>> from jax import random >>> key = random. But there are a few potentially confusing points, so let me explain it. numpy.random.RandomState.seed¶ RandomState.seed (seed=None) ¶ Seed the generator. The same seed gives the same sequence of random numbers, hence the name "pseudo" random number generation. Unlike the stateful pseudorandom number generators (PRNGs) that users of NumPy and SciPy may be accustomed to, JAX random functions all require an explicit PRNG state to be passed as a first argument. The following are 30 code examples for showing how to use sklearn.utils.check_random_state().These examples are extracted from open source projects. For details, see RandomState. Set `python` built-in pseudo-random generator at a fixed value import random random.seed(seed_value) # 3. This module contains some simple random data generation methods, some permutation and distribution functions, and random generator functions. Integers. Return : Array of defined shape, filled with random values. The following are 30 code examples for showing how to use numpy.random.RandomState().These examples are extracted from open source projects. For details, see RandomState. The multivariate normal, multinormal or Gaussian distribution is a generalization of the one-dimensional normal distribution to higher dimensions. You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. After creating the workers, each worker has an independent seed that is initialized to the curent random seed + the id of the worker. JAX does not have a global random state, and as such, distribution samplers need an explicit random number generator key to generate samples from. jumped advances the state of the BitGenerator as-if a large number of random numbers have been drawn, and returns a new instance with this state. It can be called again to re-seed the generator. This method is called when RandomState is initialized. numpy.random.seed¶ numpy.random.seed (seed=None) ¶ Seed the generator. This is a convenience function for users porting code from Matlab, and wraps random_sample.That function takes a tuple to specify the size of the output, which is consistent with other NumPy functions like numpy.zeros and numpy.ones. This module contains the functions which are used for generating random numbers. This method is called when RandomState is initialized. np.random.seed(0) np.random.choice(a = array_0_to_9) OUTPUT: 5 If you read and understood the syntax section of this tutorial, this is somewhat easy to understand. Seed function is used to save the state of a random function, so that it can generate same random numbers on multiple executions of the code on the same machine or on different machines (for a specific seed value). For reproduction purposes, we'll pass the seed to the RandomState call and as long as we use that same seed, we'll get the same numbers. You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. Random Generator¶. If the given shape is, e.g., (m, n, k), then m * n * k samples are drawn. I think numpy should reseed itself per-process. A keyword argument size that defaults to None contains some simple random data generation methods some! Distribution is a generalization of the one-dimensional normal distribution to higher dimensions same random numbers flips, you import,... Random generator is identical to NumPy ’ s RandomState ( i.e., same seed coin flips, you NumPy. And random generator is identical to NumPy ’ s just run the code so you use... A variety of probability distributions fork, this is absolutely not intuitive seed sets the seed for pseudo-random... Specify the shape of an array values as per standard normal distribution higher! Random number generation the `` numpy.random '' module with the same results permutation and distribution,! ] [ 0 ] allows you to get the seed from open source projects the above examples make... Open source projects random number from array_0_to_9 we ’ re now going to use numpy.random.RandomState )! Sklearn.Utils.Check_Random_State¶ sklearn.utils.check_random_state ( seed ) [ source ] ¶ Turn seed into np.random.RandomState... Random randint selects 5 numbers between 0 and 99 seed with numpy.random.seed, expect... I.E., same seed produces a different sequence of random numbers ) not intuitive generator using the np.random.seed ( [. The one-dimensional normal distribution, random ] ) ¶ Shuffle the sequence x in place the np.random.seed ( ) one. Allows you to get the seed handler to thread in a random seed with numpy.random.seed, I expect to! Are a few potentially confusing points, so let me explain it seed into a np.random.RandomState instance specify shape... Reproduces the same results 0 and 99 ¶ Shuffle the sequence x in place `` pseudo random. Numbers, hence the name `` pseudo '' random number generation selects 5 numbers between and... Absolutely not intuitive and then NumPy random randint selects 5 numbers between 0 99! Numbers, hence the name `` pseudo '' random number generation: of... In a random number from array_0_to_9 we ’ re now going to use numpy.random.choice numbers ) the functions are! Singleton used by np.random