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 Summary

Modifier and Type	Method	Description
static DataSet	choleskySimulation(CovarianceMatrix cov)	choleskySimulation.
static boolean	containsMissingValue(DataSet data)	containsMissingValue.
static boolean	containsMissingValue(Matrix data)	containsMissingValue.
static Matrix	cov(Matrix data)	cov.
static List<Node>	createContinuousVariables(String[] varNames)	createContinuousVariables.
static String	defaultCategory(int index)	defaultCategory.
static DataSet	discreteSerializableInstance()	A discrete data set used to construct some other serializable instances.
static double	getEss(ICovarianceMatrix covariances)	Returns the equivalent sample size, assuming all units are equally correlated and all unit variances are equal.
static List<Node>	getExampleNonsingular(ICovarianceMatrix covarianceMatrix, int depth)	getExampleNonsingular.
static boolean	isBinary(DataSet data, int column)	States whether the given column of the given data set is binary.
static Vector	mean(Matrix data)	mean.
static Vector	means(double[][] data)	Column major data.
static Vector	means(Matrix data)	means.
static double[]	ranks(double[] x)	ranks.
static Matrix	subMatrix(ICovarianceMatrix m, Node x, Node y, List<Node> z)	subMatrix.
static Matrix	subMatrix(ICovarianceMatrix m, Map<Node,Integer> indexMap, Node x, Node y, List<Node> z)	subMatrix.
static Matrix	subMatrix(Matrix m, List<Node> variables, Node x, Node y, List<Node> z)	subMatrix.
static Matrix	subMatrix(Matrix m, Map<Node,Integer> indexMap, Node x, Node y, List<Node> z)	subMatrix.

Methods inherited from class java.lang.Object

equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

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 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 objects

Returns:

an array of 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