python按指定概率抽样_概率加权的随机抽样 (Weighted Random Sampling) – A-Res 蓄水池算法...
时间: 2024-03-21 13:37:43 浏览: 12
可以使用A-Res蓄水池算法来实现概率加权的随机抽样。该算法的基本思想是通过一个动态的蓄水池来实现随机抽样。具体实现步骤如下:
1. 对样本进行编号,并计算每个样本的权重。
2. 初始化一个大小为k的蓄水池,将前k个样本放入蓄水池中。
3. 对于第i个样本(i > k),以概率k/i将其放入蓄水池中,同时随机选择一个蓄水池中的样本将其替换。
4. 重复步骤3直到遍历完所有样本。
5. 最终蓄水池中的k个样本即为概率加权的随机抽样结果。
下面是一个Python实现示例:
```python
import random
def weighted_random_sampling(data, k):
n = len(data)
weights = [data[i][1] for i in range(n)]
samples = [data[i][0] for i in range(n)]
reservoir = samples[:k]
for i in range(k, n):
p = k / (i + 1)
if random.random() < p:
j = random.randint(0, k - 1)
reservoir[j] = samples[i]
return reservoir
```
其中,data为样本列表,每个元素为一个二元组(样本,权重),k为抽样数量。函数返回抽样结果列表。
相关问题
Write pytorch-based Python code to implement a neural network that solves a regression problem with an output layer of a positively weighted sub-network plus a negatively weighted sub-network.
Sure! Here's an example of how you can implement a PyTorch-based neural network that solves a regression problem using a combination of positively and negatively weighted sub-networks in the output layer:
```python
import torch
import torch.nn as nn
class RegressionNet(nn.Module):
def __init__(self, input_size, hidden_size):
super(RegressionNet, self).__init__()
self.positive_net = nn.Linear(input_size, hidden_size)
self.negative_net = nn.Linear(input_size, hidden_size)
self.output_layer = nn.Linear(hidden_size, 1)
def forward(self, x):
positive_out = torch.relu(self.positive_net(x))
negative_out = torch.relu(self.negative_net(x))
output = self.output_layer(positive_out - negative_out)
return output
# Example usage
input_size = 10
hidden_size = 20
batch_size = 32
# Generate random input data
x = torch.randn(batch_size, input_size)
# Create an instance of the regression network
model = RegressionNet(input_size, hidden_size)
# Forward pass
output = model(x)
print(output)
```
In this code, we define a `RegressionNet` class that inherits from `nn.Module`. The network consists of two sub-networks: `positive_net` and `negative_net`, each with a linear layer followed by a ReLU activation function. The output of these sub-networks is then subtracted and passed through the `output_layer`, which is another linear layer that produces the final regression output.
You can adjust the `input_size` and `hidden_size` parameters to match your specific problem. The example usage demonstrates how to perform a forward pass through the network using randomly generated input data.
tf.nn.weighted_cross_entropy_with_logits
tf.nn.weighted_cross_entropy_with_logits是TensorFlow中的一个函数,用于计算加权交叉熵损失。它的输入是logits和labels,以及一个权重向量,用于加权不同类别的损失。该函数返回一个张量,表示加权交叉熵损失。