Modifier and Type | Class and Description |
---|---|
class |
SimpleDataset
A SimpleDataset that represent a whole dataset in memory.
|
Modifier and Type | Method and Description |
---|---|
static Dataset |
SimpleDataset.extractExamplesOfClasses(Dataset dataset,
List<Label> labels)
This method extracts examples of given
labels from
dataset |
Dataset |
Dataset.getShuffledDataset() |
Dataset[] |
SimpleDataset.nFolding(int n)
Returns
n datasets. |
Dataset[] |
SimpleDataset.nFoldingClassDistributionInvariant(int n)
Returns
n datasets. |
Dataset[] |
SimpleDataset.split(float percentage)
Returns two datasets created by splitting this dataset accordingly to
percentage . |
Dataset[] |
SimpleDataset.splitClassDistributionInvariant(float percentage)
Returns two datasets created by splitting this dataset accordingly to
percentage . |
Modifier and Type | Method and Description |
---|---|
void |
SimpleDataset.addExamples(Dataset datasetToBeAdded)
Add all the examples contained in
datasetToBeAdded |
static Dataset |
SimpleDataset.extractExamplesOfClasses(Dataset dataset,
List<Label> labels)
This method extracts examples of given
labels from
dataset |
Modifier and Type | Method and Description |
---|---|
void |
LearningAlgorithm.learn(Dataset dataset)
It starts the training process exploiting the provided
dataset |
void |
PassiveAggressive.learn(Dataset dataset) |
void |
MultiEpochLearning.learn(Dataset dataset) |
Modifier and Type | Method and Description |
---|---|
void |
BudgetedLearningAlgorithm.learn(Dataset dataset) |
Modifier and Type | Method and Description |
---|---|
void |
LibLinearLearningAlgorithm.learn(Dataset dataset) |
Constructor and Description |
---|
Problem(Dataset dataset,
String reprentationName,
Label label) |
Modifier and Type | Method and Description |
---|---|
void |
OneClassSvmClassification.learn(Dataset trainingSet) |
void |
BinaryNuSvmClassification.learn(Dataset trainingSet) |
void |
BinaryCSvmClassification.learn(Dataset trainingSet) |
Modifier and Type | Method and Description |
---|---|
protected float[] |
LibCSvmSolver.getCSvmAlpha(Dataset trainingSet)
Get the initial weight for the future Support Vectors
|
SvmSolution |
LibSvmSolver.solve(int l_,
Dataset dataset,
float[] p_,
int[] y_,
float[] initial_alpha)
It solves the SMO algorithm in [CC Chang & CJ Lin, 2011]
min 0.5(\alpha^T Q \alpha) + p^T \alpha
y^T \alpha = \delta
y_i = +1 or -1 0 <= alpha_i <= Cp for y_i = 1 0 <= alpha_i <= Cn for y_i = -1 Given: Q, p, y, Cp, Cn, and an initial feasible point \alpha l is the size of vectors and matrices eps is the stopping tolerance solution will be put in \alpha, objective value will be put in obj |
Modifier and Type | Method and Description |
---|---|
void |
OneVsOneLearning.learn(Dataset dataset)
This method will cause the meta-learning algorithm to learn
N*(N-1)/2 classifiers, where N is the number of classes in the dataset.
|
void |
OneVsAllLearning.learn(Dataset dataset)
This method will cause the meta-learning algorithm to learn
N classifiers, where N is the number of classes in the dataset.
|
Modifier and Type | Method and Description |
---|---|
void |
PassiveAggressiveClassification.learn(Dataset dataset) |
Modifier and Type | Method and Description |
---|---|
void |
PegasosLearningAlgorithm.learn(Dataset dataset) |
Modifier and Type | Method and Description |
---|---|
void |
Perceptron.learn(Dataset dataset) |
Modifier and Type | Method and Description |
---|---|
List<Cluster> |
ClusteringAlgorithm.cluster(Dataset dataset)
It starts the clustering process exploiting the provided
dataset |
Modifier and Type | Method and Description |
---|---|
List<Cluster> |
KernelBasedKMeansEngine.cluster(Dataset dataset) |
Modifier and Type | Method and Description |
---|---|
void |
EpsilonSvmRegression.learn(Dataset dataset) |
Modifier and Type | Method and Description |
---|---|
void |
PassiveAggressiveRegression.learn(Dataset dataset) |
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