mirror of
https://github.com/go-gitea/gitea
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5dbf36f356
* Issue search support elasticsearch * Fix lint * Add indexer name on app.ini * add a warnning on SearchIssuesByKeyword * improve code
379 lines
10 KiB
Go
Vendored
379 lines
10 KiB
Go
Vendored
// Copyright 2012-present Oliver Eilhard. All rights reserved.
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// Use of this source code is governed by a MIT-license.
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// See http://olivere.mit-license.org/license.txt for details.
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package elastic
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// MovAvgAggregation operates on a series of data. It will slide a window
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// across the data and emit the average value of that window.
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//
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// For more details, see
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// https://www.elastic.co/guide/en/elasticsearch/reference/7.0/search-aggregations-pipeline-movavg-aggregation.html
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//
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// Deprecated: The MovAvgAggregation has been deprecated in 6.4.0. Use the more generate MovFnAggregation instead.
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type MovAvgAggregation struct {
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format string
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gapPolicy string
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model MovAvgModel
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window *int
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predict *int
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minimize *bool
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meta map[string]interface{}
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bucketsPaths []string
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}
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// NewMovAvgAggregation creates and initializes a new MovAvgAggregation.
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//
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// Deprecated: The MovAvgAggregation has been deprecated in 6.4.0. Use the more generate MovFnAggregation instead.
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func NewMovAvgAggregation() *MovAvgAggregation {
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return &MovAvgAggregation{
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bucketsPaths: make([]string, 0),
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}
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}
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// Format to use on the output of this aggregation.
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func (a *MovAvgAggregation) Format(format string) *MovAvgAggregation {
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a.format = format
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return a
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}
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// GapPolicy defines what should be done when a gap in the series is discovered.
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// Valid values include "insert_zeros" or "skip". Default is "insert_zeros".
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func (a *MovAvgAggregation) GapPolicy(gapPolicy string) *MovAvgAggregation {
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a.gapPolicy = gapPolicy
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return a
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}
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// GapInsertZeros inserts zeros for gaps in the series.
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func (a *MovAvgAggregation) GapInsertZeros() *MovAvgAggregation {
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a.gapPolicy = "insert_zeros"
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return a
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}
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// GapSkip skips gaps in the series.
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func (a *MovAvgAggregation) GapSkip() *MovAvgAggregation {
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a.gapPolicy = "skip"
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return a
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}
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// Model is used to define what type of moving average you want to use
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// in the series.
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func (a *MovAvgAggregation) Model(model MovAvgModel) *MovAvgAggregation {
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a.model = model
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return a
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}
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// Window sets the window size for the moving average. This window will
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// "slide" across the series, and the values inside that window will
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// be used to calculate the moving avg value.
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func (a *MovAvgAggregation) Window(window int) *MovAvgAggregation {
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a.window = &window
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return a
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}
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// Predict sets the number of predictions that should be returned.
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// Each prediction will be spaced at the intervals in the histogram.
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// E.g. a predict of 2 will return two new buckets at the end of the
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// histogram with the predicted values.
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func (a *MovAvgAggregation) Predict(numPredictions int) *MovAvgAggregation {
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a.predict = &numPredictions
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return a
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}
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// Minimize determines if the model should be fit to the data using a
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// cost minimizing algorithm.
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func (a *MovAvgAggregation) Minimize(minimize bool) *MovAvgAggregation {
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a.minimize = &minimize
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return a
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}
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// Meta sets the meta data to be included in the aggregation response.
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func (a *MovAvgAggregation) Meta(metaData map[string]interface{}) *MovAvgAggregation {
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a.meta = metaData
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return a
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}
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// BucketsPath sets the paths to the buckets to use for this pipeline aggregator.
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func (a *MovAvgAggregation) BucketsPath(bucketsPaths ...string) *MovAvgAggregation {
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a.bucketsPaths = append(a.bucketsPaths, bucketsPaths...)
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return a
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}
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// Source returns the a JSON-serializable interface.
