mars.tensor.random.
RandomState
__init__
Initialize self. See help(type(self)) for accurate signature.
Methods
__init__([seed])
Initialize self.
beta(a, b[, size, chunk_size, gpu, dtype])
beta
Draw samples from a Beta distribution.
binomial(n, p[, size, chunk_size, gpu, dtype])
binomial
Draw samples from a binomial distribution.
bytes(length)
bytes
Return random bytes.
chisquare(df[, size, chunk_size, gpu, dtype])
chisquare
Draw samples from a chi-square distribution.
choice(a[, size, replace, p, chunk_size, gpu])
choice
Generates a random sample from a given 1-D array
dirichlet(alpha[, size, chunk_size, gpu, dtype])
dirichlet
Draw samples from the Dirichlet distribution.
exponential([scale, size, chunk_size, gpu, …])
exponential
Draw samples from an exponential distribution.
f(dfnum, dfden[, size, chunk_size, gpu, dtype])
f
Draw samples from an F distribution.
from_numpy(np_random_state)
from_numpy
gamma(shape[, scale, size, chunk_size, gpu, …])
gamma
Draw samples from a Gamma distribution.
geometric(p[, size, chunk_size, gpu, dtype])
geometric
Draw samples from the geometric distribution.
gumbel([loc, scale, size, chunk_size, gpu, …])
gumbel
Draw samples from a Gumbel distribution.
hypergeometric(ngood, nbad, nsample[, size, …])
hypergeometric
Draw samples from a Hypergeometric distribution.
laplace([loc, scale, size, chunk_size, gpu, …])
laplace
Draw samples from the Laplace or double exponential distribution with specified location (or mean) and scale (decay).
logistic([loc, scale, size, chunk_size, …])
logistic
Draw samples from a logistic distribution.
lognormal([mean, sigma, size, chunk_size, …])
lognormal
Draw samples from a log-normal distribution.
logseries(p[, size, chunk_size, gpu, dtype])
logseries
Draw samples from a logarithmic series distribution.
multinomial(n, pvals[, size, chunk_size, …])
multinomial
Draw samples from a multinomial distribution.
multivariate_normal(mean, cov[, size, …])
multivariate_normal
Draw random samples from a multivariate normal distribution.
negative_binomial(n, p[, size, chunk_size, …])
negative_binomial
Draw samples from a negative binomial distribution.
noncentral_chisquare(df, nonc[, size, …])
noncentral_chisquare
Draw samples from a noncentral chi-square distribution.
noncentral_f(dfnum, dfden, nonc[, size, …])
noncentral_f
Draw samples from the noncentral F distribution.
normal([loc, scale, size, chunk_size, gpu, …])
normal
Draw random samples from a normal (Gaussian) distribution.
pareto(a[, size, chunk_size, gpu, dtype])
pareto
Draw samples from a Pareto II or Lomax distribution with specified shape.
permutation(x[, axis, chunk_size])
permutation
Randomly permute a sequence, or return a permuted range.
poisson([lam, size, chunk_size, gpu, dtype])
poisson
Draw samples from a Poisson distribution.
power(a[, size, chunk_size, gpu, dtype])
power
Draws samples in [0, 1] from a power distribution with positive exponent a - 1.
rand(*dn, **kw)
rand
Random values in a given shape.
randint(low[, high, size, dtype, density, …])
randint
Return random integers from low (inclusive) to high (exclusive).
randn(*dn, **kw)
randn
Return a sample (or samples) from the “standard normal” distribution.
random([size, chunk_size, gpu, dtype])
random
Return random floats in the half-open interval [0.0, 1.0).
random_integers(low[, high, size, …])
random_integers
Random integers of type mt.int between low and high, inclusive.
random_sample([size, chunk_size, gpu, dtype])
random_sample
ranf([size, chunk_size, gpu, dtype])
ranf
rayleigh([scale, size, chunk_size, gpu, dtype])
rayleigh
Draw samples from a Rayleigh distribution.
sample([size, chunk_size, gpu, dtype])
sample
seed([seed])
seed
Seed the generator.
shuffle(x[, axis])
shuffle
Modify a sequence in-place by shuffling its contents.
standard_cauchy([size, chunk_size, gpu, dtype])
standard_cauchy
Draw samples from a standard Cauchy distribution with mode = 0.
standard_exponential([size, chunk_size, …])
standard_exponential
Draw samples from the standard exponential distribution.
standard_gamma(shape[, size, chunk_size, …])
standard_gamma
Draw samples from a standard Gamma distribution.
standard_normal([size, chunk_size, gpu, dtype])
standard_normal
Draw samples from a standard Normal distribution (mean=0, stdev=1).
standard_t(df[, size, chunk_size, gpu, dtype])
standard_t
Draw samples from a standard Student’s t distribution with df degrees of freedom.
to_numpy()
to_numpy
triangular(left, mode, right[, size, …])
triangular
Draw samples from the triangular distribution over the interval [left, right].
[left, right]
uniform([low, high, size, chunk_size, gpu, …])
uniform
Draw samples from a uniform distribution.
vonmises(mu, kappa[, size, chunk_size, gpu, …])
vonmises
Draw samples from a von Mises distribution.
wald(mean, scale[, size, chunk_size, gpu, dtype])
wald
Draw samples from a Wald, or inverse Gaussian, distribution.
weibull(a[, size, chunk_size, gpu, dtype])
weibull
Draw samples from a Weibull distribution.
zipf(a[, size, chunk_size, gpu, dtype])
zipf
Draw samples from a Zipf distribution.