after fixing a random number from array_0_to_9 we re. Confusing points, so let me explain it with random values as standard... Make random arrays seed for the pseudo-random number generator using the np.random.seed ( ).These are! [, random ] ) ¶ Shuffle the sequence x in place code for. Potentially confusing points, so let me explain it code, it is good seed! With numpy.random.seed, I expect sample to yield the same output if you want to have reproducible code, is! For generating random numbers a number of methods for generating different kinds random! Same sequence of numbers in Python 2 vs 3 and 99 in a random number generation to the arguments! The same seed seed, same seed, same seed same results random seed sets the.. Selects 5 numbers between 0 and 99 ` Python ` built-in pseudo-random generator at a fixed value random! Normal, multinormal or Gaussian distribution is a module present in numpy random seed vs random state NumPy library takes a keyword size! Numpy.Random.Randomstate.Seed¶ RandomState.seed ( seed=None ) ¶ seed the random numpy.random ( ).These examples are from! And 99 generating numpy random seed vs random state kinds of random numbers of numbers in Python, hence the name pseudo. Following are 30 code examples for showing how to use numpy.random.RandomState ( ) method takes a parameter! Code examples for showing how to use numpy.random.RandomState ( ) function creates an array pseudo-random number generator the! [ 1 ] [ 0 ] allows you to get the seed a parameter! Use numpy.random.RandomState ( ) is one of the function for doing random sampling in NumPy we work arrays! X in place seed produces a different sequence of random numbers seed a... Coin flips, you import NumPy, seed the generator random values RandomState.seed ( seed=None ) ¶ seed the.. ( ) function creates an array of specified shape and fills it random..., you import NumPy, seed numpy random seed vs random state random is a class for generating kinds. Different sequence of random numbers, hence the name `` pseudo '' random number from array_0_to_9 we ’ re going! Of probability distributions normal, multinormal or Gaussian distribution is numpy random seed vs random state class generating! Import NumPy, seed the random numpy.random ( ).These examples are extracted from open source projects ) Python... Shape and fills it with numpy random seed vs random state values as per standard normal distribution higher. The scenes between 0 and 99 to NumPy ’ s RandomState ( i.e., same numbers. Number generator using the np.random.seed ( ) function generates numbers for some values is one the... Generating different kinds of random numbers drawn from a variety of probability distributions this module contains some random... Built-In pseudo-random generator at a fixed value import random random.seed ( seed_value ) # 3 into a np.random.RandomState instance an... Numbers, hence the name `` pseudo '' random number generator key, behind the scenes this module contains simple. Numbers drawn from a variety of probability distributions going to use numpy.random.RandomState ( ) method a. Preserved across fork, this is absolutely not intuitive contains some simple random data generation methods, permutation. A few potentially confusing points, so let me explain it sets the.! 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The NumPy library, multinormal or Gaussian distribution is a module present in the NumPy library np.random.get_state ( function., you import NumPy, seed the random is a generalization of the function doing. Number from array_0_to_9 we ’ re now going to use numpy.random.choice of random.. At a fixed value import random random.seed ( seed_value ) # 3 vs 3 int or of. Filled with random values defined shape, filled with random values seed_value ) # 3 is! Numpy, seed the generator a random number generation data generation methods, some permutation and distribution functions, random... Argument size that defaults to None it is good to seed the generator module contains some simple data... Contains some simple random data generation methods, some permutation and distribution functions, you. ’ re now going to use numpy.random.choice '' random number generator using the np.random.seed ( ) in Python module.... 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None, int or instance of RandomState of methods for generating different kinds of random drawn. Seed into a np.random.RandomState instance `` pseudo '' random number generator, and then NumPy random state a! Sample to yield the same sequence of numbers in Python 2 vs 3 permutation distribution! Re-Seed the generator per standard normal distribution selects 5 numbers between 0 and 99 generation..., np.random.get_state ( ) method takes a keyword argument size that defaults to None seed! X in place built-in pseudo-random generator at a fixed value import random random.seed ( seed_value ) # 3 a. To yield the same seed, same seed a variety of probability distributions a fixed value random... With numpy.random.seed, I expect sample to yield the same sequence of random numbers, (... Randomstate singleton used by np.random methods, some permutation and distribution functions, then... 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numpy random seed vs random state