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func (a *MovAvgAggregation) Source() (interface{}, error) {
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source := make(map[string]interface{})
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params := make(map[string]interface{})
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source["moving_avg"] = params
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if a.format != "" {
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params["format"] = a.format
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}
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if a.gapPolicy != "" {
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params["gap_policy"] = a.gapPolicy
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}
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if a.model != nil {
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params["model"] = a.model.Name()
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settings := a.model.Settings()
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if len(settings) > 0 {
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params["settings"] = settings
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}
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}
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if a.window != nil {
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params["window"] = *a.window
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}
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if a.predict != nil {
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params["predict"] = *a.predict
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}
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if a.minimize != nil {
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params["minimize"] = *a.minimize
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}
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// Add buckets paths
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switch len(a.bucketsPaths) {
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case 0:
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case 1:
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params["buckets_path"] = a.bucketsPaths[0]
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default:
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params["buckets_path"] = a.bucketsPaths
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}
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// Add Meta data if available
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if len(a.meta) > 0 {
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source["meta"] = a.meta
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}
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return source, nil
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}
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// -- Models for moving averages --
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// See https://www.elastic.co/guide/en/elasticsearch/reference/7.0/search-aggregations-pipeline-movavg-aggregation.html#_models
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// MovAvgModel specifies the model to use with the MovAvgAggregation.
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type MovAvgModel interface {
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Name() string
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Settings() map[string]interface{}
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}
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// -- EWMA --
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// EWMAMovAvgModel calculates an exponentially weighted moving average.
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//
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// For more details, see
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// https://www.elastic.co/guide/en/elasticsearch/reference/7.0/search-aggregations-pipeline-movavg-aggregation.html#_ewma_exponentially_weighted
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type EWMAMovAvgModel struct {
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alpha *float64
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}
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// NewEWMAMovAvgModel creates and initializes a new EWMAMovAvgModel.
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func NewEWMAMovAvgModel() *EWMAMovAvgModel {
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return &EWMAMovAvgModel{}
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}
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// Alpha controls the smoothing of the data. Alpha = 1 retains no memory
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// of past values (e.g. a random walk), while alpha = 0 retains infinite
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// memory of past values (e.g. the series mean). Useful values are somewhere
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// in between. Defaults to 0.5.
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func (m *EWMAMovAvgModel) Alpha(alpha float64) *EWMAMovAvgModel {
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m.alpha = &alpha
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return m
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}
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// Name of the model.
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func (m *EWMAMovAvgModel) Name() string {
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return "ewma"
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}
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// Settings of the model.
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func (m *EWMAMovAvgModel) Settings() map[string]interface{} {
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settings := make(map[string]interface{})
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if m.alpha != nil {
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settings["alpha"] = *m.alpha
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}
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return settings
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}
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// -- Holt linear --
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// HoltLinearMovAvgModel calculates a doubly exponential weighted moving average.
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//
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// For more details, see
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// https://www.elastic.co/guide/en/elasticsearch/reference/7.0/search-aggregations-pipeline-movavg-aggregation.html#_holt_linear
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type HoltLinearMovAvgModel struct {
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alpha *float64
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beta *float64
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}
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// NewHoltLinearMovAvgModel creates and initializes a new HoltLinearMovAvgModel.
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func NewHoltLinearMovAvgModel() *HoltLinearMovAvgModel {
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return &HoltLinearMovAvgModel{}
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}
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// Alpha controls the smoothing of the data. Alpha = 1 retains no memory
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// of past values (e.g. a random walk), while alpha = 0 retains infinite
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// memory of past values (e.g. the series mean). Useful values are somewhere
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// in between. Defaults to 0.5.
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func (m *HoltLinearMovAvgModel) Alpha(alpha float64) *HoltLinearMovAvgModel {
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m.alpha = &alpha
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return m
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}
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// Beta is equivalent to Alpha but controls the smoothing of the trend
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// instead of the data.
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func (m *HoltLinearMovAvgModel) Beta(beta float64) *HoltLinearMovAvgModel {
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m.beta = &beta
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return m
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}
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// Name of the model.
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func (m *HoltLinearMovAvgModel) Name() string {
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return "holt"
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}
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// Settings of the model.
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func (m *HoltLinearMovAvgModel) Settings() map[string]interface{} {
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settings := make(map[string]interface{})
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if m.alpha != nil {
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settings["alpha"] = *m.alpha
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}
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if m.beta != nil {
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settings["beta"] = *m.beta
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}
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return settings
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}
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// -- Holt Winters --
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// HoltWintersMovAvgModel calculates a triple exponential weighted moving average.
