| Modifier and Type | Method and Description |
|---|---|
ObservableMap<ListDataSet> |
AbstractAlgorithm.getDataSetMap() |
| Modifier and Type | Method and Description |
|---|---|
void |
KNNClassifier.trainAll(ListDataSet dataSet) |
void |
ConstantClassifier.trainAll(ListDataSet dataSet) |
void |
RandomClassifier.trainAll(ListDataSet dataSet) |
| Modifier and Type | Method and Description |
|---|---|
void |
NaiveBayesClassifier.trainAll(ListDataSet dataSet) |
| Modifier and Type | Method and Description |
|---|---|
void |
SemiSupervisedClassifier.predict(ListDataSet dataSet) |
void |
SemiSupervisedClassifier.train(ListDataSet labeledData,
ListDataSet unlabeledData) |
void |
MultiClassClassifier.trainAll(ListDataSet dataSet) |
void |
Bagging.trainAll(ListDataSet dataSet) |
void |
FeatureSelector.trainAll(ListDataSet dataSet) |
void |
SemiSupervisedEM.trainAll(ListDataSet labeledData) |
| Constructor and Description |
|---|
SemiSupervisedEM(Regressor algorithm,
ListDataSet unlabeledData,
int iterations,
boolean useRawPrediction) |
| Modifier and Type | Method and Description |
|---|---|
int |
MultiLayerNetwork.determineOptimalTrainingDuration(ListDataSet dataSet,
int numberOfSteps) |
void |
MultiLayerNetwork.trainAll(ListDataSet dataSet) |
void |
MultiLayerNetwork.trainOnce(ListDataSet dataSet) |
| Modifier and Type | Method and Description |
|---|---|
void |
Clusterer.predict(ListDataSet dataSet) |
void |
AbstractClusterer.predict(ListDataSet dataSet) |
void |
Clusterer.train(ListDataSet dataSet) |
void |
SelfOrganizingMap.train(ListDataSet dataSet) |
| Modifier and Type | Method and Description |
|---|---|
void |
Compressor.compress(ListDataSet dataSet) |
void |
AbstractCompressor.compress(ListDataSet dataSet) |
void |
Compressor.decompress(ListDataSet dataSet) |
void |
AbstractCompressor.decompress(ListDataSet dataSet) |
int |
AbstractCompressor.getFeatureCount(ListDataSet dataSet) |
void |
Compressor.train(ListDataSet dataSet) |
void |
PCA.train(ListDataSet dataSet) |
| Modifier and Type | Method and Description |
|---|---|
void |
MultivariateGaussianDensityEstimator.trainAll(ListDataSet dataSet) |
| Modifier and Type | Method and Description |
|---|---|
int |
AbstractHashing.getFeatureCount(ListDataSet dataSet) |
void |
Hashing.hash(ListDataSet dataSet) |
void |
AbstractHashing.hash(ListDataSet dataSet) |
void |
HashRandomProjection.train(ListDataSet dataSet) |
void |
MinHashing.train(ListDataSet dataSet) |
void |
Hashing.train(ListDataSet dataSet) |
void |
LocalitySensitiveHashing.train(ListDataSet dataSet) |
| Modifier and Type | Method and Description |
|---|---|
ListDataSet |
AbstractIndex.search(String query) |
ListDataSet |
Index.search(String query) |
ListDataSet |
AbstractIndex.search(String query,
int count) |
ListDataSet |
Index.search(String query,
int count) |
ListDataSet |
Index.search(String query,
int start,
int count) |
ListDataSet |
MultiIndex.search(String query,
int start,
int count) |
ListDataSet |
MultiIndex.searchSimilar(Sample sample) |
ListDataSet |
MultiIndex.searchSimilar(Sample sample,
int count) |
ListDataSet |
MultiIndex.searchSimilar(Sample sample,
int start,
int count) |
| Modifier and Type | Method and Description |
|---|---|
void |
AbstractIndex.add(ListDataSet dataSet) |
void |
Index.add(ListDataSet dataSet) |
| Modifier and Type | Method and Description |
|---|---|
int |
AbstractRegressor.getClassCount(ListDataSet dataSet) |
int |
Regressor.getClassCount(ListDataSet dataSet) |
int |
AbstractRegressor.getFeatureCount(ListDataSet dataSet) |
int |
