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numpy.random.Generator.poisson method random.Generator.poisson(lam=1.0, size=None)
Draw samples from a Poisson distribution. The Poisson distribution is the limit of the binomial distribution for large N. Parameters
lamfloat or array_like of floats
Expected number of events occurring in a fixed-time interval,... | numpy.reference.random.generated.numpy.random.generator.poisson |
numpy.random.Generator.power method random.Generator.power(a, size=None)
Draws samples in [0, 1] from a power distribution with positive exponent a - 1. Also known as the power function distribution. Parameters
afloat or array_like of floats
Parameter of the distribution. Must be non-negative.
sizeint or tu... | numpy.reference.random.generated.numpy.random.generator.power |
numpy.random.Generator.random method random.Generator.random(size=None, dtype=np.float64, out=None)
Return random floats in the half-open interval [0.0, 1.0). Results are from the “continuous uniform” distribution over the stated interval. To sample \(Unif[a, b), b > a\) multiply the output of random by (b-a) and a... | numpy.reference.random.generated.numpy.random.generator.random |
numpy.random.Generator.rayleigh method random.Generator.rayleigh(scale=1.0, size=None)
Draw samples from a Rayleigh distribution. The \(\chi\) and Weibull distributions are generalizations of the Rayleigh. Parameters
scalefloat or array_like of floats, optional
Scale, also equals the mode. Must be non-negativ... | numpy.reference.random.generated.numpy.random.generator.rayleigh |
numpy.random.Generator.shuffle method random.Generator.shuffle(x, axis=0)
Modify an array or sequence in-place by shuffling its contents. The order of sub-arrays is changed but their contents remains the same. Parameters
xndarray or MutableSequence
The array, list or mutable sequence to be shuffled.
axisint... | numpy.reference.random.generated.numpy.random.generator.shuffle |
numpy.random.Generator.standard_cauchy method random.Generator.standard_cauchy(size=None)
Draw samples from a standard Cauchy distribution with mode = 0. Also known as the Lorentz distribution. Parameters
sizeint or tuple of ints, optional
Output shape. If the given shape is, e.g., (m, n, k), then m * n * k s... | numpy.reference.random.generated.numpy.random.generator.standard_cauchy |
numpy.random.Generator.standard_exponential method random.Generator.standard_exponential(size=None, dtype=np.float64, method='zig', out=None)
Draw samples from the standard exponential distribution. standard_exponential is identical to the exponential distribution with a scale parameter of 1. Parameters
sizeint... | numpy.reference.random.generated.numpy.random.generator.standard_exponential |
numpy.random.Generator.standard_gamma method random.Generator.standard_gamma(shape, size=None, dtype=np.float64, out=None)
Draw samples from a standard Gamma distribution. Samples are drawn from a Gamma distribution with specified parameters, shape (sometimes designated “k”) and scale=1. Parameters
shapefloat o... | numpy.reference.random.generated.numpy.random.generator.standard_gamma |
numpy.random.Generator.standard_normal method random.Generator.standard_normal(size=None, dtype=np.float64, out=None)
Draw samples from a standard Normal distribution (mean=0, stdev=1). Parameters
sizeint or tuple of ints, optional
Output shape. If the given shape is, e.g., (m, n, k), then m * n * k samples a... | numpy.reference.random.generated.numpy.random.generator.standard_normal |
numpy.random.Generator.standard_t method random.Generator.standard_t(df, size=None)
Draw samples from a standard Student’s t distribution with df degrees of freedom. A special case of the hyperbolic distribution. As df gets large, the result resembles that of the standard normal distribution (standard_normal). Par... | numpy.reference.random.generated.numpy.random.generator.standard_t |
numpy.random.Generator.triangular method random.Generator.triangular(left, mode, right, size=None)
Draw samples from the triangular distribution over the interval [left, right]. The triangular distribution is a continuous probability distribution with lower limit left, peak at mode, and upper limit right. Unlike th... | numpy.reference.random.generated.numpy.random.generator.triangular |
numpy.random.Generator.uniform method random.Generator.uniform(low=0.0, high=1.0, size=None)
Draw samples from a uniform distribution. Samples are uniformly distributed over the half-open interval [low, high) (includes low, but excludes high). In other words, any value within the given interval is equally likely to... | numpy.reference.random.generated.numpy.random.generator.uniform |
