Distributions#

Several functions generate values using statistical distributions, giving you control over the shape of your random data.

Numeric Distributions#

Function	Signature	Description
`exp_f` PRO	`exp_f(rate, min, max, precision)`	Exponential decay from min
`lognorm_f` PRO	`lognorm_f(mu, sigma, min, max, precision)`	Right-skewed with a long tail
`norm_f` PRO	`norm_f(mean, stddev, min, max, precision)`	Bell curve centered on mean
`pareto` PRO	`pareto(alpha, max)`	Continuous power-law, lower values dominate
`uniform`	`uniform(min, max)`	Flat distribution, every value equally likely
`zipf` PRO	`zipf(s, v, max)`	Power-law skew, low values dominate

Set Distributions#

Pick from a predefined set of values using a distribution to control which items are selected most often.

Function	Signature	Description
`set_exp` PRO	`set_exp(values, rate)`	Exponential distribution over indices; lower indices picked most often
`set_lognorm` PRO	`set_lognorm(values, mu, sigma)`	Log-normal distribution over indices; right-skewed selection
`set_norm` PRO	`set_norm(values, mean, stddev)`	Normal distribution over indices; `mean` index picked most often
`set_pareto` PRO	`set_pareto(values, alpha)`	Pareto distribution over indices; strong skew toward first items
`set_rand`	`set_rand(values, weights)`	Uniform or weighted random selection from a set
`set_zipf` PRO	`set_zipf(values, s, v)`	Zipfian distribution over indices; strong power-law skew toward first items