Package | Description |
---|---|
org.apache.ignite.ml.clustering.gmm |
Contains Gauss Mixture Model clustering algorithm (see
GmmModel ). |
org.apache.ignite.ml.clustering.kmeans |
Contains kMeans clustering algorithm.
|
org.apache.ignite.ml.composition |
Contains classes for ensemble of models implementation.
|
org.apache.ignite.ml.composition.bagging |
Contains bootstrap aggregation (bagging) trainer allowing to combine some other trainers and return a bagged version of them.
|
org.apache.ignite.ml.composition.boosting |
Contains Gradient Boosting regression and classification abstract classes
allowing regressor type selecting in child classes.
|
org.apache.ignite.ml.dataset.feature.extractor.impl |
Package contains default implementations of
Vectorizer . |
org.apache.ignite.ml.knn |
Contains main APIs for kNN algorithms.
|
org.apache.ignite.ml.knn.ann |
Contains main APIs for ANN classification algorithms.
|
org.apache.ignite.ml.naivebayes.compound |
Contains Compound naive Bayes classifier.
|
org.apache.ignite.ml.naivebayes.discrete |
Contains Bernoulli naive Bayes classifier.
|
org.apache.ignite.ml.naivebayes.gaussian |
Contains Gaussian naive Bayes classifier.
|
org.apache.ignite.ml.pipeline |
Contains Pipeline API.
|
org.apache.ignite.ml.preprocessing.binarization |
Contains binarization preprocessor.
|
org.apache.ignite.ml.preprocessing.developer |
Contains Developer API preprocessors.
|
org.apache.ignite.ml.preprocessing.encoding.label |
Contains label encoding preprocessor.
|
org.apache.ignite.ml.preprocessing.encoding.onehotencoder |
Contains one hot encoding preprocessor.
|
org.apache.ignite.ml.preprocessing.encoding.stringencoder |
Contains string encoding preprocessor.
|
org.apache.ignite.ml.preprocessing.finalization |
Contains vector finalization preprocessor.
|
org.apache.ignite.ml.preprocessing.imputing |
Contains Imputer preprocessor.
|
org.apache.ignite.ml.preprocessing.maxabsscaling |
Contains Max Abs Scaler preprocessor.
|
org.apache.ignite.ml.preprocessing.minmaxscaling |
Contains Min Max Scaler preprocessor.
|
org.apache.ignite.ml.preprocessing.normalization |
Contains Normalizer preprocessor.
|
org.apache.ignite.ml.preprocessing.standardscaling |
Contains Standard scaler preprocessor.
|
org.apache.ignite.ml.tree.randomforest |
Contains random forest implementation classes.
|
Modifier and Type | Class and Description |
---|---|
class |
GmmModel
Gaussian Mixture Model.
|
Modifier and Type | Class and Description |
---|---|
class |
KMeansModel
This class encapsulates result of clusterization by KMeans algorithm.
|
Modifier and Type | Class and Description |
---|---|
class |
ModelsComposition<M extends IgniteModel<Vector,Double>>
Model consisting of several models and prediction aggregation strategy.
|
Modifier and Type | Class and Description |
---|---|
class |
BaggedModel
This class represents model produced by
BaggedTrainer . |
Modifier and Type | Class and Description |
---|---|
class |
GDBModel
GDB model.
|
Modifier and Type | Class and Description |
---|---|
class |
BinaryObjectVectorizer<K>
Vectorizer on binary objects.
|
class |
DoubleArrayVectorizer<K>
Vectorizer on arrays of doubles.
|
class |
DummyVectorizer<K>
Vectorizer on Vector.
|
class |
LabeledDummyVectorizer<K,L>
Vectorizer on LabeledVector.
|
class |
ObjectArrayVectorizer<K>
Vectorizer on arrays of objects.
|
Modifier and Type | Class and Description |
---|---|
class |
NNClassificationModel
Common methods and fields for all kNN and aNN models
to predict label based on neighbours' labels.
|
Modifier and Type | Class and Description |
---|---|
class |
ANNClassificationModel
ANN model to predict labels in multi-class classification task.
|
Modifier and Type | Class and Description |
---|---|
class |
CompoundNaiveBayesModel
A compound Naive Bayes model which uses a composition of
GaussianNaiveBayesModel and DiscreteNaiveBayesModel . |
Modifier and Type | Class and Description |
---|---|
class |
DiscreteNaiveBayesModel
Discrete naive Bayes model which predicts result value
y belongs to a class C_k, k in [0..K] as
{@code p(C_k,y) =x_1*p_k1^x *... |
Modifier and Type | Class and Description |
---|---|
class |
GaussianNaiveBayesModel
Simple naive Bayes model which predicts result value
y belongs to a class C_k, k in [0..K] as {@code
p(C_k,y) = p(C_k)*p(y_1,C_k) *... |
Modifier and Type | Class and Description |
---|---|
class |
PipelineMdl<K,V>
Wraps the model produced by
Pipeline . |
Modifier and Type | Class and Description |
---|---|
class |
BinarizationPreprocessor<K,V>
Preprocessing function that makes binarization.
|
Modifier and Type | Class and Description |
---|---|
class |
MappedPreprocessor<K,V,L0,L1>
Mapped Preprocessor.
|
class |
PatchedPreprocessor<K,V,L1,L2>
Preprocessing function that makes binarization.
|
Modifier and Type | Class and Description |
---|---|
class |
LabelEncoderPreprocessor<K,V>
Preprocessing function that makes Label encoding.
|
Modifier and Type | Class and Description |
---|---|
class |
OneHotEncoderPreprocessor<K,V>
Preprocessing function that makes one-hot encoding.
|
Modifier and Type | Class and Description |
---|---|
class |
StringEncoderPreprocessor<K,V>
Preprocessing function that makes String encoding.
|
Modifier and Type | Class and Description |
---|---|
class |
VectorFinalizationPreprocessor<K,V>
Preprocessing function that makes vector finalization.
|
Modifier and Type | Class and Description |
---|---|
class |
ImputerPreprocessor<K,V>
Preprocessing function that makes imputing.
|
Modifier and Type | Class and Description |
---|---|
class |
MaxAbsScalerPreprocessor<K,V>
The preprocessing function that makes maxabsscaling, transforms features to the scale
[-1,+1] . |
Modifier and Type | Class and Description |
---|---|
class |
MinMaxScalerPreprocessor<K,V>
Preprocessing function that makes minmaxscaling.
|
Modifier and Type | Class and Description |
---|---|
class |
NormalizationPreprocessor<K,V>
Preprocessing function that makes normalization.
|
Modifier and Type | Class and Description |
---|---|
class |
StandardScalerPreprocessor<K,V>
The preprocessing function that makes standard scaling, transforms features to make
mean equal to 0
and variance equal to 1 . |
Modifier and Type | Class and Description |
---|---|
class |
RandomForestModel
Random Forest Model class.
|
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Ignite Database and Caching Platform : ver. 2.12.0 Release Date : January 10 2022