public class Ensemble
extends java.lang.Object
Class representing an ensemble
Modifier and Type | Field and Description |
---|---|
double[] |
betta
Ensemble weights.
|
double[][][][] |
cache
Ensemble weights.
|
int |
TEST
Training flag.
|
int |
TRAIN
Training flag.
|
int |
VAL
Training flag.
|
double[][] |
weights
Ensemble weights.
|
Constructor and Description |
---|
Ensemble(EnsembleParameters global)
Constructor
|
Modifier and Type | Method and Description |
---|---|
void |
EnsembleOutput(double[] inputs,
double[] outputs)
Output of every network
|
void |
GetAdaWeights()
Calculate weights using Ada method
|
void |
GetGEMWeights(EnsembleParameters global,
double[][] data,
int n)
Calculate weights using GEM method
|
void |
LoadEnsemble(java.lang.String file_name)
Load ensemble from file_name
|
void |
SaveEnsemble(java.lang.String file_name,
java.lang.String header)
Save ensemble at file_name
|
void |
SaveOutputFile(java.lang.String file_name,
double[][] data,
int n,
java.lang.String problem,
double[] a,
double[] b)
Save data in output file
|
double |
TestEnsembleInClassification(EnsembleParameters global,
double[][] data,
int npatterns)
Test ensemble in classification
|
double |
TestEnsembleInRegression(EnsembleParameters global,
double[][] data,
int npatterns)
Test ensemble in regression
|
void |
TrainEnsemble(EnsembleParameters global,
Data data)
Train Ensemble
|
void |
TrainEnsembleAda(EnsembleParameters global,
Data data)
Train ensemble using Ada
|
void |
TrainEnsembleArcing(EnsembleParameters global,
Data data)
Train ensemble using Arcing
|
void |
TrainEnsembleBagging(EnsembleParameters global,
Data data)
Train ensemble using Bagging
|
void |
TrainEnsembleNoSampling(EnsembleParameters global,
Data data)
Train ensemble without sampling
|
void |
UpdateCache(EnsembleParameters global,
Data data)
Update cache data
|
public double[][] weights
public double[] betta
public double[][][][] cache
public final int TRAIN
public final int TEST
public final int VAL
public Ensemble(EnsembleParameters global)
Constructor
global
- Global definition parameterspublic void TrainEnsemble(EnsembleParameters global, Data data)
Train Ensemble
global
- Global definition parametersdata
- Input datapublic void TrainEnsembleNoSampling(EnsembleParameters global, Data data)
Train ensemble without sampling
global
- Global definition parametersdata
- Input datapublic void TrainEnsembleBagging(EnsembleParameters global, Data data)
Train ensemble using Bagging
global
- Global definition parametersdata
- Input datapublic void TrainEnsembleArcing(EnsembleParameters global, Data data)
Train ensemble using Arcing
global
- Global definition parametersdata
- Input data.public void TrainEnsembleAda(EnsembleParameters global, Data data)
Train ensemble using Ada
global
- Global definition parametersdata
- Input datapublic void EnsembleOutput(double[] inputs, double[] outputs)
Output of every network
inputs
- Input dataoutputs
- Output datapublic double TestEnsembleInClassification(EnsembleParameters global, double[][] data, int npatterns)
Test ensemble in classification
global
- Global definition parametersdata
- Input datanpatterns
- No of patternspublic double TestEnsembleInRegression(EnsembleParameters global, double[][] data, int npatterns)
Test ensemble in regression
global
- Global definition parametersdata
- Input datanpatterns
- No of patternspublic void SaveEnsemble(java.lang.String file_name, java.lang.String header)
Save ensemble at file_name
file_name
- File nameheader
- header of the data set for which the network has been adjusted topublic void LoadEnsemble(java.lang.String file_name)
Load ensemble from file_name
file_name
- File namepublic void GetGEMWeights(EnsembleParameters global, double[][] data, int n)
Calculate weights using GEM method
global
- Global definition parametersdata
- Input datan
- matrix order (no of rows and colms in data matrix)public void GetAdaWeights()
Calculate weights using Ada method
public void SaveOutputFile(java.lang.String file_name, double[][] data, int n, java.lang.String problem, double[] a, double[] b)
Save data in output file
file_name
- File namedata
- Data to be savedn
- No of patternsproblem
- Type of problem (CLASSIFICATION | REGRESSION)a
- Scaling parameter (a).b
- Scaling parameter (b).public void UpdateCache(EnsembleParameters global, Data data)
Update cache data
global
- Global definition parametersdata
- Input data