dummyalgo {CAISEr} | R Documentation |

This is a dummy algorithm routine to test the sampling procedures, in
combination with `dummyinstance()`

.
`dummyalgo()`

receives two parameters that determine the distribution of
performances it will exhibit on a hypothetical problem class:
`distribution.fun`

(with the name of a random number generation function,
e.g. `rnorm`

, `runif`

, `rexp`

etc.); and `distribution.pars`

, a named list of
parameters to be passed on to `distribution.fun`

.
The third parameter is an instance object (see `calc_nreps()`

for details),
which is a named list with the following fields:

`FUN = "dummyinstance"`

- must always be "dummyinstance" (will be ignored otherwise).`distr`

- the name of a random number generation function.`...`

- other named fields with parameters to be passed down to the function in`distr`

.

dummyalgo( distribution.fun = "rnorm", distribution.pars = list(mean = 0, sd = 1), instance = list(FUN = "dummyinstance", distr = "rnorm", mean = 0, sd = 1) )

`distribution.fun` |
name of a function that generates random values according to a given distribution, e.g., "rnorm", "runif", "rexp" etc. |

`distribution.pars` |
list of named parameters required by the function
in |

`instance` |
instance parameters (see |

`distribution.fun`

and `distribution.pars`

regulate the mean performance of
the dummy algorithm on a given (hypothetical) problem class, and the
between-instances variance of performance. The instance specification in
`instance`

regulates the within-instance variability of results. Ideally the
distribution parameters passed to the `instance`

should result in a
within-instance distribution of values with zero mean, so that the mean of
the values returned by `dummyalgo`

is regulated only by `distribution.fun`

and `distribution.pars`

.

The value returned by dummyalgo is sampled as follows:

offset <- do.call(distribution.fun, args = distribution.pars) y <- offset + do.call("dummyinstance", args = instance)

a list object with a single field `$value`

, containing a scalar
numerical value distributed as described at the end of `Details`

.

Felipe Campelo (fcampelo@ufmg.br, f.campelo@aston.ac.uk)

# Make a dummy instance with a centered (zero-mean) exponential distribution: instance = list(FUN = "dummyinstance", distr = "rexp", rate = 5, bias = -1/5) # Simulate a dummy algorithm that has a uniform distribution of expected # performance values, between -25 and 50. dummyalgo(distribution.fun = "runif", distribution.pars = list(min = -25, max = 50), instance = instance)

[Package *CAISEr* version 1.0.16 Index]