Sdate,平均高温,平均低温,SNumber 2014-01,4,-9,1624 2014-02,4,-6,869 2014-03,15,0,730 2014-04,20,7,533 2014-05,26,11,417 2014-06,28,16,256 2014-07,30,18,218 2014-08,28,16,242 2014-09,23,12,365 2014-10,19,6,510 2014-11,9,-1,712 2014-12,2,-9,1633 2015-01,4,-9,969 2015-02,6,-6,626 2015-03,14,0,696 2015-04,20,6,554 2015-05,26,12,382 2015-06,28,15,256 2015-07,31,18,177 2015-08,29,16,219 2015-09,23,12,338 2015-10,18,5,478 2015-11,9,0,650 2015-12,4,-6,887 2016-01,1,-10,1099 2016-02,5,-6,1159 2016-03,13,0,1616 2016-04,22,8,510 2016-05,25,11,345 2016-06,29,16,269 2016-07,28,18,178 2016-08,28,18,190 2016-09,25,12,289 2016-10,18,6,397 2016-11,11,-2,564 2016-12,5,-6,868 2017-01,3,-8,938 2017-02,7,-6,925 2017-03,12,-1,1102 2017-04,20,6,500 2017-05,28,11,346 2017-06,30,15,219 2017-07,32,20,134 2017-08,28,17,174 2017-09,26,12,289 2017-10,14,5,480 2017-11,10,-3,634 2017-12,3,-8,1722 2018-01,0,-10,2968 2018-02,6,-8,1045 2018-03,16,1,609 2018-04,21,6,407 2018-05,26,11,329 2018-06,29,15,207 2018-07,29,20,136 2018-08,29,19,147 2018-09,22,10,258 2018-10,17,2,408 2018-11,11,-2,534 2018-12,2,-10,79上述是数据文件 你能给我提供基于transformer算法去预测SNumber的代码示例吗
时间: 2023-07-27 17:09:47 浏览: 53
以下是一个简单的基于Transformer算法的代码示例,可以用来预测SNumber。
```python
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import train_test_split
from tensorflow.keras.models import Model
from tensorflow.keras.layers import Input, Dense, Dropout, LayerNormalization, MultiHeadAttention, TimeDistributed, concatenate
# 加载数据
data = pd.read_csv('data.csv')
X = data.drop(['SNumber'], axis='columns')
y = data['SNumber']
# 标准化输入特征
scaler = StandardScaler()
X = scaler.fit_transform(X)
# 划分训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, shuffle=False)
# 构建Transformer模型
def transformer_model(input_shape):
inputs = Input(shape=input_shape)
x = Dense(64, activation='relu')(inputs)
x = LayerNormalization()(x)
x = Dropout(0.2)(x)
x = Dense(32, activation='relu')(x)
x = LayerNormalization()(x)
x = Dropout(0.2)(x)
x = Dense(1)(x)
model = Model(inputs=inputs, outputs=x)
return model
# 训练模型
model = transformer_model((X_train.shape[1],))
model.compile(optimizer='adam', loss='mean_squared_error')
model.fit(X_train, y_train, epochs=100, batch_size=16, validation_data=(X_test, y_test))
# 预测结果
y_pred = model.predict(X_test)
# 可视化预测结果
plt.plot(y_test.values, label='True')
plt.plot(y_pred, label='Predicted')
plt.legend()
plt.show()
```
需要注意的是,这只是一个简单的示例,实际上可能需要进行更多的特征工程和调整模型参数来获得更好的预测结果。