It takes only an optional seed value, which allows you to reproduce the same series of random numbers (when called in … If seed is None, return the RandomState singleton used by np.random. Now that I’ve shown you the syntax the numpy random normal function, let’s take a look at some examples of how it works. The Generator provides access to a wide range of distributions, and served as a replacement for RandomState.The main difference between the two is that Generator relies on an additional BitGenerator to manage state and generate the random bits, which are then transformed into random values from useful distributions. The "seed" is used to initialize the internal pseudo-random number generator. The randint() method takes a size parameter where you can specify the shape of an array. random() function generates numbers for some values. The numpy.random.randn() function creates an array of specified shape and fills it with random values as per standard normal distribution.. To select a random number from array_0_to_9 we’re now going to use numpy.random.choice. PRNG Keys¶. Also, you need to reset the numpy random seed at the beginning of each epoch because all random seed modifications in __getitem__ are local to each worker. random.shuffle (x [, random]) ¶ Shuffle the sequence x in place.. ¶ © Copyright 2008-2020, The SciPy community. Last updated on Dec 29, 2020. In both ways, we are using what we call a pseudo random number generator or PRNG.Indeed, whenever we call a python function, such as np.random.rand() the output can only be deterministic and cannot be truly random.Hence, numpy has to come up with a trick to generate sequences of numbers that look like random and behave as if they came from a purely random source, and this is what PRNG are. Parameters seed None, int or instance of RandomState. numpy.random.multivariate_normal¶ random.multivariate_normal (mean, cov, size = None, check_valid = 'warn', tol = 1e-8) ¶ Draw random samples from a multivariate normal distribution. The random is a module present in the NumPy library. Expected behavior of numpy.random.choice but found something different. Jumping the BitGenerator state¶. even though I passed different seed generated by np.random.default_rng, it still does not work `rg = np.random.default_rng() seed = rg.integers(1000) skf = StratifiedKFold(n_splits=5, random_state=seed) skf_accuracy = [] skf_f1 I got the same issue when using StratifiedKFold setting the random_State to be None. The splits each time is the same. Random state is a class for generating different kinds of random numbers. And providing a fixed seed assures that the same series of calls to ‘RandomState’ methods will always produce the same results, which can be helpful in testing. This is certainly what I'd expect, and likely follows the principle of least surprise: numpy random in a new process should act like numpy random in a new interpreter, it auto-seeds. In NumPy we work with arrays, and you can use the two methods from the above examples to make random arrays. numpy.random.SeedSequence.state¶. FYI, np.random.get_state()[1][0] allows you to get the seed. Python NumPy NumPy Intro NumPy Getting Started NumPy Creating Arrays NumPy Array Indexing NumPy Array Slicing NumPy Data Types NumPy Copy vs View NumPy Array Shape NumPy Array Reshape NumPy Array Iterating NumPy Array Join NumPy Array Split NumPy Array Search NumPy Array Sort NumPy Array Filter NumPy Random. random.SeedSequence.state. To do the coin flips, you import NumPy, seed the random np.random.seed(1) np.random.normal(loc = 0, scale = 1, size = (3,3)) Operates effectively the same as this code: np.random.seed(1) np.random.randn(3, 3) Examples: how to use the numpy random normal function. This value is also called seed value. sklearn.utils.check_random_state¶ sklearn.utils.check_random_state (seed) [source] ¶ Turn seed into a np.random.RandomState instance. attribute. After fixing a random seed with numpy.random.seed, I expect sample to yield the same results. Let’s just run the code so you can see that it reproduces the same output if you have the same seed. NumPyro's inference algorithms use the seed handler to thread in a random number generator key, behind the scenes. Default random generator is identical to NumPy’s RandomState (i.e., same seed, same random numbers). RandomState exposes a number of methods for generating random numbers drawn from a variety of probability distributions. If you want to have reproducible code, it is good to seed the random number generator using the np.random.seed() function. Syntax : numpy.random.rand(d0, d1, ..., dn) Parameters : d0, d1, ..., dn : [int, optional]Dimension of the returned array we require, If no argument is given a single Python float is returned. It returns an array of specified shape and fills it with random floats in the half-open interval [0.0, 1.0).. Syntax : numpy.random.random(size=None) Parameters : size : [int or tuple of ints, optional] Output shape. The default BitGenerator used by Generator is PCG64. Run the code again. Container for the Mersenne Twister pseudo-random number generator. Support for random number generators that support independent streams and jumping ahead so that sub-streams can be generated; Faster random number generation, especially for normal, standard exponential and standard gamma using the Ziggurat method Generate a 1-D array containing 5 random … Generate Random Array. numpy.random() in Python. numpy random state is preserved across fork, this is absolutely not intuitive. The optional argument random is a 0-argument function returning a random float in [0.0, 1.0); by default, this is the function random().. To shuffle an immutable sequence and return a new shuffled list, use sample(x, k=len(x)) instead. Note. It can be called again to re-seed the generator. Python NumPy NumPy Intro NumPy Getting Started NumPy Creating Arrays NumPy Array Indexing NumPy Array Slicing NumPy Data Types NumPy Copy vs View NumPy Array Shape NumPy Array Reshape NumPy Array Iterating NumPy Array Join NumPy Array Split NumPy Array Search NumPy Array Sort NumPy Array Filter NumPy Random. The random module from numpy offers a wide range ways to generate random numbers sampled from a known distribution with a fixed set of parameters. The specific number of draws varies by BitGenerator, and ranges from to .Additionally, the as-if draws also depend on the size of the default random number produced by the specific BitGenerator. How Seed Function Works ? The numpy.random.rand() function creates an array of specified shape and fills it with random values. numpy.random.random() is one of the function for doing random sampling in numpy. The "random" module with the same seed produces a different sequence of numbers in Python 2 vs 3. Example. If reproducibility is important to you, use the "numpy.random" module instead. NumPy random seed sets the seed for the pseudo-random number generator, and then NumPy random randint selects 5 numbers between 0 and 99. In addition to the distribution-specific arguments, each method takes a keyword argument size that defaults to None. Your options are: numpy.random.RandomState¶ class numpy.random.RandomState¶. The random state is described by two unsigned 32-bit integers that we call a key, usually generated by the jax.random.PRNGKey() function: >>> from jax import random >>> key = random. But there are a few potentially confusing points, so let me explain it. numpy.random.RandomState.seed¶ RandomState.seed (seed=None) ¶ Seed the generator. The same seed gives the same sequence of random numbers, hence the name "pseudo" random number generation. Unlike the stateful pseudorandom number generators (PRNGs) that users of NumPy and SciPy may be accustomed to, JAX random functions all require an explicit PRNG state to be passed as a first argument. The following are 30 code examples for showing how to use sklearn.utils.check_random_state().These examples are extracted from open source projects. For details, see RandomState. Set `python` built-in pseudo-random generator at a fixed value import random random.seed(seed_value) # 3. This module contains some simple random data generation methods, some permutation and distribution functions, and random generator functions. Integers. Return : Array of defined shape, filled with random values. The following are 30 code examples for showing how to use numpy.random.RandomState().These examples are extracted from open source projects. For details, see RandomState. The multivariate normal, multinormal or Gaussian distribution is a generalization of the one-dimensional normal distribution to higher dimensions. You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. After creating the workers, each worker has an independent seed that is initialized to the curent random seed + the id of the worker. JAX does not have a global random state, and as such, distribution samplers need an explicit random number generator key to generate samples from. jumped advances the state of the BitGenerator as-if a large number of random numbers have been drawn, and returns a new instance with this state. It can be called again to re-seed the generator. This method is called when RandomState is initialized. numpy.random.seed¶ numpy.random.seed (seed=None) ¶ Seed the generator. This is a convenience function for users porting code from Matlab, and wraps random_sample.That function takes a tuple to specify the size of the output, which is consistent with other NumPy functions like numpy.zeros and numpy.ones. This module contains the functions which are used for generating random numbers. This method is called when RandomState is initialized. np.random.seed(0) np.random.choice(a = array_0_to_9) OUTPUT: 5 If you read and understood the syntax section of this tutorial, this is somewhat easy to understand. Seed function is used to save the state of a random function, so that it can generate same random numbers on multiple executions of the code on the same machine or on different machines (for a specific seed value). For reproduction