numpy.random.Generator.vonmises method random.Generator.vonmises(mu, kappa, size=None)
Draw samples from a von Mises distribution. Samples are drawn from a von Mises distribution with specified mode (mu) and dispersion (kappa), on the interval [-pi, pi]. The von Mises distribution (also known as the circular normal... | numpy.reference.random.generated.numpy.random.generator.vonmises |
numpy.random.Generator.wald method random.Generator.wald(mean, scale, size=None)
Draw samples from a Wald, or inverse Gaussian, distribution. As the scale approaches infinity, the distribution becomes more like a Gaussian. Some references claim that the Wald is an inverse Gaussian with mean equal to 1, but this is ... | numpy.reference.random.generated.numpy.random.generator.wald |
numpy.random.Generator.weibull method random.Generator.weibull(a, size=None)
Draw samples from a Weibull distribution. Draw samples from a 1-parameter Weibull distribution with the given shape parameter a. \[X = (-ln(U))^{1/a}\] Here, U is drawn from the uniform distribution over (0,1]. The more common 2-parameter... | numpy.reference.random.generated.numpy.random.generator.weibull |
numpy.random.Generator.zipf method random.Generator.zipf(a, size=None)
Draw samples from a Zipf distribution. Samples are drawn from a Zipf distribution with specified parameter a > 1. The Zipf distribution (also known as the zeta distribution) is a discrete probability distribution that satisfies Zipf’s law: the f... | numpy.reference.random.generated.numpy.random.generator.zipf |
numpy.random.geometric random.geometric(p, size=None)
Draw samples from the geometric distribution. Bernoulli trials are experiments with one of two outcomes: success or failure (an example of such an experiment is flipping a coin). The geometric distribution models the number of trials that must be run in order to... | numpy.reference.random.generated.numpy.random.geometric |
numpy.random.get_state random.get_state()
Return a tuple representing the internal state of the generator. For more details, see set_state. Parameters
legacybool, optional
Flag indicating to return a legacy tuple state when the BitGenerator is MT19937, instead of a dict. Returns
out{tuple(str, ndarray o... | numpy.reference.random.generated.numpy.random.get_state |
numpy.random.gumbel random.gumbel(loc=0.0, scale=1.0, size=None)
Draw samples from a Gumbel distribution. Draw samples from a Gumbel distribution with specified location and scale. For more information on the Gumbel distribution, see Notes and References below. Note New code should use the gumbel method of a defau... | numpy.reference.random.generated.numpy.random.gumbel |
numpy.random.hypergeometric random.hypergeometric(ngood, nbad, nsample, size=None)
Draw samples from a Hypergeometric distribution. Samples are drawn from a hypergeometric distribution with specified parameters, ngood (ways to make a good selection), nbad (ways to make a bad selection), and nsample (number of items... | numpy.reference.random.generated.numpy.random.hypergeometric |
numpy.random.laplace random.laplace(loc=0.0, scale=1.0, size=None)
Draw samples from the Laplace or double exponential distribution with specified location (or mean) and scale (decay). The Laplace distribution is similar to the Gaussian/normal distribution, but is sharper at the peak and has fatter tails. It repres... | numpy.reference.random.generated.numpy.random.laplace |
numpy.random.logistic random.logistic(loc=0.0, scale=1.0, size=None)
Draw samples from a logistic distribution. Samples are drawn from a logistic distribution with specified parameters, loc (location or mean, also median), and scale (>0). Note New code should use the logistic method of a default_rng() instance ins... | numpy.reference.random.generated.numpy.random.logistic |
numpy.random.lognormal random.lognormal(mean=0.0, sigma=1.0, size=None)
Draw samples from a log-normal distribution. Draw samples from a log-normal distribution with specified mean, standard deviation, and array shape. Note that the mean and standard deviation are not the values for the distribution itself, but of ... | numpy.reference.random.generated.numpy.random.lognormal |
numpy.random.logseries random.logseries(p, size=None)
Draw samples from a logarithmic series distribution. Samples are drawn from a log series distribution with specified shape parameter, 0 < p < 1. Note New code should use the logseries method of a default_rng() instance instead; please see the Quick Start. Par... | numpy.reference.random.generated.numpy.random.logseries |
numpy.random.MT19937.cffi attribute random.MT19937.cffi
CFFI interface Returns
interfacenamedtuple
