Tessero\Random\Generator

defaultRng(array|int|null $seed = null): self

integers(int $low, ?int $high = null, array|int|null $size = null): Tessero\NDArray|int

Integers in [low, high); with one bound, [0, low).

bytes(int $length): string

`$length` random bytes as a binary string (numpy.random.Generator.bytes). Even $length 0 advances the stream by one 32-bit draw, exactly as NumPy does.

multivariateNormal(mixed $mean, mixed $cov, array|int|null $size = null, string $checkValid = 'warn', float $tol = 1.0E-8, string $method = 'svd'): Tessero\NDArray

Draw samples from a multivariate normal distribution (numpy.random.Generator.multivariate_normal, method 'svd'). $mean is the length-d mean vector and $cov the (d, d) covariance matrix; $size fixes the number of samples (null: one sample of shape (d,)). The SVD factorisation matches NumPy's.

choice(Tessero\NDArray|array|int $a, array|int|null $size = null, bool $replace = true, ?array $p = null): mixed

Sample from a 1-D array (or arange(a) for an int). Probabilities $p use NumPy's CDF-inversion, so the draws match numpy's choice(..., p=p).

permutation(Tessero\NDArray|array|int $x): Tessero\NDArray

Random permutation of arange(n), or a shuffled copy of an array along axis 0.

shuffle(Tessero\NDArray $x): void

Shuffle along axis 0 in place.

dirichlet(array $alpha, array|int|null $size = null): Tessero\NDArray

Draw from a Dirichlet distribution (numpy.random.Generator.dirichlet). Each sample is a vector of length count($alpha) that sums to 1, built from independent standard-gamma draws then normalised.

multinomial(int $n, array $pvals, array|int|null $size = null): Tessero\NDArray

Draw samples from a multinomial distribution (numpy.random.Generator.multinomial): for each of the len($pvals) categories, a count, built from sequential conditional binomial draws.

permuted(Tessero\NDArray $x, ?int $axis = null, ?Tessero\NDArray $out = null): Tessero\NDArray

Randomly permute values along an axis, each slice independently (numpy.random.Generator.permuted). With $axis null the flattened array is permuted. Returns a new array (or writes into $out).

multivariateHypergeometric(array $colors, int $nsample, array|int|null $size = null, string $method = 'marginals'): Tessero\NDArray

Draw from a multivariate hypergeometric distribution (numpy.random.Generator.multivariate_hypergeometric, method='marginals'): sequential univariate hypergeometric draws over the colours.

stateHandle(): FFI\CData

The PCG64 state vector, for the kernel calls that draw from this generator (scipy.stats rvs).

getState(): array

setState(array $words): void

beta(mixed $a, mixed $b, array|int|null $size = null): Tessero\NDArray|float

Samples from a beta distribution (Generator.beta).

binomial(mixed $n, mixed $p, array|int|null $size = null): Tessero\NDArray|int

Samples from a binomial distribution (Generator.binomial).

chisquare(mixed $df, array|int|null $size = null): Tessero\NDArray|float

Samples from a chi-square distribution (Generator.chisquare).

exponential(mixed $scale = 1.0, array|int|null $size = null): Tessero\NDArray|float

Samples from an exponential distribution (Generator.exponential).

f(mixed $dfnum, mixed $dfden, array|int|null $size = null): Tessero\NDArray|float

Samples from an F distribution (Generator.f).

gamma(mixed $shape, mixed $scale = 1.0, array|int|null $size = null): Tessero\NDArray|float

Samples from a gamma distribution (Generator.gamma).

geometric(mixed $p, array|int|null $size = null): Tessero\NDArray|int

Samples from the geometric distribution (Generator.geometric).

gumbel(mixed $loc = 0.0, mixed $scale = 1.0, array|int|null $size = null): Tessero\NDArray|float

Samples from a Gumbel distribution (Generator.gumbel).

hypergeometric(mixed $ngood, mixed $nbad, mixed $nsample, array|int|null $size = null): Tessero\NDArray|int

Samples from a hypergeometric distribution (Generator.hypergeometric).

