dataY.append(dataset[i+look_back:i+look_back+look_forward, 0]) 什么意思

这行代码是将原始数据集（dataset）按照时间步（look_back）窗口的大小分割，并将每个时间步对应的未来预测值（look_forward）作为预测目标，存储在一个名为dataY的列表中。

具体来说，假设原始数据集有N个时间步，look_back是窗口大小，look_forward是未来预测的时间步数。那么这行代码将会从第一个时间步开始，每次取出look_back个时间步的数据作为一个样本的特征，同时取出接下来的look_forward个时间步的数据作为这个样本的预测目标。然后将这个样本的预测目标存储在dataY列表中，以便后续使用。这个过程会一直持续到第N-look_back个时间步，因为在这之后就无法再取出look_back个时间步的数据了。

相关问题

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

这段程序定义了一个函数 `create_dataset`，它的作用是将给定的数据集转换成输入和输出数据集，用于后续的模型训练。

该函数需要三个参数：

- `dataset`：原始数据集，它是一个二维数组，每行代表一个时间步的数据，每列代表不同的特征。
- `look_back`：输入数据集中包含的过去时间步数。
- `look_forward`：输出数据集中包含的未来时间步数。

函数首先创建两个空列表 `dataX` 和 `dataY`，用于存储输入和输出数据集。接着，通过一个循环，依次处理每个时间步的数据。对于每个时间步，从 `dataset` 中取出 `look_back` 个过去时间步的数据，组成一个二维数组 `a`，并将其添加到 `dataX` 列表中。同时，从 `dataset` 中取出接下来的 `look_forward` 个时间步的第一个特征值，也就是下一个时间步的目标值，添加到 `dataY` 列表中。

最后，函数将 `dataX` 和 `dataY` 转换成 NumPy 数组，并将它们作为元组返回。

import numpy import numpy as np import matplotlib.pyplot as plt import math import torch from torch import nn from torch.utils.data import DataLoader, Dataset import os os.environ['KMP_DUPLICATE_LIB_OK']='True' dataset = [] for data in np.arange(0, 3, .01): data = math.sin(data * math.pi) dataset.append(data) dataset = np.array(dataset) dataset = dataset.astype('float32') max_value = np.max(dataset) min_value = np.min(dataset) scalar = max_value - min_value print(scalar) dataset = list(map(lambda x: x / scalar, dataset)) def create_dataset(dataset, look_back=3): dataX, dataY = [], [] for i in range(len(dataset) - look_back): a = dataset[i:(i + look_back)] dataX.append(a) dataY.append(dataset[i + look_back]) return np.array(dataX), np.array(dataY) data_X, data_Y = create_dataset(dataset) train_X, train_Y = data_X[:int(0.8 * len(data_X))], data_Y[:int(0.8 * len(data_Y))] test_X, test_Y = data_Y[int(0.8 * len(data_X)):], data_Y[int(0.8 * len(data_Y)):] train_X = train_X.reshape(-1, 1, 3).astype('float32') train_Y = train_Y.reshape(-1, 1, 3).astype('float32') test_X = test_X.reshape(-1, 1, 3).astype('float32') train_X = torch.from_numpy(train_X) train_Y = torch.from_numpy(train_Y) test_X = torch.from_numpy(test_X) class RNN(nn.Module): def __init__(self, input_size, hidden_size, output_size=1, num_layer=2): super(RNN, self).__init__() self.input_size = input_size self.hidden_size = hidden_size self.output_size = output_size self.num_layer = num_layer self.rnn = nn.RNN(input_size, hidden_size, batch_first=True) self.linear = nn.Linear(hidden_size, output_size) def forward(self, x): out, h = self.rnn(x) out = self.linear(out[0]) return out net = RNN(3, 20) criterion = nn.MSELoss(reduction='mean') optimizer = torch.optim.Adam(net.parameters(), lr=1e-2) train_loss = [] test_loss = [] for e in range(1000): pred = net(train_X) loss = criterion(pred, train_Y) optimizer.zero_grad() # 反向传播 loss.backward() optimizer.step() if (e + 1) % 100 == 0: print('Epoch:{},loss:{:.10f}'.format(e + 1, loss.data.item())) train_loss.append(loss.item()) plt.plot(train_loss, label='train_loss') plt.legend() plt.show()请适当修改代码，并写出预测值和真实值的代码

以下是适当修改后的代码以及预测值和真实值的代码：

import numpy as np 
import matplotlib.pyplot as plt 
import math 
import torch 
from torch import nn 
from torch.utils.data import DataLoader, Dataset 
import os 

os.environ['KMP_DUPLICATE_LIB_OK']='True' 

dataset = [] 
for data in np.arange(0, 3, .01): 
    data = math.sin(data * math.pi) 
    dataset.append(data) 

dataset = np.array(dataset) 
dataset = dataset.astype('float32') 

max_value = np.max(dataset) 
min_value = np.min(dataset) 

scalar = max_value - min_value 
print(scalar) 

dataset = list(map(lambda x: x / scalar, dataset)) 

def create_dataset(dataset, look_back=3): 
    dataX, dataY = [], [] 
    for i in range(len(dataset) - look_back): 
        a = dataset[i:(i + look_back)] 
        dataX.append(a) 
        dataY.append(dataset[i + look_back]) 
    return np.array(dataX), np.array(dataY) 

data_X, data_Y = create_dataset(dataset) 
train_X, train_Y = data_X[:int(0.8 * len(data_X))], data_Y[:int(0.8 * len(data_Y))] 
test_X, test_Y = data_Y[int(0.8 * len(data_X)):], data_Y[int(0.8 * len(data_Y)):] 

train_X = train_X.reshape(-1, 1, 3).astype('float32') 
train_Y = train_Y.reshape(-1, 1, 1).astype('float32') 
test_X = test_X.reshape(-1, 1, 3).astype('float32') 

train_X = torch.from_numpy(train_X) 
train_Y = torch.from_numpy(train_Y) 
test_X = torch.from_numpy(test_X) 

class RNN(nn.Module): 
    def __init__(self, input_size, hidden_size, output_size=1, num_layer=2): 
        super(RNN, self).__init__() 
        self.input_size = input_size 
        self.hidden_size = hidden_size 
        self.output_size = output_size 
        self.num_layer = num_layer 
        self.rnn = nn.RNN(input_size, hidden_size, batch_first=True) 
        self.linear = nn.Linear(hidden_size, output_size) 

    def forward(self, x): 
        out, h = self.rnn(x) 
        out = self.linear(out[:, -1, :]) 
        return out 

net = RNN(3, 20) 

criterion = nn.MSELoss(reduction='mean') 
optimizer = torch.optim.Adam(net.parameters(), lr=1e-2) 

train_loss = [] 
test_loss = [] 

for e in range(1000): 
    pred = net(train_X) 
    loss = criterion(pred, train_Y) 
    optimizer.zero_grad() 
    loss.backward() 
    optimizer.step() 
    if (e + 1) % 100 == 0: 
        print('Epoch:{},loss:{:.10f}'.format(e + 1, loss.data.item())) 
    train_loss.append(loss.item()) 

plt.plot(train_loss, label='train_loss') 
plt.legend() 
plt.show() 

# 预测值和真实值的代码
net.eval() 
pred_test = net(test_X) 
pred_test = pred_test.detach().numpy() * scalar 
test_Y = test_Y.reshape(-1, 1) * scalar 
plt.plot(pred_test, label='predict') 
plt.plot(test_Y, label='true') 
plt.legend() 
plt.show()