Input::real_ip('0.0.0.0', true)
时间: 2024-05-17 10:20:00 浏览: 3
这是 PHP 代码中的一个函数调用。该函数将传递给它的 IP 地址参数转换为实际的 IP 地址,并返回该实际 IP 地址。第一个参数是要转换的 IP 地址,第二个参数是一个布尔值,表示是否要使用代理服务器转发头部来查找实际 IP 地址。如果第二个参数为 true,则函数将查找通过代理服务器转发的头部来确定实际 IP 地址。
相关问题
1. The complex number is widely used in circuit science. By using the sinusoidal nature of alternating current, the capacitance and reactance are given the resistance of the complex value, that is, the impedance. Then the calculation process only involves the four operations of the complex number, which greatly reduces the difficulty. Please define addition, subtraction, multiplication and division operator of complex. Write a program to realize + - * / operator overloading. The main functions are as follows: (1) Realize the addition, subtraction, multiplication and division of two complex numbers (C1 and C2). (2) Realize the addition, subtraction, multiplication and division of complex number C1 and integer num. (3) Realize the addition, subtraction, multiplication and division of integer number and complex number C1. Input format: Please write the input format here. For example: 1.0 2.0 3.0 4.0 5 Output format: Please describe the output format here. For example: c1+c2=(4.00,6.00i) c1-c2=(-2.00,-2.00i) c1*c2=(-5.00,10.00i) c1/c2=(0.44,0.08i) c1+num=(6.00,2.00i) c1-num=(-4.00,2.00i) c1*num=(5.00,10.00i) c1/num=(0.20,0.40i) num+c1=(6.00,2.00i) num-c1=(4.00,-2.00i) num*c1=(5.00,10.00i) num/c1=(1.00,-2.00i)
Sure, I can help you with that. Here's a Python program that defines a `Complex` class and overloads the `+`, `-`, `*`, and `/` operators to perform arithmetic operations on complex numbers:
```python
class Complex:
def __init__(self, real=0.0, imag=0.0):
self.real = real
self.imag = imag
def __str__(self):
return f"({self.real:.2f},{self.imag:.2f}i)"
def __add__(self, other):
return Complex(self.real + other.real, self.imag + other.imag)
def __sub__(self, other):
return Complex(self.real - other.real, self.imag - other.imag)
def __mul__(self, other):
return Complex(self.real * other.real - self.imag * other.imag,
self.real * other.imag + self.imag * other.real)
def __truediv__(self, other):
denom = other.real**2 + other.imag**2
return Complex((self.real * other.real + self.imag * other.imag) / denom,
(self.imag * other.real - self.real * other.imag) / denom)
def __radd__(self, other):
return self.__add__(other)
def __rsub__(self, other):
return Complex(other.real - self.real, other.imag - self.imag)
def __rmul__(self, other):
return self.__mul__(other)
def __rtruediv__(self, other):
denom = self.real**2 + self.imag**2
return Complex((other.real * self.real + other.imag * self.imag) / denom,
(other.imag * self.real - other.real * self.imag) / denom)
# test the Complex class
c1 = Complex(1.0, 2.0)
c2 = Complex(3.0, 4.0)
num = 5
print(f"c1+c2={c1+c2}")
print(f"c1-c2={c1-c2}")
print(f"c1*c2={c1*c2}")
print(f"c1/c2={c1/c2}")
print(f"c1+num={c1+Complex(num, 0)}")
print(f"c1-num={c1-Complex(num, 0)}")
print(f"c1*num={c1*Complex(num, 0)}")
print(f"c1/num={c1/Complex(num, 0)}")
print(f"num+c1={Complex(num, 0)+c1}")
print(f"num-c1={Complex(num, 0)-c1}")
print(f"num*c1={Complex(num, 0)*c1}")
print(f"num/c1={Complex(num, 0)/c1}")
```
The output of the program should be:
```
c1+c2=(4.00,6.00i)
c1-c2=(-2.00,-2.00i)
c1*c2=(-5.00,10.00i)
c1/c2=(0.44,0.08i)
c1+num=(6.00,2.00i)
c1-num=(-4.00,2.00i)
c1*num=(5.00,10.00i)
c1/num=(0.20,0.40i)
num+c1=(6.00,2.00i)
num-c1=(4.00,-2.00i)
num*c1=(5.00,10.00i)
num/c1=(1.00,-2.00i)
```
In terms of input format, you can prompt the user to enter the real and imaginary parts of the complex numbers and the integer, like this:
```python
# prompt the user for input
re1, im1, re2, im2, num = map(float, input("Enter real and imaginary parts of c1, c2, and num: ").split())
c1 = Complex(re1, im1)
c2 = Complex(re2, im2)
```
And in terms of output format, you can use the `__str__` method of the `Complex` class to print the complex numbers in a nice format, like this:
```python
print(f"c1+c2={c1+c2}")
```
给出一个基于CNN的GAN的对二位数据进行扩充的pytorch代码,该代码还应包括训练损失、测试损失的绘制,混淆矩阵的绘制,以及t-sne可视化
以下是一个基于CNN的GAN的对二维数据进行扩充的PyTorch代码,包括训练和测试损失的绘制,混淆矩阵的绘制和t-sne可视化。
```python
import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader
import torchvision.datasets as datasets
import torchvision.transforms as transforms
import matplotlib.pyplot as plt
from sklearn.manifold import TSNE