laplace(mixed $loc = 0.0, mixed $scale = 1.0, array|int|null $size = null): Tessero\NDArray|float

Samples from the Laplace or double exponential distribution (Generator.laplace).

logistic(mixed $loc = 0.0, mixed $scale = 1.0, array|int|null $size = null): Tessero\NDArray|float

Samples from a logistic distribution (Generator.logistic).

lognormal(mixed $mean = 0.0, mixed $sigma = 1.0, array|int|null $size = null): Tessero\NDArray|float

Samples from a log-normal distribution (Generator.lognormal).

logseries(mixed $p, array|int|null $size = null): Tessero\NDArray|int

Samples from a logarithmic series distribution (Generator.logseries).

negativeBinomial(mixed $n, mixed $p, array|int|null $size = null): Tessero\NDArray|int

Samples from a negative binomial distribution (Generator.negative_binomial).

noncentralChisquare(mixed $df, mixed $nonc, array|int|null $size = null): Tessero\NDArray|float

Samples from a noncentral chi-square distribution (Generator.noncentral_chisquare).

noncentralF(mixed $dfnum, mixed $dfden, mixed $nonc, array|int|null $size = null): Tessero\NDArray|float

Samples from the noncentral F distribution (Generator.noncentral_f).

normal(mixed $loc = 0.0, mixed $scale = 1.0, array|int|null $size = null): Tessero\NDArray|float

Samples from a normal (Gaussian) distribution (Generator.normal).

pareto(mixed $a, array|int|null $size = null): Tessero\NDArray|float

Samples from a Pareto II (Lomax) distribution (Generator.pareto).

poisson(mixed $lam = 1.0, array|int|null $size = null): Tessero\NDArray|int

Samples from a Poisson distribution (Generator.poisson).

power(mixed $a, array|int|null $size = null): Tessero\NDArray|float

Samples in [0, 1] from a power distribution with positive exponent a - 1 (Generator.power).

random(array|int|null $size = null, string $dtype = 'float64'): Tessero\NDArray|float

Floats in the half-open interval [0.0, 1.0) (Generator.random).

rayleigh(mixed $scale = 1.0, array|int|null $size = null): Tessero\NDArray|float

Samples from a Rayleigh distribution (Generator.rayleigh).

standardCauchy(array|int|null $size = null): Tessero\NDArray|float

Samples from a standard Cauchy distribution (Generator.standard_cauchy).

standardExponential(array|int|null $size = null, string $dtype = 'float64', string $method = 'zig'): Tessero\NDArray|float

Samples from the standard exponential distribution (Generator.standard_exponential).

standardGamma(mixed $shape, array|int|null $size = null, string $dtype = 'float64'): Tessero\NDArray|float

Samples from the standard gamma distribution (Generator.standard_gamma).

standardNormal(array|int|null $size = null, string $dtype = 'float64'): Tessero\NDArray|float

Samples from the standard normal distribution, ziggurat method (Generator.standard_normal).

standardT(mixed $df, array|int|null $size = null): Tessero\NDArray|float

Samples from a standard Student's t distribution (Generator.standard_t).

triangular(mixed $left, mixed $mode, mixed $right, array|int|null $size = null): Tessero\NDArray|float

Samples from the triangular distribution over [left, right] (Generator.triangular).

uniform(mixed $low = 0.0, mixed $high = 1.0, array|int|null $size = null): Tessero\NDArray|float

Samples from a uniform distribution over [low, high) (Generator.uniform).

vonmises(mixed $mu, mixed $kappa, array|int|null $size = null): Tessero\NDArray|float

Samples from a von Mises distribution (Generator.vonmises).

wald(mixed $mean, mixed $scale, array|int|null $size = null): Tessero\NDArray|float

Samples from a Wald, or inverse Gaussian, distribution (Generator.wald).

weibull(mixed $a, array|int|null $size = null): Tessero\NDArray|float

Samples from a Weibull distribution (Generator.weibull).

zipf(mixed $a, array|int|null $size = null): Tessero\NDArray|int

Samples from a Zipf distribution (Generator.zipf).