下面这段代码什么意思：for i in range(1,backward+1): df['avgDiff'+str(i)] = df['avgVehicleSpeed'].shift(i-1)/ df['avgVehicleSpeed'].shift(i) - 1 df['avgDiff'+str(i)].replace([np.inf, -np.inf], np.nan,inplace=True) df['avgDiff'+str(i)].fillna(method='bfill') df['flowDiff'+str(i)] = df['vehicleFlowRate'].shift(i-1)/ df['vehicleFlowRate'].shift(i) - 1 df['flowDiff'+str(i)].replace([np.inf, -np.inf], np.nan,inplace=True) df['flowDiff'+str(i)].fillna(method='bfill') df['flowTraffic'+str(i)] = df['trafficConcentration'].shift(i-1)/ df['trafficConcentration'].shift(i) - 1 df['flowTraffic'+str(i)].replace([np.inf, -np.inf], np.nan,inplace=True) df['flowTraffic'+str(i)].fillna(method='bfill') # EWL df['EWMavg']=df['avgVehicleSpeed'].ewm(span=3, adjust=False).mean() df['EWMflow']=df['vehicleFlowRate'].ewm(span=3, adjust=False).mean() df['EWMtraffic']=df['trafficConcentration'].ewm(span=3, adjust=False).mean() return df def generateXYspeed20(df): df['ydiff'] = df['avgVehicleSpeed'].shift(forward)/df['avgVehicleSpeed'] - 1 df['y'] = 0 df.loc[df['ydiff']<-0.2,['y']]=1 df.dropna(inplace=True) y = df['y'] X = df.drop(['y','ydiff'], axis=1) return X , y def generateXYspeedUnder(df): mean = df['avgVehicleSpeed'].mean() df['ydiff'] = df['avgVehicleSpeed'].shift(forward) df['y'] = 0 df.loc[df['ydiff']<mean*0.6,['y']]=1 df.dropna(inplace=True) y = df['y'] X = df.drop(['y','ydiff'], axis=1) return X , y def generateXYspeedAndFlowUnder(df): means = df['avgVehicleSpeed'].mean() meanf = df['vehicleFlowRate'].mean() df['ydiffSpeed'] = df['avgVehicleSpeed'].shift(forward) df['ydiffFlow'] = df['vehicleFlowRate'].shift(forward) df['y'] = 0 df.loc[(df['ydiffSpeed']<means*0.6) &(df['ydiffFlow']<meanf*0.6),['y']]=1 df.dropna(inplace=True) y = df['y'] X = df.drop(['y','ydiffSpeed','ydiffFlow'], axis=1) return X , y def print_metrics(y_true,y_pred): conf_mx = confusion_matrix(y_true,y_pred) print(conf_mx) print (" Accuracy : ", accuracy_score(y_true,y_pred)) print (" Precision : ", precision_score(y_true,y_pred)) print (" Sensitivity : ", recall_score(y_true,y_pred))

这段代码是一个循环，它遍历了一个数列，数列的起点是1，终点是backward减1。在循环中，代码对数据框df的不同列进行了操作。首先，代码计算了df['avgVehicleSpeed']列按照向后位移i-1后与向后位移i后的比率的差值，并将其存储到df['avgDiff' str(i)]中。然后，代码将df['avgDiff' str(i)]中的无穷大值和负无穷大值替换成了NaN，并用后一个非NaN值填充缺失的NaN值。接下来，代码对df['vehicleFlowRate']列和df['trafficConcentration']列做了类似的操作。最终，代码将计算得到的结果存储到不同的列中，并返回修改后的数据框df。

相关问题

将上述代码进行提取整合

为了简化代码并将其整合在一起，你可以创建一个名为`main.py`的文件，按照以下结构编写：

# 导入所需的库
import pandas as pd
import numpy as np
import torch
import torch.nn as nn
from sklearn.model_selection import train_test_split

# 定义二进制标签函数
def binary_label(temp):
    return format(int(temp), '08b')

# 加载数据
df = pd.read_excel('weather_data.xlsx')

# 数据预处理
df['binary_label'] = df['temperature'].apply(binary_label)
features = df.iloc[:, :-1]
labels = df['binary_label']

# 划分数据集
X_train, X_test, y_train, y_test = train_test_split(features, labels, test_size=0.2, random_state=42)

# 数据张量化
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
X_train = torch.tensor(X_train.values, dtype=torch.float32).to(device)
y_train = torch.tensor(y_train.values, dtype=torch.long).to(device)
X_test = torch.tensor(X_test.values, dtype=torch.float32).to(device)
y_test = torch.tensor(y_test.values, dtype=torch.long).to(device)

# 神经网络模型
input_dim = features.shape[1]
hidden_dim = 64
output_dim = 8
model = BinaryTemperaturePredictor(input_dim, hidden_dim, output_dim).to(device)

