本文整理了Java中eu.amidst.core.variables.Variables类的一些代码示例，展示了Variables类的具体用法。这些代码示例主要来源于Github/Stackoverflow/Maven等平台，是从一些精选项目中提取出来的代码，具有较强的参考意义，能在一定程度帮忙到你。Variables类的具体详情如下：
包路径：eu.amidst.core.variables.Variables
类名称：Variables

Variables介绍

暂无

代码示例

代码示例来源：origin: amidst/toolbox

public static BayesianNetwork createBN(int nVars) throws Exception {
  Variables dynamicVariables = new Variables();
  Variable classVar = dynamicVariables.newMultinomialVariable("C", 2);
  for (int i = 0; i < nVars; i++) {
    dynamicVariables.newGaussianVariable("A" + i);
  }
  DAG dag = new DAG(dynamicVariables);
  for (int i = 0; i < nVars; i++) {
    dag.getParentSet(dynamicVariables.getVariableByName("A" + i)).addParent(classVar);
  }
  dag.setName("dbn1");
  BayesianNetwork bn = new BayesianNetwork(dag);
  bn.randomInitialization(new Random(1));
  return bn;
}

代码示例来源：origin: amidst/toolbox

/**
 * This method contains the code needed to build the NaiveBayes DAG with a global hidden variable modelling
 * concept drift.
 * @return A poperly created {@link DAG} object.
 * @throws Exception if an error occurs while reading the file.
 */
public static DAG modelBuilding() throws Exception {
  //We load the data for one month
  DataStream<DataInstance> instances = DataStreamLoader.open("./datasets/bnaic2015/BCC/Month0.arff");
  //Define the variables. By default, a random variable is created for each attribute
  Variables variables  = new Variables(instances.getAttributes());
  //We create a new global hidden Gaussian variable
  Variable hiddenGaussian = variables.newGaussianVariable("HiddenGaussian");
  //We get the variable Default
  Variable defaultVariable = variables.getVariableByName("default");
  //We define the DAG
  DAG dag = new DAG(variables);
  //We add the links of the DAG
  dag.getVariables()
      .getListOfVariables()
      .stream()
      .filter(var -> var != defaultVariable)
      .filter(var -> var != hiddenGaussian)
      .forEach(var -> {
        dag.getParentSet(var).addParent(defaultVariable);
        dag.getParentSet(var).addParent(hiddenGaussian);
      });
  return dag;
}

代码示例来源：origin: amidst/toolbox

/**
 * This method returns a DAG object with naive Bayes structure for the attributes of the passed data stream.
 * @param dataStream object of the class DataStream<DataInstance>
 * @param classIndex integer value indicating the position of the class
 * @return object of the class DAG
 */
public static DAG getNaiveBayesStructure(DataStream<DataInstance> dataStream, int classIndex){
  //We create a Variables object from the attributes of the data stream
  Variables modelHeader = new Variables(dataStream.getAttributes());
  //We define the predicitive class variable
  Variable classVar = modelHeader.getVariableById(classIndex);
  //Then, we create a DAG object with the defined model header
  DAG dag = new DAG(modelHeader);
  //We set the linkds of the DAG.
  dag.getParentSets().stream().filter(w -> w.getMainVar() != classVar).forEach(w -> w.addParent(classVar));
  return dag;
}

代码示例来源：origin: amidst/toolbox

/**
 * Constructor from a list of attributes.
 * The default parameters are used: the class variable is the last one and the
 * diagonal flag is set to false (predictive variables are NOT independent).
 * @param attributes list of attributes of the classifier (i.e. its variables)
 * @throws WrongConfigurationException
 */
public BayesianLinearRegression(Attributes attributes) throws WrongConfigurationException {
  super(attributes);
  classVar = vars.getListOfVariables().get(vars.getNumberOfVars()-1);
  this.diagonal = false;
}

