StrLearningGA_BNM: Structure Learning for BNM by simple GA

In exametrika: Test Theory Analysis and Biclustering

Structure Learning for BNM by simple GA

Description

Generating a DAG from data using a genetic algorithm.

Usage

StrLearningGA_BNM(
  U,
  Z = NULL,
  w = NULL,
  na = NULL,
  seed = 123,
  population = 20,
  Rs = 0.5,
  Rm = 0.005,
  maxParents = 2,
  maxGeneration = 100,
  successiveLimit = 5,
  crossover = 0,
  elitism = 0,
  filename = NULL,
  verbose = TRUE
)

Arguments

U	U is either a data class of exametrika, or raw data. When raw data is given, it is converted to the exametrika class with the dataFormat function.
Z	Z is a missing indicator matrix of the type matrix or data.frame
w	w is item weight vector
na	na argument specifies the numbers or characters to be treated as missing values.
seed	seed for random.
population	Population size. The default is 20
Rs	Survival Rate. The default is 0.5
Rm	Mutation Rate. The default is 0.005
maxParents	Maximum number of edges emanating from a single node. The default is 2.
maxGeneration	Maximum number of generations.
successiveLimit	Termination conditions. If the optimal individual does not change for this number of generations, it is considered to have converged.
crossover	Configure crossover using numerical values. Specify 0 for uniform crossover, where bits are randomly copied from both parents. Choose 1 for single-point crossover with one crossover point, and 2 for two-point crossover with two crossover points. The default is 0.
elitism	Number of elites that remain without crossover when transitioning to the next generation.
filename	Specify the filename when saving the generated adjacency matrix in CSV format. The default is null, and no output is written to the file.
verbose	verbose output Flag. default is TRUE

Details

This function generates a DAG from data using a genetic algorithm. Depending on the size of the data and the settings, the computation may take a significant amount of computational time. For details on the settings or algorithm, see Shojima(2022), section 8.5

Value

adj

Optimal adjacency matrix

testlength

Length of the test. The number of items included in the test.

TestFitIndices

Overall fit index for the test.See also TestFit

nobs

Sample size. The number of rows in the dataset.

testlength

Length of the test. The number of items included in the test.

crr

correct response ratio

TestFitIndices

Overall fit index for the test.See also TestFit

adj

Adjacency matrix

param

Learned Parameters

CCRR_table

Correct Response Rate tables

Examples

# Perform Structure Learning for Bayesian Network Model using Genetic Algorithm
# Parameters are set for balanced exploration and computational efficiency
StrLearningGA_BNM(J5S10,
  population = 20, # Size of population in each generation
  Rs = 0.5, # 50% survival rate for next generation
  Rm = 0.002, # 0.2% mutation rate for genetic diversity
  maxParents = 2, # Maximum of 2 parent nodes per item
  maxGeneration = 100, # Maximum number of evolutionary steps
  crossover = 2, # Use two-point crossover method
  elitism = 2 # Keep 2 best solutions in each generation
)

exametrika documentation built on Aug. 21, 2025, 5:27 p.m.