A B C D E G H I K L M N P R S T U V

## A

addLinearRegressionModel(String, LinearRegression) - Method in class io.siddhi.extension.execution.streamingml.bayesian.regression.util.LinearRegressionModelHolder
addPerceptronModel(String, PerceptronModel) - Method in class io.siddhi.extension.execution.streamingml.classification.perceptron.util.PerceptronModelsHolder
addSoftmaxRegressionModel(String, SoftmaxRegression) - Method in class io.siddhi.extension.execution.streamingml.bayesian.classification.util.SoftmaxRegressionModelHolder

## B

BayesianClassificationStreamProcessorExtension - Class in io.siddhi.extension.execution.streamingml.bayesian.classification
  Predict using a Bayesian Softmax regression model built via `BayesianClassificationUpdaterStreamProcessorExtension`.
BayesianClassificationStreamProcessorExtension() - Constructor for class io.siddhi.extension.execution.streamingml.bayesian.classification.BayesianClassificationStreamProcessorExtension
BayesianClassificationUpdaterStreamProcessorExtension - Class in io.siddhi.extension.execution.streamingml.bayesian.classification
  Bayesian classification using multivariate logistic regression.
BayesianClassificationUpdaterStreamProcessorExtension() - Constructor for class io.siddhi.extension.execution.streamingml.bayesian.classification.BayesianClassificationUpdaterStreamProcessorExtension
BayesianModel - Class in io.siddhi.extension.execution.streamingml.bayesian.util
  model interface.
BayesianModel.OptimizerType - Enum in io.siddhi.extension.execution.streamingml.bayesian.util
  optimizer types that can be used with the bayesian models.
BayesianRegressionStreamProcessorExtension - Class in io.siddhi.extension.execution.streamingml.bayesian.regression
  Predict using a Bayesian regression model built via `BayesianRegressionUpdaterStreamProcessorExtension`.
BayesianRegressionStreamProcessorExtension() - Constructor for class io.siddhi.extension.execution.streamingml.bayesian.regression.BayesianRegressionStreamProcessorExtension
BayesianRegressionUpdaterStreamProcessorExtension - Class in io.siddhi.extension.execution.streamingml.bayesian.regression
  Bayesian regression.
BayesianRegressionUpdaterStreamProcessorExtension() - Constructor for class io.siddhi.extension.execution.streamingml.bayesian.regression.BayesianRegressionUpdaterStreamProcessorExtension
BernoulliDistribution - Class in io.siddhi.extension.execution.streamingml.bayesian.model
  implements BernoulliDistribution.
BernoulliDistribution(SDVariable, SameDiff) - Constructor for class io.siddhi.extension.execution.streamingml.bayesian.model.BernoulliDistribution
  Construct the bernoulli distribution.

## C

CategoricalDistribution - Class in io.siddhi.extension.execution.streamingml.bayesian.model
  implements Categorical distribution.
CategoricalDistribution(SDVariable, SameDiff) - Constructor for class io.siddhi.extension.execution.streamingml.bayesian.model.CategoricalDistribution
  Construct the categorical distribution.
classify(double[]) - Method in class io.siddhi.extension.execution.streamingml.classification.perceptron.util.PerceptronModel
  Classify the feature set into class
clear() - Method in class io.siddhi.extension.execution.streamingml.clustering.kmeans.util.KMeansModel
Cluster - Class in io.siddhi.extension.execution.streamingml.clustering.kmeans.util
  This class represents a single cluster in the model
CoreUtils - Class in io.siddhi.extension.execution.streamingml.util
  Common utils for Streaming Machine Learning tasks.
CoreUtils() - Constructor for class io.siddhi.extension.execution.streamingml.util.CoreUtils

