dataset = data.Global_active_power.values.astype('float32') # Reshape the numpy array into a 2D array with 1 column dataset = np.reshape(dataset, (-1, 1)) # Create an instance of the MinMaxScaler class to scale the values between 0 and 1 scaler = MinMaxScaler(feature_range=(0, 1)) # Fit the MinMaxScaler to the transformed data and transform the values dataset = scaler.fit_transform(dataset) # Split the transformed data into a training set (80%) and a test set (20%)

这段代码是用于数据预处理的，主要包括以下几个步骤：

1. 将原始数据集中的Global_active_power列提取出来，并将数据类型转换为float32。

2. 将提取出来的数据集变成一个二维数组，其中只有一列。

3. 利用MinMaxScaler将数据集中的数值缩放到0-1之间。

4. 将缩放后的数据集按照8:2的比例分割成训练集和测试集。

这些预处理步骤可以帮助我们更好地训练模型和评估模型的性能。

相关问题

修改一下这段代码在pycharm中的实现，import pandas as pd import numpy as np from sklearn.model_selection import train_test_split import torch import torch.nn as nn import torch.nn.functional as F import torch.optim as optim #from torchvision import datasets,transforms import torch.utils.data as data #from torch .nn:utils import weight_norm import matplotlib.pyplot as plt from sklearn.metrics import precision_score from sklearn.metrics import recall_score from sklearn.metrics import f1_score from sklearn.metrics import cohen_kappa_score data_ = pd.read_csv(open(r"C:\Users\zhangjinyue\Desktop\rice.csv"),header=None) data_ = np.array(data_).astype('float64') train_data =data_[:,:520] train_Data =np.array(train_data).astype('float64') train_labels=data_[:,520] train_labels=np.array(train_data).astype('float64') train_data,train_data,train_labels,train_labels=train_test_split(train_data,train_labels,test_size=0.33333) train_data=torch.Tensor(train_data) train_data=torch.LongTensor(train_labels) train_data=train_data.reshape(-1,1,20,26) train_data=torch.Tensor(train_data) train_data=torch.LongTensor(train_labels) train_data=train_data.reshape(-1,1,20,26) start_epoch=1 num_epoch=1 BATCH_SIZE=70 Ir=0.001 classes=('0','1','2','3','4','5') device=torch.device("cuda"if torch.cuda.is_available()else"cpu") torch.backends.cudnn.benchmark=True best_acc=0.0 train_dataset=data.TensorDataset(train_data,train_labels) test_dataset=data.TensorDataset(train_data,train_labels) train_loader=torch.utills.data.DataLoader(dtaset=train_dataset,batch_size=BATCH_SIZE,shuffle=True) test_loader=torch.utills.data.DataLoader(dtaset=train_dataset,batch_size=BATCH_SIZE,shuffle=True)

修改后的代码如下所示：

import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import torch.utils.data as data
import matplotlib.pyplot as plt
from sklearn.metrics import precision_score, recall_score, f1_score, cohen_kappa_score

data_ = pd.read_csv(r"C:\Users\zhangjinyue\Desktop\rice.csv", header=None)
data_ = np.array(data_).astype('float64')

train_data = data_[:, :520]
train_labels = data_[:, 520]
train_data, _, train_labels, _ = train_test_split(train_data, train_labels, test_size=0.33333)

train_data = torch.Tensor(train_data)
train_labels = torch.LongTensor(train_labels)
train_data = train_data.reshape(-1, 1, 20, 26)

start_epoch = 1
num_epoch = 1
BATCH_SIZE = 70
Ir = 0.001
classes = ('0', '1', '2', '3', '4', '5')
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
torch.backends.cudnn.benchmark = True
best_acc = 0.0

train_dataset = data.TensorDataset(train_data, train_labels)
test_dataset = data.TensorDataset(train_data, train_labels)

train_loader = torch.utils.data.DataLoader(dataset=train_dataset, batch_size=BATCH_SIZE, shuffle=True)
test_loader = torch.utils.data.DataLoader(dataset=train_dataset, batch_size=BATCH_SIZE, shuffle=True)

请注意，我做了以下修改：

1. 移除了不需要的导入语句。
2. 修复了变量名拼写错误。
3. 移除了重复的代码行。
4. 修正了 `torch.utils.data.DataLoader` 的拼写错误。
5. 修正了数据集分割时的变量名错误。

请根据你的实际需求进一步调整代码。

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()