Named tuple containing CFFI wrapper state_address - Memory address of the state struct state - pointer to the state struct next_uint64 - function pointer to produce 64 bit integers next_uint32 - function pointe... | numpy.reference.random.bit_generators.generated.numpy.random.mt19937.cffi |
numpy.random.MT19937.ctypes attribute random.MT19937.ctypes
ctypes interface Returns
interfacenamedtuple
Named tuple containing ctypes wrapper state_address - Memory address of the state struct state - pointer to the state struct next_uint64 - function pointer to produce 64 bit integers next_uint32 - functio... | numpy.reference.random.bit_generators.generated.numpy.random.mt19937.ctypes |
numpy.random.MT19937.jumped method random.MT19937.jumped(jumps=1)
Returns a new bit generator with the state jumped The state of the returned big generator is jumped as-if 2**(128 * jumps) random numbers have been generated. Parameters
jumpsinteger, positive
Number of times to jump the state of the bit genera... | numpy.reference.random.bit_generators.generated.numpy.random.mt19937.jumped |
numpy.random.MT19937.state attribute random.MT19937.state
Get or set the PRNG state Returns
statedict
Dictionary containing the information required to describe the state of the PRNG | numpy.reference.random.bit_generators.generated.numpy.random.mt19937.state |
numpy.random.multinomial random.multinomial(n, pvals, size=None)
Draw samples from a multinomial distribution. The multinomial distribution is a multivariate generalization of the binomial distribution. Take an experiment with one of p possible outcomes. An example of such an experiment is throwing a dice, where th... | numpy.reference.random.generated.numpy.random.multinomial |
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 multivariate normal, multinormal or Gaussian distribution is a generalization of the one-dimensional normal distribution to higher dimensio... | numpy.reference.random.generated.numpy.random.multivariate_normal |
numpy.random.negative_binomial random.negative_binomial(n, p, size=None)
Draw samples from a negative binomial distribution. Samples are drawn from a negative binomial distribution with specified parameters, n successes and p probability of success where n is > 0 and p is in the interval [0, 1]. Note New code shou... | numpy.reference.random.generated.numpy.random.negative_binomial |
numpy.random.noncentral_chisquare random.noncentral_chisquare(df, nonc, size=None)
Draw samples from a noncentral chi-square distribution. The noncentral \(\chi^2\) distribution is a generalization of the \(\chi^2\) distribution. Note New code should use the noncentral_chisquare method of a default_rng() instance ... | numpy.reference.random.generated.numpy.random.noncentral_chisquare |
numpy.random.noncentral_f random.noncentral_f(dfnum, dfden, nonc, size=None)
Draw samples from the noncentral F distribution. Samples are drawn from an F distribution with specified parameters, dfnum (degrees of freedom in numerator) and dfden (degrees of freedom in denominator), where both parameters > 1. nonc is ... | numpy.reference.random.generated.numpy.random.noncentral_f |
numpy.random.normal random.normal(loc=0.0, scale=1.0, size=None)
Draw random samples from a normal (Gaussian) distribution. The probability density function of the normal distribution, first derived by De Moivre and 200 years later by both Gauss and Laplace independently [2], is often called the bell curve because ... | numpy.reference.random.generated.numpy.random.normal |
numpy.random.pareto random.pareto(a, size=None)
Draw samples from a Pareto II or Lomax distribution with specified shape. The Lomax or Pareto II distribution is a shifted Pareto distribution. The classical Pareto distribution can be obtained from the Lomax distribution by adding 1 and multiplying by the scale param... | numpy.reference.random.generated.numpy.random.pareto |
numpy.random.PCG64.advance method random.PCG64.advance(delta)
Advance the underlying RNG as-if delta draws have occurred. Parameters
deltainteger, positive
Number of draws to advance the RNG. Must be less than the size state variable in the underlying RNG. Returns
selfPCG64
RNG advanced delta steps ... | numpy.reference.random.bit_generators.generated.numpy.random.pcg64.advance |
numpy.random.PCG64.cffi attribute random.PCG64.cffi
CFFI interface Returns
interfacenamedtuple
Named tuple containing CFFI wrapper state_address - Memory address of the state struct state - pointer to the state struct next_uint64 - function pointer to produce 64 bit integers next_uint32 - function pointer to... | numpy.reference.random.bit_generators.generated.numpy.random.pcg64.cffi |
numpy.random.PCG64.ctypes attribute random.PCG64.ctypes
ctypes interface Returns
interfacenamedtuple