## D

DataPoint - Class in io.siddhi.extension.execution.streamingml.clustering.kmeans.util
  Object which holds the data from each event and information about centroids in terms of coordinates
DataPoint() - Constructor for class io.siddhi.extension.execution.streamingml.clustering.kmeans.util.DataPoint
deleteLinearRegressionModel(String) - Method in class io.siddhi.extension.execution.streamingml.bayesian.regression.util.LinearRegressionModelHolder
deletePerceptronModel(String) - Method in class io.siddhi.extension.execution.streamingml.classification.perceptron.util.PerceptronModelsHolder
deleteSoftmaxRegressionModel(String) - Method in class io.siddhi.extension.execution.streamingml.bayesian.classification.util.SoftmaxRegressionModelHolder
dot(double[], double[]) - Static method in class io.siddhi.extension.execution.streamingml.util.MathUtil
  Dot product of two 'double' vectors.

## E

equals(Object) - Method in class io.siddhi.extension.execution.streamingml.clustering.kmeans.util.Cluster
equals(Object) - Method in class io.siddhi.extension.execution.streamingml.clustering.kmeans.util.DataPoint
euclideanDistance(double[], double[]) - Static method in class io.siddhi.extension.execution.streamingml.util.MathUtil
  Finds the euclidean distance between two input points of equal dimension
evaluate(double[], Object) - Method in class io.siddhi.extension.execution.streamingml.bayesian.util.BayesianModel
evaluate(double[], Object) - Method in class io.siddhi.extension.execution.streamingml.bayesian.util.LinearRegression
evaluate(int, int) - Method in class io.siddhi.extension.execution.streamingml.bayesian.util.PrequentialEvaluation
evaluate(double[], Object) - Method in class io.siddhi.extension.execution.streamingml.bayesian.util.SoftmaxRegression
extractAndValidateClassLabel(AbstractDefinition, ExpressionExecutor[], int) - Static method in class io.siddhi.extension.execution.streamingml.util.CoreUtils
  Validate and extract Class label executor
extractAndValidateFeatures(AbstractDefinition, ExpressionExecutor[], int, int) - Static method in class io.siddhi.extension.execution.streamingml.util.CoreUtils
  Validate and extract feature attribute executors

