def adds(x): return x+1 a = [1,2,3,4,5]
时间: 2024-02-10 15:59:44 浏览: 24
To add 1 to each element in the list 'a' using the function 'adds', we can use the map() function as follows:
a = list(map(adds, a))
This will apply the function 'adds' to each element in the list 'a', resulting in a new list with the updated values:
[2, 3, 4, 5, 6]
相关问题
下面pytorch代码转化为paddlepaddle2.2.2代码: import torch import torch.distributions as tdist def random_noise_levels(): """Generates random noise levels from a log-log linear distribution.""" log_min_shot_noise = np.log(0.0001) log_max_shot_noise = np.log(0.012) log_shot_noise = torch.FloatTensor(1).uniform_(log_min_shot_noise, log_max_shot_noise) shot_noise = torch.exp(log_shot_noise) line = lambda x: 2.18 * x + 1.20 n = tdist.Normal(loc=torch.tensor([0.0]), scale=torch.tensor([0.26])) log_read_noise = line(log_shot_noise) + n.sample() read_noise = torch.exp(log_read_noise) return shot_noise, read_noise def add_noise(image, shot_noise=0.01, read_noise=0.0005): """Adds random shot (proportional to image) and read (independent) noise.""" image = image.permute(1, 2, 0) # Permute the image tensor to HxWxC format from CxHxW format variance = image * shot_noise + read_noise n = tdist.Normal(loc=torch.zeros_like(variance), scale=torch.sqrt(variance)) noise = n.sample() out = image + noise out = out.permute(2, 0, 1) # Re-Permute the tensor back to CxHxW format return out
以下是将该代码转化为PaddlePaddle2.2.2的代码:
```
import paddle
import paddle.distribution as pdist
import numpy as np
def random_noise_levels():
"""Generates random noise levels from a log-log linear distribution."""
log_min_shot_noise = np.log(0.0001)
log_max_shot_noise = np.log(0.012)
log_shot_noise = paddle.uniform(shape=[1], min=log_min_shot_noise, max=log_max_shot_noise)
shot_noise = paddle.exp(log_shot_noise)
line = lambda x: 2.18 * x + 1.20
n = pdist.Normal(loc=paddle.zeros([1]), scale=paddle.ones([1]) * 0.26)
log_read_noise = line(log_shot_noise) + n.sample()
read_noise = paddle.exp(log_read_noise)
return shot_noise, read_noise
def add_noise(image, shot_noise=0.01, read_noise=0.0005):
"""Adds random shot (proportional to image) and read (independent) noise."""
image = image.permute([1, 2, 0]) # Permute the image tensor to HxWxC format from CxHxW format
variance = image * shot_noise + read_noise
n = pdist.Normal(loc=paddle.zeros_like(variance), scale=paddle.sqrt(variance))
noise = n.sample()
out = image + noise
out = out.permute([2, 0, 1]) # Re-Permute the tensor back to CxHxW format
return out
```
请注意,PaddlePaddle的分布API(`paddle.distribution`)与PyTorch的分布API(`torch.distributions`)略有不同,需要相应地更改代码。此外,PaddlePaddle的张量API(如`paddle.exp`)与PyTorch的张量API(如`torch.exp`)也略有不同,需要相应地更改代码。
def size1(n): result = "" for i in range(n): result = "".rjust(2 * i + 1, "*") + "\n" return result
The function size1(n) appears to be incomplete and contains errors. Here is a revised version of the function that will return a string of asterisks (*) arranged in a pyramid shape:
```
def size1(n):
result = ""
for i in range(1, n + 1):
row = "* " * i
result += row.center(n * 2 - 1) + "\n"
return result
```
Explanation:
1. `result = ""` initializes an empty string that will be used to store the pyramid.
2. `for i in range(1, n + 1):` loops through the numbers 1 to n (inclusive).
3. `row = "* " * i` creates a string of asterisks and spaces, with i asterisks and i - 1 spaces.
4. `result += row.center(n * 2 - 1) + "\n"` centers the row string in a field of width n * 2 - 1 (to account for the spaces between asterisks), adds a newline character at the end, and appends it to the result string.
5. `return result` returns the completed pyramid string.
Example usage:
```
>>> print(size1(5))
*
* *
* * *
* * * *
* * * * *
```
Note that the pyramid is centered and has a base width of n * 2 - 1.