org.elasticsearch.search.aggregations.pipeline.movavg.models

Class MovAvgModel

java.lang.Object

org.elasticsearch.search.aggregations.pipeline.movavg.models.MovAvgModel

All Implemented Interfaces:

NamedWriteable, Writeable, ToXContent

Direct Known Subclasses:

EwmaModel, HoltLinearModel, HoltWintersModel, LinearModel, SimpleModel

public abstract class MovAvgModel
extends java.lang.Object
implements NamedWriteable, ToXContent

Nested Class Summary

Nested Classes

Modifier and Type	Class and Description
static class	MovAvgModel.AbstractModelParser Abstract class which also provides some concrete parsing functionality.

Nested classes/interfaces inherited from interface org.elasticsearch.common.io.stream.Writeable

Writeable.Reader<V>, Writeable.Writer<V>

Nested classes/interfaces inherited from interface org.elasticsearch.common.xcontent.ToXContent

ToXContent.DelegatingMapParams, ToXContent.MapParams, ToXContent.Params

Field Summary

Fields inherited from interface org.elasticsearch.common.xcontent.ToXContent

EMPTY_PARAMS

Constructor Summary

Constructors

Constructor and Description
MovAvgModel()

Method Summary

Modifier and Type	Method and Description
abstract boolean	canBeMinimized() Returns if the model can be cost minimized.
abstract MovAvgModel	clone() Clone the model, returning an exact copy
protected abstract <T extends java.lang.Number> double[]	doPredict(java.util.Collection<T> values, int numPredictions) Calls to the model-specific implementation which actually generates the predictions
protected double[]	emptyPredictions(int numPredictions) Returns an empty set of predictions, filled with NaNs
abstract boolean	equals(java.lang.Object obj)
abstract int	hashCode()
boolean	hasValue(int valuesAvailable) Checks to see this model can produce a new value, without actually running the algo.
boolean	minimizeByDefault() Should this model be fit to the data via a cost minimizing algorithm by default?
abstract MovAvgModel	neighboringModel() Generates a "neighboring" model, where one of the tunable parameters has been randomly mutated within the allowed range.
abstract <T extends java.lang.Number> double	next(java.util.Collection<T> values) Returns the next value in the series, according to the underlying smoothing model
<T extends java.lang.Number> double[]	predict(java.util.Collection<T> values, int numPredictions) Predicts the next `n` values in the series.
abstract void	writeTo(StreamOutput out) Write the model to the output stream

Methods inherited from class java.lang.Object

finalize, getClass, notify, notifyAll, toString, wait, wait, wait

Methods inherited from interface org.elasticsearch.common.io.stream.NamedWriteable

getWriteableName

Methods inherited from interface org.elasticsearch.common.xcontent.ToXContent

isFragment, toXContent

Constructor Detail

MovAvgModel

public MovAvgModel()

Method Detail

minimizeByDefault

public boolean minimizeByDefault()

Should this model be fit to the data via a cost minimizing algorithm by default?

canBeMinimized

public abstract boolean canBeMinimized()

Returns if the model can be cost minimized. Not all models have parameters which can be tuned / optimized.

neighboringModel

public abstract MovAvgModel neighboringModel()

Generates a "neighboring" model, where one of the tunable parameters has been randomly mutated within the allowed range. Used for minimization

hasValue

public boolean hasValue(int valuesAvailable)

Checks to see this model can produce a new value, without actually running the algo. This can be used for models that have certain preconditions that need to be met in order to short-circuit execution

Parameters:

valuesAvailable - Number of values in the current window of values

Returns:

Returns `true` if calling next() will produce a value, `false` otherwise

next

public abstract <T extends java.lang.Number> double next(java.util.Collection<T> values)

Returns the next value in the series, according to the underlying smoothing model

Type Parameters:

T - Type of numeric

Parameters:

values - Collection of numerics to movingAvg, usually windowed

Returns:

Returns a double, since most smoothing methods operate on floating points

predict

public <T extends java.lang.Number> double[] predict(java.util.Collection<T> values,
                                                     int numPredictions)

Predicts the next `n` values in the series.

Type Parameters:

T - Type of numeric

Parameters:

values - Collection of numerics to movingAvg, usually windowed

numPredictions - Number of newly generated predictions to return

Returns:

Returns an array of doubles, since most smoothing methods operate on floating points

doPredict

protected abstract <T extends java.lang.Number> double[] doPredict(java.util.Collection<T> values,
                                                                   int numPredictions)

Calls to the model-specific implementation which actually generates the predictions

Type Parameters:

T - Type of numeric

Parameters:

values - Collection of numerics to movingAvg, usually windowed

numPredictions - Number of newly generated predictions to return

Returns:

Returns an array of doubles, since most smoothing methods operate on floating points

emptyPredictions

protected double[] emptyPredictions(int numPredictions)

Returns an empty set of predictions, filled with NaNs

Parameters:

numPredictions - Number of empty predictions to generate

writeTo

public abstract void writeTo(StreamOutput out)
                      throws java.io.IOException

Write the model to the output stream

Specified by:

writeTo in interface Writeable

Parameters:

out - Output stream

Throws:

java.io.IOException

clone

public abstract MovAvgModel clone()

Clone the model, returning an exact copy

Overrides:

clone in class java.lang.Object

hashCode

public abstract int hashCode()

Overrides:

hashCode in class java.lang.Object

equals

public abstract boolean equals(java.lang.Object obj)

Overrides:

equals in class java.lang.Object