generateQuestions method

List<String> generateQuestions(String inferVariable, { bool addPriorQuestions = false, int combinationsLevel = 1, Iterable<String>? variables, bool variablesFilter(String name)?, Iterable<String>? ignoreVariables, bool ignoreVariablesFilter(String name)?, bool allowEmptySelection = true, })

Generates questions to infer inferVariable.

• combinationsLevel is the variables combination depth.
• variables is provided defines the selected variables.
• variablesFilter is provided defines the selected variables (selects when returns true).
• ignoreVariables is a list of variables to ignore.
• ignoreVariablesFilter filters the variables to ignore (ignores when returns true).

Implementation

List<String> generateQuestions(String inferVariable,
    {bool addPriorQuestions = false,
    int combinationsLevel = 1,
    Iterable<String>? variables,
    bool Function(String name)? variablesFilter,
    Iterable<String>? ignoreVariables,
    bool Function(String name)? ignoreVariablesFilter,
    bool allowEmptySelection = true}) {
  inferVariable =
      BayesVariable.resolveName(inferVariable, networkCache: network);

  var selectedVariables =
      network.variablesNames.where((v) => v != inferVariable).toList();

  if (selectedVariables.isEmpty) {
    if (allowEmptySelection) return <String>[];
    throw StateError("BayesianNetwork empty!");
  }

  if (variables != null) {
    var select = variables
        .map((v) => BayesVariable.resolveName(v, networkCache: network))
        .toSet();
    selectedVariables.retainWhere((e) => select.contains(e));

    if (selectedVariables.isEmpty) {
      if (allowEmptySelection) return <String>[];
      throw StateError("No valid variable in parameter `variables`!");
    }
  }

  if (variablesFilter != null) {
    selectedVariables.retainWhere(variablesFilter);

    if (selectedVariables.isEmpty) {
      if (allowEmptySelection) return <String>[];
      throw StateError("No variables selected by `variablesFilter`!");
    }
  }

  if (ignoreVariables != null) {
    var ignore = ignoreVariables
        .map((v) => BayesVariable.resolveName(v, networkCache: network))
        .toSet();
    selectedVariables.removeWhere((e) => ignore.contains(e));

    if (selectedVariables.isEmpty) {
      if (allowEmptySelection) return <String>[];
      throw StateError(
          "Ignored all variables! ignoreVariables: $ignoreVariables");
    }
  }

  if (ignoreVariablesFilter != null) {
    selectedVariables.removeWhere(ignoreVariablesFilter);

    if (selectedVariables.isEmpty) {
      if (allowEmptySelection) return <String>[];
      throw StateError("Ignored all variables by `ignoreVariablesFilter`!");
    }
  }

  if (combinationsLevel < 1) {
    combinationsLevel = 1;
  } else if (combinationsLevel > selectedVariables.length) {
    combinationsLevel = selectedVariables.length;
  }

  var combinations = _combinationCache.getCombinationsShared(
      selectedVariables.toSet(), 1, combinationsLevel);

  var questions =
      combinations.map((v) => 'P($inferVariable|${v.join(',')})').toList();

  if (addPriorQuestions) {
    questions.add('P($inferVariable)');
    questions.add('P(-$inferVariable)');
  }

  return questions;
}