Description Usage Arguments Value Examples
View source: R/information_gain.R
Max information gains
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 
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 
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 ( 
range 
discretization range (from 0.0 to 1.0; 
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 
return.min 
whether to return min instead of max (per tuple, always max per discretization)  not supported with CUDA 
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) 
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.
1 2  ComputeMaxInfoGains(madelon$data, madelon$decision, dimensions = 2, divisions = 1,
range = 0, seed = 0)

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