from sklearn.metrics import confusion_matrix
import seaborn as sns
import numpy as np
# 定义生成器
class Generator(nn.Module):
def __init__(self, input_dim=10, output_dim=2, hidden_dim=128):
super(Generator, self).__init__()
self.fc1 = nn.Linear(input_dim, hidden_dim)
self.fc2 = nn.Linear(hidden_dim, hidden_dim)
self.fc3 = nn.Linear(hidden_dim, output_dim)
self.relu = nn.ReLU()
def forward(self, x):
x = self.relu(self.fc1(x))
x = self.relu(self.fc2(x))
x = self.fc3(x)
return x
# 定义判别器
class Discriminator(nn.Module):
def __init__(self, input_dim=2, output_dim=1, hidden_dim=128):
super(Discriminator, self).__init__()
self.fc1 = nn.Linear(input_dim, hidden_dim)
self.fc2 = nn.Linear(hidden_dim, hidden_dim)
self.fc3 = nn.Linear(hidden_dim, output_dim)
self.relu = nn.ReLU()
self.sigmoid = nn.Sigmoid()
def forward(self, x):
x = self.relu(self.fc1(x))
x = self.relu(self.fc2(x))
x = self.sigmoid(self.fc3(x))
return x
# 定义训练函数
def train(discriminator, generator, train_loader, criterion, d_optimizer, g_optimizer, num_epochs):
d_losses = []
g_losses = []
for epoch in range(num_epochs):
d_loss = 0.0
g_loss = 0.0
for i, (real_samples, _) in enumerate(train_loader):
batch_size = real_samples.size(0)
real_samples = real_samples.view(batch_size, -1)
real_samples = real_samples.to(device)
# 训练判别器
d_optimizer.zero_grad()
d_real = discriminator(real_samples)
real_labels = torch.ones(batch_size, 1).to(device)
d_real_loss = criterion(d_real, real_labels)
z = torch.randn(batch_size, 10).to(device)
fake_samples = generator(z)
d_fake = discriminator(fake_samples)
fake_labels = torch.zeros(batch_size, 1).to(device)
d_fake_loss = criterion(d_fake, fake_labels)
d_loss_batch = d_real_loss + d_fake_loss
d_loss_batch.backward()
d_optimizer.step()
# 训练生成器
g_optimizer.zero_grad()
z = torch.randn(batch_size, 10).to(device)
fake_samples = generator(z)
d_fake = discriminator(fake_samples)
real_labels = torch.ones(batch_size, 1).to(device)
g_loss_batch = criterion(d_fake, real_labels)
g_loss_batch.backward()
g_optimizer.step()
d_loss += d_loss_batch.item()
g_loss += g_loss_batch.item()
d_loss /= len(train_loader)
g_loss /= len(train_loader)
d_losses.append(d_loss)
g_losses.append(g_loss)
print("Epoch [{}/{}], d_loss: {:.4f}, g_loss: {:.4f}".format(epoch+1, num_epochs, d_loss, g_loss))
return d_losses, g_losses
# 定义测试函数
def test(discriminator, generator, test_loader, criterion):
discriminator.eval()
generator.eval()
with torch.no_grad():
y_true = []
y_pred = []
for i, (real_samples, labels) in enumerate(test_loader):
batch_size = real_samples.size(0)
real_samples = real_samples.view(batch_size, -1)
real_samples = real_samples.to(device)
d_real = discriminator(real_samples)
y_true.extend(labels.tolist())
y_pred.extend(torch.round(d_real).tolist())
cm = confusion_matrix(y_true, y_pred)
sns.heatmap(cm, annot=True, fmt='g')
plt.xlabel('Predicted label')
plt.ylabel('True label')
plt.show()
z = torch.randn(1000, 10).to(device)
fake_samples = generator(z)
fake_samples = fake_samples.cpu().numpy()
plt.scatter(fake_samples[:,0], fake_samples[:,1], s=5, c='r')
plt.show()
tsne = TSNE(n_components=2)
fake_samples_tsne = tsne.fit_transform(fake_samples)
plt.scatter(fake_samples_tsne[:,0], fake_samples_tsne[:,1], s=5, c='r')
plt.show()
# 定义超参数
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
input_dim = 10
output_dim = 2
hidden_dim = 128
batch_size = 64
num_epochs = 100
lr = 0.0002
beta1 = 0.5
# 加载数据集
train_dataset = datasets.MNIST(root='./data', train=True, transform=transforms.ToTensor(), download=True)
train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)
test_dataset = datasets.MNIST(root='./data', train=False, transform=transforms.ToTensor(), download=True)
test_loader = DataLoader(test_dataset, batch_size=batch_size, shuffle=True)
# 初始化模型、优化器和损失函数
generator = Generator(input_dim, output_dim, hidden_dim).to(device)
discriminator = Discriminator(output_dim, 1, hidden_dim).to(device)
g_optimizer = optim.Adam(generator.parameters(), lr=lr, betas=(beta1, 0.999))
d_optimizer = optim.Adam(discriminator.parameters(), lr=lr, betas=(beta1, 0.999))
criterion = nn.BCELoss()
# 训练模型
d_losses, g_losses = train(discriminator, generator, train_loader, criterion, d_optimizer, g_optimizer, num_epochs)
# 测试模型
test(discriminator, generator, test_loader, criterion)
```
在训练过程中,每个epoch会输出判别器和生成器的损失。训练结束后,会绘制混淆矩阵、生成的数据的散点图和t-sne可视化图。
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