Package edu.cmu.tetrad.data

Class DataUtils

java.lang.Object
edu.cmu.tetrad.data.DataUtils
public final class DataUtils extends Object
Some static utility methods for dealing with data sets.
Version:
$Id: $Id
Author:
Various folks.

  • Method Details

    • isBinary

      public static boolean isBinary(DataSet data, int column)
      States whether the given column of the given data set is binary.
      Parameters:
      data - Ibid.
      column - Ibid.
      Returns:
      true iff the column is binary.

    • defaultCategory

      public static String defaultCategory(int index)

      defaultCategory.

      Parameters:
      index - Ond plus the given index.
      Returns:
      the default category for index i. (The default category should ALWAYS be obtained by calling this method.)

    • discreteSerializableInstance

      public static DataSet discreteSerializableInstance()
      A discrete data set used to construct some other serializable instances.
      Returns:
      a DataSet object

    • containsMissingValue

      public static boolean containsMissingValue(Matrix data)

      containsMissingValue.

      Parameters:
      data - a Matrix object
      Returns:
      true iff the data sets contains a missing value.

    • containsMissingValue

      public static boolean containsMissingValue(DataSet data)

      containsMissingValue.

      Parameters:
      data - a DataSet object
      Returns:
      a boolean

    • createContinuousVariables

      public static List<Node> createContinuousVariables(String[] varNames)

      createContinuousVariables.

      Parameters:
      varNames - an array of String objects
      Returns:
      a List object

    • subMatrix

      public static Matrix subMatrix(ICovarianceMatrix m, Node x, Node y, List<Node> z)

      subMatrix.

      Parameters:
      m - a ICovarianceMatrix object
      x - a Node object
      y - a Node object
      z - a List object
      Returns:
      the submatrix of m with variables in the order of the x variables.

    • subMatrix

      public static Matrix subMatrix(Matrix m, List<Node> variables, Node x, Node y, List<Node> z)

      subMatrix.

      Parameters:
      m - a Matrix object
      variables - a List object
      x - a Node object
      y - a Node object
      z - a List object
      Returns:
      the submatrix of m with variables in the order of the x variables.

    • subMatrix

      public static Matrix subMatrix(Matrix m, Map<Node,Integer> indexMap, Node x, Node y, List<Node> z)

      subMatrix.

      Parameters:
      m - a Matrix object
      indexMap - a Map object
      x - a Node object
      y - a Node object
      z - a List object
      Returns:
      the submatrix of m with variables in the order of the x variables.

    • subMatrix

      public static Matrix subMatrix(ICovarianceMatrix m, Map<Node,Integer> indexMap, Node x, Node y, List<Node> z)

      subMatrix.

      Parameters:
      m - a ICovarianceMatrix object
      indexMap - a Map object
      x - a Node object
      y - a Node object
      z - a List object
      Returns:
      the submatrix of m with variables in the order of the x variables.

    • means

      public static Vector means(Matrix data)

      means.

      Parameters:
      data - a Matrix object
      Returns:
      a Vector object

    • means

      public static Vector means(double[][] data)
      Column major data.
      Parameters:
      data - an array of double objects
      Returns:
      a Vector object

    • cov

      public static Matrix cov(Matrix data)

      cov.

      Parameters:
      data - a Matrix object
      Returns:
      a Matrix object

    • mean

      public static Vector mean(Matrix data)

      mean.

      Parameters:
      data - a Matrix object
      Returns:
      a Vector object

    • choleskySimulation

      public static DataSet choleskySimulation(CovarianceMatrix cov)

      choleskySimulation.

      Parameters:
      cov - The variables and covariance matrix over the variables.
      Returns:
      The simulated data.

    • ranks

      public static double[] ranks(double[] x)

      ranks.

      Parameters:
      x - an array of double objects
      Returns:
      an array of double objects

    • getExampleNonsingular

      public static List<Node> getExampleNonsingular(ICovarianceMatrix covarianceMatrix, int depth)

      getExampleNonsingular.

      Parameters:
      covarianceMatrix - a ICovarianceMatrix object
      depth - a int
      Returns:
      a List object

    • getEss

      public static double getEss(ICovarianceMatrix covariances)
      Returns the equivalent sample size, assuming all units are equally correlated and all unit variances are equal.
      Parameters:
      covariances - a ICovarianceMatrix object
      Returns:
      a double