K
- Type of a key in upstream
data.V
- Type of a value in upstream
data.public class Pipeline<K,V,C extends Serializable,L> extends Object
fit()
method is called, the stages are executed in order.Constructor and Description |
---|
Pipeline() |
Modifier and Type | Method and Description |
---|---|
Pipeline<K,V,C,L> |
addPreprocessingTrainer(PreprocessingTrainer preprocessingTrainer)
Adds a preprocessor.
|
Pipeline<K,V,C,L> |
addTrainer(DatasetTrainer trainer)
Adds a trainer.
|
Pipeline<K,V,C,L> |
addVectorizer(Vectorizer<K,V,C,L> vectorizer) |
PipelineMdl<K,V> |
fit(DatasetBuilder datasetBuilder)
Fits the pipeline to the input dataset builder.
|
PipelineMdl<K,V> |
fit(Ignite ignite,
IgniteCache<K,V> cache)
Fits the pipeline to the input cache.
|
PipelineMdl<K,V> |
fit(Map<K,V> data,
int parts)
Fits the pipeline to the input mock data.
|
DatasetTrainer |
getTrainer()
Returns trainer.
|
void |
setEnvironmentBuilder(LearningEnvironmentBuilder envBuilder)
Set learning environment builder.
|
public Pipeline<K,V,C,L> addVectorizer(Vectorizer<K,V,C,L> vectorizer)
vectorizer
- Vectorizer.public Pipeline<K,V,C,L> addPreprocessingTrainer(PreprocessingTrainer preprocessingTrainer)
preprocessingTrainer
- The parameter value.public Pipeline<K,V,C,L> addTrainer(DatasetTrainer trainer)
trainer
- The parameter value.public DatasetTrainer getTrainer()
public PipelineMdl<K,V> fit(Ignite ignite, IgniteCache<K,V> cache)
ignite
- Ignite instance.cache
- Ignite cache with upstream
data.public void setEnvironmentBuilder(LearningEnvironmentBuilder envBuilder)
envBuilder
- Learning environment builder.public PipelineMdl<K,V> fit(Map<K,V> data, int parts)
data
- Data.parts
- Number of partitions.public PipelineMdl<K,V> fit(DatasetBuilder datasetBuilder)
GridGain In-Memory Computing Platform : ver. 8.9.14 Release Date : November 5 2024