data = self.scaler.transform(df_data.values)

这行代码是将 DataFrame 数据 df_data 的值进行标准化处理，并赋值给变量 data。假设 self.scaler 是一个已经进行 fit 操作的 StandardScaler 对象，transform 方法将使用之前计算得到的均值和标准差，对 df_data 的每个特征进行标准化处理，使得数据具有零均值和单位方差。最终得到的标准化后的数据将赋值给 data 变量。

相关问题

将冒号后面的代码改写成一个nn.module类：data1 = pd.read_csv("终极1.csv", usecols=[17], encoding='gb18030') df = data1.fillna(method='ffill') data = df.values.reshape(-1, 1) scaler = MinMaxScaler(feature_range=(0, 1)) data = scaler.fit_transform(data) train_size = int(len(data) * 0.8) test_size = len(data) - train_size train, test = data[0:train_size, :], data[train_size:len(data), :] def create_dataset(dataset, look_back=1): dataX, dataY = [], [] for i in range(len(dataset)-look_back-1): a = dataset[i:(i+look_back), 0] dataX.append(a) dataY.append(dataset[i + look_back, 0]) return np.array(dataX), np.array(dataY) look_back = 30 trainX, trainY = create_dataset(train, look_back) testX, testY = create_dataset(test, look_back) trainX = np.reshape(trainX, (trainX.shape[0], 1, trainX.shape[1])) testX = np.reshape(testX, (testX.shape[0], 1, testX.shape[1])) model = Sequential() model.add(LSTM(50, input_shape=(1, look_back), return_sequences=True)) model.add(LSTM(50)) model.add(Dense(1)) model.compile(loss='mean_squared_error', optimizer='adam') model.fit(trainX, trainY, epochs=6, batch_size=1, verbose=2) trainPredict = model.predict(trainX) testPredict = model.predict(testX) trainPredict = scaler.inverse_transform(trainPredict) trainY = scaler.inverse_transform([trainY]) testPredict = scaler.inverse_transform(testPredict) testY = scaler.inverse_transform([testY])

以下是将代码改写为nn.module类的示例：

import torch
import torch.nn as nn
import numpy as np
import pandas as pd
from sklearn.preprocessing import MinMaxScaler

class LSTMModel(nn.Module):
    def __init__(self, input_dim, hidden_dim, output_dim, num_layers):
        super(LSTMModel, self).__init__()
        self.hidden_dim = hidden_dim
        self.num_layers = num_layers
        self.lstm = nn.LSTM(input_dim, hidden_dim, num_layers, batch_first=True)
        self.fc = nn.Linear(hidden_dim, output_dim)

    def forward(self, x):
        h0 = torch.zeros(self.num_layers, x.size(0), self.hidden_dim).requires_grad_()
        c0 = torch.zeros(self.num_layers, x.size(0), self.hidden_dim).requires_grad_()
        out, (hn, cn) = self.lstm(x, (h0.detach(), c0.detach()))
        out = self.fc(out[:, -1, :])
        return out

# 读取数据
data1 = pd.read_csv("终极1.csv", usecols=[17], encoding='gb18030')
df = data1.fillna(method='ffill')
data = df.values.reshape(-1, 1)

# 数据归一化
scaler = MinMaxScaler(feature_range=(0, 1))
data = scaler.fit_transform(data)

# 划分数据集
train_size = int(len(data) * 0.8)
test_size = len(data) - train_size
train, test = data[0:train_size, :], data[train_size:len(data), :]

# 创建数据集
def create_dataset(dataset, look_back=1):
    dataX, dataY = [], []
    for i in range(len(dataset)-look_back-1):
        a = dataset[i:(i+look_back), 0]
        dataX.append(a)
        dataY.append(dataset[i + look_back, 0])
    return np.array(dataX), np.array(dataY)

look_back = 30
trainX, trainY = create_dataset(train, look_back)
testX, testY = create_dataset(test, look_back)

trainX = np.reshape(trainX, (trainX.shape[0], trainX.shape[1], 1))
testX = np.reshape(testX, (testX.shape[0], testX.shape[1], 1))

# 模型训练
input_dim = 1
hidden_dim = 50
output_dim = 1
num_layers = 2

model = LSTMModel(input_dim=input_dim, hidden_dim=hidden_dim, output_dim=output_dim, num_layers=num_layers)
criterion = nn.MSELoss()
optimizer = torch.optim.Adam(model.parameters(), lr=0.001)

num_epochs = 6
for epoch in range(num_epochs):
    outputs = model(trainX)
    optimizer.zero_grad()
    loss = criterion(outputs, trainY)
    loss.backward()
    optimizer.step()
    if epoch % 1 == 0:
        print("Epoch: %d, loss: %1.5f" % (epoch, loss.item()))

# 预测结果
trainPredict = model(trainX)
testPredict = model(testX)

trainPredict = scaler.inverse_transform(trainPredict.detach().numpy())
trainY = scaler.inverse_transform([trainY])
testPredict = scaler.inverse_transform(testPredict.detach().numpy())
testY = scaler.inverse_transform([testY])