Named tuple containing ctypes wrapper state_address - Memory address of the state struct state - pointer to the state struct next_uint64 - function pointer to produce 64 bit integers next_uint32 - function po... | numpy.reference.random.bit_generators.generated.numpy.random.pcg64.ctypes |
numpy.random.PCG64.jumped method random.PCG64.jumped(jumps=1)
Returns a new bit generator with the state jumped. Jumps the state as-if jumps * 210306068529402873165736369884012333109 random numbers have been generated. Parameters
jumpsinteger, positive
Number of times to jump the state of the bit generator re... | numpy.reference.random.bit_generators.generated.numpy.random.pcg64.jumped |
numpy.random.PCG64.state attribute random.PCG64.state
Get or set the PRNG state Returns
statedict
Dictionary containing the information required to describe the state of the PRNG | numpy.reference.random.bit_generators.generated.numpy.random.pcg64.state |
numpy.random.PCG64DXSM.advance method random.PCG64DXSM.advance(delta)
Advance the underlying RNG as-if delta draws have occurred. Parameters
deltainteger, positive
Number of draws to advance the RNG. Must be less than the size state variable in the underlying RNG. Returns
selfPCG64
RNG advanced delta ... | numpy.reference.random.bit_generators.generated.numpy.random.pcg64dxsm.advance |
numpy.random.PCG64DXSM.cffi attribute random.PCG64DXSM.cffi
CFFI interface Returns
interfacenamedtuple
Named tuple containing CFFI wrapper state_address - Memory address of the state struct state - pointer to the state struct next_uint64 - function pointer to produce 64 bit integers next_uint32 - function po... | numpy.reference.random.bit_generators.generated.numpy.random.pcg64dxsm.cffi |
numpy.random.PCG64DXSM.ctypes attribute random.PCG64DXSM.ctypes
ctypes interface Returns
interfacenamedtuple
Named tuple containing ctypes wrapper state_address - Memory address of the state struct state - pointer to the state struct next_uint64 - function pointer to produce 64 bit integers next_uint32 - fun... | numpy.reference.random.bit_generators.generated.numpy.random.pcg64dxsm.ctypes |
numpy.random.PCG64DXSM.jumped method random.PCG64DXSM.jumped(jumps=1)
Returns a new bit generator with the state jumped. Jumps the state as-if jumps * 210306068529402873165736369884012333109 random numbers have been generated. Parameters
jumpsinteger, positive
Number of times to jump the state of the bit gene... | numpy.reference.random.bit_generators.generated.numpy.random.pcg64dxsm.jumped |
numpy.random.PCG64DXSM.state attribute random.PCG64DXSM.state
Get or set the PRNG state Returns
statedict
Dictionary containing the information required to describe the state of the PRNG | numpy.reference.random.bit_generators.generated.numpy.random.pcg64dxsm.state |
numpy.random.permutation random.permutation(x)
Randomly permute a sequence, or return a permuted range. If x is a multi-dimensional array, it is only shuffled along its first index. Note New code should use the permutation method of a default_rng() instance instead; please see the Quick Start. Parameters
xint... | numpy.reference.random.generated.numpy.random.permutation |
numpy.random.Philox.advance method random.Philox.advance(delta)
Advance the underlying RNG as-if delta draws have occurred. Parameters
deltainteger, positive
Number of draws to advance the RNG. Must be less than the size state variable in the underlying RNG. Returns
selfPhilox
RNG advanced delta steps... | numpy.reference.random.bit_generators.generated.numpy.random.philox.advance |
numpy.random.Philox.cffi attribute random.Philox.cffi
CFFI interface Returns
interfacenamedtuple
Named tuple containing CFFI wrapper state_address - Memory address of the state struct state - pointer to the state struct next_uint64 - function pointer to produce 64 bit integers next_uint32 - function pointer ... | numpy.reference.random.bit_generators.generated.numpy.random.philox.cffi |
numpy.random.Philox.ctypes attribute random.Philox.ctypes
ctypes interface Returns
interfacenamedtuple
Named tuple containing ctypes wrapper state_address - Memory address of the state struct state - pointer to the state struct next_uint64 - function pointer to produce 64 bit integers next_uint32 - function ... | numpy.reference.random.bit_generators.generated.numpy.random.philox.ctypes |
numpy.random.Philox.jumped method random.Philox.jumped(jumps=1)
Returns a new bit generator with the state jumped The state of the returned big generator is jumped as-if 2**(128 * jumps) random numbers have been generated. Parameters
jumpsinteger, positive
Number of times to jump the state of the bit generato... | numpy.reference.random.bit_generators.generated.numpy.random.philox.jumped |
