SAS写一段代码建立LSTM模型
时间: 2023-04-04 11:03:25 浏览: 111
你好,以下是建立 LSTM 模型的 SAS 代码:
/* 导入数据 */
proc import datafile="your_data_file.csv" out=data dbms=csv replace;
run;
/* 划分训练集和测试集 */
data data_train data_test;
set data;
if mod(_n_, 5) = 0 then output data_test;
else output data_train;
run;
/* 建立 LSTM 模型 */
proc deepLearn data=data_train outweights=weights;
input x1-x10;
target y;
datalines;
...
;
/* 使用测试集评估模型 */
proc deepLearn evalweights=weights data=data_test;
input x1-x10;
target y;
run;
希望这个回答对你有所帮助。
相关问题
用一段代码实现lstm模型对股票的分析预测
以下是使用 Python 代码实现 LSTM 模型对股票进行分析预测的示例:
```python
import numpy as np
import pandas as pd
from keras.models import Sequential
from keras.layers import Dense, LSTM
# 读取股票数据
data = pd.read_csv('stock_data.csv')
# 数据预处理
data = data.dropna()
data = data[['Open', 'High', 'Low', 'Close']]
data = data.values
# 划分训练集和测试集
train_size = int(len(data) * .8)
train_data = data[:train_size, :]
test_data = data[train_size:, :]
# 归一化处理
from sklearn.preprocessing import MinMaxScaler
scaler = MinMaxScaler(feature_range=(, 1))
train_data = scaler.fit_transform(train_data)
test_data = scaler.transform(test_data)
# 生成训练数据
def create_dataset(dataset, look_back=1):
X, Y = [], []
for i in range(len(dataset)-look_back):
X.append(dataset[i:(i+look_back), :])
Y.append(dataset[i+look_back, 3])
return np.array(X), np.array(Y)
look_back = 60
train_X, train_Y = create_dataset(train_data, look_back)
test_X, test_Y = create_dataset(test_data, look_back)
# 构建 LSTM 模型
model = Sequential()
model.add(LSTM(units=50, input_shape=(look_back, 4)))
model.add(Dense(units=1))
model.compile(optimizer='adam', loss='mean_squared_error')
# 训练模型
model.fit(train_X, train_Y, epochs=100, batch_size=32)
# 预测测试集
test_predict = model.predict(test_X)
test_predict = scaler.inverse_transform(np.concatenate((test_X[:, -1, :3], test_predict), axis=1))[:, -1]
# 计算误差
from sklearn.metrics import mean_squared_error
mse = mean_squared_error(test_Y, test_predict)
print('MSE:', mse)
```
以上代码实现了一个简单的 LSTM 模型,用于对股票进行分析预测。具体来说,它将股票的开盘价、最高价、最低价和收盘价作为输入,使用前 60 天的数据来预测下一天的收盘价。模型使用了 Adam 优化器和均方误差损失函数进行训练,并在测试集上计算了均方误差。
那写一个PyTorch 的lstm 模型代码
好的,以下是一个简单的 PyTorch LSTM 模型代码:
``` python
import torch
import torch.nn as nn
class LSTMModel(nn.Module):
def __init__(self, input_size, hidden_size, num_layers, num_classes):
super(LSTMModel, self).__init__()
self.hidden_size = hidden_size
self.num_layers = num_layers
self.lstm = nn.LSTM(input_size, hidden_size, num_layers, batch_first=True)
self.fc = nn.Linear(hidden_size, num_classes)
def forward(self, x):
h0 = torch.zeros(self.num_layers, x.size(0), self.hidden_size).to(device)
c0 = torch.zeros(self.num_layers, x.size(0), self.hidden_size).to(device)
out, _ = self.lstm(x, (h0, c0))
out = self.fc(out[:, -1, :])
return out
```
希望能对您有所帮助。