1):
# 初始化种群
population = np.random.uniform(low=[0.1, 0.1], high=[100, 10], size=(pop_size, 2))
velocity = np.zeros((pop_size, 2))
best_fitness = np.zeros(num_generations)
best_individual = np.zeros(2)
best_individual_fitness = 0
for i in range(num_generations):
# 计算适应度
fitness = np.zeros(pop_size)
for j in range(pop_size):
fitness[j] = svm_fitness(population[j])
if fitness[j] > best_individual_fitness:
best_individual_fitness = fitness[j]
best_individual = population[j]
best_fitness[i] = best_individual_fitness
# 更新速度和位置
for j in range(pop_size):
r1 = np.random.rand()
r2 = np.random.rand()
velocity[j] = w * velocity[j] + c1 * r1 * (best_individual - population[j]) + c2 * r2 * (best_individual - population[j])
# 限制速度
velocity[j][velocity[j] > v_max] = v_max
velocity[j][velocity[j] < -v_max] = -v_max
population[j] += velocity[j]
# 限制位置
population[j][population[j] < 0.1] = 0.1
population[j][population[j] > 100] = 100
return best_individual, best_fitness
# 运行遗传算法
best_individual_ga, best_fitness_ga = genetic_algorithm()
# 运行粒子群优化算法
best_individual_pso, best_fitness_pso = pso()
print("遗传算法得到的最佳个体:", best_individual_ga)
print("遗传算法得到的最佳适应度:", np.max(best_fitness_ga))
print("粒子群优化算法得到的最佳个体:", best_individual_pso)
print("粒子群优化算法得到的最佳适应度:", np.max(best_fitness_pso))
```
在上面的代码中,我们使用了Scikit-learn库生成了一组模拟数据并将其分为训练集和测试集。然后,我们定义了适应度函数,即SVM的精度,以及两种优化算法:遗传算法和粒子群优化算法。最后,我们运行两种算法并输出它们得到的最佳个体和最佳适应度。
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