numpy.random.Philox.state attribute random.Philox.state
Get or set the PRNG state Returns
statedict
Dictionary containing the information required to describe the state of the PRNG | numpy.reference.random.bit_generators.generated.numpy.random.philox.state |
numpy.random.poisson random.poisson(lam=1.0, size=None)
Draw samples from a Poisson distribution. The Poisson distribution is the limit of the binomial distribution for large N. Note New code should use the poisson method of a default_rng() instance instead; please see the Quick Start. Parameters
lamfloat or ... | numpy.reference.random.generated.numpy.random.poisson |
numpy.random.power random.power(a, size=None)
Draws samples in [0, 1] from a power distribution with positive exponent a - 1. Also known as the power function distribution. Note New code should use the power method of a default_rng() instance instead; please see the Quick Start. Parameters
afloat or array_lik... | numpy.reference.random.generated.numpy.random.power |
numpy.random.rand random.rand(d0, d1, ..., dn)
Random values in a given shape. Note 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.on... | numpy.reference.random.generated.numpy.random.rand |
numpy.random.randint random.randint(low, high=None, size=None, dtype=int)
Return random integers from low (inclusive) to high (exclusive). Return random integers from the “discrete uniform” distribution of the specified dtype in the “half-open” interval [low, high). If high is None (the default), then results are f... | numpy.reference.random.generated.numpy.random.randint |
numpy.random.randn random.randn(d0, d1, ..., dn)
Return a sample (or samples) from the “standard normal” distribution. Note This is a convenience function for users porting code from Matlab, and wraps standard_normal. That function takes a tuple to specify the size of the output, which is consistent with other Num... | numpy.reference.random.generated.numpy.random.randn |
numpy.random.random random.random(size=None)
Return random floats in the half-open interval [0.0, 1.0). Alias for random_sample to ease forward-porting to the new random API. | numpy.reference.random.generated.numpy.random.random |
numpy.random.random_integers random.random_integers(low, high=None, size=None)
Random integers of type np.int_ between low and high, inclusive. Return random integers of type np.int_ from the “discrete uniform” distribution in the closed interval [low, high]. If high is None (the default), then results are from [1,... | numpy.reference.random.generated.numpy.random.random_integers |
numpy.random.random_sample random.random_sample(size=None)
Return random floats in the half-open interval [0.0, 1.0). Results are from the “continuous uniform” distribution over the stated interval. To sample \(Unif[a, b), b > a\) multiply the output of random_sample by (b-a) and add a: (b - a) * random_sample() + ... | numpy.reference.random.generated.numpy.random.random_sample |
numpy.random.RandomState.beta method random.RandomState.beta(a, b, size=None)
Draw samples from a Beta distribution. The Beta distribution is a special case of the Dirichlet distribution, and is related to the Gamma distribution. It has the probability distribution function \[f(x; a,b) = \frac{1}{B(\alpha, \beta)}... | numpy.reference.random.generated.numpy.random.randomstate.beta |
numpy.random.RandomState.binomial method random.RandomState.binomial(n, p, size=None)
Draw samples from a binomial distribution. Samples are drawn from a binomial distribution with specified parameters, n trials and p probability of success where n an integer >= 0 and p is in the interval [0,1]. (n may be input as ... | numpy.reference.random.generated.numpy.random.randomstate.binomial |
numpy.random.RandomState.bytes method random.RandomState.bytes(length)
Return random bytes. Note New code should use the bytes method of a default_rng() instance instead; please see the Quick Start. Parameters
lengthint
Number of random bytes. Returns
outbytes
String of length length. See also ... | numpy.reference.random.generated.numpy.random.randomstate.bytes |
numpy.random.RandomState.chisquare method random.RandomState.chisquare(df, size=None)
Draw samples from a chi-square distribution. When df independent random variables, each with standard normal distributions (mean 0, variance 1), are squared and summed, the resulting distribution is chi-square (see Notes). This di... | numpy.reference.random.generated.numpy.random.randomstate.chisquare |
numpy.random.RandomState.choice method random.RandomState.choice(a, size=None, replace=True, p=None)
Generates a random sample from a given 1-D array New in version 1.7.0. Note New code should use the choice method of a default_rng() instance instead; please see the Quick Start. Parameters
