de.lmu.ifi.dbs.elki.utilities.scaling.outlier
Class MinusLogGammaScaling

java.lang.Object
  de.lmu.ifi.dbs.elki.logging.AbstractLoggable
      de.lmu.ifi.dbs.elki.utilities.scaling.outlier.OutlierGammaScaling
          de.lmu.ifi.dbs.elki.utilities.scaling.outlier.MinusLogGammaScaling

All Implemented Interfaces:

Parameterizable, OutlierScalingFunction, ScalingFunction

public class MinusLogGammaScaling
extends OutlierGammaScaling

Scaling that can map arbitrary values to a probability in the range of [0:1], by assuming a Gamma distribution on the data and evaluating the Gamma CDF.

Author:

Erich Schubert

Field Summary

(package private) double	max Maximum value seen
(package private) double	mlogmax Minimum value (after log step, so maximum again)

Fields inherited from class de.lmu.ifi.dbs.elki.utilities.scaling.outlier.OutlierGammaScaling

atmean, k, mean, meta, normalize, NORMALIZE_FLAG, theta, var

Fields inherited from class de.lmu.ifi.dbs.elki.logging.AbstractLoggable

debug, logger

Constructor Summary

MinusLogGammaScaling()
Constructor, adhering to Parameterizable

Method Summary

void	prepare(Database<?> db, Result result, OutlierResult or) Prepare is called once for each data set, before getScaled() will be called.
protected double	preScale(double score) Normalize data if necessary.

Methods inherited from class de.lmu.ifi.dbs.elki.utilities.scaling.outlier.OutlierGammaScaling

getMax, getMin, getScaled

Methods inherited from class de.lmu.ifi.dbs.elki.logging.AbstractLoggable

debugFine, debugFiner, debugFinest, exception, progress, verbose, warning

Methods inherited from class java.lang.Object

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

Field Detail

max

double max

Maximum value seen

mlogmax

double mlogmax

Minimum value (after log step, so maximum again)

Constructor Detail

MinusLogGammaScaling

public MinusLogGammaScaling()

Constructor, adhering to Parameterizable

Method Detail

preScale

protected double preScale(double score)

Description copied from class: OutlierGammaScaling

Normalize data if necessary. Note: this is overridden by MinusLogGammaScaling!

Overrides:

preScale in class OutlierGammaScaling

Parameters:

score - Original score

Returns:

Normalized score.

prepare

public void prepare(Database<?> db,
                    Result result,
                    OutlierResult or)

Description copied from interface: OutlierScalingFunction

Prepare is called once for each data set, before getScaled() will be called. This function can be used to extract global parameters such as means, minimums or maximums from the Database, Result or Annotation.

Specified by:

prepare in interface OutlierScalingFunction

Overrides:

prepare in class OutlierGammaScaling

Parameters:

db - Database used

result - Result object

or - Outlier result to use