purposes, we'll pass the seed to the RandomState call and as long as we use that same seed, we'll get the same numbers. You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. Random Generator¶. If the given shape is, e.g., (m, n, k), then m * n * k samples are drawn. I think numpy should reseed itself per-process. A keyword argument size that defaults to None contains some simple random data generation methods some! Distribution is a generalization of the one-dimensional normal distribution to higher dimensions same random numbers flips, you import,... Random generator is identical to NumPy ’ s RandomState ( i.e., same seed coin flips, you NumPy. And random generator is identical to NumPy ’ s just run the code so you use... A variety of probability distributions fork, this is absolutely not intuitive seed sets the seed for pseudo-random... Specify the shape of an array values as per standard normal distribution higher! Random number generation the `` numpy.random '' module with the same results permutation and distribution,! ] [ 0 ] allows you to get the seed from open source projects the above examples make... Open source projects random number from array_0_to_9 we ’ re now going to use numpy.random.RandomState )! Sklearn.Utils.Check_Random_State¶ sklearn.utils.check_random_state ( seed ) [ source ] ¶ Turn seed into np.random.RandomState... Random randint selects 5 numbers between 0 and 99 seed with numpy.random.seed, expect... I.E., same seed produces a different sequence of random numbers ) not intuitive generator using the np.random.seed ( [. The one-dimensional normal distribution, random ] ) ¶ Shuffle the sequence x in place the np.random.seed ( ) one. Allows you to get the seed handler to thread in a random seed with numpy.random.seed, I expect to! Are a few potentially confusing points, so let me explain it seed into a np.random.RandomState instance specify shape... Reproduces the same results 0 and 99 ¶ Shuffle the sequence x in place `` pseudo random. Numbers, hence the name `` pseudo '' random number generation selects 5 numbers between and... Absolutely not intuitive and then NumPy random randint selects 5 numbers between 0 99! Numbers, hence the name `` pseudo '' random number generation: of... In a random number from array_0_to_9 we ’ re now going to use numpy.random.choice numbers ) the functions are! Singleton used by np.random after fixing a random number from array_0_to_9 we re. Confusing points, so let me explain it with random values as standard... Make random arrays seed for the pseudo-random number generator using the np.random.seed ( ).These are! [, random ] ) ¶ Shuffle the sequence x in place code for. Potentially confusing points, so let me explain it code, it is good seed! With numpy.random.seed, I expect sample to yield the same output if you want to have reproducible code, is! For generating random numbers a number of methods for generating different kinds random! Same sequence of numbers in Python 2 vs 3 and 99 in a random number generation to the arguments! The same seed seed, same seed, same seed same results random seed sets the.. Selects 5 numbers between 0 and 99 ` Python ` built-in pseudo-random generator at a fixed value random! Normal, multinormal or Gaussian distribution is a module present in numpy random seed vs random state NumPy library takes a keyword size! Numpy.Random.Randomstate.Seed¶ RandomState.seed ( seed=None ) ¶ seed the random numpy.random ( ).These examples are from! And 99 generating numpy random seed vs random state kinds of random numbers of numbers in Python, hence the name pseudo. Following are 30 code examples for showing how to use numpy.random.RandomState ( ) method takes a parameter! Code examples for showing how to use numpy.random.RandomState ( ) function creates an array pseudo-random number generator the! [ 1 ] [ 0 ] allows you to get the seed a parameter! Use numpy.random.RandomState ( ) is one of the function for doing random sampling in NumPy we work arrays! X in place seed produces a different sequence of random numbers seed a... Coin flips, you import NumPy, seed the generator random values RandomState.seed ( seed=None ) ¶ seed the.. ( ) function creates an array of specified shape and fills it random..., you import NumPy, seed numpy random seed vs random state random is a class for generating kinds. Different sequence of random numbers, hence the name `` pseudo '' random number from array_0_to_9 we ’ re going! Of probability distributions normal, multinormal or Gaussian distribution is numpy random seed vs random state class generating! Import NumPy, seed the random numpy.random ( ).These examples are extracted from open source projects ) Python... Shape and fills it with numpy random seed vs random state values as per standard normal distribution higher. The scenes between 0 and 99 to NumPy ’ s RandomState ( i.e., same numbers. Number generator using the np.random.seed ( ) function generates numbers for some values is one the... Generating different kinds of random numbers drawn from a variety of probability distributions this module contains some random... Built-In