C 语言 print a number at random from the set
时间: 2024-09-29 15:11:15 浏览: 18
在C语言中,如果你想从一组特定数字中随机打印出一个,你可以使用`<stdlib.h>`库中的`rand()`函数配合`<time.h>`库来生成随机数,然后结合数组索引来选择随机元素。这里是一个简单的示例:
```c
#include <stdio.h>
#include <stdlib.h>
#include <time.h> // 引入时间函数库
#define NUMBERS {1, 2, 3, 4, 5} // 定义你想要的数字集合
int numbers[NUMBERS] = {}; // 创建包含这些数字的数组
void print_random_number() {
// 设置随机数种子,确保每次程序运行时都得到不同的随机数序列
srand(time(0));
// 生成一个介于0到数组长度减一之间的随机整数
int random_index = rand() % (sizeof(numbers) / sizeof(int));
// 打印随机选择的数字
printf("Randomly selected number: %d\n", numbers[random_index]);
}
int main() {
print_random_number();
return 0;
}
```
当你运行这个程序时,它会打印出数组`numbers`中的一个随机数。注意,`rand()`返回的是整型随机数,所以你需要对结果进行适当的处理使其落在指定范围内。
相关问题
import random password_set = set() while len(password_set)<5: number_list = random.choices("0123456789",k=6) password = ''.join(number_list) password_set.add(password) print(password_set)
这是一段 Python 代码,它生成了一个包含 5 个随机密码的集合(每个密码由 6 个数字组成)。其中使用了 random 模块的 choices() 函数来从数字列表中随机选择 6 个数字,然后使用 join() 函数将这些数字连接起来形成一个密码。密码集合的长度小于 5 时,就会一直生成新的密码并添加到集合中,直到集合中包含 5 个不同的密码为止。最后打印输出密码集合。
Write pytorch-based Python code to implement a neural network that solves binary classification problem with an output layer of 3 weighted sub-networks. The first weight is Positive, the second is negative, which is random and take the opposite of the absolute value of the standard normal distribution, the third random weight obeys a standard normal distribution. The exact value of the 3 weights are obtained from the objective function by Back propagation. The activation function is set to tanh.
Certainly! Here's an example of PyTorch-based Python code that implements a neural network for binary classification with an output layer of 3 weighted sub-networks using the tanh activation function:
```python
import torch
import torch.nn as nn
import torch.optim as optim
class NeuralNetwork(nn.Module):
def __init__(self, input_size):
super(NeuralNetwork, self).__init__()
self.fc1 = nn.Linear(input_size, 10)
self.fc2 = nn.Linear(10, 10)
self.fc3 = nn.Linear(10, 3)
def forward(self, x):
x = torch.tanh(self.fc1(x))
x = torch.tanh(self.fc2(x))
x = self.fc3(x)
return x
# Generate random weights for the second sub-network
negative_weights = -torch.abs(torch.randn(10))
# Create an instance of the neural network
input_size = 10 # Replace with the actual input size
model = NeuralNetwork(input_size)
# Define the loss function and optimizer
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(model.parameters(), lr=0.1)
# Training loop
for epoch in range(100):
# Forward pass
outputs = model(inputs) # Replace inputs with your actual input data
loss = criterion(outputs, labels) # Replace labels with your actual labels
# Backward and optimize
optimizer.zero_grad()
loss.backward()
optimizer.step()
# Update the second sub-network's weight using backpropagation
model.fc2.weight.data = negative_weights
# Print the loss during training
print(f"Epoch {epoch+1}/{num_epochs}, Loss: {loss.item():.4f}")
```
In this code, we define a `NeuralNetwork` class that inherits from `nn.Module` and implements the forward pass. The network consists of three fully connected layers (`nn.Linear`) with tanh activation for the hidden layers. The second sub-network's weight is initialized with random negative values obtained from the standard normal distribution.
During the training loop, we compute the forward pass, calculate the loss with the `nn.CrossEntropyLoss` function, perform backpropagation, and update the weights using the optimizer (`optim.SGD`). The second sub-network's weight is updated after each backpropagation step.
Please replace `input_size`, `inputs`, and `labels` with your actual input size, input data, and labels, respectively. Adjust the hyperparameters and the number of training epochs according to your specific problem.
Note: This code is a basic example and may need modifications to fit your specific use case.
阅读全文