Python中的random模块用于生成随机数。下面介绍一下random模块中最常用的几个函数。 random.random random.random()用于生成一个0到1的随机符点数: 0 <= n < 1.0 random.uniform random.uniform的函数原型为:random.uniform(a, b),用于生成一个指定范围内的随机符点数,两个参数其中一个是上限,一个是下限。如果...
step -- 指定递增基数。(也就是说最后print variable的值减除范围开始的值能被步长整除) v = random.randrange(100, 1000, 3) print va Traceback (most recent call last): File "<stdin>", line 1, in <module> NameError: name 'va' is not defined print v 802 3.random.choice(" ") print r...
"""Random variable generators. integers --- uniform within range sequences --- pick random element pick random sample generate random permutation distributions on the real line: --- uniform triangular normal (Gaussian) lognormal negative exponential gamma beta pareto Weibull distributions on the circl...
seed:一个 Python 整数.用于为分布创建一个随机种子.查看 tf.set_random_seed 行为. name:操作的名称(可选). 返回: 用于填充随机均匀值的指定形状的张量. 可能引发的异常: ValueError:如果 dtype 是整数并且 maxval 没有被指定. tf.random_uniform((5, 5), minval=low,maxval=high,dtype=tf.float32)))返...
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Returns the initial seed for generating random numbers as aPythonlong. 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. ...
Python random ModuleNumPy CounterpartUse random() rand() Random float in [0.0, 1.0) randint(a, b) random_integers() Random integer in [a, b] randrange(a, b[, step]) randint() Random integer in [a, b) uniform(a, b) uniform() Random float in [a, b] choice(seq) choice() ...
Python >>> rng = np.random.default_rng() >>> rng.integers(size=(2, 3), low=1, high=5) array([[4, 2, 3], [1, 1, 2]], dtype=int64) >>> rng.uniform(size=(2, 3), low=1, high=5) array([[4.97441068, 1.02042664, 1.43584549], [2.87965746, 1.99063036, 2.86212453]]) ...
利用Python的两个模块,分别为pandas和scikit-learn来实现随机森林。 from sklearn.datasets import load_irisfrom sklearn.ensemble import RandomForestClassifierimport pandas as pdimport numpy as np iris = load_iris() df = pd.DataFrame(iris.data, columns=iris.feature_names) ...
python setup.py install By default, GPU support is built if CUDA is found and torch.cuda.is_available() is True. Additionally, it is possible to force building GPU support by setting the FORCE_CUDA=1 environment variable, which is useful when building a docker image. ...