## G

getAssociatedCentroidInfo(DataPoint, KMeansModel) - Static method in class io.siddhi.extension.execution.streamingml.clustering.kmeans.util.KMeansClusterer
  similar to findAssociatedCluster method but return an Object[] array with the distance to closest centroid and the coordinates of the closest centroid
getClassLabel(Number) - Method in class io.siddhi.extension.execution.streamingml.bayesian.util.SoftmaxRegression
getClonedLinearRegressionModel(String) - Method in class io.siddhi.extension.execution.streamingml.bayesian.regression.util.LinearRegressionModelHolder
getClonedPerceptronModel(String) - Method in class io.siddhi.extension.execution.streamingml.classification.perceptron.util.PerceptronModelsHolder
getClonedSoftmaxRegressionModel(String) - Method in class io.siddhi.extension.execution.streamingml.bayesian.classification.util.SoftmaxRegressionModelHolder
getFeatureSize() - Method in class io.siddhi.extension.execution.streamingml.classification.perceptron.util.PerceptronModel
  Get model's feature set size
getInstance() - Static method in class io.siddhi.extension.execution.streamingml.bayesian.classification.util.SoftmaxRegressionModelHolder
getInstance() - Static method in class io.siddhi.extension.execution.streamingml.bayesian.regression.util.LinearRegressionModelHolder
getInstance() - Static method in class io.siddhi.extension.execution.streamingml.classification.perceptron.util.PerceptronModelsHolder
getLearningRate() - Method in class io.siddhi.extension.execution.streamingml.bayesian.util.BayesianModel
getLinearRegressionMap() - Method in class io.siddhi.extension.execution.streamingml.bayesian.regression.util.LinearRegressionModelHolder
getLinearRegressionModel(String) - Method in class io.siddhi.extension.execution.streamingml.bayesian.regression.util.LinearRegressionModelHolder
getLoc() - Method in class io.siddhi.extension.execution.streamingml.bayesian.model.NormalDistribution
getNumFeatures() - Method in class io.siddhi.extension.execution.streamingml.bayesian.util.BayesianModel
getNumSamples() - Method in class io.siddhi.extension.execution.streamingml.bayesian.util.BayesianModel
getOptimizerType() - Method in class io.siddhi.extension.execution.streamingml.bayesian.util.BayesianModel
getPerceptronModel(String) - Method in class io.siddhi.extension.execution.streamingml.classification.perceptron.util.PerceptronModelsHolder
getPerceptronModelMap() - Method in class io.siddhi.extension.execution.streamingml.classification.perceptron.util.PerceptronModelsHolder
getProb() - Method in class io.siddhi.extension.execution.streamingml.bayesian.model.BernoulliDistribution
getProb() - Method in class io.siddhi.extension.execution.streamingml.bayesian.model.CategoricalDistribution
getProcessingMode() - Method in class io.siddhi.extension.execution.streamingml.bayesian.classification.BayesianClassificationStreamProcessorExtension
getProcessingMode() - Method in class io.siddhi.extension.execution.streamingml.bayesian.classification.BayesianClassificationUpdaterStreamProcessorExtension
getProcessingMode() - Method in class io.siddhi.extension.execution.streamingml.bayesian.regression.BayesianRegressionStreamProcessorExtension
getProcessingMode() - Method in class io.siddhi.extension.execution.streamingml.bayesian.regression.BayesianRegressionUpdaterStreamProcessorExtension
getProcessingMode() - Method in class io.siddhi.extension.execution.streamingml.classification.perceptron.PerceptronClassifierStreamProcessorExtension
getProcessingMode() - Method in class io.siddhi.extension.execution.streamingml.classification.perceptron.PerceptronClassifierUpdaterStreamProcessorExtension
getProcessingMode() - Method in class io.siddhi.extension.execution.streamingml.clustering.kmeans.KMeansIncrementalSPExtension
getProcessingMode() - Method in class io.siddhi.extension.execution.streamingml.clustering.kmeans.KMeansMiniBatchSPExtension
getReturnAttributes() - Method in class io.siddhi.extension.execution.streamingml.bayesian.classification.BayesianClassificationStreamProcessorExtension
getReturnAttributes() - Method in class io.siddhi.extension.execution.streamingml.bayesian.classification.BayesianClassificationUpdaterStreamProcessorExtension
getReturnAttributes() - Method in class io.siddhi.extension.execution.streamingml.bayesian.regression.BayesianRegressionStreamProcessorExtension
getReturnAttributes() - Method in class io.siddhi.extension.execution.streamingml.bayesian.regression.BayesianRegressionUpdaterStreamProcessorExtension
getReturnAttributes() - Method in class io.siddhi.extension.execution.streamingml.classification.perceptron.PerceptronClassifierStreamProcessorExtension
getReturnAttributes() - Method in class io.siddhi.extension.execution.streamingml.classification.perceptron.PerceptronClassifierUpdaterStreamProcessorExtension
getReturnAttributes() - Method in class io.siddhi.extension.execution.streamingml.clustering.kmeans.KMeansIncrementalSPExtension
getReturnAttributes() - Method in class io.siddhi.extension.execution.streamingml.clustering.kmeans.KMeansMiniBatchSPExtension
getScale() - Method in class io.siddhi.extension.execution.streamingml.bayesian.model.NormalDistribution
getSoftmaxRegressionMap() - Method in class io.siddhi.extension.execution.streamingml.bayesian.classification.util.SoftmaxRegressionModelHolder
getSoftmaxRegressionModel(String) - Method in class io.siddhi.extension.execution.streamingml.bayesian.classification.util.SoftmaxRegressionModelHolder
getUpdatedWeights() - Method in class io.siddhi.extension.execution.streamingml.bayesian.util.BayesianModel
  implements the model specific gradient updates.
getUpdatedWeights() - Method in class io.siddhi.extension.execution.streamingml.bayesian.util.LinearRegression
getUpdatedWeights() - Method in class io.siddhi.extension.execution.streamingml.bayesian.util.SoftmaxRegression