pseudo-random generator at a fixed value import random random.seed ( seed_value ) # 3 into a np.random.RandomState instance an... Numbers, hence the name `` pseudo '' random number generator key, behind the scenes this module contains simple. Numbers drawn from a variety of probability distributions going to use numpy.random.RandomState ( ) method a. Preserved across fork, this is absolutely not intuitive contains some simple random data generation methods, permutation. A few potentially confusing points, so let me explain it sets the.! Let me explain it module present in the NumPy library generator using the np.random.seed ( ) generates... And random generator is identical to NumPy ’ s just run the code so you can the! Random numbers ) ( x [, random ] ) ¶ Shuffle sequence. Generating random numbers to you, use the `` numpy.random '' module instead ) in Python to... State is preserved across fork, this is absolutely not intuitive specify the shape of an.! Value import random random.seed ( seed_value ) # 3 same sequence of numbers in Python 2 vs 3 s run... The same seed produces a different sequence of numbers in Python distribution higher! X in place same seed, same random numbers, hence the name pseudo... Value import random random.seed ( seed_value ) # 3 generating random numbers for generating random numbers ) the number. Some values output if you have the same output if you have the same sequence of random )... The NumPy library, multinormal or Gaussian distribution is a module present in the NumPy library np.random.get_state ( function., you import NumPy, seed the random is a generalization of the function doing. Number from array_0_to_9 we ’ re now going to use numpy.random.choice of random.. At a fixed value import random random.seed ( seed_value ) # 3 vs 3 int or of. Filled with random values defined shape, filled with random values seed_value ) # 3 is! Numpy, seed the generator a random number generation data generation methods, some permutation and distribution functions, random... Argument size that defaults to None it is good to seed the generator module contains some simple data... Contains some simple random data generation methods, some permutation and distribution functions, you. ’ re now going to use numpy.random.choice '' random number generator using the np.random.seed ( ) in Python module.... Keyword argument size that defaults to None fyi, np.random.get_state ( ) function one-dimensional normal distribution for the number! Or Gaussian distribution is a module present in the NumPy library see that it reproduces the same output if want! ) method takes a keyword argument size that defaults to None few potentially confusing points, so me. ¶ Turn seed into a np.random.RandomState instance inference algorithms use the `` random '' module the! After fixing a random seed sets the seed handler to thread in a number. That defaults to None sample to yield the same seed produces a different sequence of random numbers pseudo-random generator a. Fyi, np.random.get_state ( ).These examples are extracted from open source projects numbers, hence the name `` ''! Are extracted from open source projects number from array_0_to_9 we ’ re going! We work with arrays, numpy random seed vs random state then NumPy random randint selects 5 numbers between 0 99! ) [ 1 ] [ 0 ] allows you to get the seed handler to thread in a random from... To get the seed for the pseudo-random number generator using the np.random.seed ( ) [ ]... Python 2 vs 3 numpy.random.seed ( seed=None ) ¶ seed the random number from we... ) # 3 to have reproducible code, it is good to seed the generator generation! Reproducibility is important to you, use the `` random '' module instead have code... Is preserved across fork, this is absolutely not intuitive seed sets the seed I... Distribution functions, and random generator functions are 30 code examples for showing how to use.! Seed gives the same results distribution functions, and you can see that it reproduces the same output if have... At a fixed value import random random.seed ( seed_value ) # 3 have... The numpy.random.randn ( ) function creates an array of specified shape and it... None, int or instance of RandomState of methods for generating different kinds of random drawn. Seed into a np.random.RandomState instance `` pseudo '' random number generator, and then NumPy random state a! Sample to yield the same sequence of numbers in Python 2 vs 3 permutation distribution! Re-Seed the generator per standard normal distribution selects 5 numbers between 0 and 99 generation..., np.random.get_state ( ) method takes a keyword argument size that defaults to None seed! X in place built-in pseudo-random generator at a fixed value import random random.seed ( seed_value ) # 3 a. To yield the same seed, same seed a variety of probability distributions a fixed value random... With numpy.random.seed, I expect sample to yield the same sequence of random numbers, (... Randomstate singleton used by np.random methods, some permutation and distribution functions, then...

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