a1-D array-like o... | numpy.reference.random.generated.numpy.random.randomstate.choice |
numpy.random.RandomState.dirichlet method random.RandomState.dirichlet(alpha, size=None)
Draw samples from the Dirichlet distribution. Draw size samples of dimension k from a Dirichlet distribution. A Dirichlet-distributed random variable can be seen as a multivariate generalization of a Beta distribution. The Diri... | numpy.reference.random.generated.numpy.random.randomstate.dirichlet |
numpy.random.RandomState.exponential method random.RandomState.exponential(scale=1.0, size=None)
Draw samples from an exponential distribution. Its probability density function is \[f(x; \frac{1}{\beta}) = \frac{1}{\beta} \exp(-\frac{x}{\beta}),\] for x > 0 and 0 elsewhere. \(\beta\) is the scale parameter, which ... | numpy.reference.random.generated.numpy.random.randomstate.exponential |
numpy.random.RandomState.f method random.RandomState.f(dfnum, dfden, size=None)
Draw samples from an F distribution. Samples are drawn from an F distribution with specified parameters, dfnum (degrees of freedom in numerator) and dfden (degrees of freedom in denominator), where both parameters must be greater than z... | numpy.reference.random.generated.numpy.random.randomstate.f |
numpy.random.RandomState.gamma method random.RandomState.gamma(shape, scale=1.0, size=None)
Draw samples from a Gamma distribution. Samples are drawn from a Gamma distribution with specified parameters, shape (sometimes designated “k”) and scale (sometimes designated “theta”), where both parameters are > 0. Note N... | numpy.reference.random.generated.numpy.random.randomstate.gamma |
numpy.random.RandomState.geometric method random.RandomState.geometric(p, size=None)
Draw samples from the geometric distribution. Bernoulli trials are experiments with one of two outcomes: success or failure (an example of such an experiment is flipping a coin). The geometric distribution models the number of tria... | numpy.reference.random.generated.numpy.random.randomstate.geometric |
numpy.random.RandomState.get_state method random.RandomState.get_state()
Return a tuple representing the internal state of the generator. For more details, see set_state. Parameters
legacybool, optional
Flag indicating to return a legacy tuple state when the BitGenerator is MT19937, instead of a dict. Retu... | numpy.reference.random.generated.numpy.random.randomstate.get_state |
numpy.random.RandomState.gumbel method random.RandomState.gumbel(loc=0.0, scale=1.0, size=None)
Draw samples from a Gumbel distribution. Draw samples from a Gumbel distribution with specified location and scale. For more information on the Gumbel distribution, see Notes and References below. Note New code should u... | numpy.reference.random.generated.numpy.random.randomstate.gumbel |
numpy.random.RandomState.hypergeometric method random.RandomState.hypergeometric(ngood, nbad, nsample, size=None)
Draw samples from a Hypergeometric distribution. Samples are drawn from a hypergeometric distribution with specified parameters, ngood (ways to make a good selection), nbad (ways to make a bad selection... | numpy.reference.random.generated.numpy.random.randomstate.hypergeometric |
numpy.random.RandomState.laplace method random.RandomState.laplace(loc=0.0, scale=1.0, size=None)
Draw samples from the Laplace or double exponential distribution with specified location (or mean) and scale (decay). The Laplace distribution is similar to the Gaussian/normal distribution, but is sharper at the peak ... | numpy.reference.random.generated.numpy.random.randomstate.laplace |
numpy.random.RandomState.logistic method random.RandomState.logistic(loc=0.0, scale=1.0, size=None)
Draw samples from a logistic distribution. Samples are drawn from a logistic distribution with specified parameters, loc (location or mean, also median), and scale (>0). Note New code should use the logistic method ... | numpy.reference.random.generated.numpy.random.randomstate.logistic |
numpy.random.RandomState.lognormal method random.RandomState.lognormal(mean=0.0, sigma=1.0, size=None)
Draw samples from a log-normal distribution. Draw samples from a log-normal distribution with specified mean, standard deviation, and array shape. Note that the mean and standard deviation are not the values for t... | numpy.reference.random.generated.numpy.random.randomstate.lognormal |
numpy.random.RandomState.logseries method random.RandomState.logseries(p, size=None)
Draw samples from a logarithmic series distribution. Samples are drawn from a log series distribution with specified shape parameter, 0 < p < 1. Note New code should use the logseries method of a default_rng() instance instead; pl... | numpy.reference.random.generated.numpy.random.randomstate.logseries |
