本文整理了Java中eu.amidst.core.variables.Variables.newMultinomialVariable()方法的一些代码示例，展示了Variables.newMultinomialVariable()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.newMultinomialVariable()方法的具体详情如下：
包路径：eu.amidst.core.variables.Variables
类名称：Variables
方法名：newMultinomialVariable

Variables.newMultinomialVariable介绍

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代码示例

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 DAG over the set of variables given with the structure of the model
 */
@Override
protected void buildDAG() {
  hiddenVar = vars.newMultinomialVariable("HiddenVar",numStatesHiddenVar);
  //We create a standard naive Bayes
  dag = new DAG(vars);
  dag.getParentSets().stream().filter(w -> !w.getMainVar().equals(hiddenVar)).forEach(w -> w.addParent(hiddenVar));
  // if it is not diagonal add the links between the attributes (features)
  if(!isDiagonal()) {
    List<Variable> attrVars = vars.getListOfVariables().stream().filter(v -> !v.equals(hiddenVar)).collect(Collectors.toList());
    for (int i=0; i<attrVars.size()-1; i++){
      for(int j=i+1; j<attrVars.size(); j++) {
        // Add the links
        dag.getParentSet(attrVars.get(i)).addParent(attrVars.get(j));
      }
    }
  }
}

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));
  }
}

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

/**
 * 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.newMultinomialVariable(groupedClassName + "_t" + Integer.toString(mergedClassVarIndex), nStatesMAPVariable);
});

代码示例来源：origin: amidst/toolbox

@Override
protected void buildDAG() {
  Variable superParentVar = vars.newMultinomialVariable("superParentVar",getNumStates());
  dag = new DAG(vars);
  dag.getParentSets()
      .stream()
      .filter(w -> !w.getMainVar().equals(classVar))
      .filter(w -> !w.getMainVar().equals(superParentVar))
      .forEach(w -> {
        w.addParent(classVar);
        w.addParent(superParentVar);
      });
  dag.getParentSet(superParentVar).addParent(classVar);
}

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

topicIndicator = variables.newMultinomialVariable("TopicIndicator", nTopics);

代码示例来源：origin: amidst/toolbox

topicIndicator = variables.newMultinomialVariable("TopicIndicator", nTopics);

代码示例来源：origin: amidst/toolbox

Variable globalHiddenDiscrete = modelHeader.newMultinomialVariable("globalHiddenDiscrete", 2);

代码示例来源：origin: amidst/toolbox

Variable globalHiddenVar = modelHeader.newMultinomialVariable("GlobalHidden", 2);

代码示例来源：origin: amidst/toolbox

.forEach(i -> variables.newMultinomialVariable("DiscreteVar" + i, getNumStates()));
Variable classVar = variables.newMultinomialVariable("ClassVar", getNumStates());
Variable discreteHiddenVar = variables.newMultinomialVariable("DiscreteSPVar", getNumStatesHiddenDiscVars());

代码示例来源：origin: amidst/toolbox

.forEach(i -> variables.newMultinomialVariable("DiscreteVar" + i, getNumStates()));
Variable classVar = variables.newMultinomialVariable("ClassVar", getNumStates());
Variable discreteHiddenVar = variables.newMultinomialVariable("DiscreteSPVar", getNumStatesHiddenDiscVars());

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

@Override
public void buildClusterer(Instances data) throws Exception {
  attributes_ = Converter.convertAttributes(data.enumerateAttributes());
  Variables modelHeader = new Variables(attributes_);
  clusterVar_ = modelHeader.newMultinomialVariable("clusterVar", this.numberOfClusters());
  inferenceAlgorithm_ = new ImportanceSampling();
  inferenceAlgorithm_.setSeed(this.getSeed());
  dag = new DAG(modelHeader);
  /* Set DAG structure. */
  /* Add the hidden cluster variable as a parent of all the predictive attributes. */
  dag.getParentSets().stream()
  .filter(w -> w.getMainVar().getVarID() != clusterVar_.getVarID())
  .filter(w -> w.getMainVar().isObservable())
  .forEach(w -> w.addParent(clusterVar_));
  System.out.println(dag.toString());
  parameterLearningAlgorithm_ = new SVB();
  parameterLearningAlgorithm_.setDAG(dag);
  DataOnMemoryListContainer<DataInstance> batch_ = new DataOnMemoryListContainer(attributes_);
  data.stream().forEach(instance ->
      batch_.add(new DataInstanceFromDataRow(new DataRowWeka(instance, this.attributes_)))
  );
  parameterLearningAlgorithm_.setDataStream(batch_);
  parameterLearningAlgorithm_.initLearning();
  parameterLearningAlgorithm_.runLearning();
  bnModel_ = parameterLearningAlgorithm_.getLearntBayesianNetwork();
  System.out.println(bnModel_);
  inferenceAlgorithm_.setModel(bnModel_);
}

代码示例来源：origin: amidst/toolbox

clusterVar_ = modelHeader.newMultinomialVariable("clusterVar", getNumClusters());

代码示例来源：origin: amidst/toolbox

/**
 * Define the Fire Dectector Model's DAG.
 * @param attributes
 * @return
 */
public static DAG creatFireDectectorModel(Attributes attributes){
  /********** Model Definition ************/
  //Create the object handling the random variables of the model
  Variables variables = new Variables();
  //Create the random variables of the model. Some of them are associated to one attribute to retrieve its observed values from the data set.
  Variable fire = variables.newMultinomialVariable(attributes.getAttributeByName("Fire"));
  Variable temperature = variables.newGaussianVariable("Temperature");
  Variable smoke = variables.newMultinomialVariable("Smoke",2);
  Variable sensorT1 = variables.newGaussianVariable(attributes.getAttributeByName("SensorTemp1"));
  Variable sensorT2 = variables.newGaussianVariable(attributes.getAttributeByName("SensorTemp2"));
  Variable sensorSmoke = variables.newGaussianVariable(attributes.getAttributeByName("SensorSmoke"));
  //Create the directed acyclic graph object encoding the conditional independe relaionship among the variables of the model.
  DAG dag = new DAG(variables);
  //Define the parent set for each random variable
  dag.getParentSet(sensorT1).addParent(temperature);
  dag.getParentSet(sensorT2).addParent(temperature);
  dag.getParentSet(sensorSmoke).addParent(smoke);
  dag.getParentSet(temperature).addParent(fire);
  dag.getParentSet(smoke).addParent(fire);
  return dag;
}

代码示例来源：origin: amidst/toolbox

Variable fire = variables.newMultinomialVariable("Fire", 2);
Variable temperature = variables.newGaussianVariable("Temperature");
Variable smoke = variables.newMultinomialVariable("Smoke", 2);
Variable sensorT1 = variables.newGaussianVariable("SensorTemp1");
Variable sensorT2 = variables.newGaussianVariable("SensorTemp2");

代码示例来源：origin: amidst/toolbox

Variable fire = variables.newMultinomialVariable("Fire", 2);
Variable temperature = variables.newGaussianVariable("Temperature");
Variable smoke = variables.newMultinomialVariable("Smoke", 2);
Variable sensorT1 = variables.newGaussianVariable("SensorTemp1");
Variable sensorT2 = variables.newGaussianVariable("SensorTemp2");