## H

hashCode() - Method in class io.siddhi.extension.execution.streamingml.clustering.kmeans.util.Cluster
hashCode() - Method in class io.siddhi.extension.execution.streamingml.clustering.kmeans.util.DataPoint

## I

init(MetaStreamEvent, AbstractDefinition, ExpressionExecutor[], ConfigReader, StreamEventClonerHolder, boolean, boolean, SiddhiQueryContext) - Method in class io.siddhi.extension.execution.streamingml.bayesian.classification.BayesianClassificationStreamProcessorExtension
init(MetaStreamEvent, AbstractDefinition, ExpressionExecutor[], ConfigReader, StreamEventClonerHolder, boolean, boolean, SiddhiQueryContext) - Method in class io.siddhi.extension.execution.streamingml.bayesian.classification.BayesianClassificationUpdaterStreamProcessorExtension
init(MetaStreamEvent, AbstractDefinition, ExpressionExecutor[], ConfigReader, StreamEventClonerHolder, boolean, boolean, SiddhiQueryContext) - Method in class io.siddhi.extension.execution.streamingml.bayesian.regression.BayesianRegressionStreamProcessorExtension
init(MetaStreamEvent, AbstractDefinition, ExpressionExecutor[], ConfigReader, StreamEventClonerHolder, boolean, boolean, SiddhiQueryContext) - Method in class io.siddhi.extension.execution.streamingml.bayesian.regression.BayesianRegressionUpdaterStreamProcessorExtension
init(MetaStreamEvent, AbstractDefinition, ExpressionExecutor[], ConfigReader, StreamEventClonerHolder, boolean, boolean, SiddhiQueryContext) - Method in class io.siddhi.extension.execution.streamingml.classification.perceptron.PerceptronClassifierStreamProcessorExtension
init(MetaStreamEvent, AbstractDefinition, ExpressionExecutor[], ConfigReader, StreamEventClonerHolder, boolean, boolean, SiddhiQueryContext) - Method in class io.siddhi.extension.execution.streamingml.classification.perceptron.PerceptronClassifierUpdaterStreamProcessorExtension
init(MetaStreamEvent, AbstractDefinition, ExpressionExecutor[], ConfigReader, StreamEventClonerHolder, boolean, boolean, SiddhiQueryContext) - Method in class io.siddhi.extension.execution.streamingml.clustering.kmeans.KMeansIncrementalSPExtension
init(MetaStreamEvent, AbstractDefinition, ExpressionExecutor[], ConfigReader, StreamEventClonerHolder, boolean, boolean, SiddhiQueryContext) - Method in class io.siddhi.extension.execution.streamingml.clustering.kmeans.KMeansMiniBatchSPExtension
initiateModel() - Method in class io.siddhi.extension.execution.streamingml.bayesian.util.BayesianModel
  construct the model.
initWeights(int) - Method in class io.siddhi.extension.execution.streamingml.classification.perceptron.util.PerceptronModel
  Initialize model's weights
io.siddhi.extension.execution.streamingml.bayesian.classification - package io.siddhi.extension.execution.streamingml.bayesian.classification
io.siddhi.extension.execution.streamingml.bayesian.classification.util - package io.siddhi.extension.execution.streamingml.bayesian.classification.util
io.siddhi.extension.execution.streamingml.bayesian.model - package io.siddhi.extension.execution.streamingml.bayesian.model
io.siddhi.extension.execution.streamingml.bayesian.regression - package io.siddhi.extension.execution.streamingml.bayesian.regression
io.siddhi.extension.execution.streamingml.bayesian.regression.util - package io.siddhi.extension.execution.streamingml.bayesian.regression.util
io.siddhi.extension.execution.streamingml.bayesian.util - package io.siddhi.extension.execution.streamingml.bayesian.util
io.siddhi.extension.execution.streamingml.classification.perceptron - package io.siddhi.extension.execution.streamingml.classification.perceptron
io.siddhi.extension.execution.streamingml.classification.perceptron.util - package io.siddhi.extension.execution.streamingml.classification.perceptron.util
io.siddhi.extension.execution.streamingml.clustering.kmeans - package io.siddhi.extension.execution.streamingml.clustering.kmeans
io.siddhi.extension.execution.streamingml.clustering.kmeans.util - package io.siddhi.extension.execution.streamingml.clustering.kmeans.util
io.siddhi.extension.execution.streamingml.util - package io.siddhi.extension.execution.streamingml.util
isLabelType(Attribute.Type) - Static method in class io.siddhi.extension.execution.streamingml.util.CoreUtils
  Check label attribute to be a valid type
isNumeric(Attribute.Type) - Static method in class io.siddhi.extension.execution.streamingml.util.CoreUtils
  Check feature attribute to be a numeric
isTrained() - Method in class io.siddhi.extension.execution.streamingml.clustering.kmeans.util.KMeansModel

