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Web ini bisa digunakan membuat Bilangan Biner secara Acak dengan cara yang mudah, cukup start dan stop saja. Bilangan Binner secara random akan dibita oleh web ini secara cepat dengan jumlah bit yang sangat banyak. Untuk menggunakannya tinggal select dan copy bilangan ini. Web ini membuat Bilangan Biner secara Acak dengan cara mengenerate dari webnya berikut ini contoh tampilan saat web ini diakases paa 28 April 2021. Web ini membuat Bilangan Biner secara AcakAlamat web ini ada di sini : https://qrng.anu.edu.au/random-binary/ atau bisa diakese secara langsung di web dibawah ini. Untuk membuat aplikasi di atas, di dalam kode program diperlukan proses untuk merandom sebuah bilangan. Bagaimana cara merandom bilangan? baik bilangan riil maupun bilangan bulat (integer)? Serta dalam kasus lainnya, apabila terdapat sekumpulan bilangan dalam sebuah list, bagaimana mengambil secara acak sebuah bilangan dari list tersebut? Cara membut web random ini bisa dengan berbagai fungsi yang ada di bahasa program misalkan menggunakan
Post Views: 1,026 Post navigationThe Construct a new Generator with the default BitGenerator (PCG64). Parametersseed{None, int, array_like[ints], SeedSequence, BitGenerator, Generator}, optionalA
seed to initialize the The initialized generator object. Notes If
Examples
Here we use
>>> import numpy as np >>> rng = np.random.default_rng(12345) >>> print(rng) Generator(PCG64) >>> rfloat = rng.random() >>> rfloat 0.22733602246716966 >>> type(rfloat) <class 'float'> Here we use >>> import numpy as np >>> rng = np.random.default_rng(12345) >>> rints = rng.integers(low=0, high=10, size=3) >>> rints array([6, 2, 7]) >>> type(rints[0]) <class 'numpy.int64'> Here we specify a seed so that we have reproducible results: >>> import numpy as np >>> rng = np.random.default_rng(seed=42) >>> print(rng) Generator(PCG64) >>> arr1 = rng.random((3, 3)) >>> arr1 array([[0.77395605, 0.43887844, 0.85859792], [0.69736803, 0.09417735, 0.97562235], [0.7611397 , 0.78606431, 0.12811363]]) If we exit and restart our Python interpreter, we’ll see that we generate the same random numbers again: >>> import numpy as np >>> rng = np.random.default_rng(seed=42) >>> arr2 = rng.random((3, 3)) >>> arr2 array([[0.77395605, 0.43887844, 0.85859792], [0.69736803, 0.09417735, 0.97562235], [0.7611397 , 0.78606431, 0.12811363]])class numpy.random.Generator(bit_generator)# Container for the BitGenerators.
The function
No Compatibility Guarantee
BitGenerator to use as the core generator. Notes The Python stdlib module Examples >>> from numpy.random import Generator, PCG64 >>> rng = Generator(PCG64()) >>> rng.standard_normal() -0.203 # random Accessing the BitGenerator#
Simple random data#
Permutations#The methods for randomly permuting a sequence are
The following table summarizes the behaviors of the methods.
The following subsections provide more details about the differences. In-place vs. copy#The main difference between
By default,
>>> rng = np.random.default_rng() >>> x = np.arange(0, 15).reshape(3, 5) >>> x array([[ 0, 1, 2, 3, 4], [ 5, 6, 7, 8, 9], [10, 11, 12, 13, 14]]) >>> y = rng.permuted(x, axis=1, out=x) >>> x array([[ 1, 0, 2, 4, 3], # random [ 6, 7, 8, 9, 5], [10, 14, 11, 13, 12]]) Note that when Handling the axis parameter#An important distinction for these
methods is how they handle the >>> rng = np.random.default_rng() >>> x = np.arange(0, 15).reshape(3, 5) >>> x array([[ 0, 1, 2, 3, 4], [ 5, 6, 7, 8, 9], [10, 11, 12, 13, 14]]) >>> rng.permutation(x, axis=1) array([[ 1, 3, 2, 0, 4], # random [ 6, 8, 7, 5, 9], [11, 13, 12, 10, 14]]) Note that the columns have been rearranged “in bulk”: the values within each column have not changed. The method
>>> rng.permuted(x, axis=1) array([[ 1, 0, 2, 4, 3], # random [ 5, 7, 6, 9, 8], [10, 14, 12, 13, 11]]) In this example, the
values within each row (i.e. the values along Shuffling non-NumPy sequences#
>>> rng = np.random.default_rng() >>> a = ['A', 'B', 'C', 'D', 'E'] >>> rng.shuffle(a) # shuffle the list in-place >>> a ['B', 'D', 'A', 'E', 'C'] # random Distributions#
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