用Keras框架写出WOA优化LSTM参数进行多变量预测代码

以下是用Keras框架编写WOA优化LSTM参数进行多变量预测的代码：

首先，我们需要导入必要的库：

import numpy as np
import pandas as pd
from keras.models import Sequential
from keras.layers import Dense, LSTM
from keras.optimizers import Adam

然后，我们需要定义一些超参数：

n_features = 4  # 输入特征数
n_neurons = 50  # LSTM神经元数量
n_epochs = 100  # 训练轮数
batch_size = 32  # 批处理大小

接下来，我们需要定义WOA优化算法的相关函数：

def woa_init_search_space(num_params, search_space):
    return np.random.uniform(search_space[0], search_space[1], size=(num_params,))

def woa_get_fitness(model, X_train, y_train, X_valid, y_valid):
    model.fit(X_train, y_train, epochs=n_epochs, batch_size=batch_size, verbose=0)
    score = model.evaluate(X_valid, y_valid, verbose=0)
    return score[0]

def woa_get_parameters(model):
    return model.get_weights()

def woa_set_parameters(model, params):
    model.set_weights(params)

def woa_search(model, X_train, y_train, X_valid, y_valid, search_space, max_iter=100):
    lb, ub = search_space
    num_params = len(model.get_weights())
    best_fitness = float('inf')
    best_params = None
    a = 2
    a2 = -1
    for i in range(max_iter):
        for j in range(num_params):
            r1 = np.random.random()
            r2 = np.random.random()
            A = 2 * a * r1 - a
            C = 2 * r2
            p = np.random.random()
            b = 1
            l = (a2 - i) * (ub - lb) / max_iter + lb
            if p < 0.5:
                if abs(A) < 1:
                    D = abs(C * best_params[j] - model.get_weights()[j])
                    new_pos = best_params[j] - A * D
                else:
                    rand_param = woa_init_search_space(1, [lb, ub])[0]
                    D = abs(C * rand_param - model.get_weights()[j])
                    new_pos = rand_param - A * D
            else:
                D = abs(best_params[j] - model.get_weights()[j])
                new_pos = best_params[j] - b * D
            if new_pos < lb:
                new_pos = lb
            elif new_pos > ub:
                new_pos = ub
            model.get_weights()[j] = new_pos
        fitness = woa_get_fitness(model, X_train, y_train, X_valid, y_valid)
        if fitness < best_fitness:
            best_fitness = fitness
            best_params = woa_get_parameters(model)
    return best_params

现在，我们可以加载并准备数据：

data = pd.read_csv('data.csv')
X = data.iloc[:, :n_features].values
y = data.iloc[:, n_features:].values
n_samples = len(data)
n_train = int(n_samples * 0.8)
X_train, y_train = X[:n_train], y[:n_train]
X_valid, y_valid = X[n_train:], y[n_train:]

然后，我们可以构建模型并使用WOA优化算法来优化参数：

model = Sequential()
model.add(LSTM(n_neurons, input_shape=(None, n_features)))
model.add(Dense(y.shape[1]))
model.compile(optimizer=Adam(), loss='mse')
search_space = [-5, 5]
woa_params = woa_search(model, X_train.reshape(-1, 1, n_features), y_train, X_valid.reshape(-1, 1, n_features), y_valid, search_space)
woa_set_parameters(model, woa_params)

最后，我们可以使用训练好的模型来进行预测：

X_test = np.array([[0.1, 0.2, 0.3, 0.4], [0.2, 0.3, 0.4, 0.5], [0.3, 0.4, 0.5, 0.6]])
y_pred = model.predict(X_test.reshape(-1, 1, n_features))
print(y_pred)

以上就是用Keras框架编写WOA优化LSTM参数进行多变量预测的完整代码。