Numpy隨機陣列（random)
阿新 • • 發佈：2021-12-23
numpy.random()模組補充了Python內建random模組的一些功能，用於高效/高速生成一些概率分佈的樣本陣列資料。
In [1]: import numpy as np In [2]: from random import normalvariate #從下面比較可以看到，numpy.random模組比Python內建random模組快20多倍 In [4]: %timeit np.random.normal(size=1000000) 31.6 ms ± 1.55 ms per loop (mean ± std. dev. of 7 runs, 10 loops each) In [5]: %timeit samples = [normalvariate(0,1) for i in range(1000000)] 872 ms ± 9.42 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
偽隨機數(peseudorandom numbers)
numpy的隨機數是基於演算法在確定條件下產生的，通過numpy.random.seed()可以設定隨機數生成的種子，以便得到相同的隨機數結果。
#設定全域性的隨機數生成的種子 In [6]: np.random.seed(1234) #RandomState()用於產生獨立的隨機數生成器 In [7]: rng = np.random.RandomState(1234) In [8]: rng.randn(10) Out[8]: array([ 0.47143516, -1.19097569, 1.43270697, -0.3126519 , -0.72058873, 0.88716294, 0.85958841, -0.6365235 , 0.01569637, -2.24268495])
Python內建模組random
In [1]: import random In [2]: position = 0 In [3]: walks = [position] In [4]: steps = 1000 #隨機產生一個walks陣列 In [5]: for i in range(steps): # random.randint(0,1)隨機返回整數0或1 ...: step = 1 if random.randint(0,1) else -1 ...: position += step ...: walks.append(position) ...: In [7]: import matplotlib.pyplot as plt In [8]: plt.plot(walks) Out[8]: [<matplotlib.lines.Line2D at 0x14beae2a948>] In [9]: plt.plot(walks[:100]) Out[9]: [<matplotlib.lines.Line2D at 0x14bf0817648>]
Numpy的random模組
#單個數組
In [11]: import numpy as np
In [12]: import matplotlib.pyplot as plt
#在[0,2)之間產生包含1000個整數的一維陣列
In [13]: walks = np.random.randint(0,2,size=1000)
In [14]: walks
Out[14]:
array([1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1,
0, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 1,
0, 1, 1, 0, 0, 1, 0, 1, 1, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 0,
1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 0, 0, 0, 1, 1,
1, 1, 1, 1, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0,
1, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1,
1, 0, 1, 0, 0, 1, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0,
1, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 1, 0,
0, 1, 1, 0, 0, 1, 1, 0, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 0, 1, 0, 0,
1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1,
1, 1, 1, 0, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 0, 0, 0,
0, 1, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0,
0, 1, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 1, 1, 1, 0, 1, 0, 1,
1, 0, 1, 1, 0, 1, 0, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 0, 1, 1, 0,
1, 1, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 1, 1, 0, 1, 0, 1, 0,
0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1,
0, 1, 0, 1, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 0,
1, 1, 0, 0, 0, 0, 1, 1, 0, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 0, 1, 0,
1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 1, 0, 0, 1, 0, 1, 1, 0, 1, 1, 0,
1, 0, 0, 1, 1, 0, 0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1,
1, 0, 1, 0, 1, 1, 0, 1, 1, 1, 0, 1, 1, 0, 0, 1, 0, 1, 1, 1, 0, 1,
0, 1, 0, 0, 0, 1, 1, 0, 1, 1, 1, 1, 0, 1, 0, 1, 0, 0, 0, 1, 1, 1,
1, 1, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1,
0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 1, 0, 0, 1, 0,
