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

This feature has been removed due to the isuue at https://github.com/siddhi-io/siddhi-execution-streamingml/issues/75

BayesianClassificationStreamProcessorExtension() - Constructor for class io.siddhi.extension.execution.streamingml.bayesian.classification.BayesianClassificationStreamProcessorExtension

BayesianClassificationUpdaterStreamProcessorExtension - Class in io.siddhi.extension.execution.streamingml.bayesian.classification

This feature has been removed due to the isuue at https://github.com/siddhi-io/siddhi-execution-streamingml/issues/75

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.

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