public final class ParameterLearningOutput extends Object implements Cloneable
ParameterLearning.learn(com.bayesserver.data.EvidenceReaderCommand, com.bayesserver.learning.parameters.ParameterLearningOptions)
.Modifier and Type | Method and Description |
---|---|
Double |
getBIC()
Gets the Bayesian Information Criterion (BIC) for the final learnt
Network based on the learning data. |
double |
getCaseCount()
Gets the number of cases (records) in the learning data.
|
boolean |
getConverged()
Gets a value indicating whether this parameter learning converged.
|
int |
getIterationCount()
Gets the number of iterations performed during learning.
|
Double |
getLogLikelihood()
Gets the log likelihood of the learning data with the final learnt
Network . |
int |
getSeed()
Gets the seed used to generate random numbers for initialization.
|
long |
getUnweightedCaseCount()
Gets the unweighted case count in the learning data.
|
double |
getWeightedCaseCount()
Gets the weighted case count in the learning data.
|
public int getSeed()
ParameterLearningOptions.getMaximumConcurrency()
is 1.public boolean getConverged()
ParameterLearningOptions.getTolerance()
.true
if converged; otherwise, false
.public Double getLogLikelihood()
Network
.
The log likelihood increases as the model fits the data better.
This value is only calculated if ParameterLearningOptions.getCalculateStatistics()
is true
.
public int getIterationCount()
public double getCaseCount()
public double getWeightedCaseCount()
public long getUnweightedCaseCount()
public Double getBIC()
Network
based on the learning data.
When comparing two models using the BIC heuristic, a lower value is preferred.
This statistic is only calculated if ParameterLearningOptions.getCalculateStatistics()
is true
.
The BIC statistics is a Log Likelihood based statistic which penalizes models with more parameters. It can be used as an heuristic to compare networks with different parameters learnt with the same data.
Copyright © 2023. All rights reserved.