# 定义损失函数和优化器
criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters(), lr=0.001)

# 训练循环
num_epochs = 100
for epoch in range(num_epochs):
    optimizer.zero_grad()
    outputs = model(X_train)
    loss = criterion(outputs, y_train)
    loss.backward()
    optimizer.step()
    
    # 打印训练进度
    if (epoch + 1) % 10 == 0:
        print(f"Epoch {epoch+1}/{num_epochs}, Loss: {loss.item()}")

# 预测并验证
model.eval()  # 设置为评估模式
with torch.no_grad():
    predictions = model(X_test)
    predicted_temps = [int(''.join(str(p) for p in pred.argmax(dim=1)), 2) for pred in predictions]

accuracy = sum(predicted_temps == y_test.tolist()) / len(y_test)
print(f"Final Accuracy: {accuracy}")

# 结果保存
torch.save(model.state_dict(), 'temperature_predictor.pth')

在这个版本的代码中，我们已经整合了数据加载、预处理、模型定义、训练、预测以及最终准确性的计算。记得替换`'weather_data.xlsx'`为你实际的数据路径。

基于bert文本分类的源代码

基于BERT的文本分类是一种常见的自然语言处理任务，利用BERT（Bidirectional Encoder Representations from Transformers）模型进行文本分类的源代码通常包括以下几个步骤：

1. **数据预处理**：将文本数据转换为BERT模型可以接受的格式。
2. **加载预训练模型**：使用Hugging Face的Transformers库加载预训练的BERT模型和分词器。
3. **定义分类模型**：在BERT模型的基础上添加一个全连接层来进行分类。
4. **训练模型**：使用训练数据进行模型训练。
5. **评估模型**：使用验证集或测试集评估模型性能。

以下是一个简单的基于BERT的文本分类源代码示例：

import torch
from torch.utils.data import DataLoader, Dataset
from transformers import BertTokenizer, BertForSequenceClassification
from transformers import AdamW, get_linear_schedule_with_warmup
import pandas as pd

# 自定义数据集类
class TextDataset(Dataset):
    def __init__(self, texts, labels, tokenizer, max_len):
        self.texts = texts
        self.labels = labels
        self.tokenizer = tokenizer
        self.max_len = max_len
    
    def __len__(self):
        return len(self.texts)
    
    def __getitem__(self, idx):
        text = str(self.texts[idx])
        label = self.labels[idx]
        encoding = self.tokenizer.encode_plus(
            text,
            add_special_tokens=True,
            max_length=self.max_len,
            return_token_type_ids=False,
            padding='max_length',
            truncation=True,
            return_attention_mask=True,
            return_tensors='pt',
        )
        return {
            'text': text,
            'input_ids': encoding['input_ids'].flatten(),
            'attention_mask': encoding['attention_mask'].flatten(),
            'labels': torch.tensor(label, dtype=torch.long)
        }

# 加载数据
df = pd.read_csv('data.csv')
texts = df['text'].tolist()
labels = df['label'].tolist()

# 划分训练集和验证集
from sklearn.model_selection import train_test_split
train_texts, val_texts, train_labels, val_labels = train_test_split(texts, labels, test_size=0.2)

# 初始化分词器和数据集
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
train_dataset = TextDataset(train_texts, train_labels, tokenizer, max_len=128)
val_dataset = TextDataset(val_texts, val_labels, tokenizer, max_len=128)

# 初始化数据加载器
train_loader = DataLoader(train_dataset, batch_size=16, shuffle=True)
val_loader = DataLoader(val_dataset, batch_size=16, shuffle=False)

# 加载预训练模型
model = BertForSequenceClassification.from_pretrained('bert-base-uncased', num_labels=2)

# 训练模型
device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')
model = model.to(device)
optimizer = AdamW(model.parameters(), lr=2e-5, correct_bias=False)
total_steps = len(train_loader) * 2
scheduler = get_linear_schedule_with_warmup(optimizer, num_warmup_steps=0, num_training_steps=total_steps)

for epoch in range(2):
    model.train()
    for batch in train_loader:
        optimizer.zero_grad()
        input_ids = batch['input_ids'].to(device)
        attention_mask = batch['attention_mask'].to(device)
        labels = batch['labels'].to(device)
        outputs = model(input_ids=input_ids, attention_mask=attention_mask, labels=labels)
        loss = outputs.loss
        loss.backward()
        optimizer.step()
        scheduler.step()
    
    # 验证模型
    model.eval()
    correct_predictions = 0
    for batch in val_loader:
        with torch.no_grad():
            input_ids = batch['input_ids'].to(device)
            attention_mask = batch['attention_mask'].to(device)
            labels = batch['labels'].to(device)
            outputs = model(input_ids=input_ids, attention_mask=attention_mask)
            _, preds = torch.max(outputs.logits, dim=1)
            correct_predictions += torch.sum(preds == labels)
    print(f'Epoch {epoch + 1}, Accuracy: {correct_predictions.double() / len(val_dataset)}')