PNC2 Rule Induction System

Source Documentation

 Some general notes
 Something on the terms used in comments
 Class overview: Kernels
 Class overview: Gui
 Class overview: Command line version
 Some notes on the variable types symbolic and continuous
 Some notes on the command line version
 Known bugs

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Some general notes


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Something on the terms used in comments


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Class overview: Kernels

Class Module Description
TPnc
TCuboid
TIntervals
TCuboids
TCuboidsEx
TCuboidsLists
TAdjMat
TAdjMatEx
TAdjArr
pnc.h
pnc.cpp
Contains the class TPnc and all other necessary sub-classes for the PNC2 cluster algorithm.
TParameter para.h
para.cpp
TParameter is derived from TParaSet and serves as a struct with all parameters needed by the PNC2 cluster algorithm.
TCluster
TDFunc
cluster.h
cluster.cpp

TCluster encapsulates a model learned with the PNC2 cluster algorithm. It is used in combination with TPrediction.

TDFunc encapsulates two function pointers that are used to improve performance of distance calculations in the input space.

TPrediction prediction.h
prediction.cpp
TPrediction predicts the unknown output value given the input based on the model learned with the PNC2 cluster algorithm. Note: The class TCluster encapsulates such a model.
TLossFunction lossfunc.h
lossfunc.cpp
Tracks some loss function results like mean absolute error (MAE) or relative number of misclassifications (MCE).
TData data.h
data.cpp
Class TData encapsulates data matrix with nTup rows and nVar columns. Note: The first column is regarded as output! Use MoveToFirst() to met this condition!
TDataData ddata.h
ddata.cpp
Encapsulates some necessary information from the learn data file like # variables, minima, maxima, feature, weights ...
TParaSet paraset.h
paraset.cpp
Base class for TParameter. Encapsulates only those parameters that are chosen within the parameter tuning.
TProject project.h
project.cpp
Encapsulates project settings. Abstract base class! GUI and batch interface each derive their own project classes.
TExceptionAB
TExceptionU
exception.h
exception.cpp

TExceptionAB encapsulates type 'A' and 'B' exceptions. These exceptions are intended for program development only and they should never been thrown in the released program. Note: Type 'B' exceptions are only generated in debug mode, i.e. if DEBUG is defined.

TExceptionU encapsulates type 'U' exceptions. These exceptions are intended to be catched and shown to the user. They may occur in practise.

TParaSetList ParaSetList.h
ParaSetList.cpp
Type definition and utilities to store and use several parameter sets (TParaSet) in a single linked list (TStdList).
TStdList stdList.h Simple but time efficient single linked list template.
TTask task.h
task.cpp
A kind of struct with load and save functionality. Encapsulates a task, i.e. a set of parameter values for the PNC2 algorithm, that are used to generate a TParaSetList by building all possible combinations.
- defines.h Defines
- util.h
util.cpp
General utility functions to sort and randomize, to write values or times as formatted text, to quantize and de-quantize values and to write bits.
- fileutil.h
fileutil.cpp
Utility functions to read values or strings from a file stream. You can realize format-sensitive reading without the always read a whoole line concept.


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Class overview: Gui

Class Module Description
TProjectG ProjectG.h
ProjectG.cpp
Encapsulates project settings. Derived from TProject.
TTune tune.h
tune.cpp
Tune parameters, i.e. learn and test model on several different data sets that are build randomly from a given TData object. Load and save functionality.
ThrUseModel ThrUseModel.h
ThrUseModel.cpp
Thread function: Use given model (TCluster) with given parameters (TParameter) to predict the unknown ouput values using only the inputs for a given TData object. Employs the class TPrediction.
ThrTune ThrTune.h
ThrTune.cpp
Tune parameters using TTune.
ThrOpenProject ThrOpenProject.h
ThrOpenProject.cpp
Thread function: Loads model, tuning results and data as specifed in given TProjectG object. Calls back to given c-style function when finished.
ThrLoadData ThrLoadData.h
ThrLoadData.cpp
Thread function: Calls TData::Load() in its execute function.
ThrLearnModel ThrLearnModel.h
ThrLearnModel.cpp
Thread function: Learns a model using TPnc and transforms the learned model afterwards to TCluster by calling TPnc::ToTCluster().
FVarTypes FVarTypes.h
FVarTypes.cpp
Dialog to show and edit the variable types. Symbolic variables can be toggled between nominal and nominal (symbolic) type. Additional information like extrema, # symbols etc. is displayed for each variable.
FOptions FOptions.h
FOptions.cpp
Options dialog: Set those parameters of the PNC2 algorithm, that are not tuned within the parameter tuning.
FOpenProject FOpenProject.h
FOpenProject.cpp
Dialog that displays progress and other information when loading a model and data as specified in associated TProjectG object. Loading is done using the thread object ThrOpenProject.
FMain FMain.h
FMain.cpp
Main form of the GUI for the PNC2 cluster algorithm.
FGuiOptions FGuiOptions.h
FGuiOptions.cpp
Options dialog for Gui specific settings - directly saves to registry.
FAbout FAbout.h
FAbout.cpp
About-dialog with a few lines of text and an animated icon playing pinball with the dialogs frame.
FEditSets FEditSets.h
FEditSets.cpp
Form to edit the parameter sets for the parameter tuning.


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Class overview: Command line version

Class Module Description
TBatchInterface bap.h
bap.cpp
Encapsulates batch interface for the PNC2 cluster algorithm.
TParaSetResult TParaSetResults SetResult.h
SetResult.cpp
TParaSetResult encapsulates loss function results and model characteristics for a model learned and tested several times on different data. TParaSetResults encapsulates several TParaSetResult objects, i.e. stores the results of several parameter sets used to learn and test a model on several different data sets.
TProjectB ProjectB.h
ProjectB.cpp
Encapsulates project settings. Derived from TProject.


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Some notes on the variable types symbolic and continuous



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Some notes on the command line version



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Known bugs

last updated: 22 January 2004   © 2000-2004 by Lars Haendel