ai.libs.jaicore.ml.tsc.classifier.trees

Class LearnPatternSimilarityLearningAlgorithm

java.lang.Object

ai.libs.jaicore.basic.algorithm.AAlgorithm<TimeSeriesDataset,C>

ai.libs.jaicore.ml.tsc.classifier.ASimplifiedTSCLearningAlgorithm<java.lang.Integer,LearnPatternSimilarityClassifier>

ai.libs.jaicore.ml.tsc.classifier.trees.LearnPatternSimilarityLearningAlgorithm

All Implemented Interfaces:

ai.libs.jaicore.basic.algorithm.IAlgorithm<TimeSeriesDataset,LearnPatternSimilarityClassifier>, ai.libs.jaicore.basic.Cancelable, ai.libs.jaicore.basic.ILoggingCustomizable, java.lang.Iterable<ai.libs.jaicore.basic.algorithm.events.AlgorithmEvent>, java.util.concurrent.Callable<LearnPatternSimilarityClassifier>, java.util.Iterator<ai.libs.jaicore.basic.algorithm.events.AlgorithmEvent>

public class LearnPatternSimilarityLearningAlgorithm
extends ASimplifiedTSCLearningAlgorithm<java.lang.Integer,LearnPatternSimilarityClassifier>

Algorithm training a LearnPatternSimilarityClassifier as described in Baydogan, Mustafa & Runger, George. (2015). Time series representation and similarity based on local autopatterns. Data Mining and Knowledge Discovery. 30. 1-34. 10.1007/s10618-015-0425-y.

Nested Class Summary

Nested Classes

Modifier and Type	Class and Description
static interface	LearnPatternSimilarityLearningAlgorithm.IPatternSimilarityConfig

Constructor Summary

Constructors

Constructor and Description
LearnPatternSimilarityLearningAlgorithm(LearnPatternSimilarityLearningAlgorithm.IPatternSimilarityConfig config, LearnPatternSimilarityClassifier model, TimeSeriesDataset dataset) Standard constructor.

Method Summary

Modifier and Type	Method and Description
LearnPatternSimilarityClassifier	call() Training procedure for a LearnPatternSimilarityClassifier.
static void	collectLeafCounts(int[] leafNodeCountsForInstance, weka.core.Instance instance, AccessibleRandomTree regTree) Function collecting the leaf counts for the given instance as predicted by regTree.
void	generateSegmentsAndDifferencesForTree(int[] segments, int[] segmentsDifference, int length, int timeSeriesLength, java.util.Random random) Method generating the segment start indices and the segment difference locations randomly using random.
static weka.core.Instance	generateSubseriesFeatureInstance(double[] instValues, int[] segments, int[] segmentsDifference, int len) Function generating subseries feature instances based on the given segments and segmentsDifference matrices.
static weka.core.Instances	generateSubseriesFeaturesInstances(java.util.List<weka.core.Attribute> attributes, int length, int[] segments, int[] segmentsDifference, double[][] dataMatrix) Function generating a dataset storing the features being generated as described in the original paper.
LearnPatternSimilarityLearningAlgorithm.IPatternSimilarityConfig	getConfig()
AccessibleRandomTree	initializeRegressionTree(int numInstances) Initializes a new instance of RandomRegressionTree.
ai.libs.jaicore.basic.algorithm.events.AlgorithmEvent	nextWithException()

Methods inherited from class ai.libs.jaicore.ml.tsc.classifier.ASimplifiedTSCLearningAlgorithm

getClassifier

Methods inherited from class ai.libs.jaicore.basic.algorithm.AAlgorithm

activate, announceTimeoutDetected, avoidReinterruptionOnShutdownOnCurrentThread, cancel, checkAndConductTermination, checkTermination, computeTimeoutAware, getActivationTime, getId, getInput, getLoggerName, getNumCPUs, getRemainingTimeToDeadline, getState, getTimeout, getTimeoutPrecautionOffset, hasNext, hasThreadBeenInterruptedDuringShutdown, interruptThreadAsPartOfShutdown, isCanceled, isShutdownInitialized, isStopCriterionSatisfied, isTimeouted, iterator, next, post, registerActiveThread, registerListener, resolveShutdownInterruptOnCurrentThread, setConfig, setLoggerName, setMaxNumThreads, setNumCPUs, setState, setTimeout, setTimeout, setTimeoutPrecautionOffset, shutdown, terminate, unregisterActiveThread, unregisterThreadAndShutdown

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Methods inherited from interface java.lang.Iterable

forEach, spliterator

Methods inherited from interface java.util.Iterator

forEachRemaining, remove

Constructor Detail

LearnPatternSimilarityLearningAlgorithm

public LearnPatternSimilarityLearningAlgorithm(LearnPatternSimilarityLearningAlgorithm.IPatternSimilarityConfig config,
                                               LearnPatternSimilarityClassifier model,
                                               TimeSeriesDataset dataset)

Standard constructor.