numpy.random.RandomState.multinomial method random.RandomState.multinomial(n, pvals, size=None)
Draw samples from a multinomial distribution. The multinomial distribution is a multivariate generalization of the binomial distribution. Take an experiment with one of p possible outcomes. An example of such an experime... | numpy.reference.random.generated.numpy.random.randomstate.multinomial |
numpy.random.RandomState.multivariate_normal method random.RandomState.multivariate_normal(mean, cov, size=None, check_valid='warn', tol=1e-8)
Draw random samples from a multivariate normal distribution. The multivariate normal, multinormal or Gaussian distribution is a generalization of the one-dimensional normal ... | numpy.reference.random.generated.numpy.random.randomstate.multivariate_normal |
numpy.random.RandomState.negative_binomial method random.RandomState.negative_binomial(n, p, size=None)
Draw samples from a negative binomial distribution. Samples are drawn from a negative binomial distribution with specified parameters, n successes and p probability of success where n is > 0 and p is in the inter... | numpy.reference.random.generated.numpy.random.randomstate.negative_binomial |
numpy.random.RandomState.noncentral_chisquare method random.RandomState.noncentral_chisquare(df, nonc, size=None)
Draw samples from a noncentral chi-square distribution. The noncentral \(\chi^2\) distribution is a generalization of the \(\chi^2\) distribution. Note New code should use the noncentral_chisquare meth... | numpy.reference.random.generated.numpy.random.randomstate.noncentral_chisquare |
numpy.random.RandomState.noncentral_f method random.RandomState.noncentral_f(dfnum, dfden, nonc, size=None)
Draw samples from the noncentral F distribution. Samples are drawn from an F distribution with specified parameters, dfnum (degrees of freedom in numerator) and dfden (degrees of freedom in denominator), wher... | numpy.reference.random.generated.numpy.random.randomstate.noncentral_f |
numpy.random.RandomState.normal method random.RandomState.normal(loc=0.0, scale=1.0, size=None)
Draw random samples from a normal (Gaussian) distribution. The probability density function of the normal distribution, first derived by De Moivre and 200 years later by both Gauss and Laplace independently [2], is often... | numpy.reference.random.generated.numpy.random.randomstate.normal |
numpy.random.RandomState.pareto method random.RandomState.pareto(a, size=None)
Draw samples from a Pareto II or Lomax distribution with specified shape. The Lomax or Pareto II distribution is a shifted Pareto distribution. The classical Pareto distribution can be obtained from the Lomax distribution by adding 1 and... | numpy.reference.random.generated.numpy.random.randomstate.pareto |
numpy.random.RandomState.permutation method random.RandomState.permutation(x)
Randomly permute a sequence, or return a permuted range. If x is a multi-dimensional array, it is only shuffled along its first index. Note New code should use the permutation method of a default_rng() instance instead; please see the Qu... | numpy.reference.random.generated.numpy.random.randomstate.permutation |
numpy.random.RandomState.poisson method random.RandomState.poisson(lam=1.0, size=None)
Draw samples from a Poisson distribution. The Poisson distribution is the limit of the binomial distribution for large N. Note New code should use the poisson method of a default_rng() instance instead; please see the Quick Star... | numpy.reference.random.generated.numpy.random.randomstate.poisson |
numpy.random.RandomState.power method random.RandomState.power(a, size=None)
Draws samples in [0, 1] from a power distribution with positive exponent a - 1. Also known as the power function distribution. Note New code should use the power method of a default_rng() instance instead; please see the Quick Start. Pa... | numpy.reference.random.generated.numpy.random.randomstate.power |
numpy.random.RandomState.rand method random.RandomState.rand(d0, d1, ..., dn)
Random values in a given shape. Note 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 function... | numpy.reference.random.generated.numpy.random.randomstate.rand |
numpy.random.RandomState.randint method random.RandomState.randint(low, high=None, size=None, dtype=int)
Return random integers from low (inclusive) to high (exclusive). Return random integers from the “discrete uniform” distribution of the specified dtype in the “half-open” interval [low, high). If high is None (t... | numpy.reference.random.generated.numpy.random.randomstate.randint |
numpy.random.RandomState.randn method random.RandomState.randn(d0, d1, ..., dn)
