magpie.data.utilities.normalizers

Class ZScoreNormalizer

java.lang.Object

magpie.data.utilities.normalizers.BaseDatasetNormalizer

magpie.data.utilities.normalizers.IndependentVariableNormalizer

magpie.data.utilities.normalizers.ZScoreNormalizer

All Implemented Interfaces:

java.io.Serializable, java.lang.Cloneable, Commandable, Options, Printable

Direct Known Subclasses:

SoftmaxNormalizer

public class ZScoreNormalizer
extends IndependentVariableNormalizer

Scale variables by calculating their Z scores. Applies the transformation x’ = ( x - mean(X) ) / stdev(X) where x is the value of an attribute, X is the set of all values of that variable in the training set, and x’ is the normalized attribute. For examples where the range of X is measured to be 0, the range is assumed to be 1.

Usage: *No options*

See Also:

Serialized Form

Constructor Summary

Constructors

Constructor and Description
ZScoreNormalizer()

Method Summary

Modifier and Type	Method and Description
ZScoreNormalizer	clone()
protected void	computeAttributeStatistics(int attributeNumber, double[] values) Compute statistics required to perform normalization/restoration.
protected void	computeClassStatistics(double[] values) Compute statistics regarding the class variable.
protected double	normalizationFunction(double variable, double mean, double stdev) Compute the normalized value of a variable
protected void	normalizeAttributes(double[] attributes) Normalize each attribute for an entry
protected double	normalizeClassVariable(double value) Normalize a class variable
protected void	prepareAttributeArrays(int NAttributes) Prepare arrays that will hold attribute statistics.
java.lang.String	printUsage() Print out required format for options.
protected double	restorationFunction(double variable, double mean, double stdev) Compute the restored value a variable
protected void	restoreAttributes(double[] attributes) Restore each attribute for an entry
protected double	restoreClassVariable(double value) Restore a class variable
void	setOptions(java.util.List<java.lang.Object> Options) Set any options for this object.

Methods inherited from class magpie.data.utilities.normalizers.IndependentVariableNormalizer

normalizeAttributes, normalizeClassVariable, restoreAttributes, restoreClassVariable, trainOnAttributes, trainOnMeasuredClass

Methods inherited from class magpie.data.utilities.normalizers.BaseDatasetNormalizer

about, isTrained, normalize, printCommand, printDescription, restore, runCommand, setToNormalizeAttributes, setToNormalizeClass, test, train, willNormalizeAttributes, willNormalizeClass

Methods inherited from class java.lang.Object

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

Constructor Detail

ZScoreNormalizer

public ZScoreNormalizer()

Method Detail

clone

public ZScoreNormalizer clone()

Overrides:

clone in class BaseDatasetNormalizer

setOptions

public void setOptions(java.util.List<java.lang.Object> Options)
                throws java.lang.Exception

Description copied from interface: Options

Set any options for this object. If parsing the options fails, return an Exception with the usage as its message.

Parameters:

Options - Array of options as Objects - can be null

Throws:

java.lang.Exception - if problem with inputs

printUsage

public java.lang.String printUsage()

Description copied from interface: Options

Print out required format for options. For consistency, should start with "Usage: ".

Returns:

Usage for this function

prepareAttributeArrays

protected void prepareAttributeArrays(int NAttributes)

Description copied from class: IndependentVariableNormalizer

Prepare arrays that will hold attribute statistics.

Specified by:

prepareAttributeArrays in class IndependentVariableNormalizer

computeAttributeStatistics

protected void computeAttributeStatistics(int attributeNumber,
                                          double[] values)

Description copied from class: IndependentVariableNormalizer

Compute statistics required to perform normalization/restoration.

Specified by:

computeAttributeStatistics in class IndependentVariableNormalizer

Parameters:

attributeNumber - Attribute number

computeClassStatistics

protected void computeClassStatistics(double[] values)

Description copied from class: IndependentVariableNormalizer

Compute statistics regarding the class variable.

Specified by:

computeClassStatistics in class IndependentVariableNormalizer

Parameters:

values - Measured values of the class variable

normalizeAttributes

protected void normalizeAttributes(double[] attributes)

Description copied from class: IndependentVariableNormalizer

Normalize each attribute for an entry

Specified by:

normalizeAttributes in class IndependentVariableNormalizer

Parameters:

attributes - Attributes to be normalized

normalizeClassVariable

protected double normalizeClassVariable(double value)

Description copied from class: IndependentVariableNormalizer

Normalize a class variable

Specified by:

normalizeClassVariable in class IndependentVariableNormalizer

Parameters:

value - Value of class variable

Returns:

Normalized value

normalizationFunction

protected double normalizationFunction(double variable,
                                       double mean,
                                       double stdev)

Compute the normalized value of a variable

Parameters:

variable - Value of variable to be normalized

mean - Mean of this variable (from training set)

stdev - Standard deviation of this variable

Returns:

Normalized value

restoreAttributes

protected void restoreAttributes(double[] attributes)

Description copied from class: IndependentVariableNormalizer

Restore each attribute for an entry

Specified by:

restoreAttributes in class IndependentVariableNormalizer

Parameters:

attributes - Normalized attributes to be restored

restoreClassVariable

protected double restoreClassVariable(double value)

Description copied from class: IndependentVariableNormalizer

Restore a class variable

Specified by:

restoreClassVariable in class IndependentVariableNormalizer

Parameters:

value - Normalized value of class variable

Returns:

Original value

restorationFunction

protected double restorationFunction(double variable,
                                     double mean,
                                     double stdev)

Compute the restored value a variable

Parameters:

variable - Value of variable to be restored

mean - Mean of variable (from training set)

stdev - Measured standard deviation of variable

Returns:

Restored value