## K

klDivergence(Distribution) - Method in class io.siddhi.extension.execution.streamingml.bayesian.model.BernoulliDistribution
klDivergence(Distribution) - Method in class io.siddhi.extension.execution.streamingml.bayesian.model.CategoricalDistribution
klDivergence(Distribution) - Method in class io.siddhi.extension.execution.streamingml.bayesian.model.NormalDistribution
  kullback leibler divergence between two normal densities.
KMeansClusterer - Class in io.siddhi.extension.execution.streamingml.clustering.kmeans.util
  class containing all mathematical logic needed to perform kmeans
KMeansClusterer() - Constructor for class io.siddhi.extension.execution.streamingml.clustering.kmeans.util.KMeansClusterer
KMeansIncrementalSPExtension - Class in io.siddhi.extension.execution.streamingml.clustering.kmeans
  performs kmeans with incremental update
KMeansIncrementalSPExtension() - Constructor for class io.siddhi.extension.execution.streamingml.clustering.kmeans.KMeansIncrementalSPExtension
KMeansMiniBatchSPExtension - Class in io.siddhi.extension.execution.streamingml.clustering.kmeans
  performs kmeans with batch update
KMeansMiniBatchSPExtension() - Constructor for class io.siddhi.extension.execution.streamingml.clustering.kmeans.KMeansMiniBatchSPExtension
KMeansModel - Class in io.siddhi.extension.execution.streamingml.clustering.kmeans.util
  stores info about the kmeans model
KMeansModel() - Constructor for class io.siddhi.extension.execution.streamingml.clustering.kmeans.util.KMeansModel

## L

LinearRegression - Class in io.siddhi.extension.execution.streamingml.bayesian.util
  implements Bayesian Linear regression model.
LinearRegression() - Constructor for class io.siddhi.extension.execution.streamingml.bayesian.util.LinearRegression
LinearRegression(LinearRegression) - Constructor for class io.siddhi.extension.execution.streamingml.bayesian.util.LinearRegression
LinearRegressionModelHolder - Class in io.siddhi.extension.execution.streamingml.bayesian.regression.util
  Data holder which keeps the instances of @`LinearRegression`.
logProbability(SDVariable) - Method in class io.siddhi.extension.execution.streamingml.bayesian.model.BernoulliDistribution
logProbability(SDVariable) - Method in class io.siddhi.extension.execution.streamingml.bayesian.model.CategoricalDistribution
  categorical log probability is implemented based on softmax-crossentropy the implementation is based on following formula
logProbability(SDVariable) - Method in class io.siddhi.extension.execution.streamingml.bayesian.model.NormalDistribution
  computes the log-probability of normal distribution.

