Question

我有一个形状为(1000,)的一维numpy数组X。我想在随机（均匀）位置注入10个随机（正常）值，从而获得形状(1010,)的numpy数组。如何在numpy中有效地做到这一点？

Answer 1

您可以将np.insert与np.random.choice一起使用：

n = 10
np.insert(a, np.random.choice(len(a), size=n), np.random.normal(size=n))

Answer 2

这里是基于遮罩的-

def addrand(a, N):
    n = len(a)
    m = np.concatenate((np.ones(n, dtype=bool), np.zeros(N, dtype=bool)))
    np.random.shuffle(m)
    out = np.empty(len(a)+N, dtype=a.dtype)
    out[m] = a
    out[~m] = np.random.uniform(N)
    return out

样品运行-

In [22]: a = 10+np.random.rand(20)

In [23]: a
Out[23]: 
array([10.65458302, 10.18034826, 10.08652451, 10.03342622, 10.63930492,
       10.48439184, 10.2859206 , 10.91419282, 10.56905636, 10.01595702,
       10.21063965, 10.23080433, 10.90546147, 10.02823502, 10.67987108,
       10.00583747, 10.24664158, 10.78030108, 10.33638157, 10.32471524])

In [24]: addrand(a, N=3) # adding 3 rand numbers
Out[24]: 
array([10.65458302, 10.18034826, 10.08652451, 10.03342622,  0.79989563,
       10.63930492, 10.48439184, 10.2859206 , 10.91419282, 10.56905636,
       10.01595702,  0.23873077, 10.21063965, 10.23080433, 10.90546147,
       10.02823502,  0.66857723, 10.67987108, 10.00583747, 10.24664158,
       10.78030108, 10.33638157, 10.32471524])

时间：

In [71]: a = np.random.rand(1000)

In [72]: %timeit addrand(a, N=10)
37.3 µs ± 273 ns per loop (mean ± std. dev. of 7 runs, 10000 loops each)

# @a_guest's soln
In [73]: %timeit np.insert(a, np.random.choice(len(a), size=10), np.random.normal(size=10))
63.3 µs ± 2.18 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each)

注意：如果使用更大的数组，似乎np.insert会做得更好。

Answer 3

不确定这是否是最有效的方法，但至少可以奏效。

A = np.arange(1000)
for i in np.random.randint(low = 0, high = 1000, size = 10):
    A = np.concatenate((A[:i], [np.random.normal(),], A[i:]))

编辑，检查性能：

def insert_random(A):
    for i in np.random.randint(low = 0, high = len(A), size = 10):
        A = np.concatenate((A[:i], [np.random.normal(),], A[i:]))
    return A

A = np.arange(1000)
%timeit test(A)

83.2 µs ± 2.47 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each)

所以绝对不是最有效的。 np.insert似乎是要走的路。

Answer 4

您可以使用import numpy as np a = np.arange(1000) a = np.insert(a, np.random.randint(low = 1, high = 999, size=10), np.random.normal(loc=0.0, scale=1.0, size=10))。

insert

请记住，using Distributed addprocs(2) @everywhere using DistributedArrays @everywhere using LinearAlgebra n=10 Z=zeros(n,n) #Z[1,:].=200 #Z[:,end].=200 Z=distribute(Z; dist=(2,1)) K=ones(n,1) #K[1,:].=200 #K[end,:].=200 K=distribute(K; dist=(2,1)) #(i+1) % 2)+1,j @sync @distributed for x in 1:nworkers() localpart(Z)[1,:].=200 @sync @distributed for i in 2:length(localindices(Z)[1]) for j in 1:length(localindices(Z)[2]) localpart(Z)[i,j]=10*log(myid())+localpart(K)[i] end end end end Z不会自动更改原始数组，但是会返回修改后的副本。

在一维numpy数组中的随机位置注入随机数

4 个答案: