stochastic_search: Stochastic Search
In kcf-jackson/wandeRer: A package of stochastic search

Description Usage Arguments Details

View source: R/stochastic_search.R

An stochasic algorithm for optimisation

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stochastic_search(dim_param, perf_fun, loss_fun = ls_loss, target_perf,
  max_iter = 100, tol = 0, curiosity = 1, block_num, block_size,
  lambda = 0, reg_fun = zero_reg, param)

`dim_param`	integer; the dimension of the parameters.
`perf_fun`	function; the performance function mapping parameters to an outcome, e.g. a curve.
`loss_fun`	function; the loss function of two outcomes.
`target_perf`	function; the performance to be matched, e.g. a target data curve.
`max_iter`	integer; maximum number of iterations.
`tol`	numeric; tolerence for the loss functions.
`curiosity`	numeric; search parameter.
`block_num`	integer.
`block_size`	integer.
`lambda`	numeric; regularisation parameter.
`reg_fun`	function; regularisation funciton, see 'zero_reg', 'L1_reg', 'L2_reg'.
`param`	numeric vector; parameter from previous run of the function.

Denote the dimension of the parameters by d. If the stochastic search didn't improve in 5*d block iterations, then it will increase the curiosity by 10 folds. On the other hand, all improvements are associated with a 0.001 decrease curiosity. This is to create oscillatary behaviour of the curiosity parameter.

kcf-jackson/wandeRer documentation built on May 20, 2019, 8:15 a.m.