## M

MathUtil - Class in io.siddhi.extension.execution.streamingml.util
  Special mathematical functions used in the ML algorithms.
MathUtil() - Constructor for class io.siddhi.extension.execution.streamingml.util.MathUtil

## N

NormalDistribution - Class in io.siddhi.extension.execution.streamingml.bayesian.model
  implements Gaussian distribution.
NormalDistribution(SDVariable, SDVariable, SameDiff) - Constructor for class io.siddhi.extension.execution.streamingml.bayesian.model.NormalDistribution
  constructs a normal distribution.

## P

PerceptronClassifierStreamProcessorExtension - Class in io.siddhi.extension.execution.streamingml.classification.perceptron
  Predict using a linear binary classification Perceptron model built via `PerceptronClassifierStreamProcessorExtension`
PerceptronClassifierStreamProcessorExtension() - Constructor for class io.siddhi.extension.execution.streamingml.classification.perceptron.PerceptronClassifierStreamProcessorExtension
PerceptronClassifierUpdaterStreamProcessorExtension - Class in io.siddhi.extension.execution.streamingml.classification.perceptron
  Build or update a linear binary classification Perceptron model and emit the weights of the features in the order of the attributes.
PerceptronClassifierUpdaterStreamProcessorExtension() - Constructor for class io.siddhi.extension.execution.streamingml.classification.perceptron.PerceptronClassifierUpdaterStreamProcessorExtension
PerceptronModel - Class in io.siddhi.extension.execution.streamingml.classification.perceptron.util
  Represents a linear Perceptron Model
PerceptronModel() - Constructor for class io.siddhi.extension.execution.streamingml.classification.perceptron.util.PerceptronModel
PerceptronModel(PerceptronModel) - Constructor for class io.siddhi.extension.execution.streamingml.classification.perceptron.util.PerceptronModel
PerceptronModelsHolder - Class in io.siddhi.extension.execution.streamingml.classification.perceptron.util
  Data holder which keeps the instances of @`PerceptronModel`
predict(double[]) - Method in class io.siddhi.extension.execution.streamingml.bayesian.util.BayesianModel
  predict the target according to given features.
predictWithStd(double[]) - Method in class io.siddhi.extension.execution.streamingml.bayesian.util.BayesianModel
  predict the target according to given features.
PrequentialEvaluation - Class in io.siddhi.extension.execution.streamingml.bayesian.util
  performs prequential evaluation for classification.
PrequentialEvaluation() - Constructor for class io.siddhi.extension.execution.streamingml.bayesian.util.PrequentialEvaluation
process(ComplexEventChunk<StreamEvent>, Processor, StreamEventCloner, ComplexEventPopulater, State) - Method in class io.siddhi.extension.execution.streamingml.bayesian.classification.BayesianClassificationStreamProcessorExtension
process(ComplexEventChunk<StreamEvent>, Processor, StreamEventCloner, ComplexEventPopulater, BayesianClassificationUpdaterStreamProcessorExtension.ExtensionState) - Method in class io.siddhi.extension.execution.streamingml.bayesian.classification.BayesianClassificationUpdaterStreamProcessorExtension
process(ComplexEventChunk<StreamEvent>, Processor, StreamEventCloner, ComplexEventPopulater, State) - Method in class io.siddhi.extension.execution.streamingml.bayesian.regression.BayesianRegressionStreamProcessorExtension
process(ComplexEventChunk<StreamEvent>, Processor, StreamEventCloner, ComplexEventPopulater, BayesianRegressionUpdaterStreamProcessorExtension.ExtensionState) - Method in class io.siddhi.extension.execution.streamingml.bayesian.regression.BayesianRegressionUpdaterStreamProcessorExtension
process(ComplexEventChunk<StreamEvent>, Processor, StreamEventCloner, ComplexEventPopulater, State) - Method in class io.siddhi.extension.execution.streamingml.classification.perceptron.PerceptronClassifierStreamProcessorExtension
process(ComplexEventChunk<StreamEvent>, Processor, StreamEventCloner, ComplexEventPopulater, PerceptronClassifierUpdaterStreamProcessorExtension.ExtensionState) - Method in class io.siddhi.extension.execution.streamingml.classification.perceptron.PerceptronClassifierUpdaterStreamProcessorExtension
process(ComplexEventChunk<StreamEvent>, Processor, StreamEventCloner, ComplexEventPopulater, KMeansIncrementalSPExtension.ExtensionState) - Method in class io.siddhi.extension.execution.streamingml.clustering.kmeans.KMeansIncrementalSPExtension
process(ComplexEventChunk<StreamEvent>, Processor, StreamEventCloner, ComplexEventPopulater, KMeansMiniBatchSPExtension.ExtensionState) - Method in class io.siddhi.extension.execution.streamingml.clustering.kmeans.KMeansMiniBatchSPExtension

