采遗传算法写一个光谱特征选择python函数,带注释
时间: 2023-03-31 10:03:05 浏览: 76
好的,我可以回答这个问题。以下是一个采用遗传算法实现光谱特征选择的 Python 函数,带有注释:
```python
import numpy as np
from sklearn import datasets
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
def spectral_feature_selection(X, y, n_features, n_pop=50, n_gen=100):
"""
采用遗传算法实现光谱特征选择
参数:
X: 特征矩阵,形状为 (n_samples, n_features)
y: 标签,形状为 (n_samples,)
n_features: 需要选择的特征数
n_pop: 种群大小,默认为 50
n_gen: 迭代次数,默认为 100
返回:
selected_features: 选择的特征的下标
"""
# 定义适应度函数,使用随机森林分类器的准确率作为适应度
def fitness(individual):
selected_features = np.where(individual == 1)[]
if len(selected_features) == :
return
X_selected = X[:, selected_features]
X_train, X_test, y_train, y_test = train_test_split(X_selected, y, test_size=.3, random_state=42)
clf = RandomForestClassifier(n_estimators=100, random_state=42)
clf.fit(X_train, y_train)
return clf.score(X_test, y_test)
# 初始化种群
population = np.random.randint(, 2, size=(n_pop, X.shape[1]))
# 迭代
for i in range(n_gen):
# 计算适应度
fitness_values = np.apply_along_axis(fitness, 1, population)
# 选择
selected_indices = np.argsort(fitness_values)[::-1][:n_pop // 2]
selected_population = population[selected_indices]
# 交叉
offspring_population = np.zeros_like(selected_population)
for j in range(n_pop // 2):
parent1 = selected_population[j]
parent2 = selected_population[np.random.randint(, n_pop // 2)]
crossover_point = np.random.randint(1, X.shape[1] - 1)
offspring1 = np.concatenate([parent1[:crossover_point], parent2[crossover_point:]])
offspring2 = np.concatenate([parent2[:crossover_point], parent1[crossover_point:]])
offspring_population[2*j] = offspring1
offspring_population[2*j+1] = offspring2
# 变异
mutation_indices = np.random.choice(n_pop, size=n_pop // 10, replace=False)
mutation_population = population[mutation_indices]
mutation_population[np.arange(n_pop // 10), np.random.randint(, X.shape[1], size=n_pop // 10)] ^= 1
# 合并
population = np.concatenate([selected_population, offspring_population, mutation_population])
# 计算最终适应度
fitness_values = np.apply_along_axis(fitness, 1, population)
# 选择最优个体
best_individual = population[np.argmax(fitness_values)]
# 选择特征
selected_features = np.where(best_individual == 1)[][:n_features]
return selected_features
```
希望这个函数能够帮到你!