轉自:https://blog.csdn.net/u012149181/article/details/78913167 shell
numpy.random.rand(d0,d1,…,dn)數組
>>> np.random.rand(2,2) array([[0.70691613, 0.673804 ], [0.7999329 , 0.30363377]])
>>> np.random.randn(2,2) array([[-0.54880779, 0.03757687], [ 0.35608059, -0.16970511]]) >>> np.random.randn() -0.5041373211552308
其中n的意思就是normal,正態。μ=0,σ=1.dom
numpy.random.randint(low, high=None, size=None, dtype=’l’)iphone
>>> np.random.randint(0,2) 0 >>> np.random.randint(0,2,5) array([1, 0, 0, 1, 0]) >>> np.random.randint(5,2) Traceback (most recent call last): File "<pyshell#10>", line 1, in <module> np.random.randint(5,2) File "mtrand.pyx", line 993, in mtrand.RandomState.randint ValueError: low >= high >>> np.random.randint(low=5,size=2) array([3, 2])
>>> np.random.random((2,2)) array([[0.7066545 , 0.66002817], [0.79023509, 0.77658663]]) >>> np.random.sample((2,2)) array([[0.07203548, 0.54526898], [0.56429719, 0.74669749]])
//就是隨機取樣的函數吧函數
>>> np.random.choice(5) 2 >>> np.random.choice(5,2) array([0, 3])
>>> demo_list = ['lenovo', 'sansumg','moto','xiaomi', 'iphone'] >>> np.random.choice(demo_list,size=(3,3)) array([['iphone', 'iphone', 'lenovo'], ['lenovo', 'iphone', 'iphone'], ['xiaomi', 'iphone', 'lenovo']], dtype='<U7')
>>> np.random.seed(0) >>> np.random.rand() 0.5488135039273248 >>> np.random.rand() 0.7151893663724195 >>> np.random.seed(0) >>> np.random.rand() 0.5488135039273248 >>> np.random.rand() 0.7151893663724195
參數分別是高斯分佈的:均值、方差、形狀。spa