代码示例来源：origin: amidst/toolbox

/**
 * Returns the class variable.
 * @param modelContext a {@link weka.core.Instances} object.
 * @param atts a set of of {@link Attributes}.
 * @return a {@link Variable} object that represents the class variable.
 */
public static Variable getClassVariable(Instances modelContext, Attributes atts){
  Variables variables = new Variables(atts);
  String className = modelContext.classAttribute().name();
  return variables.getVariableByName(className);
}

代码示例来源：origin: amidst/toolbox

public static void main(String[] args) throws Exception {

    //We first create an empty Variables object
    Variables variables = new Variables();

    //We invoke the "new" methods of the object Variables to create new variables.
    //Now we create a Gaussian variables
    Variable gaussianVar = variables.newGaussianVariable("Gaussian");

    //Now we create a Multinomial variable with two states
    Variable multinomialVar = variables.newMultinomialVariable("Multinomial", 2);

    //Now we create a Multinomial variable with two states: TRUE and FALSE
    Variable multinomialVar2 = variables.newMultinomialVariable("Multinomial2", Arrays.asList("TRUE, FALSE"));

    //For Multinomial variables we can iterate over their different states
    FiniteStateSpace states = multinomialVar2.getStateSpaceType();
    states.getStatesNames().forEach(System.out::println);

    //Variable objects can also be used, for example, to know if one variable can be set as parent of some other variable
    System.out.println("Can a Gaussian variable be parent of Multinomial variable? " +
        (multinomialVar.getDistributionType().isParentCompatible(gaussianVar)));

    System.out.println("Can a Multinomial variable be parent of Gaussian variable? " +
        (gaussianVar.getDistributionType().isParentCompatible(multinomialVar)));

  }
}

代码示例来源：origin: amidst/toolbox

/**
 * Method to set the class variable.
 * @param className String with the name of the class variable
 */
public BayesianLinearRegression setClassName(String className){
  setClassVar(vars.getVariableByName(className));
  return this;
}

代码示例来源：origin: amidst/toolbox

Variables modelHeader = new Variables(attributes);
Variable globalHiddenVar = modelHeader.newMultinomialVariable("GlobalHidden", 2);
Variable globalHiddenGaussian = modelHeader.newGaussianVariable("globalHiddenGaussian");
Variable classVar = modelHeader.getVariableById(0);

代码示例来源：origin: amidst/toolbox

Variables variables = new Variables();
    .forEach(i -> variables.newMultinomialVariable("DiscreteVar" + i, getNumStates()));
    .forEach(i -> variables.newGaussianVariable("GaussianVar" + i));
Variable classVar = variables.newMultinomialVariable("ClassVar", getNumStates());
System.out.println("\n Number of variables \n " + bn.getDAG().getVariables().getNumberOfVars());
System.out.println(dag.toString());

代码示例来源：origin: amidst/toolbox

/**
 * Builds the DAG structure of a Naive Bayes classifier with a global hidden Gaussian variable.
 */
private void buildGlobalDAG(){
  Variables variables = new Variables(attributes);
  String className = attributes.getFullListOfAttributes().get(classIndex).getName();
  hiddenVars = new ArrayList<Variable>();
  for (int i = 0; i < this.numberOfGlobalVars ; i++) {
    hiddenVars.add(variables.newGaussianVariable("GlobalHidden_"+i));
  }
  Variable classVariable = variables.getVariableByName(className);
  this.globalDAG = new DAG(variables);
  for (Attribute att : attributes.getListOfNonSpecialAttributes()) {
    if (att.getName().equals(className))
      continue;
    Variable variable = variables.getVariableByName(att.getName());
    globalDAG.getParentSet(variable).addParent(classVariable);
    for (int i = 0; i < this.numberOfGlobalVars ; i++) {
      globalDAG.getParentSet(variable).addParent(hiddenVars.get(i));
    }
  }
  System.out.println(globalDAG.toString());
}

