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

Variables.<init>介绍

暂无

代码示例

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

/**
 * Returns the class variable.
 * @param modelContext a {@link moa.core.InstancesHeader} object.
 * @param atts a set of of {@link Attributes}.
 * @return a {@link Variable} object that represents the class variable.
 */
public static Variable getClassVariable(InstancesHeader 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 Model(Attributes attributes) {
  this.atts = attributes;
  vars = new Variables(attributes);
  if(!this.isValidConfiguration()) {
    throw new WrongConfigurationException(getErrorMessage());
  }
}

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

Variables variables = new Variables();

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

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

Variables variables = new Variables();
word = variables.newSparseMultionomialVariable(attributes.getAttributeByName(wordDocumentName));

代码示例来源：origin: amidst/toolbox

Variables variables = new Variables();
word = variables.newSparseMultionomialVariable(attributes.getAttributeByName(wordDocumentName));

代码示例来源：origin: amidst/toolbox

public static DAG getHiddenNaiveBayesStructure(DataFlink<DataInstance> dataStream) {
  Variables modelHeader = new Variables(dataStream.getAttributes());

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

/**
 * 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

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

Variables variables = new Variables();

代码示例来源：origin: amidst/toolbox

Variables variables = new Variables();

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

/**
 * {@inheritDoc}
 */
@Override
public void initLearning() {
  super.initLearning();
  truncatedExpVar = new Variables().newTruncatedExponential("TruncatedExponentialVar");
  this.ef_TExpP = truncatedExpVar.getDistributionType().newEFUnivariateDistribution(this.getDelta());
  prior = this.plateuStructure.getPlateauNaturalParameterPrior();
  int size = prior.getNumberOfBaseVectors();
  this.ef_TExpQ = new EF_TruncatedExponential[size];
  for (int i = 0; i < size; i++) {
    this.ef_TExpQ[i] = truncatedExpVar.getDistributionType().newEFUnivariateDistribution(this.getDelta());
  }
  firstBatch=true;
}

代码示例来源：origin: amidst/toolbox

/**
 * {@inheritDoc}
 */
@Override
public void initLearning() {
  super.initLearning();
  truncatedExpVar = new Variables().newTruncatedExponential("TruncatedExponentialVar");
  this.ef_TExpP = truncatedExpVar.getDistributionType().newEFUnivariateDistribution(this.getDelta());
  prior = this.plateuStructure.getPlateauNaturalParameterPrior();
  for (Variable variable : this.plateuStructure.getNonReplicatedVariables()) {
    if (variable.getNumberOfStates()>maxsize)
      maxsize=variable.getNumberOfStates();
  }
  this.ef_TExpQ = new EF_TruncatedExponential[prior.getNumberOfBaseVectors()][maxsize];
  for (int i = 0; i < this.ef_TExpQ.length; i++) {
    for (int j = 0; j < this.ef_TExpQ[i].length; j++) {
      this.ef_TExpQ[i][j] = truncatedExpVar.getDistributionType().newEFUnivariateDistribution(this.getDelta());
    }
  }
  firstBatch=true;
}

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

public static BayesianNetwork createFireDetectorModel(double tempMean) {
  Variables variables = new Variables();