Package | Description |
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org.apache.ignite.ml.knn |
Contains main APIs for kNN algorithms.
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org.apache.ignite.ml.knn.ann |
Contains main APIs for ANN classification algorithms.
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org.apache.ignite.ml.knn.utils |
Contains util functionality for kNN algorithms.
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org.apache.ignite.ml.structures |
Contains some internal utility structures.
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org.apache.ignite.ml.structures.partition |
Contains internal APIs for dataset partitioned labeled datasets.
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org.apache.ignite.ml.structures.preprocessing |
Contains internal APIs for dataset pre-processing.
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Modifier and Type | Method and Description |
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protected LabeledVectorSet<LabeledVector> |
NNClassificationModel.buildLabeledDatasetOnListOfVectors(List<LabeledVector> neighborsFromPartitions) |
Modifier and Type | Method and Description |
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protected @NotNull TreeMap<Double,Set<Integer>> |
NNClassificationModel.getDistances(Vector v,
LabeledVectorSet<LabeledVector<Double>> trainingData)
Computes distances between given vector and each vector in training dataset.
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protected @NotNull LabeledVector[] |
NNClassificationModel.getKClosestVectors(LabeledVectorSet<LabeledVector> trainingData,
TreeMap<Double,Set<Integer>> distanceIdxPairs)
Iterates along entries in distance map and fill the resulting k-element array.
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Modifier and Type | Method and Description |
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LabeledVectorSet<LabeledVector> |
ANNClassificationModel.getCandidates() |
LabeledVectorSet<LabeledVector> |
ANNModelFormat.getCandidates() |
Constructor and Description |
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ANNClassificationModel(LabeledVectorSet<LabeledVector> centers,
ANNClassificationTrainer.CentroidStat centroindsStat)
Build the model based on a candidates set.
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ANNModelFormat(int k,
DistanceMeasure distanceMeasure,
boolean weighted,
LabeledVectorSet<LabeledVector> candidates,
ANNClassificationTrainer.CentroidStat candidatesStat)
Creates an instance.
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Modifier and Type | Method and Description |
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static <K,V,C extends Serializable> |
KNNUtils.buildDataset(LearningEnvironmentBuilder envBuilder,
DatasetBuilder<K,V> datasetBuilder,
Preprocessor<K,V> vectorizer)
Builds dataset.
|
Modifier and Type | Method and Description |
---|---|
LabeledVectorSet |
LabeledVectorSet.copy()
Makes copy with new Label objects and old features and Metadata objects.
|
LabeledVectorSet |
LabeledVectorSetTestTrainPair.test()
Test subset of the whole dataset.
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LabeledVectorSet |
LabeledVectorSetTestTrainPair.train()
Train subset of the whole dataset.
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Constructor and Description |
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LabeledVectorSetTestTrainPair(LabeledVectorSet dataset,
double testPercentage)
Creates two subsets of given dataset.
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Modifier and Type | Method and Description |
---|---|
LabeledVectorSet<LabeledVector> |
LabeledDatasetPartitionDataBuilderOnHeap.build(LearningEnvironment env,
Iterator<UpstreamEntry<K,V>> upstreamData,
long upstreamDataSize,
C ctx)
Builds a new partition
data from a partition upstream data and partition context . |
Modifier and Type | Method and Description |
---|---|
static LabeledVectorSet |
LabeledDatasetLoader.loadFromTxtFile(Path pathToFile,
String separator,
boolean isDistributed,
boolean isFallOnBadData)
Datafile should keep class labels in the first column.
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GridGain In-Memory Computing Platform : ver. 8.9.14 Release Date : November 5 2024