MLTrain (encog-java-core 3.4 API)

All Known Subinterfaces:

Train

All Known Implementing Classes:

Backpropagation, BaseBaumWelch, BasicTraining, BasicTrainSOM, CrossTraining, CrossValidationKFold, FreeformBackPropagation, FreeformPropagationTraining, FreeformResilientPropagation, LevenbergMarquardtTraining, ManhattanPropagation, MLMethodGeneticAlgorithm, MLMethodGeneticAlgorithm.MLMethodGeneticAlgorithmHelper, NelderMeadTraining, NeuralPSO, NeuralSimulatedAnnealing, Propagation, QuickPropagation, ResilientPropagation, ScaledConjugateGradient, SOMClusterCopyTraining, StochasticGradientDescent, SVDTraining, SVMSearchTrain, SVMTrain, TrainAdaline, TrainBasicPNN, TrainBaumWelch, TrainBaumWelchScaled, TrainBayesian, TrainEA, TrainGaussian, TrainInstar, TrainKMeans, TrainLinearRegression, TrainOutstar
```
public interface MLTrain
```
Defines a training method for a machine learning method. Most MLMethod objects need to be trained in some way before they are ready for use.

Method Summary

All Methods Instance Methods Abstract Methods
Modifier and Type	Method and Description
`void`	`addStrategy(Strategy strategy)` Training strategies can be added to improve the training results.
`boolean`	`canContinue()`
`void`	`finishTraining()` Should be called once training is complete and no more iterations are needed.
`double`	`getError()`
`TrainingImplementationType`	`getImplementationType()`
`int`	`getIteration()`
`MLMethod`	`getMethod()` Get the current best machine learning method from the training.
`java.util.List<Strategy>`	`getStrategies()`
`MLDataSet`	`getTraining()`
`boolean`	`isTrainingDone()`
`void`	`iteration()` Perform one iteration of training.
`void`	`iteration(int count)` Perform a number of training iterations.
`TrainingContinuation`	`pause()` Pause the training to continue later.
`void`	`resume(TrainingContinuation state)` Resume training.
`void`	`setError(double error)`
`void`	`setIteration(int iteration)` Set the current training iteration.

- Method Detail
  - getImplementationType
```
TrainingImplementationType getImplementationType()
```
    Returns:
    
    The training implementation type.
  - isTrainingDone
```
boolean isTrainingDone()
```
    Returns:
    
    True if training can progress no further.
  - getTraining
```
MLDataSet getTraining()
```
    Returns:
    
    The training data to use.
  - iteration
```
void iteration()
```
    Perform one iteration of training.
  - getError
```
double getError()
```
    Returns:
    
    Returns the training error. This value is calculated as the training data is evaluated by the iteration function. This has two important ramifications. First, the value returned by getError() is meaningless prior to a call to iteration. Secondly, the error is calculated BEFORE training is applied by the call to iteration. The timing of the error calculation is done for performance reasons.
  - finishTraining
```
void finishTraining()
```
    Should be called once training is complete and no more iterations are needed. Calling iteration again will simply begin the training again, and require finishTraining to be called once the new training session is complete. It is particularly important to call finishTraining for multithreaded training techniques.
  - iteration
```
void iteration(int count)
```
    Perform a number of training iterations.
    
    Parameters:
    
    count - The number of iterations to perform.
  - getIteration
```
int getIteration()
```
    Returns:
    
    The current training iteration.
  - canContinue
```
boolean canContinue()
```
    Returns:
    
    True if the training can be paused, and later continued.
  - pause
```
TrainingContinuation pause()
```
    Pause the training to continue later.
    
    Returns:
    
    A training continuation object.
  - resume
```
void resume(TrainingContinuation state)
```
    Resume training.
    
    Parameters:
    
    state - The training continuation object to use to continue.
  - addStrategy
```
void addStrategy(Strategy strategy)
```
    Training strategies can be added to improve the training results. There are a number to choose from, and several can be used at once.
    
    Parameters:
    
    strategy - The strategy to add.
  - getMethod
```
MLMethod getMethod()
```
    Get the current best machine learning method from the training.
    
    Returns:
    
    The best machine learning method.
  - getStrategies
```
java.util.List<Strategy> getStrategies()
```
    Returns:
    
    The strategies to use.
  - setError
```
void setError(double error)
```
    Parameters:
    
    error - Set the current error rate. This is usually used by training strategies.
  - setIteration
```
void setIteration(int iteration)
```
    Set the current training iteration.
    
    Parameters:
    
    iteration - Iteration.

Interface MLTrain

Method Summary

Method Detail

getImplementationType

isTrainingDone

getTraining

iteration

getError

finishTraining

iteration

getIteration

canContinue

pause

resume

addStrategy

getMethod

getStrategies

setError

setIteration