Package | Description |
---|---|
org.neuroph.core |
Provides base classes and basic building components for neural networks.
|
org.neuroph.core.data |
Provides data set related classes and manipulation methods.
|
org.neuroph.core.learning |
Provides base classes for neural network learning algorithms.
|
org.neuroph.eval | |
org.neuroph.eval.classification | |
org.neuroph.nnet.learning |
Provides implementations of specific neural network learning algorithms.
|
org.neuroph.nnet.learning.kmeans | |
org.neuroph.util |
Provides various utility classes for creating neural networks,
type codes, parsing vectors, etc.
|
org.neuroph.util.data.norm |
Provides data normalization techniques
|
org.neuroph.util.data.sample |
Provides data sampling techniques
|
Modifier and Type | Method and Description |
---|---|
void |
NeuralNetwork.learn(DataSet trainingSet)
Learn the specified training set
|
void |
NeuralNetwork.learn(DataSet trainingSet,
L learningRule)
Learn the specified training set, using specified learning rule
|
Modifier and Type | Class and Description |
---|---|
class |
BufferedDataSet
This class can be used for large training sets, which are partialy read from
file during the training.
|
Modifier and Type | Method and Description |
---|---|
static DataSet |
DataSet.createFromFile(String filePath,
int inputsCount,
int outputsCount,
String delimiter)
Creates and returns data set from specified csv file
|
static DataSet |
DataSet.createFromFile(String filePath,
int inputsCount,
int outputsCount,
String delimiter,
boolean loadColumnNames)
Creates and returns data set from specified csv file
|
DataSet[] |
DataSet.createTrainingAndTestSubsets(int trainSetPercent,
int testSetPercent)
Returns training and test subsets in the specified percent ratio
|
static DataSet |
DataSet.load(String filePath)
Loads training set from the specified file
TODO: throw checked exceptionse here
|
Modifier and Type | Method and Description |
---|---|
List<DataSet> |
DataSet.sample(Sampling sampling) |
List<DataSet> |
DataSet.split(int... sizePercents) |
Modifier and Type | Field and Description |
---|---|
protected DataSet |
LearningRule.trainingSet
Training data set
|
Modifier and Type | Method and Description |
---|---|
DataSet |
LearningRule.getTrainingSet()
Gets training set
|
Modifier and Type | Method and Description |
---|---|
void |
UnsupervisedLearning.doLearningEpoch(DataSet trainingSet)
This method does one learning epoch for the unsupervised learning rules.
|
void |
SupervisedLearning.doLearningEpoch(DataSet trainingSet)
This method implements basic logic for one learning epoch for the
supervised learning algorithms.
|
abstract void |
IterativeLearning.doLearningEpoch(DataSet trainingSet)
Override this method to implement specific learning epoch - one learning
iteration, one pass through whole training set
|
void |
IterativeLearning.doOneLearningIteration(DataSet trainingSet)
Runs one learning iteration with the specified training set and fires
event to notify observers.
|
abstract void |
LearningRule.learn(DataSet trainingSet)
Override this method to implement specific learning procedures
|
void |
IterativeLearning.learn(DataSet trainingSet) |
void |
SupervisedLearning.learn(DataSet trainingSet,
double maxError)
Trains network for the specified training set and maxError
|
void |
SupervisedLearning.learn(DataSet trainingSet,
double maxError,
int maxIterations)
Trains network for the specified training set, maxError and number of iterations
|
void |
IterativeLearning.learn(DataSet trainingSet,
int maxIterations)
Trains network for the specified training set and number of iterations
|
void |
LearningRule.setTrainingSet(DataSet trainingSet)
Sets training set for this learning rule
|
Modifier and Type | Method and Description |
---|---|
DataSet |
EvaluationResult.getDataSet() |
Modifier and Type | Method and Description |
---|---|
EvaluationResult |
Evaluation.evaluateDataSet(NeuralNetwork neuralNetwork,
DataSet dataSet)
Runs evaluation procedure for given neural network and data set through all evaluatoors
Evaluation results are stored in evaluators
|
static void |
Evaluation.runFullEvaluation(NeuralNetwork<?> neuralNet,
DataSet dataSet)
Out of the box method (util) which computes all metrics for given neural network and test data set
|
void |
EvaluationResult.setDataSet(DataSet dataSet) |
Constructor and Description |
---|
CrossValidation(NeuralNetwork neuralNetwork,
DataSet dataSet,
int foldCount)
Creates a new instance of crrossvalidation for specified neural network, data set and number of folds.
|
Modifier and Type | Method and Description |
---|---|
boolean |
McNemarTest.evaluateNetworks(NeuralNetwork network1,
NeuralNetwork network2,
DataSet dataSet) |
Modifier and Type | Method and Description |
---|---|
void |
UnsupervisedHebbianLearning.doLearningEpoch(DataSet trainingSet)
This method does one learning epoch for the unsupervised learning rules.
|
void |
SimulatedAnnealingLearning.doLearningEpoch(DataSet trainingSet)
Perform one simulated annealing epoch.
|
void |
DynamicBackPropagation.doLearningEpoch(DataSet trainingSet) |
void |
CompetitiveLearning.doLearningEpoch(DataSet trainingSet)
This method does one learning epoch for the unsupervised learning rules.
|
void |
SimulatedAnnealingLearning.doLearningEpoch(DataSet trainingSet,
double randomChance) |
void |
KohonenLearning.learn(DataSet trainingSet) |
void |
HopfieldLearning.learn(DataSet trainingSet)
Calculates weights for the hopfield net to learn the specified training
set
|
Modifier and Type | Method and Description |
---|---|
DataSet |
KMeansClustering.getDataSet() |
Modifier and Type | Method and Description |
---|---|
void |
KMeansClustering.setDataSet(DataSet vectors) |
Constructor and Description |
---|
KMeansClustering(DataSet dataSet) |
KMeansClustering(DataSet dataSet,
int numberOfClusters) |
Modifier and Type | Method and Description |
---|---|
DataSet |
DataSetStatistics.getDataSet()
Get original data set.
|
static DataSet |
TrainingSetImport.importFromFile(String filePath,
int inputsCount,
int outputsCount,
String separator) |
Modifier and Type | Method and Description |
---|---|
static double[] |
DataSetStats.calculateMaxByColumns(DataSet dataSet) |
static double[] |
DataSetStats.calculateMeanByColumns(DataSet dataSet) |
static double[] |
DataSetStats.calculateMinByColumns(DataSet dataSet) |
Constructor and Description |
---|
DataSetStatistics(DataSet dataSet) |
Modifier and Type | Method and Description |
---|---|
void |
ZeroMeanNormalizer.normalize(DataSet dataSet) |
void |
RangeNormalizer.normalize(DataSet dataSet) |
void |
Normalizer.normalize(DataSet dataSet)
Normalize specified data set
|
void |
MaxNormalizer.normalize(DataSet dataSet) |
void |
MaxMinNormalizer.normalize(DataSet dataSet) |
void |
DecimalScaleNormalizer.normalize(DataSet dataSet) |
Modifier and Type | Method and Description |
---|---|
List<DataSet> |
SubSampling.sample(DataSet dataSet) |
List<DataSet> |
Sampling.sample(DataSet dataSet) |
Modifier and Type | Method and Description |
---|---|
List<DataSet> |
SubSampling.sample(DataSet dataSet) |
List<DataSet> |
Sampling.sample(DataSet dataSet) |
Copyright © 2017 Neuroph Project. All rights reserved.