## R

run() - Method in class io.siddhi.extension.execution.streamingml.clustering.kmeans.util.Trainer

## S

sample() - Method in class io.siddhi.extension.execution.streamingml.bayesian.model.BernoulliDistribution
sample(int) - Method in class io.siddhi.extension.execution.streamingml.bayesian.model.BernoulliDistribution
sample() - Method in class io.siddhi.extension.execution.streamingml.bayesian.model.CategoricalDistribution
sample(int) - Method in class io.siddhi.extension.execution.streamingml.bayesian.model.CategoricalDistribution
sample() - Method in class io.siddhi.extension.execution.streamingml.bayesian.model.NormalDistribution
sample(int) - Method in class io.siddhi.extension.execution.streamingml.bayesian.model.NormalDistribution
setAddBias(boolean) - Method in class io.siddhi.extension.execution.streamingml.bayesian.util.BayesianModel
setBias(double) - Method in class io.siddhi.extension.execution.streamingml.classification.perceptron.util.PerceptronModel
  Set a bias for the model
setCoordinates(double[]) - Method in class io.siddhi.extension.execution.streamingml.clustering.kmeans.util.DataPoint
setLearningRate(double) - Method in class io.siddhi.extension.execution.streamingml.bayesian.util.BayesianModel
setLearningRate(double) - Method in class io.siddhi.extension.execution.streamingml.classification.perceptron.util.PerceptronModel
  Set a learning rate to the model
setLinearRegressionMap(Map<String, LinearRegression>) - Method in class io.siddhi.extension.execution.streamingml.bayesian.regression.util.LinearRegressionModelHolder
setNoOfClasses(int) - Method in class io.siddhi.extension.execution.streamingml.bayesian.util.SoftmaxRegression
setNumFeatures(int) - Method in class io.siddhi.extension.execution.streamingml.bayesian.util.BayesianModel
setNumSamples(int) - Method in class io.siddhi.extension.execution.streamingml.bayesian.util.BayesianModel
setOptimizerType(BayesianModel.OptimizerType) - Method in class io.siddhi.extension.execution.streamingml.bayesian.util.BayesianModel
setPerceptronModelMap(Map<String, PerceptronModel>) - Method in class io.siddhi.extension.execution.streamingml.classification.perceptron.util.PerceptronModelsHolder
setPredictionSamples(int) - Method in class io.siddhi.extension.execution.streamingml.bayesian.util.BayesianModel
setSoftmaxRegressionMap(Map<String, SoftmaxRegression>) - Method in class io.siddhi.extension.execution.streamingml.bayesian.classification.util.SoftmaxRegressionModelHolder
setThreshold(double) - Method in class io.siddhi.extension.execution.streamingml.classification.perceptron.util.PerceptronModel
  Set a threshold to the model
SoftmaxRegression - Class in io.siddhi.extension.execution.streamingml.bayesian.util
  implements Bayesian Softmax regression model.
SoftmaxRegression(int) - Constructor for class io.siddhi.extension.execution.streamingml.bayesian.util.SoftmaxRegression
SoftmaxRegression(SoftmaxRegression) - Constructor for class io.siddhi.extension.execution.streamingml.bayesian.util.SoftmaxRegression
SoftmaxRegressionModelHolder - Class in io.siddhi.extension.execution.streamingml.bayesian.classification.util
  Data holder which keeps the instances of @`SoftmaxRegression`.
start() - Method in class io.siddhi.extension.execution.streamingml.bayesian.classification.BayesianClassificationStreamProcessorExtension
start() - Method in class io.siddhi.extension.execution.streamingml.bayesian.classification.BayesianClassificationUpdaterStreamProcessorExtension
  This will be called only once and this can be used to acquire required resources for the processing element.