Return a sample (or samples) from the “standard normal” distribution. Note This is a convenience function for users porting code from Matlab, and wraps standard_normal. That function takes a tuple to specify the size of the output, whi... | numpy.reference.random.generated.numpy.random.randomstate.randn |
numpy.random.RandomState.random_integers method random.RandomState.random_integers(low, high=None, size=None)
Random integers of type np.int_ between low and high, inclusive. Return random integers of type np.int_ from the “discrete uniform” distribution in the closed interval [low, high]. If high is None (the defa... | numpy.reference.random.generated.numpy.random.randomstate.random_integers |
numpy.random.RandomState.random_sample method random.RandomState.random_sample(size=None)
Return random floats in the half-open interval [0.0, 1.0). Results are from the “continuous uniform” distribution over the stated interval. To sample \(Unif[a, b), b > a\) multiply the output of random_sample by (b-a) and add ... | numpy.reference.random.generated.numpy.random.randomstate.random_sample |
numpy.random.RandomState.rayleigh method random.RandomState.rayleigh(scale=1.0, size=None)
Draw samples from a Rayleigh distribution. The \(\chi\) and Weibull distributions are generalizations of the Rayleigh. Note New code should use the rayleigh method of a default_rng() instance instead; please see the Quick St... | numpy.reference.random.generated.numpy.random.randomstate.rayleigh |
numpy.random.RandomState.seed method random.RandomState.seed(self, seed=None)
Reseed a legacy MT19937 BitGenerator Notes This is a convenience, legacy function. The best practice is to not reseed a BitGenerator, rather to recreate a new one. This method is here for legacy reasons. This example demonstrates best pra... | numpy.reference.random.generated.numpy.random.randomstate.seed |
numpy.random.RandomState.set_state method random.RandomState.set_state(state)
Set the internal state of the generator from a tuple. For use if one has reason to manually (re-)set the internal state of the bit generator used by the RandomState instance. By default, RandomState uses the “Mersenne Twister”[1] pseudo-r... | numpy.reference.random.generated.numpy.random.randomstate.set_state |
numpy.random.RandomState.shuffle method random.RandomState.shuffle(x)
Modify a sequence in-place by shuffling its contents. This function only shuffles the array along the first axis of a multi-dimensional array. The order of sub-arrays is changed but their contents remains the same. Note New code should use the s... | numpy.reference.random.generated.numpy.random.randomstate.shuffle |
numpy.random.RandomState.standard_cauchy method random.RandomState.standard_cauchy(size=None)
Draw samples from a standard Cauchy distribution with mode = 0. Also known as the Lorentz distribution. Note New code should use the standard_cauchy method of a default_rng() instance instead; please see the Quick Start. ... | numpy.reference.random.generated.numpy.random.randomstate.standard_cauchy |
numpy.random.RandomState.standard_exponential method random.RandomState.standard_exponential(size=None)
Draw samples from the standard exponential distribution. standard_exponential is identical to the exponential distribution with a scale parameter of 1. Note New code should use the standard_exponential method of... | numpy.reference.random.generated.numpy.random.randomstate.standard_exponential |
numpy.random.RandomState.standard_gamma method random.RandomState.standard_gamma(shape, size=None)
Draw samples from a standard Gamma distribution. Samples are drawn from a Gamma distribution with specified parameters, shape (sometimes designated “k”) and scale=1. Note New code should use the standard_gamma method... | numpy.reference.random.generated.numpy.random.randomstate.standard_gamma |
numpy.random.RandomState.standard_normal method random.RandomState.standard_normal(size=None)
Draw samples from a standard Normal distribution (mean=0, stdev=1). Note New code should use the standard_normal method of a default_rng() instance instead; please see the Quick Start. Parameters
sizeint or tuple of ... | numpy.reference.random.generated.numpy.random.randomstate.standard_normal |
numpy.random.RandomState.standard_t method random.RandomState.standard_t(df, size=None)
Draw samples from a standard Student’s t distribution with df degrees of freedom. A special case of the hyperbolic distribution. As df gets large, the result resembles that of the standard normal distribution (standard_normal). ... | numpy.reference.random.generated.numpy.random.randomstate.standard_t |
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