Parameters:

seed - See LearnPatternSimilarityLearningAlgorithm#seed.

numTrees - See LearnPatternSimilarityLearningAlgorithm#numTrees.

maxTreeDepth - See LearnPatternSimilarityLearningAlgorithm#maxTreeDepth.

numSegments - See LearnPatternSimilarityLearningAlgorithm#numSegments.

Method Detail

call

public LearnPatternSimilarityClassifier call()
                                      throws ai.libs.jaicore.basic.algorithm.exceptions.AlgorithmException,
                                             ai.libs.jaicore.basic.algorithm.exceptions.AlgorithmTimeoutedException

Training procedure for a LearnPatternSimilarityClassifier. At first, it generates randomly subsequences (segments) and subsequence difference locations used for feature generation. The generated features are used to train a forest of RandomRegressionTree models. The predicted leaf nodes are stored in the model for a 1NN search for an equally generated prediction leaf node matrix.

Throws:

ai.libs.jaicore.basic.algorithm.exceptions.AlgorithmException

ai.libs.jaicore.basic.algorithm.exceptions.AlgorithmTimeoutedException

generateSegmentsAndDifferencesForTree

public void generateSegmentsAndDifferencesForTree(int[] segments,
                                                  int[] segmentsDifference,
                                                  int length,
                                                  int timeSeriesLength,
                                                  java.util.Random random)

Method generating the segment start indices and the segment difference locations randomly using random. This method used the length to specify the interval being generated. LearnPatternSimilarityLearningAlgorithm#numSegments * 2 many indices are generated. The start indices can only be between [0, timeSeriesLength - length] and the segment differences can only between [0, timeSeriesLength - length -1] (-1 due to the fact that the next index is used for the difference calculation).

Parameters:

segments - Segment start indices used for feature generation

segmentsDifference - Segment difference start indices used for feature generation

length - The length of the segments

timeSeriesLength - The total length of the complete time series

random - Generator for the random numbers

initializeRegressionTree

public AccessibleRandomTree initializeRegressionTree(int numInstances)

Initializes a new instance of RandomRegressionTree.

Parameters:

numInstances - The number of instance used for latter training (used for setting the minimum number of instances per leaf)

Returns:

Returns the initialized tree

collectLeafCounts

public static void collectLeafCounts(int[] leafNodeCountsForInstance,
                                     weka.core.Instance instance,
                                     AccessibleRandomTree regTree)
                              throws PredictionException

Function collecting the leaf counts for the given instance as predicted by regTree. The result is stored at the induced leaf node index in leafNodeCountsForInstance.

Parameters:

leafNodeCountsForInstance - The vector storing the frequencies for each leaf node of the tree being the last node within the prediction

instance - The given Weka instance which is fed to the regTree

regTree - The regression tree used for prediction

Throws:

PredictionException - Thrown if the random regression tree could not predict anything for the given instance

generateSubseriesFeaturesInstances

public static weka.core.Instances generateSubseriesFeaturesInstances(java.util.List<weka.core.Attribute> attributes,
                                                                     int length,
                                                                     int[] segments,
                                                                     int[] segmentsDifference,
                                                                     double[][] dataMatrix)

Function generating a dataset storing the features being generated as described in the original paper. The segments and segmentsDifference indices are used to extract subsequences of the given dataMatrix and generating value differences, respectively.

Parameters:

attributes - The attributes used by Weka to create the dataset

length - The length considered for the feature generation

segments - Segment start indices used for feature generation

segmentsDifference - Segment difference start indices used for feature generation

dataMatrix - Matrix storing the instance values used for feature generation

Returns:

Returns Weka instances storing the generated features

generateSubseriesFeatureInstance

public static weka.core.Instance generateSubseriesFeatureInstance(double[] instValues,
                                                                  int[] segments,
                                                                  int[] segmentsDifference,
                                                                  int len)

Function generating subseries feature instances based on the given segments and segmentsDifference matrices. The len parameter indicates which subsequence instance is generated within this call. The values are extracted and used for calculation (for difference) from instValues.

Parameters:

instValues - Instance values used for feature generation

segments - Segment start indices used for feature generation

segmentsDifference - Segment difference start indices used for feature generation

len - Current length (is added to the segment and segment difference locations)

Returns:

Returns a Weka instance storing the generated features

nextWithException

public ai.libs.jaicore.basic.algorithm.events.AlgorithmEvent nextWithException()

getConfig

public LearnPatternSimilarityLearningAlgorithm.IPatternSimilarityConfig getConfig()

Specified by:

getConfig in interface ai.libs.jaicore.basic.algorithm.IAlgorithm<TimeSeriesDataset,LearnPatternSimilarityClassifier>

Overrides:

getConfig in class ai.libs.jaicore.basic.algorithm.AAlgorithm<TimeSeriesDataset,LearnPatternSimilarityClassifier>