np.fromfunction
时间: 2023-07-04 08:26:28 浏览: 63
`np.fromfunction` 是一个 NumPy 函数,用于根据指定的函数和形状创建数组。它的语法如下:
```python
np.fromfunction(function, shape, **kwargs)
```
其中,`function` 是一个函数,该函数接受一个或多个数组作为输入,并返回一个数组,用于计算要创建的数组的每个元素的值。`shape` 是一个整数元组,用于指定要创建的数组的形状。
例如,下面的代码使用 `np.fromfunction` 创建一个 3x3 的数组,并将每个元素设置为其行列坐标的和:
```python
import numpy as np
def sum_of_indices(i, j):
return i + j
arr = np.fromfunction(sum_of_indices, (3, 3))
print(arr)
```
输出:
```
[[0. 1. 2.]
[1. 2. 3.]
[2. 3. 4.]]
```
相关问题
import numpy as np def f(a, b): return 2 * a + 3*b x = np.fromfunction(f, (3, 4), dtype=int) print(x[2,1])#将打印
9。
这段代码中,np.fromfunction(f, (3, 4), dtype=int) 的作用是创建一个3行4列的数组x,其中的每个元素都是通过函数f计算得到的。具体来说,x[i,j]的值为f(i,j)的返回值。
在这个例子中,我们可以看到f(a, b) = 2 * a + 3 * b。因此,x[2,1] = f(2,1) = 2 * 2 + 3 * 1 = 7 + 2 = 9。
因此,打印x[2,1]的结果为9。
np.random.laplace
The np.random.laplace function in NumPy generates random numbers from a Laplace distribution.
The Laplace distribution is also known as the double exponential distribution and is a probability distribution that is similar to the normal or Gaussian distribution, but has heavier tails. It is often used in statistics to model data that has large outliers or extreme values.
The function takes several arguments:
- loc: the mean of the distribution (default is 0)
- scale: the scale parameter (default is 1)
- size: the size or shape of the output array (default is None)
Here's an example of how to use np.random.laplace to generate a random sample from a Laplace distribution with mean 0 and scale 1:
``` python
import numpy as np
# Generate 1000 random numbers from a Laplace distribution
sample = np.random.laplace(loc=0, scale=1, size=1000)
# Compute the mean and standard deviation of the sample
mean = np.mean(sample)
std = np.std(sample)
print("Sample mean:", mean)
print("Sample standard deviation:", std)
```
This will output something like:
```
Sample mean: -0.018013482992065523
Sample standard deviation: 1.353447003090671
```
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