Package adams.flow.control.removeoutliers

Class AbstractNumericOutlierDetector

java.lang.Object

adams.core.logging.LoggingObject

adams.core.logging.CustomLoggingLevelObject

adams.core.option.AbstractOptionHandler

adams.flow.control.removeoutliers.AbstractOutlierDetector

adams.flow.control.removeoutliers.AbstractNumericOutlierDetector

All Implemented Interfaces:

Destroyable, ErrorProvider, GlobalInfoSupporter, LoggingLevelHandler, LoggingSupporter, OptionHandler, QuickInfoSupporter, SizeOfHandler, Serializable

Direct Known Subclasses:

IQR, MeanAbsoluteError, StdDev

public abstract class AbstractNumericOutlierDetector
extends AbstractOutlierDetector

Ancestor for numeric outlier detection.

Version:

$Revision$

Author:

FracPete (fracpete at waikato dot ac dot nz)

See Also:

Serialized Form

Field Summary

Fields inherited from class adams.flow.control.removeoutliers.AbstractOutlierDetector

m_LastError

Fields inherited from class adams.core.option.AbstractOptionHandler

m_OptionManager

Fields inherited from class adams.core.logging.LoggingObject

m_Logger, m_LoggingIsEnabled, m_LoggingLevel

Constructor Summary

Constructors

Constructor	Description
AbstractNumericOutlierDetector()

Method Summary

Modifier and Type	Method	Description
String	check(SpreadSheet sheet, SpreadSheetColumnIndex actual, SpreadSheetColumnIndex predicted)	Check method before detection.
protected double[]	diff(SpreadSheet sheet, SpreadSheetColumnIndex actual, SpreadSheetColumnIndex predicted, boolean useRelative)	Computes the difference between the two columns.
protected double[]	diff(Double[] actual, Double[] predicted, boolean useRelative)	Computes the difference between the two columns.
protected Double[]	extractColumn(SpreadSheet sheet, SpreadSheetColumnIndex col)	Extracts the double values from the specified column.

Methods inherited from class adams.flow.control.removeoutliers.AbstractOutlierDetector

detect, doDetect, getLastError, getQuickInfo, hasLastError

Methods inherited from class adams.core.option.AbstractOptionHandler

cleanUpOptions, defineOptions, destroy, finishInit, getDefaultLoggingLevel, getOptionManager, globalInfo, initialize, loggingLevelTipText, newOptionManager, reset, setLoggingLevel, toCommandLine, toString

Methods inherited from class adams.core.logging.LoggingObject

configureLogger, getLogger, getLoggingLevel, initializeLogging, isLoggingEnabled, sizeOf

Methods inherited from class java.lang.Object

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

Methods inherited from interface adams.core.logging.LoggingLevelHandler

getLoggingLevel

Constructor Detail

AbstractNumericOutlierDetector

public AbstractNumericOutlierDetector()

Method Detail

check

public String check(SpreadSheet sheet,
                    SpreadSheetColumnIndex actual,
                    SpreadSheetColumnIndex predicted)

Check method before detection.

Overrides:

check in class AbstractOutlierDetector

Parameters:

sheet - the spreadsheet to analyze

actual - the column with the actual values

predicted - the column with the predicted values

Returns:

null if check passed, otherwise error message

extractColumn

protected Double[] extractColumn(SpreadSheet sheet,
                                 SpreadSheetColumnIndex col)

Extracts the double values from the specified column.

Parameters:

sheet - the sheet to get the data from

col - the column

Returns:

the data, null entries for non-numeric or missing values

diff

protected double[] diff(Double[] actual,
                        Double[] predicted,
                        boolean useRelative)

Computes the difference between the two columns.

Parameters:

actual - the column with the actual values

predicted - the column with the predicted values

useRelative - whether to divide the difference by the actual value (NaN if 0)

Returns:

the difference, uses NaN if either column has non-numeric or missing value

diff

protected double[] diff(SpreadSheet sheet,
                        SpreadSheetColumnIndex actual,
                        SpreadSheetColumnIndex predicted,
                        boolean useRelative)

Computes the difference between the two columns.

Parameters:

sheet - the sheet to get the data from

actual - the column with the actual values

predicted - the column with the predicted values

useRelative - whether to divide the difference by the actual value (NaN if 0)

Returns:

the difference, uses NaN if either column has non-numeric or missing value