代码示例来源：origin: amidst/toolbox

@Override
protected void buildDAG() {
  /** Create a set of variables from the given attributes**/
  Variables variables = new Variables(attributes);
  /** Create a hidden variable with two hidden states*/
  Variable hiddenVar = variables.newMultinomialVariable("HiddenVar",2);
  //We create a standard naive Bayes
  DAG dag = new DAG(variables);
  for (Variable variable: variables){
    if (variable==hiddenVar)
      continue;
    dag.getParentSet(variable).addParent(hiddenVar);
  }
  //This is needed to maintain coherence in the Model class.
  this.dag=dag;
  this.vars = variables;
}

代码示例来源：origin: amidst/toolbox

Variables modelHeader = new Variables(dataStream.getAttributes());
this.targetVar = modelHeader.getVariableByName(this.nameTarget);
DAG dag = new DAG(modelHeader);
BayesianNetwork bn = new BayesianNetwork(dag);
  Variable var = bn.getDAG().getVariables().getVariableById(i);
  Node n = nodeList.get(i);
try {
  dagLearned = (BNConverterToAMIDST.convertToAmidst(huginNetwork)).getDAG();
  dagLearned.getVariables().setAttributes(dataStream.getAttributes());
} catch (ExceptionHugin exceptionHugin) {
  System.out.println("ParallelTan LearnDAG Error 6");

代码示例来源：origin: amidst/toolbox

Set<Variable> dynAssigVariables = dynamicAssignment.getVariables();
  for (Variable dynVariable : dynAssigVariables) {
    Variable staticVariable = unfoldedStaticModel.getVariables().getVariableByName(dynVariable.getName() + "_t" + Integer.toString(time));
    double varValue = dynamicAssignment.getValue(dynVariable);
    staticEvidence.setValue(staticVariable, varValue);
((ImportanceSamplingRobust)staticModelInference).setVariablesAPosteriori(unfoldedStaticModel.getVariables().getListOfVariables().stream().filter(variable -> variable.getName().contains(MAPvarName)).collect(Collectors.toList()));
Variables variables = Serialization.deepCopy(this.unfoldedStaticModel.getVariables());
Variable currentVar;
if (variables.getVariableByName(MAPvarName + "_t" + Integer.toString(t))!=null) {
  currentVar  = variables.getVariableByName(MAPvarName + "_t" + Integer.toString(t));
  currentVar  = variables.newMultinomialVariable(MAPvarName + "_t" + Integer.toString(t), MAPvariable.getNumberOfStates());

代码示例来源：origin: amidst/toolbox

/**
 * tests if the attributes passed as an argument in the constructor are suitable
 * @return boolean value with the result of the test.
 */
@Override
public boolean isValidConfiguration() {
  boolean isValid  = vars.getListOfVariables().stream()
      .allMatch(Variable::isNormal);
  if(!isValid) {
    String errorMsg = "Invalid configuration: All variables must be real";
    this.setErrorMessage(errorMsg);
  }
  return isValid;
}

代码示例来源：origin: amidst/toolbox

Variables variables = new Variables();
word = variables.newSparseMultionomialVariable(attributes.getAttributeByName(wordDocumentName));
topicIndicator = variables.newMultinomialVariable("TopicIndicator", nTopics);

代码示例来源：origin: amidst/toolbox

/**
 * Builds the DAG over the set of variables given with the naive Bayes structure
 */
@Override
protected void buildDAG() {
  //Obtain the predictive attributes
  List<Variable> attrVars = vars.getListOfVariables().stream()
      .filter(v -> !v.equals(classVar)).collect(Collectors.toList());
  //Create the hidden variabels
  hiddenMultinomial = vars.newMultinomialVariable("M", numStatesHidden);
  contHiddenList = new ArrayList<Variable>();
  for(int i=0; i<numContinuousHidden; i++) {
    contHiddenList.add(vars.newGaussianVariable("Z"+Integer.toString(i)));
  }
  dag = new DAG(vars);
  //arcs from the class to the hidden variables
  dag.getParentSet(hiddenMultinomial).addParent(classVar);
  contHiddenList.stream().forEach(z -> dag.getParentSet(z).addParent(classVar));
  //arcs from the hidden vars to each attribute
  attrVars.stream().forEach(x->dag.getParentSet(x).addParent(hiddenMultinomial));
  for (Variable z : contHiddenList) {
    attrVars.stream().forEach(x->dag.getParentSet(x).addParent(z));
  }
}
/*

