ai.libs.jaicore.ml.tsc.classifier.shapelets

Interface ILearnShapeletsLearningAlgorithmConfig

All Superinterfaces:

org.aeonbits.owner.Config, ai.libs.jaicore.basic.algorithm.IAlgorithmConfig, ai.libs.jaicore.basic.IConfig, ai.libs.jaicore.basic.algorithm.IRandomAlgorithmConfig, org.aeonbits.owner.Mutable, java.io.Serializable

public interface ILearnShapeletsLearningAlgorithmConfig
extends ai.libs.jaicore.basic.algorithm.IRandomAlgorithmConfig

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

Field Summary

Fields

Modifier and Type	Field and 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_NUM_SHAPELETS
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.algorithm.IRandomAlgorithmConfig

K_SEED

Fields inherited from interface ai.libs.jaicore.basic.algorithm.IAlgorithmConfig

K_CPUS, K_MEMORY, K_THREADS, K_TIMEOUT

Method Summary

Modifier and Type	Method and 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 ai.libs.jaicore.basic.algorithm.IRandomAlgorithmConfig

seed

Methods inherited from interface ai.libs.jaicore.basic.algorithm.IAlgorithmConfig

cpus, memory, threads, timeout

Methods inherited from interface ai.libs.jaicore.basic.IConfig

loadPropertiesFromFile, loadPropertiesFromList, loadPropertiesFromResource

Methods inherited from interface org.aeonbits.owner.Mutable

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

Field Detail

K_NUM_SHAPELETS

static final java.lang.String K_NUM_SHAPELETS

See Also:

Constant Field Values

K_LEARNINGRATE

static final java.lang.String K_LEARNINGRATE

See Also:

Constant Field Values

K_REGULARIZATION

static final java.lang.String K_REGULARIZATION

See Also:

Constant Field Values

K_SHAPELETLENGTH_MIN

static final java.lang.String K_SHAPELETLENGTH_MIN

See Also:

Constant Field Values

K_SHAPELETLENGTH_RELMIN

static final java.lang.String K_SHAPELETLENGTH_RELMIN

See Also:

Constant Field Values

K_SCALER

static final java.lang.String K_SCALER

See Also:

Constant Field Values

K_MAXITER

static final java.lang.String K_MAXITER

See Also:

Constant Field Values

K_GAMMA

static final java.lang.String K_GAMMA

See Also:

Constant Field Values

K_ESTIMATEK

static final java.lang.String K_ESTIMATEK

See Also:

Constant Field Values

Method Detail

numShapelets

@Config.Key(value="numshapelets")
 @Config.DefaultValue(value="1")
int numShapelets()

Parameter which determines how many of the most-informative shapelets should be used. Corresponds to K in the paper

learningRate

@Config.Key(value="learningrate")
 @Config.DefaultValue(value="0.01")
double learningRate()

The learning rate used within the SGD.

regularization

@Config.Key(value="regularization")
 @Config.DefaultValue(value="0.01")
double regularization()

The regularization used wihtin the SGD.

minShapeletLength

@Config.Key(value="minshapeletlength")
int minShapeletLength()

The minimum shapelet of the shapelets to be learned. Internally derived by the time series lengths and the minShapeLengthPercentage.

minShapeLengthPercentage

@Config.Key(value="relativeminshapeletlength")
 @Config.DefaultValue(value="0.1")
double minShapeLengthPercentage()

The minimum shape length percentage used to calculate the minimum shape length.

scaleR

@Config.Key(value="scaler")
 @Config.DefaultValue(value="2")
int scaleR()

The number of scales used for the shapelet lengths.

maxIterations

@Config.Key(value="maxiter")
 @Config.DefaultValue(value="300")
int maxIterations()

The maximum iterations used for the SGD.

gamma

@Config.Key(value="gamma")
 @Config.DefaultValue(value="0.5")
double gamma()

Gamma value used for momentum during gradient descent. Defaults to 0.5.

estimateK

@Config.Key(value="estimatek")
 @Config.DefaultValue(value="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.