importnumpyasnp# 使用默认的Mersenne Twister生成器np.random.seed(13579)print(f"Default RNG from numpyarray.com:{np.random.rand()}")# 使用PCG64生成器rng=np.random.Generator(np.random.PCG64(seed=13579))print(f"PCG64 RNG from numpyarray.com:{rng.random()}")# 使用Philox生成器rng=np.random.Generator(np.random.Philo...
NumPy允许我们创建自定义的随机数生成器,这对于需要更精细控制的场景非常有用。 importnumpyasnp# 创建一个自定义的随机数生成器rng=np.random.default_rng(seed=12345)# 使用自定义生成器生成随机数custom_random=rng.random(5)print("Custom random numbers from numpyarray.com:",custom_random)# 使用自定义生成...
rng = np.random.default_rng(seed=12345) data=rng.standard_normal((2,3))permutation:返回一个序列的随机排列,或返回一个随机排列的范围 shuffle:随机打乱一个序列 uniform:从均匀分布中抽取样本 integers:从一个由低到高的范围抽取随机整数 Standard_normal:从均值为0,标准差为1的正态分布中抽取样本 binomial...
Calldefault_rngto get a new instance of aGenerator, then call its methods to obtain samples from different distributions. By default,Generatoruses bits provided byPCG64which has better statistical properties than the legacyMT19937used inRandomState. 请调用default_rng以获取Generator的新实例,然后调用其...
>>> import numpy as np >>> rng = np.random.default_rng(seed=42) >>> xarr = rng.random((3, 3)) >>> xarr array([[0.77395605, 0.43887844, 0.85859792], [0.69736803, 0.09417735, 0.97562235], [0.7611397 , 0.78606431, 0.12811363]]) >>> R1 = np.corrcoef(xarr) >>> R1 array([[ 1\...
请调用default_rng以获取Generator的新实例,然后调用其方法从不同的分布中获取样本。默认情况下,Generator使用PCG64提供的位,其统计特性比RandomState使用的旧版MT19937更好。 In [9]: # Do this (new version) ...: from numpy.random import default_rng ...
Using numpy.random.binomial may change the RNG state vs. numpy < 1.9 Random seed enforced to be a 32 bit unsigned integer Argmin and argmax out argument Einsum Indexing Non-integer reduction axis indexes are deprecated promote_types and string dtype can_cast and string dtype astype...
import pandas as pd import numpy as np # 创建一个时间序列数据 rng = pd.date_range('1/1/2021', periods=100, freq='D') ts = pd.Series(np.random.randn(len(rng)), index=rng) # 将数据按月份进行重采样,并计算每个月的平均值 ts.resample('M').mean() 9.DataFrame.plot( ) 使用pandas...
rng = np.random.default_rng(42) arr = rng.random((5, 4)) arr array([[0.77395605, 0.43887844, 0.85859792, 0.69736803], [0.09417735, 0.97562235, 0.7611397 , 0.78606431], [0.12811363, 0.45038594, 0.37079802, 0.92676499], [0.64386512, 0.82276161, 0.4434142 , 0.22723872], ...
4) ax.set_ylim(0, 4) ax.tick_params(which='major', width=1.0) ax.tick_params(which='...