Regressor.getFeatureCount(ListDataSet dataSet) |
boolean |
AbstractRegressor.isDiscrete(ListDataSet dataSet) |
boolean |
Regressor.isDiscrete(ListDataSet dataSet) |
void |
AbstractRegressor.predictAll(ListDataSet dataSet) |
void |
Regressor.predictAll(ListDataSet dataSet) |
void |
LinearRegressionGradientDescent.trainAll(ListDataSet dataSet) |
void |
Regressor.trainAll(ListDataSet dataSet) |
void |
LinearRegression.trainAll(ListDataSet dataSet) |
| Modifier and Type | Method and Description |
|---|---|
ListDataSet |
MarketBasketAnalysis.calculate(ListDataSet dataSet) |
| Modifier and Type | Method and Description |
|---|---|
ListDataSet |
MarketBasketAnalysis.calculate(ListDataSet dataSet) |
| Modifier and Type | Method and Description |
|---|---|
ListDataSet |
SimilaritySearcher.searchSimilar(Sample sample) |
ListDataSet |
SimilaritySearcher.searchSimilar(Sample sample,
int count) |
ListDataSet |
SimilaritySearcher.searchSimilar(Sample sample,
int start,
int count) |
| Modifier and Type | Method and Description |
|---|---|
void |
AbstractTagger.tag(ListDataSet dataSet) |
void |
Tagger.tag(ListDataSet dataSet) |
| Modifier and Type | Method and Description |
|---|---|
void |
Tokenizer.tokenize(String variableKey,
ListDataSet dataSet) |
void |
AbstractTokenizer.tokenize(String variableKey,
ListDataSet dataSet) |
| Modifier and Type | Class and Description |
|---|---|
class |
AbstractListDataSet |
class |
DefaultListDataSet |
class |
MatrixDataSet |
| Modifier and Type | Method and Description |
|---|---|
ListDataSet |
AbstractListDataSet.bootstrap() |
ListDataSet |
DataSet.bootstrap() |
ListDataSet |
AbstractListDataSet.bootstrap(int numberOfSamples) |
ListDataSet |
DataSet.bootstrap(int count) |
abstract ListDataSet |
AbstractListDataSet.clone() |
ListDataSet |
DataSet.clone() |
ListDataSet |
MatrixDataSet.clone() |
ListDataSet |
DefaultListDataSet.clone() |
ListDataSet |
DefaultDataSetFactory.CountActive(int number) |
ListDataSet |
DataSetFactory.CountActive(int number) |
ListDataSet |
DefaultDataSetFactory.emptyDataSet() |
ListDataSet |
DataSetFactory.emptyDataSet() |
ListDataSet |
DefaultDataSetFactory.HenonMap(int sampleCount,
int inputLength,
int predictionLength) |
ListDataSet |
DataSetFactory.HenonMap(int sampleCount,
int inputLength,
int predictionLength) |
ListDataSet |
DefaultDataSetFactory.importFromClipboard() |
ListDataSet |
DataSetFactory.importFromClipboard() |
ListDataSet |
DefaultDataSetFactory.importFromFile(FileFormat format,
File file,
Object... parameters) |
ListDataSet |
DataSetFactory.importFromFile(FileFormat format,
File file,
Object... parameters) |
ListDataSet |
DefaultDataSetFactory.importFromJDBC(DBType type,
String host,
int port,
String database,
String sqlStatement,
String username,
String password) |
ListDataSet |
DataSetFactory.importFromJDBC(DBType type,
String host,
int port,
String database,
String sqlStatement,
String username,
String password) |
ListDataSet |
DefaultDataSetFactory.importFromJDBC(String url,
String sqlStatement,
String username,
String password) |
ListDataSet |
DataSetFactory.importFromJDBC(String url,
String sqlStatement,
String username,
String password) |
ListDataSet |
DefaultDataSetFactory.importFromURL(FileFormat fileFormat,
URL url,
Object... parameters) |
ListDataSet |
DataSetFactory.importFromURL(FileFormat fileFormat,
URL url,
Object... parameters) |
ListDataSet |
DefaultDataSetFactory.IRIS() |
ListDataSet |
DataSetFactory.IRIS() |
ListDataSet |
DefaultDataSetFactory.Linear1() |
ListDataSet |
DataSetFactory.Linear1() |
