ComputeMaxInfoGains: Max information gains

In MDFS: MultiDimensional Feature Selection

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
)

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)

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.

Examples

ComputeMaxInfoGains(madelon$data, madelon$decision, dimensions = 2, divisions = 1,
                    range = 0, seed = 0)

MDFS documentation built on May 31, 2023, 7:31 p.m.