将冒号后面的代码改写成一个nn.module类：import pandas as pd import numpy as np from sklearn.preprocessing import MinMaxScaler import matplotlib.pyplot as plt from keras.models import Sequential from keras.layers import Dense, LSTM data1 = pd.read_csv("终极1.csv", usecols=[17], encoding='gb18030') df = data1.fillna(method='ffill') data = df.values.reshape(-1, 1) scaler = MinMaxScaler(feature_range=(0, 1)) data = scaler.fit_transform(data) train_size = int(len(data) * 0.8) test_size = len(data) - train_size train, test = data[0:train_size, :], data[train_size:len(data), :] def create_dataset(dataset, look_back=1): dataX, dataY = [], [] for i in range(len(dataset)-look_back-1): a = dataset[i:(i+look_back), 0] dataX.append(a) dataY.append(dataset[i + look_back, 0]) return np.array(dataX), np.array(dataY) look_back = 30 trainX, trainY = create_dataset(train, look_back) testX, testY = create_dataset(test, look_back) trainX = np.reshape(trainX, (trainX.shape[0], 1, trainX.shape[1])) testX = np.reshape(testX, (testX.shape[0], 1, testX.shape[1])) model = Sequential() model.add(LSTM(50, input_shape=(1, look_back), return_sequences=True)) model.add(LSTM(50)) model.add(Dense(1)) model.compile(loss='mean_squared_error', optimizer='adam') model.fit(trainX, trainY, epochs=6, batch_size=1, verbose=2) trainPredict = model.predict(trainX) testPredict = model.predict(testX) trainPredict = scaler.inverse_transform(trainPredict) trainY = scaler.inverse_transform([trainY]) testPredict = scaler.inverse_transform(testPredict) testY = scaler.inverse_transform([testY])

以下是将代码改写成一个nn.module类：

import torch.nn as nn
import pandas as pd 
import numpy as np 
from sklearn.preprocessing import MinMaxScaler 
import matplotlib.pyplot as plt 
from keras.models import Sequential 
from keras.layers import Dense, LSTM 

class LSTMModel(nn.Module):
    def __init__(self, input_size, hidden_size, output_size, num_layers, look_back):
        super().__init__()
        self.hidden_size = hidden_size
        self.num_layers = num_layers
        self.look_back = look_back

        self.lstm1 = nn.LSTM(input_size, hidden_size, num_layers, batch_first=True)
        self.lstm2 = nn.LSTM(hidden_size, hidden_size, num_layers, batch_first=True)
        self.fc = nn.Linear(hidden_size, output_size)

    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)

        x = x.view(-1, self.look_back, 1)

        out, _ = self.lstm1(x, (h0, c0))
        out, _ = self.lstm2(out, (h0, c0))

        out = self.fc(out[:, -1, :])
        return out

data1 = pd.read_csv("终极1.csv", usecols=[17], encoding='gb18030') 
df = data1.fillna(method='ffill') 
data = df.values.reshape(-1, 1) 
scaler = MinMaxScaler(feature_range=(0, 1)) 
data = scaler.fit_transform(data) 
train_size = int(len(data) * 0.8) 
test_size = len(data) - train_size 
train, test = data[0:train_size, :], data[train_size:len(data), :] 

def create_dataset(dataset, look_back=1): 
    dataX, dataY = [], [] 
    for i in range(len(dataset)-look_back-1): 
        a = dataset[i:(i+look_back), 0] 
        dataX.append(a) 
        dataY.append(dataset[i + look_back, 0]) 
    return np.array(dataX), np.array(dataY) 

look_back = 30 
trainX, trainY = create_dataset(train, look_back) 
testX, testY = create_dataset(test, look_back) 
trainX = np.reshape(trainX, (trainX.shape[0], trainX.shape[1], 1)) 
testX = np.reshape(testX, (testX.shape[0], testX.shape[1], 1)) 

device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model = LSTMModel(input_size=1, hidden_size=50, output_size=1, num_layers=2, look_back=look_back).to(device)

criterion = nn.MSELoss()
optimizer = torch.optim.Adam(model.parameters(), lr=0.001)

num_epochs = 6
for epoch in range(num_epochs):
    for i, (inputs, labels) in enumerate(zip(trainX, trainY)):
        inputs = torch.from_numpy(inputs).float().to(device)
        labels = torch.from_numpy(labels).float().to(device)

        outputs = model(inputs)
        loss = criterion(outputs, labels)

        optimizer.zero_grad()
        loss.backward()
        optimizer.step()

        if (i+1) % 100 == 0:
            print(f'Epoch [{epoch+1}/{num_epochs}], Step [{i+1}/{len(trainX)}], Loss: {loss.item():.4f}')

model.eval()
trainPredict = scaler.inverse_transform(model(torch.from_numpy(trainX).float().to(device)).detach().cpu().numpy())
testPredict = scaler.inverse_transform(model(torch.from_numpy(testX).float().to(device)).detach().cpu().numpy())
trainY = scaler.inverse_transform([trainY])
testY = scaler.inverse_transform([testY])

这个类接受5个参数，分别是：

- input_size: LSTM的输入维度，对于这个例子来说，input_size=1
- hidden_size: LSTM的隐藏层维度
- output_size: LSTM的输出维度，对于这个例子来说，output_size=1
- num_layers: LSTM的层数
- look_back: 每个样本的序列长度，对于这个例子来说，look_back=30