ListDataSet |
DefaultDataSetFactory.Linear2() |
ListDataSet |
DataSetFactory.Linear2() |
ListDataSet |
DefaultDataSetFactory.Linear3() |
ListDataSet |
DataSetFactory.Linear3() |
ListDataSet |
DefaultDataSetFactory.linkToFile(FileFormat format,
File file,
Object... parameters) |
ListDataSet |
DataSetFactory.linkToFile(FileFormat format,
File file,
Object... parameters) |
ListDataSet |
DefaultDataSetFactory.linkToInput(Matrix input) |
ListDataSet |
DataSetFactory.linkToInput(Matrix input) |
ListDataSet |
DefaultDataSetFactory.linkToInputAndLabels(Matrix input,
Matrix labels) |
ListDataSet |
DataSetFactory.linkToInputAndLabels(Matrix input,
Matrix labels) |
ListDataSet |
DefaultDataSetFactory.linkToInputAndTarget(Matrix input,
Matrix target) |
ListDataSet |
DataSetFactory.linkToInputAndTarget(Matrix input,
Matrix target) |
ListDataSet |
DefaultDataSetFactory.linkToInputTargetAndLabel(Matrix input,
Matrix target,
Matrix labels) |
ListDataSet |
DataSetFactory.linkToInputTargetAndLabel(Matrix input,
Matrix target,
Matrix label) |
ListDataSet |
DefaultDataSetFactory.linkToJDBC(DBType type,
String host,
int port,
String database,
String sqlStatement,
String username,
String password) |
ListDataSet |
DataSetFactory.linkToJDBC(DBType type,
String host,
int port,
String database,
String sqlStatement,
String username,
String password) |
ListDataSet |
DefaultDataSetFactory.linkToJDBC(String url,
String sqlStatement,
String username,
String password) |
ListDataSet |
DataSetFactory.linkToJDBC(String url,
String sqlStatement,
String username,
String password) |
ListDataSet |
DefaultDataSetFactory.LogisticMap(int sampleCount,
int inputLength,
int predictionLength) |
ListDataSet |
DataSetFactory.LogisticMap(int sampleCount,
int inputLength,
int predictionLength) |
ListDataSet |
DefaultDataSetFactory.MNISTTest() |
ListDataSet |
DataSetFactory.MNISTTest() |
ListDataSet |
DefaultDataSetFactory.MNISTTrain() |
ListDataSet |
DataSetFactory.MNISTTrain() |
ListDataSet |
DefaultDataSetFactory.ONE() |
ListDataSet |
DataSetFactory.ONE() |
ListDataSet |
DefaultDataSetFactory.OR() |
ListDataSet |
DataSetFactory.OR() |
ListDataSet |
DefaultDataSetFactory.XOR() |
ListDataSet |
DataSetFactory.XOR() |
| Modifier and Type | Method and Description |
|---|---|
ObservableMap<ListDataSet> |
HasDataSetMap.getDataSetMap() |
List<ListDataSet> |
AbstractListDataSet.splitByClass() |
List<ListDataSet> |
AbstractListDataSet.splitByCount(boolean shuffle,
int... count) |
List<ListDataSet> |
DataSet.splitByCount(boolean shuffle,
int... count) |
List<ListDataSet> |
AbstractListDataSet.splitByPercent(boolean shuffle,
double... percent) |
List<ListDataSet> |
DataSet.splitByPercent(boolean shuffle,
double... percent) |
List<ListDataSet> |
AbstractListDataSet.splitForCV(int numberOfCVSets,
int idOfCVSet,
long randomSeed) |
List<ListDataSet> |
DataSet.splitForCV(int numberOfCVSets,
int idOfCVSet,
long randomSeed) |
List<ListDataSet> |
AbstractListDataSet.splitForStratifiedCV(int numberOfCVSets,
int idOfCVSet,
long randomSeed) |
List<ListDataSet> |
DataSet.splitForStratifiedCV(int i,
int r,
long seed) |
| Modifier and Type | Method and Description |
|---|---|
static ListMatrix<Double> |
CrossValidation.run(Classifier algorithm,
ListDataSet dataSet) |
static ListMatrix<Double> |
CrossValidation.run(Regressor algorithm,
ListDataSet dataSet,
int folds,
int runs,
long randomSeed) |
| Modifier and Type | Method and Description |
|---|---|
ObservableMap<ListDataSet> |
AbstractModule.getDataSetMap() |
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