python numpy之np.random的随机数函数使用介绍
np.random的随机数函数（1）
np.random.rand
importnumpyasnp a=np.random.rand(3,4,5) a Out[3]: array([[[0.28576737,0.96566496,0.59411491,0.47805199,0.97454449], [0.15970049,0.35184063,0.66815684,0.13571458,0.41168113], [0.66737322,0.91583297,0.68033204,0.49083857,0.33549182], [0.52797439,0.23526146,0.39731129,0.26576975,0.26846021]], [[0.46860445,0.84988491,0.92614786,0.76410349,0.00283208], [0.88036955,0.01402271,0.59294569,0.14080713,0.72076521], [0.0537956,0.08118672,0.59281986,0.60544876,0.77931621], [0.41678215,0.24321042,0.25167563,0.94738625,0.86642919]], [[0.36137271,0.21672667,0.85449629,0.51065516,0.16990425], [0.97507815,0.78870518,0.36101021,0.56538782,0.56392004], [0.93777677,0.73199966,0.97342172,0.42147127,0.73654324], [0.83139234,0.00221262,0.51822612,0.60964223,0.83029954]]])
np.random.randn
b=np.random.randn(3,4,5) b Out[5]: array([[[0.09170952,-0.36083675,-0.18189783,-0.52370155, -0.61183783], [1.05285606,-0.82944771,-0.93438396,0.32229904, -0.85316565], [1.41103666,-0.32534111,-0.02202953,1.02101228, 1.59756695], [-0.33896372,0.42234042,0.14297587,-0.70335248, 0.29436318]], [[0.73454216,0.35412624,-1.76199508,1.79502353, 1.05694614], [-0.42403323,-0.36551581,0.54033378,-0.04914723, 1.15092556], [0.48814148,1.09265266,0.65504441,-1.04280834, 0.70437122], [2.92946803,-1.73066859,-0.30184912,1.04918753, -1.58460681]], [[1.24923498,-0.65467868,-1.30427044,1.49415265, 0.87520623], [-0.26425316,-0.89014489,0.98409579,1.13291179, -0.91343016], [-0.71570644,0.81026219,-0.00906133,0.90806035, -0.914998], [0.22115875,-0.81820313,0.66359573,-0.1490853, 0.75663096]]])
np.random.randint
c=np.random.randint(100,200,(3,4)) c Out[9]: array([[104,140,161,193], [134,147,126,120], [117,141,162,137]])
numpy.random.randint的详细用法-python
函数的作用是，返回一个随机整型数，范围从低（包括）到高（不包括），即[low,high)。如果没有写参数high的值，则返回[0,low)的值。
numpy.random.randint(low,high=None,size=None,dtype='l')
参数如下：
| 参数 | 描述 |
|---|---|
| low:int | 生成的数值最低要大于等于low。 （hign=None时，生成的数值要在[0,low)区间内） |
| high:int(可选) | 如果使用这个值，则生成的数值在[low,high)区间。 |
| size:intortupleofints(可选) | 输出随机数的尺寸，比如 |
| dtype:dtype(可选)： | 想要输出的格式。如int64、int等等 |
输出：
返回一个随机数或随机数数组
例子
>>>np.random.randint(2,size=10)
array([1,0,0,0,1,1,0,0,1,0])
>>>np.random.randint(1,size=10)
array([0,0,0,0,0,0,0,0,0,0])>>>np.random.randint(5,size=(2,4))
array([[4,0,2,1],
[3,2,2,0]])>>>np.random.randint(2,high=10,size=(2,3))
array([[6,8,7],
[2,5,2]])
np.random.seed
随机种子生成器，使下一次生成的随机数为由种子数决定的“特定”的随机数，如果seed中参数为空，则生成的随机数“完全”随机。参考和文档。
np.random.seed(10) np.random.randint(100,200,(3,4)) Out[11]: array([[109,115,164,128], [189,193,129,108], [173,100,140,136]]) np.random.seed(10) np.random.randint(100,200,(3,4)) Out[13]: array([[109,115,164,128], [189,193,129,108], [173,100,140,136]])
np.random的随机数函数（2）
np.random.shuffle
a=np.random.randint(100,200,(3,4)) a Out[15]: array([[116,111,154,188], [162,133,172,178], [149,151,154,177]]) np.random.shuffle(a) a Out[17]: array([[116,111,154,188], [149,151,154,177], [162,133,172,178]]) np.random.shuffle(a) a Out[19]: array([[162,133,172,178], [116,111,154,188], [149,151,154,177]])
可以看到，a发生了变化，轴。
np.random.permutation
b=np.random.randint(100,200,(3,4)) b Out[21]: array([[113,192,186,130], [130,189,112,165], [131,157,136,127]]) np.random.permutation(b) Out[22]: array([[113,192,186,130], [130,189,112,165], [131,157,136,127]]) b Out[24]: array([[113,192,186,130], [130,189,112,165], [131,157,136,127]])
可以看到，b没有发生改变。
np.random.choice
c=np.random.randint(100,200,(8,)) c Out[26]:array([123,194,111,128,174,188,109,115]) np.random.choice(c,(3,2)) Out[27]: array([[111,123], [109,115], [123,128]])#默认可以出现重复值 np.random.choice(c,(3,2),replace=False) Out[28]: array([[188,111], [123,115], [174,128]])#不允许出现重复值 np.random.choice(c,(3,2),p=c/np.sum(c)) Out[29]: array([[194,188], [109,111], [174,109]])#指定每个值出现的概率
np.random的随机数函数（3）
u=np.random.uniform(0,10,(3,4)) u Out[31]: array([[9.83020867,4.67403279,8.75744495,2.96068699], [1.31291053,8.42817933,6.59036304,5.95439605], [4.36353698,3.56250327,5.87130925,1.49471337]]) n=np.random.normal(10,5,(3,4)) n Out[33]: array([[8.17771928,4.17423265,3.28465058,17.2669643], [10.00584724,9.94039808,13.57941572,4.07115727], [6.81836048,6.94593078,3.40304302,7.19135792]]) p=np.random.poisson(2.0,(3,4)) p Out[35]: array([[0,2,2,1], [2,0,1,3], [4,2,0,3]])
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