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//
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// For more details, see
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// https://www.elastic.co/guide/en/elasticsearch/reference/7.0/search-aggregations-pipeline-movavg-aggregation.html#_holt_winters
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type HoltWintersMovAvgModel struct {
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alpha *float64
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beta *float64
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gamma *float64
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period *int
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seasonalityType string
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pad *bool
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}
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// NewHoltWintersMovAvgModel creates and initializes a new HoltWintersMovAvgModel.
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func NewHoltWintersMovAvgModel() *HoltWintersMovAvgModel {
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return &HoltWintersMovAvgModel{}
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}
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// Alpha controls the smoothing of the data. Alpha = 1 retains no memory
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// of past values (e.g. a random walk), while alpha = 0 retains infinite
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// memory of past values (e.g. the series mean). Useful values are somewhere
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// in between. Defaults to 0.5.
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func (m *HoltWintersMovAvgModel) Alpha(alpha float64) *HoltWintersMovAvgModel {
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m.alpha = &alpha
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return m
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}
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// Beta is equivalent to Alpha but controls the smoothing of the trend
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// instead of the data.
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func (m *HoltWintersMovAvgModel) Beta(beta float64) *HoltWintersMovAvgModel {
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m.beta = &beta
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return m
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}
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func (m *HoltWintersMovAvgModel) Gamma(gamma float64) *HoltWintersMovAvgModel {
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m.gamma = &gamma
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return m
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}
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func (m *HoltWintersMovAvgModel) Period(period int) *HoltWintersMovAvgModel {
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m.period = &period
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return m
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}
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func (m *HoltWintersMovAvgModel) SeasonalityType(typ string) *HoltWintersMovAvgModel {
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m.seasonalityType = typ
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return m
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}
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func (m *HoltWintersMovAvgModel) Pad(pad bool) *HoltWintersMovAvgModel {
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m.pad = &pad
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return m
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}
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// Name of the model.
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func (m *HoltWintersMovAvgModel) Name() string {
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return "holt_winters"
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}
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// Settings of the model.
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func (m *HoltWintersMovAvgModel) Settings() map[string]interface{} {
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settings := make(map[string]interface{})
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if m.alpha != nil {
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settings["alpha"] = *m.alpha
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}
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if m.beta != nil {
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settings["beta"] = *m.beta
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}
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if m.gamma != nil {
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settings["gamma"] = *m.gamma
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}
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if m.period != nil {
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settings["period"] = *m.period
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}
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if m.pad != nil {
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settings["pad"] = *m.pad
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}
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if m.seasonalityType != "" {
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settings["type"] = m.seasonalityType
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}
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return settings
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}
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// -- Linear --
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// LinearMovAvgModel calculates a linearly weighted moving average, such
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// that older values are linearly less important. "Time" is determined
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// by position in collection.
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//
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// For more details, see
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// https://www.elastic.co/guide/en/elasticsearch/reference/7.0/search-aggregations-pipeline-movavg-aggregation.html#_linear
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type LinearMovAvgModel struct {
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}
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// NewLinearMovAvgModel creates and initializes a new LinearMovAvgModel.
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func NewLinearMovAvgModel() *LinearMovAvgModel {
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return &LinearMovAvgModel{}
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}
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// Name of the model.
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func (m *LinearMovAvgModel) Name() string {
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return "linear"
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}
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// Settings of the model.
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func (m *LinearMovAvgModel) Settings() map[string]interface{} {
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return nil
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}
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// -- Simple --
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// SimpleMovAvgModel calculates a simple unweighted (arithmetic) moving average.
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//
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// For more details, see
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// https://www.elastic.co/guide/en/elasticsearch/reference/7.0/search-aggregations-pipeline-movavg-aggregation.html#_simple
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type SimpleMovAvgModel struct {
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}
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// NewSimpleMovAvgModel creates and initializes a new SimpleMovAvgModel.
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func NewSimpleMovAvgModel() *SimpleMovAvgModel {
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return &SimpleMovAvgModel{}
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}
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// Name of the model.
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func (m *SimpleMovAvgModel) Name() string {
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return "simple"
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}
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// Settings of the model.
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func (m *SimpleMovAvgModel) Settings() map[string]interface{} {
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return nil
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}
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