View source: R/information_gain.R
ComputeMaxInfoGains | R Documentation |
Max information gains
ComputeMaxInfoGains( data, decision = NULL, dimensions = 1, divisions = NULL, discretizations = 1, seed = NULL, range = NULL, pc.xi = 0.25, return.tuples = FALSE, return.min = FALSE, interesting.vars = vector(mode = "integer"), require.all.vars = FALSE, use.CUDA = FALSE )
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) |
If decision
is omitted, this function calculates either the variable entropy
(in 1D) or mutual information (in higher dimensions).
Translate "IG" respectively to entropy or mutual information in the
rest of this function's description.
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.
ComputeMaxInfoGains(madelon$data, madelon$decision, dimensions = 2, divisions = 1, range = 0, seed = 0)
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