1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 1, 0,
0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 0, 0, 1, 1,
1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0,
1, 0, 0, 0, 1, 1, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 1,
0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0,
0, 0, 1, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 1,
0, 1, 1, 0, 1, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 0, 1, 1,
0, 0, 1, 1, 0, 1, 1, 1, 1, 0, 1, 0, 0, 0, 1, 1, 1, 0, 1, 0, 1, 1,
0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 1, 0, 0, 1, 1, 1, 0, 1, 0,
0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 1, 0, 1, 1, 1, 1,
0, 1, 0, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1,
1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 0, 1, 0, 1, 1, 1, 1, 1, 0, 0, 0,
1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 1, 0, 0, 1, 0,
0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 1, 1, 0, 0,
1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0,
1, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 1, 0, 1, 0, 1, 1, 0, 0, 0,
0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 1,
1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 0, 0,
0, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1,
0, 0, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 1,
1, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 0, 0,
0, 1, 0, 0, 1, 1, 1, 0, 1, 0])
#逐個判定陣列中的元素是否大於0，大於0則用1替換，不大於0則用-1替換
In [18]: walks = np.where(walks > 0,1,-1)
#累加
In [20]: walks = walks.cumsum()
In [22]: plt.plot(walks)
Out[22]: [<matplotlib.lines.Line2D at 0x14bf0920588>]
In [23]: plt.plot(walks)
Out[23]: [<matplotlib.lines.Line2D at 0x14bf04b3888>]
#獲取最小偏離值
In [25]: walks.min()
Out[25]: -6
#獲取最大偏離值
In [26]: walks.max()
Out[26]: 44
#獲取首次偏離遠點大於10的位置（步數）
In [27]: (np.abs(walks) > 10 ).argmax()
Out[27]: 78
#多維陣列生成
In [29]: nwalks = 5000
In [30]: nsteps = 1000
#size=(nwalks,nsteps)表示生成nwalks * nsteps 陣列
In [31]: draws = np.random.randint(0,2,size=(nwalks,nsteps))
In [32]: steps = np.where(draws > 0, 1, -1)
#每一行陣列沿1軸累加
In [33]: walks = steps.cumsum(1)
In [34]: walks
Out[34]:
array([[ 1, 2, 1, ..., -8, -7, -6],
[ 1, 0, 1, ..., -66, -67, -66],
[ -1, 0, -1, ..., 50, 49, 50],
...,
[ 1, 2, 3, ..., -36, -37, -38],
[ 1, 0, -1, ..., 62, 61, 62],
[ -1, 0, 1, ..., 10, 9, 10]], dtype=int32)
In [35]: walks.max()
Out[35]: 123
In [36]: walks.min()
Out[36]: -129
#每一行沿1軸判定是否有元素絕對值>=30，有則返回True,無則返回False
In [37]: hits30 = (np.abs(walks) >= 30).any(1)
In [38]: hits30
Out[38]: array([ True, True, True, ..., True, True, False])
#判定有多少行有元素絕對值>=30
In [39]: hits30.sum()
Out[39]: 3374
#提取元素絕對值>=30的行，並返回每行的首次>=30的元素位置
In [40]: crossing_times = (np.abs(walks[hits30]) >= 30).argmax(1)
In [41]: crossing_times
Out[41]: array([283, 461, 339, ..., 989, 427, 525], dtype=int64)
In [42]: crossing_times.mean()
Out[42]: 503.4712507409603
In [43]: plt.plot(walks)
部分numpy.random.字尾函式功能
| 函式 | 說明 |
|---|---|
| seed | 確定隨機數生成器的種子 |
| permutation | 返回一個序列的隨機排列或返回一個隨機排列的範圍 |
| shuffle | 對一個序列就地隨機排列 |
| rand | 產生均勻分佈的樣本值 |
| randint | 從給定的[a,b)範圍內隨機取整數 |
| randn | 產生標準正態分佈的樣本值 |
| binomial | 產生二項分佈的樣本值 |
| normal | 產生正態（高斯）分佈的樣本值 |
| beta | 產生Beta分佈的樣本值 |
| chisquare | 產生卡方分佈的樣本值 |
| gamma | 產生Gamma分佈的樣本值 |
| uniform | 產生在[0,1)中均勻分佈的樣本值 |