python中对一个训练完的训练集的数据进行一个去inf数据的方法，代码是什么

在Python中，可以使用numpy库中的isfinite函数来去除inf数据。具体的代码如下所示：

import numpy as np

# 假设训练集数据为train_data
train_data = np.array([...])

# 去除inf数据
train_data = train_data[np.isfinite(train_data)]

# 输出去除inf数据后的训练集数据
print(train_data)

其中，`np.isfinite(train_data)`会返回一个布尔数组，表示train_data中的元素是否为有限数（即不是inf或NaN）。然后，将这个布尔数组作为下标，即可得到去除inf数据后的训练集数据。

相关问题

python编程4个函数。dataset函数中写两个加载数据集的类。一个用于加载训练集和验证集，对coco数据集的单张自然图像进行预处理，返回tensor形式的自然图像。另一个用于加载测试集，对TNO数据集的红外与可见光图像进行预处理，返回tensor形式红外与可见光图像。net函数中构建出此融合方法的网络,在初始化函数中定义各种神经网络操作,并编写encoder、fusion和decoder函数，分别表示编码器、融合策略和解码器。train函数需要实例化训练网络、dataset和dataloader,设置优化器与训练轮数等参数。在训练中对训练集进行多轮训练，每轮训练结束后对验证集进行验证，记录每轮的平均训练损失和平均验证损失，并将模型保存到pth文件夹中。test函数需要实例化测试集、加载模型参数，融合测试集中的红外与可见光图像，并保存到result文件夹中。

很高兴为您提供代码示例：

import torch
import torch.nn as nn
import torch.optim as optim
from torchvision import transforms
from torch.utils.data import Dataset, DataLoader

# 定义加载数据集的类
class COCODataset(Dataset):
    def __init__(self, data_dir, transform=None):
        self.data_dir = data_dir
        self.transform = transform

    def __len__(self):
        return len(os.listdir(self.data_dir))

    def __getitem__(self, idx):
        img_path = os.path.join(self.data_dir, f"{idx}.jpg")
        img = Image.open(img_path).convert("RGB")

        if self.transform:
            img = self.transform(img)

        return img

class TNODataset(Dataset):
    def __init__(self, data_dir, transform=None):
        self.data_dir = data_dir
        self.transform = transform

    def __len__(self):
        return len(os.listdir(self.data_dir))

    def __getitem__(self, idx):
        ir_path = os.path.join(self.data_dir, f"{idx}_ir.jpg")
        vis_path = os.path.join(self.data_dir, f"{idx}_vis.jpg")

        ir_img = Image.open(ir_path).convert("RGB")
        vis_img = Image.open(vis_path).convert("RGB")

        if self.transform:
            ir_img = self.transform(ir_img)
            vis_img = self.transform(vis_img)

        return ir_img, vis_img

# 定义网络结构
class FusionNet(nn.Module):
    def __init__(self):
        super(FusionNet, self).__init__()

        self.encoder = nn.Sequential(
            nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1),
            nn.BatchNorm2d(64),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(kernel_size=2, stride=2),

            nn.Conv2d(64, 128, kernel_size=3, stride=1, padding=1),
            nn.BatchNorm2d(128),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(kernel_size=2, stride=2),

            nn.Conv2d(128, 256, kernel_size=3, stride=1, padding=1),
            nn.BatchNorm2d(256),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(kernel_size=2, stride=2),

            nn.Conv2d(256, 512, kernel_size=3, stride=1, padding=1),
            nn.BatchNorm2d(512),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(kernel_size=2, stride=2),
        )

        self.fusion = nn.Sequential(
            nn.Conv2d(1024, 512, kernel_size=3, stride=1, padding=1),
            nn.BatchNorm2d(512),
            nn.ReLU(inplace=True),

            nn.Conv2d(512, 256, kernel_size=3, stride=1, padding=1),
            nn.BatchNorm2d(256),
            nn.ReLU(inplace=True),

            nn.Conv2d(256, 128, kernel_size=3, stride=1, padding=1),
            nn.BatchNorm2d(128),
            nn.ReLU(inplace=True),
        )

        self.decoder = nn.Sequential(
            nn.ConvTranspose2d(128, 64, kernel_size=4, stride=2, padding=1),
            nn.BatchNorm2d(64),
            nn.ReLU(inplace=True),

            nn.ConvTranspose2d(64, 32, kernel_size=4, stride=2, padding=1),
            nn.BatchNorm2d(32),
            nn.ReLU(inplace=True),

            nn.ConvTranspose2d(32, 3, kernel_size=4, stride=2, padding=1),
            nn.Tanh(),
        )

    def forward(self, ir, vis):
        ir_enc = self.encoder(ir)
        vis_enc = self.encoder(vis)
        enc = torch.cat((ir_enc, vis_enc), dim=1)

        fusion = self.fusion(enc)

        dec = self.decoder(fusion)

        return dec

# 定义训练函数
def train(model, train_loader, val_loader, criterion, optimizer, num_epochs, device):
    best_loss = float('inf')

    for epoch in range(num_epochs):
        train_loss = 0.0
        val_loss = 0.0

        model.train() # 将模型设为训练模式
        for i, data in enumerate(train_loader):
            ir, vis = data
            ir = ir.to(device)
            vis = vis.to(device)

            optimizer.zero_grad()

            outputs = model(ir, vis)

            loss = criterion(outputs, ir)

            loss.backward()

            optimizer.step()

            train_loss += loss.item()

        model.eval() # 将模型设为验证模式
        with torch.no_grad():
            for i, data in enumerate(val_loader):
                ir, vis = data
                ir = ir.to(device)
                vis = vis.to(device)

                outputs = model(ir, vis)

                loss = criterion(outputs, ir)

                val_loss += loss.item()

        train_loss /= len(train_loader)
        val_loss /= len(val_loader)

        print(f"Epoch {epoch+1} - Train Loss: {train_loss:.4f} - Val Loss: {val_loss:.4f}")

        if val_loss < best_loss:
            best_loss = val_loss
            torch.save(model.state_dict(), "pth/model.pth")

# 定义测试函数
def test(model, test_loader, device):
    model.load_state_dict(torch.load("pth/model.pth"))

    model.eval() # 将模型设为测试模式
    with torch.no_grad():
        for i, data in enumerate(test_loader):
            ir, vis = data
            ir = ir.to(device)
            vis = vis.to(device)

            outputs = model(ir, vis)

            img = torch.cat((ir, vis, outputs), dim=0)
            img = transforms.ToPILImage()(img.cpu())
            img.save(f"result/{i}.jpg")

# 数据预处理
transform_train = transforms.Compose([
    transforms.Resize((256, 256)),
    transforms.RandomHorizontalFlip(),
    transforms.ToTensor(),
    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
])

transform_test = transforms.Compose([
    transforms.Resize((256, 256)),
    transforms.ToTensor(),
    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
])

# 实例化数据集和数据加载器
train_val_dataset = COCODataset("data/train_val", transform_train)
test_dataset = TNODataset("data/test", transform_test)

train_dataset, val_dataset = torch.utils.data.random_split(train_val_dataset, [80000, 20000])

train_loader = DataLoader(train_dataset, batch_size=64, shuffle=True)
val_loader = DataLoader(val_dataset, batch_size=64, shuffle=False)
test_loader = DataLoader(test_dataset, batch_size=1, shuffle=False)

# 实例化模型和优化器
model = FusionNet().to(device)
criterion = nn.MSELoss()
optimizer = optim.Adam(model.parameters(), lr=0.001)

# 训练模型
train(model, train_loader, val_loader, criterion, optimizer, num_epochs=10, device=device)

# 测试模型
test(model, test_loader, device=device)

具有训练集测试集验证集的arima模型python代码

### ARIMA 模型的时间序列预测代码

对于时间序列分析中的ARIMA模型，在Python中可以利用`statsmodels`库来构建并应用该模型。为了确保数据集被合理划分为训练集、测试集以及验证集，下面提供了一段完整的代码示例[^1]。

import pandas as pd
from statsmodels.tsa.arima.model import ARIMA
from sklearn.metrics import mean_squared_error
import numpy as np
import matplotlib.pyplot as plt

# 加载数据
data = pd.read_csv('your_time_series_data.csv', parse_dates=['date'], index_col='date')

# 数据预处理：如果存在缺失值则填充或删除
data.fillna(method='ffill', inplace=True)

# 划分训练/验证/测试集
train_size = int(len(data) * 0.7)
val_size = int(len(data) * 0.2)
test_size = len(data) - train_size - val_size

train, validate, test = data[:train_size], data[train_size:train_size+val_size], data[-test_size:]

# 定义评估函数
def evaluate_arima_model(X_train, X_val, p_values, d_values, q_values):
    best_score, best_cfg = float("inf"), None
    for p in p_values:
        for d in d_values:
            for q in q_values:
                try:
                    model = ARIMA(X_train.values, order=(p,d,q))
                    model_fit = model.fit()
                    forecast = model_fit.forecast(steps=len(X_val))[0]
                    error = mean_squared_error(X_val.values, forecast)
                    if error < best_score:
                        best_score, best_cfg = error, (p,d,q)
                except:
                    continue
    print(f'Best ARIMA{best_cfg} MSE={best_score:.3f}')
    return best_cfg

# 调整参数范围
p_values = range(0, 5)
d_values = range(0, 3)
q_values = range(0, 5)

# 找到最优配置
best_config = evaluate_arima_model(train['value'], validate['value'], p_values, d_values, q_values)

# 使用最佳配置重新拟合整个训练加验证的数据，并对未来进行预测
final_model = ARIMA(pd.concat([train,validate])['value'].values, order=best_config).fit()

forecast_steps = final_model.forecast(steps=len(test))

plt.figure(figsize=(12,8))
plt.plot(data.index, data['value'], label="Original Data", color='blue')
plt.plot(test.index, forecast_steps, label="Forecasted Values", color='red')
plt.legend(loc='upper left')
plt.show()

这段代码展示了如何加载时间序列数据、对其进行分割成三个不同的子集（即训练集、验证集和测试集），并通过网格搜索找到最适合给定数据的最佳(p,d,q)组合。最后一步则是基于选定的超参数对最终模型进行了训练，并绘制了原始数据与预测结果之间的对比图。