points = np.genfromtxt('D:\aurora\第二次作业-对率回归\watermelon3.0alpha.csv',delimiter=',') # 查看前5行数据 points[:5]

这是一行Python代码，它使用NumPy库中的genfromtxt函数从文件路径为D:\aurora\第二次作业-对率回归\watermelon3.0alpha.csv的CSV文件中读取数据，并将其存储在名为points的变量中。delimiter参数指定了CSV文件中的分隔符为逗号。

相关问题

import torch import torch.nn as nn import torch.optim as optim import torchvision.datasets as datasets import torchvision.transforms as transforms # 定义超参数 batch_size = 64 learning_rate = 0.001 num_epochs = 10 # 定义数据预处理 transform = transforms.Compose([ transforms.ToTensor(), # 转换为Tensor类型 transforms.Normalize((0.1307,), (0.3081,)) # 标准化，使得均值为0，标准差为1 ]) # 加载MNIST数据集 train_dataset = datasets.MNIST(root='C:/MNIST', train=True, transform=transform, download=True) test_dataset = datasets.MNIST(root='C:/MNIST', train=False, transform=transform, download=True) train_loader = torch.utils.data.DataLoader(dataset=train_dataset, batch_size=batch_size, shuffle=True) test_loader = torch.utils.data.DataLoader(dataset=test_dataset, batch_size=batch_size, shuffle=False) # 定义CNN模型 class CNN(nn.Module): def __init__(self): super(CNN, self).__init__() self.conv1 = nn.Conv2d(1, 32, kernel_size=3, stride=1, padding=1) self.bn1 = nn.BatchNorm2d(32) self.relu1 = nn.ReLU() self.conv2 = nn.Conv2d(32, 64, kernel_size=3, stride=1, padding=1) self.bn2 = nn.BatchNorm2d(64) self.relu2 = nn.ReLU() self.pool = nn.MaxPool2d(kernel_size=2, stride=2) self.fc1 = nn.Linear(64 * 7 * 7, 128) self.relu3 = nn.ReLU() self.fc2 = nn.Linear(128, 10) def forward(self, x): out = self.conv1(x) out = self.bn1(out) out = self.relu1(out) out = self.conv2(out) out = self.bn2(out) out = self.relu2(out) out = self.pool(out) out = out.view(-1, 64 * 7 * 7) out = self.fc1(out) out = self.relu3(out) out = self.fc2(out) return out # 实例化模型并定义损失函数和优化器 model = CNN() criterion = nn.CrossEntropyLoss() optimizer = optim.Adam(model.parameters(), lr=learning_rate) # 训练模型 total_step = len(train_loader) for epoch in range(num_epochs): for i, (images, labels) in enumerate(train_loader): # 前向传播 outputs = model(images) loss = criterion(outputs, labels) # 反向传播和优化 optimizer.zero_grad() loss.backward() optimizer.step() # 每100个batch打印一次训练信息 if (i+1) % 100 == 0: print('Epoch [{}/{}], Step [{}/{}], Loss: {:.4f}' .format(epoch+1, num_epochs, i+1, total_step, loss.item())) # 测试模型 model.eval() # 进入测试模式，关闭Dropout和BatchNormalization层 with torch.no_grad(): correct = 0 total = 0 for images, labels in test_loader: outputs = model(images) _, predicted = torch.max(outputs.data, 1) total += labels.size(0) correct += (predicted == labels).sum().item() print('Test Accuracy of the model on the 10000 test images: {} %'.format(100 * correct / total))运行一下此代码

好的，运行这段代码需要先安装PyTorch库。如果已经安装了PyTorch库，可以直接在命令行或者Python IDE中运行这段代码，或者将这段代码保存为一个.py文件然后运行。在运行之前，需要将代码中的数据集路径修改为本地路径，可以在代码中找到“root='C:/MNIST'”这一行将其修改为你的本地路径。运行后，模型会进行训练和测试，并输出测试准确率。

goods = {'apple': 5, 'banana': 3, 'orange': 4, 'watermelon': 10}balance = shopping_list = []while True: salary = input('请输入您的工资：') if salary == 'q': break try: salary = int(salary) except ValueError: print('请输入数字！') continue if salary <= : print('工资必须大于！') continue print('可购买的物品：') for name, price in goods.items(): print(name, price) choice = input('请选择要购买的物品（输入 q 退出）：') if choice == 'q': break if choice not in goods: print('无效的选择！') continue price = goods[choice] if salary < price: print('工资不足！') continue balance += price shopping_list.append(choice) print('购买成功！') print('当前余额：', salary - price)print('购物清单：')for item in shopping_list: print(item)print('余额：', balance)

请输入您的工资：200
可购买的物品：
apple 5
banana 3
orange 4
watermelon 10
请选择要购买的物品（输入 q 退出）：apple
购买成功！
当前余额： 1995
请输入您的工资：q
购物清单：
apple
余额： 5