ComputeMaxInfoGains: Max information gains

Description

Max information gains

Usage

ComputeMaxInfoGains(
  data,
  decision,
  contrast_data = NULL,
  dimensions = 1,
  divisions = 1,
  discretizations = 1,
  seed = NULL,
  range = NULL,
  pc.xi = 0.25,
  return.tuples = FALSE,
  interesting.vars = vector(mode = "integer"),
  require.all.vars = FALSE,
  use.CUDA = FALSE
)

Value

A data.frame with the following columns:

IG -- max information gain (of each variable)
Tuple.1, Tuple.2, ... -- corresponding tuple (up to dimensions columns, available only when return.tuples == T)
Discretization.nr -- corresponding discretization number (available only when return.tuples == T)

Additionally attribute named run.params with run parameters is set on the result.

Arguments

data: input data where columns are variables and rows are observations (all numeric)
decision: decision variable as a binary sequence of length equal to number of observations
contrast_data: the contrast counterpart of data, has to have the same number of observations - not supported with CUDA
dimensions: number of dimensions (a positive integer; 5 max)
divisions: number of divisions (from 1 to 15; additionally limited by dimensions if using CUDA)
discretizations: number of discretizations
seed: seed for PRNG used during discretizations (NULL for random)
range: discretization range (from 0.0 to 1.0; NULL selects probable optimal number)
pc.xi: parameter xi used to compute pseudocounts (the default is recommended not to be changed)
return.tuples: whether to return tuples (and relevant discretization number) where max IG was observed (one tuple and relevant discretization number per variable) - not supported with CUDA nor in 1D
interesting.vars: variables for which to check the IGs (none = all) - not supported with CUDA
require.all.vars: boolean whether to require tuple to consist of only interesting.vars
use.CUDA: whether to use CUDA acceleration (must be compiled with CUDA)

Examples

Run this code

# \donttest{
ComputeMaxInfoGains(madelon$data, madelon$decision, dimensions = 2, divisions = 1,
                    range = 0, seed = 0)
# }

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