import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler

from keras.models import Sequential
from keras.layers import LSTM, Dense, Dropout, Attention

# 数据预处理
data = pd.read_csv("data.csv")
data = data.dropna()
data = data.astype("float32")

# 归一化
scaler = StandardScaler()
data = scaler.fit_transform(data)

# 划分训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(data, labels, test_size=0.2)

# 定义模型
model = Sequential()
model.add(LSTM(128, input_shape=(data.shape[1],)))
model.add(Attention())
model.add(Dropout(0.2))
model.add(Dense(3, activation="softmax"))

# 编译模型
model.compile(loss="categorical_crossentropy", optimizer="adam", metrics=["accuracy"])

# 训练模型
model.fit(X_train, y_train, epochs=100)

# 评估模型
score = model.evaluate(X_test, y_test, verbose=0)
print("Test loss:", score[0])
print("Test accuracy:", score[1])

# 保存模型
model.save("model.h5")