examples.bindings-probcurves.yaml Maven / Gradle / Ivy

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Runtime-Only CLI for nosqlbench
There is a newer version: 5.17.0
description: examples of all probability distributions

scenarios:
  default:
    readout1: run driver===stdout format=readout cycles=1

bindings:
  cycle: Identity()
  # parameter types and symbolic names included below with links
  #
  # https://en.wikipedia.org/wiki/L%C3%A9vy_distribution
  # (double location, double scale)
  levy: Levy(0.0,0.5)
  # https://en.wikipedia.org/wiki/Nakagami_distribution
  # (double shape, double spread)
  nakagami: Nakagami(0.5,1.0)
  # https://en.wikipedia.org/wiki/Triangular_distribution
  # (double a, double b, double c)
  triangular: Triangular(1.0, 2.0, 3.0)
  # http://homepage.divms.uiowa.edu/~mbognar/applets/exp.html
  # https://en.wikipedia.org/wiki/Exponential_distribution
  # (double rate)
  exponential: Exponential(1.5)
  # https://en.wikipedia.org/wiki/Logistic_distribution
  # (double mean, double scale)
  logistic: Logistic(5.0,2.0)
  # https://en.wikipedia.org/wiki/Wrapped_asymmetric_Laplace_distribution
  # (double location, double scale)
  laplace: Laplace(1.0,2.0)
  # https://en.wikipedia.org/wiki/Log-normal_distribution
  # (double mean, double stddev)
  log_normal: LogNormal(1.0,0.25)
  # https://en.wikipedia.org/wiki/Cauchy_distribution
  # (double location, double scale)
  cauchy: Cauchy(0.0,0.5)
  # https://en.wikipedia.org/wiki/F-distribution
  # (double d1, double d2)
  f: F(10.0,1.0)
  # https://en.wikipedia.org/wiki/Student%27s_t-distribution
  # (double dof)
  t: T(2.0)
  # https://en.wikipedia.org/wiki/Empirical_distribution_function
  # (int bincount)
  # empirical: empirical(10)
  # https://en.wikipedia.org/wiki/Normal_distribution
  # (double mean, double variance)
  normal: Normal(5.0, 1.0)
  # https://en.wikipedia.org/wiki/Weibull_distribution
  # (double scale, double shape)
  weibull: Weibull(1.0,1.5)
  # https://en.wikipedia.org/wiki/Chi-squared_distribution
  # (double dof)
  chi_squared: ChiSquared(5.0)
  # https://en.wikipedia.org/wiki/Gumbel_distribution
  # (double location, double scale)
  gumbel: Gumbel(0.5,2.0)
  # https://en.wikipedia.org/wiki/Beta_distribution
  # (double shape1, double shape2)
  beta: Beta(2.0,2.0)
  # https://en.wikipedia.org/wiki/Pareto_distribution
  # (double scale, double shape)
  pareto: Pareto(1.0, 3.0)
  # https://en.wikipedia.org/wiki/Gamma_distribution
  # (double shape, double scale)
  gamma: Gamma(3.0,2.0)
  # https://en.wikipedia.org/wiki/Uniform_distribution_(continuous)
  # (double min, double max)
  uniform_real: Uniform(0.0,100.0) -> double
  # http://homepage.divms.uiowa.edu/~mbognar/applets/hg.html
  # https://en.wikipedia.org/wiki/Hypergeometric_distribution
  # (int pop, int successes, int sample)
  hypergeometric: Hypergeometric(40,20,10)
  # (int min, int max)
  uniform_int: Uniform(0,100) -> int
  # http://homepage.divms.uiowa.edu/~mbognar/applets/geo1.html
  # https://en.wikipedia.org/wiki/Geometric_distribution
  # (double probability)
  geometric: Geometric(0.5)
  # https://en.wikipedia.org/wiki/Poisson_distribution
  # (double avgrate)
  poisson: Poisson(5.0)
  # https://en.wikipedia.org/wiki/Zipf%27s_law
  # (int elements, double exponent)
  zipf: Zipf(10,5.0)
  # https://en.wikipedia.org/wiki/Binomial_distribution
  # (int trials, double probability)
  binomial: Binomial(8,0.5)
  # https://en.wikipedia.org/wiki/Negative_binomial_distribution
  # (int successes, double probability)
  pascal: Pascal(10,0.33)