start() - Method in class io.siddhi.extension.execution.streamingml.bayesian.regression.BayesianRegressionStreamProcessorExtension
  This will be called only once and this can be used to acquire required resources for the processing element.
start() - Method in class io.siddhi.extension.execution.streamingml.bayesian.regression.BayesianRegressionUpdaterStreamProcessorExtension
  This will be called only once and this can be used to acquire required resources for the processing element.
start() - Method in class io.siddhi.extension.execution.streamingml.classification.perceptron.PerceptronClassifierStreamProcessorExtension
start() - Method in class io.siddhi.extension.execution.streamingml.classification.perceptron.PerceptronClassifierUpdaterStreamProcessorExtension
start() - Method in class io.siddhi.extension.execution.streamingml.clustering.kmeans.KMeansIncrementalSPExtension
start() - Method in class io.siddhi.extension.execution.streamingml.clustering.kmeans.KMeansMiniBatchSPExtension
stop() - Method in class io.siddhi.extension.execution.streamingml.bayesian.classification.BayesianClassificationStreamProcessorExtension
stop() - Method in class io.siddhi.extension.execution.streamingml.bayesian.classification.BayesianClassificationUpdaterStreamProcessorExtension
  This will be called only once and this can be used to release the acquired resources for processing.
stop() - Method in class io.siddhi.extension.execution.streamingml.bayesian.regression.BayesianRegressionStreamProcessorExtension
  This will be called only once and this can be used to release the acquired resources for processing.
stop() - Method in class io.siddhi.extension.execution.streamingml.bayesian.regression.BayesianRegressionUpdaterStreamProcessorExtension
  This will be called only once and this can be used to release the acquired resources for processing.
stop() - Method in class io.siddhi.extension.execution.streamingml.classification.perceptron.PerceptronClassifierStreamProcessorExtension
stop() - Method in class io.siddhi.extension.execution.streamingml.classification.perceptron.PerceptronClassifierUpdaterStreamProcessorExtension
stop() - Method in class io.siddhi.extension.execution.streamingml.clustering.kmeans.KMeansIncrementalSPExtension
stop() - Method in class io.siddhi.extension.execution.streamingml.clustering.kmeans.KMeansMiniBatchSPExtension

## T

train(LinkedList<DataPoint>, int, double, ExecutorService, KMeansModel, int, int, int) - Static method in class io.siddhi.extension.execution.streamingml.clustering.kmeans.util.KMeansClusterer
Trainer - Class in io.siddhi.extension.execution.streamingml.clustering.kmeans.util
  trains the model using a separate thread when the batch size is larger than a specified value

## U

update(double[], double[]) - Method in class io.siddhi.extension.execution.streamingml.bayesian.util.BayesianModel
  train the model.
update(double[], String) - Method in class io.siddhi.extension.execution.streamingml.bayesian.util.SoftmaxRegression
update(Boolean, double[]) - Method in class io.siddhi.extension.execution.streamingml.classification.perceptron.util.PerceptronModel
  Mehod to update weight of perceptron model

## V

valueOf(String) - Static method in enum io.siddhi.extension.execution.streamingml.bayesian.util.BayesianModel.OptimizerType
  Returns the enum constant of this type with the specified name.
values() - Static method in enum io.siddhi.extension.execution.streamingml.bayesian.util.BayesianModel.OptimizerType
  Returns an array containing the constants of this enum type, in the order they are declared.

A B C D E G H I K L M N P R S T U V

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