Package ai.libs.jaicore.ml.weka.classification.singlelabel.timeseries.learner.shapelets

Interface LearnShapeletsLearningAlgorithm.ILearnShapeletsLearningAlgorithmConfig

  • All Superinterfaces:
    org.aeonbits.owner.Accessible, org.aeonbits.owner.Config, org.api4.java.algorithm.IAlgorithmConfig, org.api4.java.common.control.IConfig, ai.libs.jaicore.basic.IOwnerBasedAlgorithmConfig, ai.libs.jaicore.basic.IOwnerBasedConfig, ai.libs.jaicore.basic.IOwnerBasedRandomizedAlgorithmConfig, org.api4.java.algorithm.IRandomAlgorithmConfig, java.util.Map<java.lang.Object,​java.lang.Object>, org.aeonbits.owner.Mutable, org.aeonbits.owner.Reloadable, java.io.Serializable
    Enclosing class:
    LearnShapeletsLearningAlgorithm
    public static interface LearnShapeletsLearningAlgorithm.ILearnShapeletsLearningAlgorithmConfig
extends ai.libs.jaicore.basic.IOwnerBasedRandomizedAlgorithmConfig

    • Nested Class Summary

      • Nested classes/interfaces inherited from interface org.aeonbits.owner.Config

        org.aeonbits.owner.Config.ConverterClass, org.aeonbits.owner.Config.DecryptorClass, org.aeonbits.owner.Config.DefaultValue, org.aeonbits.owner.Config.DisableableFeature, org.aeonbits.owner.Config.DisableFeature, org.aeonbits.owner.Config.EncryptedValue, org.aeonbits.owner.Config.HotReload, org.aeonbits.owner.Config.HotReloadType, org.aeonbits.owner.Config.Key, org.aeonbits.owner.Config.LoadPolicy, org.aeonbits.owner.Config.LoadType, org.aeonbits.owner.Config.PreprocessorClasses, org.aeonbits.owner.Config.Separator, org.aeonbits.owner.Config.Sources, org.aeonbits.owner.Config.TokenizerClass

      • Nested classes/interfaces inherited from interface java.util.Map

        java.util.Map.Entry<K extends java.lang.Object,​V extends java.lang.Object>

    • Field Summary

      Fields 
      Modifier and Type Field Description
      static java.lang.String K_ESTIMATEK  
      static java.lang.String K_GAMMA  
      static java.lang.String K_LEARNINGRATE  
      static java.lang.String K_MAXITER  
      static java.lang.String K_NUMSHAPELETS  
      static java.lang.String K_REGULARIZATION  
      static java.lang.String K_SCALER  
      static java.lang.String K_SHAPELETLENGTH_MIN  
      static java.lang.String K_SHAPELETLENGTH_RELMIN  

      • Fields inherited from interface ai.libs.jaicore.basic.IOwnerBasedAlgorithmConfig

        K_CPUS, K_MEMORY, K_THREADS, K_TIMEOUT

      • Fields inherited from interface ai.libs.jaicore.basic.IOwnerBasedRandomizedAlgorithmConfig

        K_SEED

    • Method Summary

      All Methods Instance Methods Abstract Methods 
      Modifier and Type Method Description
      boolean estimateK()
      Parameter indicator whether estimation of K (number of learned shapelets) should be derived from the number of total segments.
      double gamma()
      Gamma value used for momentum during gradient descent.
      double learningRate()
      The learning rate used within the SGD.
      int maxIterations()
      The maximum iterations used for the SGD.
      double minShapeLengthPercentage()
      The minimum shape length percentage used to calculate the minimum shape length.
      int minShapeletLength()
      The minimum shapelet of the shapelets to be learned.
      int numShapelets()
      Parameter which determines how many of the most-informative shapelets should be used.
      double regularization()
      The regularization used wihtin the SGD.
      int scaleR()
      The number of scales used for the shapelet lengths.

      • Methods inherited from interface org.aeonbits.owner.Accessible

        fill, getProperty, getProperty, list, list, propertyNames, store, storeToXML

      • Methods inherited from interface org.api4.java.common.control.IConfig

        setProperty

      • Methods inherited from interface ai.libs.jaicore.basic.IOwnerBasedAlgorithmConfig

        cpus, getTimeout, memory, threads, timeout

      • Methods inherited from interface ai.libs.jaicore.basic.IOwnerBasedConfig

        copy, loadPropertiesFromFile, loadPropertiesFromFileArray, loadPropertiesFromList, loadPropertiesFromResource

      • Methods inherited from interface ai.libs.jaicore.basic.IOwnerBasedRandomizedAlgorithmConfig

        seed

      • Methods inherited from interface java.util.Map

        clear, compute, computeIfAbsent, computeIfPresent, containsKey, containsValue, entrySet, equals, forEach, get, getOrDefault, hashCode, isEmpty, keySet, merge, put, putAll, putIfAbsent, remove, remove, replace, replace, replaceAll, size, values

      • Methods inherited from interface org.aeonbits.owner.Mutable

        addPropertyChangeListener, addPropertyChangeListener, clear, load, load, removeProperty, removePropertyChangeListener, setProperty

      • Methods inherited from interface org.aeonbits.owner.Reloadable

        addReloadListener, reload, removeReloadListener

    • Method Detail

      • numShapelets

        @Key("numshapelets")
int numShapelets()
        Parameter which determines how many of the most-informative shapelets should be used. Corresponds to K in the paper

      • learningRate

        @Key("learningrate")
double learningRate()
        The learning rate used within the SGD.

      • regularization

        @Key("regularization")
double regularization()
        The regularization used wihtin the SGD.

      • minShapeletLength

        @Key("minshapeletlength")
int minShapeletLength()
        The minimum shapelet of the shapelets to be learned. Internally derived by the time series lengths and the minShapeLengthPercentage.

      • minShapeLengthPercentage

        @Key("relativeminshapeletlength")
double minShapeLengthPercentage()
        The minimum shape length percentage used to calculate the minimum shape length.

      • scaleR

        @Key("scaler")
int scaleR()
        The number of scales used for the shapelet lengths.

      • maxIterations

        @Key("maxiter")
int maxIterations()
        The maximum iterations used for the SGD.

      • gamma

        @Key("gamma")
@DefaultValue("0.5")
double gamma()
        Gamma value used for momentum during gradient descent. Defaults to 0.5.

      • estimateK

        @Key("estimatek")
@DefaultValue("false")
boolean estimateK()
        Parameter indicator whether estimation of K (number of learned shapelets) should be derived from the number of total segments. False by default.