ga-svm多分类python代码
时间: 2023-07-22 15:15:36 浏览: 47
以下是一个使用遗传算法优化SVM多分类的Python示例代码:
```python
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
from sklearn.svm import SVC
import numpy as np
import random
# 准备数据集
X, y = load_data() # 加载数据集
# 划分训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)
# 定义SVM模型
clf = SVC()
# 定义GA算法的适应度函数
def fitness(population):
accuracies = []
for chromosome in population:
clf.set_params(C=chromosome[0], gamma=chromosome[1])
clf.fit(X_train, y_train)
y_pred = clf.predict(X_test)
accuracy = accuracy_score(y_test, y_pred)
accuracies.append(accuracy)
return accuracies
# 定义GA算法的染色体编码
def chromosome_encoding(population_size):
population = []
for i in range(population_size):
chromosome = [random.uniform(0.1, 10), random.uniform(0.001, 1)]
population.append(chromosome)
return population
# 定义GA算法的交叉操作
def crossover(parent1, parent2):
child1 = [parent1[0], parent2[1]]
child2 = [parent2[0], parent1[1]]
return child1, child2
# 定义GA算法的变异操作
def mutation(chromosome):
mutation_prob = 0.1
if random.random() < mutation_prob:
chromosome[random.randint(0, 1)] = random.uniform(0.1, 10)
return chromosome
# 实现GA算法
population_size = 50
num_generations = 100
population = chromosome_encoding(population_size)
for i in range(num_generations):
fitness_values = fitness(population)
sorted_indices = np.argsort(fitness_values)[::-1]
population = [population[i] for i in sorted_indices]
next_generation = [population[0]]
for j in range(1, population_size):
parent1 = population[random.randint(0, population_size // 2)]
parent2 = population[random.randint(0, population_size // 2)]
child1, child2 = crossover(parent1, parent2)
child1 = mutation(child1)
child2 = mutation(child2)
next_generation.extend([child1, child2])
population = next_generation[:population_size]
# 测试模型
best_chromosome = population[0]
clf.set_params(C=best_chromosome[0], gamma=best_chromosome[1])
clf.fit(X_train, y_train)
y_pred = clf.predict(X_test)
accuracy = accuracy_score(y_test, y_pred)
print("Accuracy: {:.2f}%".format(accuracy * 100))
```
在上述代码中,我们首先加载数据集并划分训练集和测试集。然后定义SVM模型、适应度函数、染色体编码、交叉操作和变异操作。接着使用遗传算法进行训练,并测试模型的准确率。最后输出模型的准确率。需要注意的是,在实际应用中,可以进行交叉验证等技术来提高模型的泛化能力。