sklearn实现ga-bp神经网络

可以使用sklearn中的MLPRegressor类来实现GA-BP神经网络。GA-BP神经网络是一种结合了遗传算法和反向传播算法的神经网络，可以用于回归问题。

具体实现步骤如下：
1. 定义GA-BP神经网络模型，包括输入层、隐藏层和输出层的神经元个数、激活函数、遗传算法参数等。
2. 使用遗传算法初始化神经网络的权重和偏置。
3. 使用反向传播算法训练神经网络，得到最优的权重和偏置。
4. 使用训练好的神经网络进行预测。

以下是一个简单的示例代码：

from sklearn.neural_network import MLPRegressor
from sklearn.metrics import mean_squared_error
import numpy as np
import random

# 定义GA-BP神经网络模型
class GABP:
    def __init__(self, input_size, hidden_size, output_size, activation='relu', max_iter=100, pop_size=50, mutation_rate=0.1):
        self.input_size = input_size
        self.hidden_size = hidden_size
        self.output_size = output_size
        self.activation = activation
        self.max_iter = max_iter
        self.pop_size = pop_size
        self.mutation_rate = mutation_rate

    # 遗传算法初始化权重和偏置
    def init_weights(self):
        weights = []
        for i in range(self.pop_size):
            w1 = np.random.randn(self.input_size, self.hidden_size)
            b1 = np.random.randn(self.hidden_size)
            w2 = np.random.randn(self.hidden_size, self.output_size)
            b2 = np.random.randn(self.output_size)
            weights.append((w1, b1, w2, b2))
        return weights

    # 计算适应度函数
    def fitness(self, X, y, weights):
        mse_list = []
        for w1, b1, w2, b2 in weights:
            model = MLPRegressor(hidden_layer_sizes=(self.hidden_size,), activation=self.activation, max_iter=self.max_iter)
            model.coefs_ = [w1, w2]
            model.intercepts_ = [b1, b2]
            y_pred = model.predict(X)
            mse = mean_squared_error(y, y_pred)
            mse_list.append(mse)
        fitness_list = [1 / (mse + 1e-6) for mse in mse_list]
        return fitness_list

    # 遗传算法选择
    def selection(self, weights, fitness_list):
        idx = random.choices(range(self.pop_size), weights=fitness_list, k=2)
        return weights[idx[0]], weights[idx[1]]

    # 遗传算法交叉
    def crossover(self, parent1, parent2):
        w1_1, b1_1, w2_1, b2_1 = parent1
        w1_2, b1_2, w2_2, b2_2 = parent2
        child1 = (w1_1, b1_2, w2_1, b2_2)
        child2 = (w1_2, b1_1, w2_2, b2_1)
        return child1, child2

    # 遗传算法变异
    def mutation(self, child):
        w1, b1, w2, b2 = child
        if random.random() < self.mutation_rate:
            w1 += np.random.randn(*w1.shape) * 0.1
        if random.random() < self.mutation_rate:
            b1 += np.random.randn(*b1.shape) * 0.1
        if random.random() < self.mutation_rate:
            w2 += np.random.randn(*w2.shape) * 0.1
        if random.random() < self.mutation_rate:
            b2 += np.random.randn(*b2.shape) * 0.1
        return w1, b1, w2, b2

    # 训练GA-BP神经网络
    def fit(self, X, y):
        weights = self.init_weights()
        for i in range(self.max_iter):
            fitness_list = self.fitness(X, y, weights)
            new_weights = []
            for j in range(self.pop_size // 2):
                parent1 = self.selection(weights, fitness_list)
                parent2 = self.selection(weights, fitness_list)
                child1, child2 = self.crossover(parent1, parent2)
                child1 = self.mutation(child1)
                child2 = self.mutation(child2)
                new_weights.append(child1)
                new_weights.append(child2)
            weights = new_weights
        fitness_list = self.fitness(X, y, weights)
        best_idx = np.argmax(fitness_list)
        w1, b1, w2, b2 = weights[best_idx]
        self.model = MLPRegressor(hidden_layer_sizes=(self.hidden_size,), activation=self.activation, max_iter=self.max_iter)
        self.model.coefs_ = [w1, w2]
        self.model.intercepts_ = [b1, b2]

    # 预测
    def predict(self, X):
        return self.model.predict(X)

# 示例
X = np.random.randn(100, 10)
y = np.random.randn(100)
model = GABP(input_size=10, hidden_size=5, output_size=1)
model.fit(X, y)
y_pred = model.predict(X)
print(mean_squared_error(y, y_pred))