Choose a random element from a non-empty sequence. # 随机取出序列中的一个元素>>> random.choice('abcdef')'d'>>> random.choice('abcdef')'f'>>> random.choice('abcdef')'b'>>> random.choice([1,22,333,4444])333>>> random.choice([1,22,333,4444])1>>> random.choice([1,22,333...
array = np.arange(10) rng.shuffle(array) array Out[R]:array([9, 8, 0, 3, 2, 1, 6, 7, 4, 5]) 也可以不是数组而仅仅是一般的Python序列: sequence = ["你","的","头","发","还","好","吗"] rng.shuffle(sequence) sequence Out[R]:[‘你’, ‘发’, ‘的’, ‘好’, ...
返回值为指定维度的 array。 我们可以创建 4 行 2 列的随机数据。 np.random.rand(4,2) #array([[0.02533197, 0.80477348], # [0.85778508, 0.01261245], # [0.04261013, 0.26928786], # [0.81136377, 0.34618951]]) 1. 2. 3. 4. 5. 我们也可以创建 2 块 2 行 3 列的随机数据。 np.random.rand(...
Output 2D Array filled with random floats : [[0.15468058 0.26536462 0.54954387]] import numpy as geek # output array out_arr = geek.random.random_sample((3, 2, 1)) print ("Output 3D Array filled with random floats : ", out_arr) Output 3D Array filled with random floats : [[[0.638022...
# array([3, 7, 4, 2, 5, 1, 7, 5, 1, 8])) # 分析:由于每次输出前都设置了相同的随机种子,所以程序得到的随机数的值相同 # 2. np.random.seed随机种子的使用:numpy.random.seed()不是线程安全的 # 如果程序中有多个线程最好使用numpy.random.RandomState实例对象来创建或者使用random.seed()来设...
Python数据分析(中英对照)·Random Choice 随机选择 1.1.5: Random Choice 随机选择 通常,当我们使用数字时,偶尔也会使用其他类型的对象,我们希望使用某种类型的随机性。 Often when we’re using numbers, but also,occasionally, with other types of objects,we would like to do some type of randomness. ...
importnumpyasnp# 生成2x3x4的三维随机整数数组,范围是0到9random_3d_array=np.random.randint(0,10,size=(2,3,4))print("3D random array from numpyarray.com:\n",random_3d_array) Python Copy Output: 这个例子生成了一个2x3x4的三维随机整数数组,每个元素都是0到9之间的随机整数。
Returns the initial seed for generating random numbers as a Python long. torch.get_rng_state()[source] Returns the random number generator state as a torch.ByteTensor. torch.set_rng_state(new_state)[source] Sets the random number generator state. ...
array([0.47143516, -1.19097569,1.43270697, -0.3126519, -0.72058873,0.88716294,0.85958841, -0.6365235,0.01569637, -2.24268495]) Python内置模块random In [1]:importrandom In [2]: position =0In [3]: walks = [position] In [4]: steps =1000#随机产生一个walks数组In [5]:foriinrange(steps):# ran...
If no argument is given a single Python float is returned. 返回: Array of defined shape, filled with random floating-point samples from the standard normal distribution. 代码1:随机构造一维数组 # Python Program illustrating# numpy.random.randn() methodimportnumpyasgeek# 1D Arrayarray = geek.rando...