代码示例来源：origin: amidst/toolbox

Variables variables = new Variables();
  variables.newMultinomialVariable(groupedClassName + "_t" + Integer.toString(mergedClassVarIndex), nStatesMAPVariable);
});
          VariableBuilder aux = dynVar.getVariableBuilder();
          aux.setName(dynVar.getName() + "_t" + Integer.toString(i));
          variables.newVariable(aux);
        })
    );

代码示例来源：origin: amidst/toolbox

/**
 * Builds the DAG
 */
@Override
protected void buildDAG() {
  List<Variable> observableVariables = new ArrayList<>();
  List<Variable> latentVariables = new ArrayList<>();
  vars.forEach(observableVariables::add);
  IntStream.range(0,numberOfLatentVariables).forEach(i -> {
    Variable latentVar = vars.newGaussianVariable("LatentVar" + i);
    latentVariables.add(latentVar);
  });
  dag = new DAG(vars);
  for (Variable variable : observableVariables) {
    latentVariables.forEach(latentVariable -> dag.getParentSet(variable).addParent(latentVariable));
  }
  IntStream.range(0,numberOfLatentVariables).forEach(i -> {
    Variable latentVarChildren = vars.getVariableByName("LatentVar" + i);
    IntStream.range(0,i).forEach(j -> {
      Variable latentVarParent = vars.getVariableByName("LatentVar" + j);
      dag.getParentSet(latentVarChildren).addParent(latentVarParent);
    });
  });
}

代码示例来源：origin: amidst/toolbox

@Override
protected void buildDAG() {
  //Obtain the predictive attributes
  List<Variable> attrVars = vars.getListOfVariables().stream()
      .filter(v -> !v.equals(classVar)).collect(Collectors.toList());
  int numAttr = attrVars.size();
  /** Create a hidden variable with two hidden states */
  Variable globalHiddenVar = vars.newMultinomialVariable("globalHiddenVar",2);
  /** Create a list of local hidden variables */
  List<Variable> localHidden = new ArrayList<Variable>();
  for(int i= 0; i< numAttr; i++) {
    localHidden.add(vars.newMultinomialVariable("locallHiddenVar_"+i,2));
  }
  /** We create a standard naive Bayes */
  DAG dag = new DAG(vars);
  /** Add the links */
  for (int i=0; i<numAttr; i++) {
    dag.getParentSet(attrVars.get(i)).addParent(localHidden.get(i));
    dag.getParentSet(attrVars.get(i)).addParent(globalHiddenVar);
    dag.getParentSet(attrVars.get(i)).addParent(classVar);
  }
  //This is needed to maintain coherence in the Model class.
  this.dag=dag;
}

代码示例来源：origin: amidst/toolbox

/**
 * Builds the graph
 */
@Override
protected void buildDAG() {
  List<Variable> observableVariables = new ArrayList<>();
  List<Variable> latentVariables = new ArrayList<>();
  vars.forEach(observableVariables::add);
  Variable discreteLatentVar = vars.newMultinomialVariable("DiscreteLatentVar",numberOfStatesLatentDiscreteVar);
  IntStream.range(0,numberOfLatentVariables).forEach(i -> {
    Variable latentVar = vars.newGaussianVariable("LatentVar" + i);
    latentVariables.add(latentVar);
  });
  dag = new DAG(vars);
  for (Variable variable : observableVariables) {
    dag.getParentSet(variable).addParent(discreteLatentVar);
    latentVariables.forEach(latentVariable -> dag.getParentSet(variable).addParent(latentVariable));
  }
}