From 502d8b0cdd279b837df73a191cc27a93aa8e1472 Mon Sep 17 00:00:00 2001 From: Nick Craig-Wood Date: Mon, 11 Jun 2018 11:31:18 +0100 Subject: [PATCH] vendor: remove github.com/VividCortex/ewma dependency --- Gopkg.lock | 9 -- .../ewma/.github/ISSUE_TEMPLATE.md | 10 -- .../ewma/.github/PULL_REQUEST_TEMPLATE.md | 10 -- vendor/github.com/VividCortex/ewma/.gitignore | 2 - vendor/github.com/VividCortex/ewma/LICENSE | 21 --- vendor/github.com/VividCortex/ewma/README.md | 140 ------------------ vendor/github.com/VividCortex/ewma/ewma.go | 126 ---------------- .../github.com/VividCortex/ewma/ewma_test.go | 103 ------------- 8 files changed, 421 deletions(-) delete mode 100644 vendor/github.com/VividCortex/ewma/.github/ISSUE_TEMPLATE.md delete mode 100644 vendor/github.com/VividCortex/ewma/.github/PULL_REQUEST_TEMPLATE.md delete mode 100644 vendor/github.com/VividCortex/ewma/.gitignore delete mode 100644 vendor/github.com/VividCortex/ewma/LICENSE delete mode 100644 vendor/github.com/VividCortex/ewma/README.md delete mode 100644 vendor/github.com/VividCortex/ewma/ewma.go delete mode 100644 vendor/github.com/VividCortex/ewma/ewma_test.go diff --git a/Gopkg.lock b/Gopkg.lock index 1ae63112c..774822e09 100644 --- a/Gopkg.lock +++ b/Gopkg.lock @@ -56,14 +56,6 @@ pruneopts = "" revision = "ef1e4c783f8f0478bd8bff0edb3dd0bade552599" -[[projects]] - digest = "1:ac226c42eb54c121e0704c6f7f64c96c7817ad6d6286e5536e8cea72807e1079" - name = "github.com/VividCortex/ewma" - packages = ["."] - pruneopts = "" - revision = "b24eb346a94c3ba12c1da1e564dbac1b498a77ce" - version = "v1.1.1" - [[projects]] branch = "master" digest = "1:391632fa3a324c4f461f28baaf45cea8b21e13630b00f27059613f855bb544bb" @@ -580,7 +572,6 @@ "github.com/Azure/go-ansiterm", "github.com/Azure/go-ansiterm/winterm", "github.com/Unknwon/goconfig", - "github.com/VividCortex/ewma", "github.com/a8m/tree", "github.com/abbot/go-http-auth", "github.com/aws/aws-sdk-go/aws", diff --git a/vendor/github.com/VividCortex/ewma/.github/ISSUE_TEMPLATE.md b/vendor/github.com/VividCortex/ewma/.github/ISSUE_TEMPLATE.md deleted file mode 100644 index f3c19086c..000000000 --- a/vendor/github.com/VividCortex/ewma/.github/ISSUE_TEMPLATE.md +++ /dev/null @@ -1,10 +0,0 @@ -Before you file an issue, please consider: - -We only accept pull requests for minor fixes or improvements. This includes: - -* Small bug fixes -* Typos -* Documentation or comments - -Please open issues to discuss new features. Pull requests for new features will be rejected, -so we recommend forking the repository and making changes in your fork for your use case. diff --git a/vendor/github.com/VividCortex/ewma/.github/PULL_REQUEST_TEMPLATE.md b/vendor/github.com/VividCortex/ewma/.github/PULL_REQUEST_TEMPLATE.md deleted file mode 100644 index 0c22b9267..000000000 --- a/vendor/github.com/VividCortex/ewma/.github/PULL_REQUEST_TEMPLATE.md +++ /dev/null @@ -1,10 +0,0 @@ -Before you create a pull request, please consider: - -We only accept pull requests for minor fixes or improvements. This includes: - -* Small bug fixes -* Typos -* Documentation or comments - -Please open issues to discuss new features. Pull requests for new features will be rejected, -so we recommend forking the repository and making changes in your fork for your use case. diff --git a/vendor/github.com/VividCortex/ewma/.gitignore b/vendor/github.com/VividCortex/ewma/.gitignore deleted file mode 100644 index 6c7104aef..000000000 --- a/vendor/github.com/VividCortex/ewma/.gitignore +++ /dev/null @@ -1,2 +0,0 @@ -.DS_Store -.*.sw? diff --git a/vendor/github.com/VividCortex/ewma/LICENSE b/vendor/github.com/VividCortex/ewma/LICENSE deleted file mode 100644 index a78d643ed..000000000 --- a/vendor/github.com/VividCortex/ewma/LICENSE +++ /dev/null @@ -1,21 +0,0 @@ -The MIT License - -Copyright (c) 2013 VividCortex - -Permission is hereby granted, free of charge, to any person obtaining a copy -of this software and associated documentation files (the "Software"), to deal -in the Software without restriction, including without limitation the rights -to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -copies of the Software, and to permit persons to whom the Software is -furnished to do so, subject to the following conditions: - -The above copyright notice and this permission notice shall be included in -all copies or substantial portions of the Software. - -THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN -THE SOFTWARE. diff --git a/vendor/github.com/VividCortex/ewma/README.md b/vendor/github.com/VividCortex/ewma/README.md deleted file mode 100644 index 7aab61b87..000000000 --- a/vendor/github.com/VividCortex/ewma/README.md +++ /dev/null @@ -1,140 +0,0 @@ -# EWMA [![GoDoc](https://godoc.org/github.com/VividCortex/ewma?status.svg)](https://godoc.org/github.com/VividCortex/ewma) ![Build Status](https://circleci.com/gh/VividCortex/moving_average.png?circle-token=1459fa37f9ca0e50cef05d1963146d96d47ea523) - -This repo provides Exponentially Weighted Moving Average algorithms, or EWMAs for short, [based on our -Quantifying Abnormal Behavior talk](https://vividcortex.com/blog/2013/07/23/a-fast-go-library-for-exponential-moving-averages/). - -### Exponentially Weighted Moving Average - -An exponentially weighted moving average is a way to continuously compute a type of -average for a series of numbers, as the numbers arrive. After a value in the series is -added to the average, its weight in the average decreases exponentially over time. This -biases the average towards more recent data. EWMAs are useful for several reasons, chiefly -their inexpensive computational and memory cost, as well as the fact that they represent -the recent central tendency of the series of values. - -The EWMA algorithm requires a decay factor, alpha. The larger the alpha, the more the average -is biased towards recent history. The alpha must be between 0 and 1, and is typically -a fairly small number, such as 0.04. We will discuss the choice of alpha later. - -The algorithm works thus, in pseudocode: - -1. Multiply the next number in the series by alpha. -2. Multiply the current value of the average by 1 minus alpha. -3. Add the result of steps 1 and 2, and store it as the new current value of the average. -4. Repeat for each number in the series. - -There are special-case behaviors for how to initialize the current value, and these vary -between implementations. One approach is to start with the first value in the series; -another is to average the first 10 or so values in the series using an arithmetic average, -and then begin the incremental updating of the average. Each method has pros and cons. - -It may help to look at it pictorially. Suppose the series has five numbers, and we choose -alpha to be 0.50 for simplicity. Here's the series, with numbers in the neighborhood of 300. - -![Data Series](https://user-images.githubusercontent.com/279875/28242350-463289a2-6977-11e7-88ca-fd778ccef1f0.png) - -Now let's take the moving average of those numbers. First we set the average to the value -of the first number. - -![EWMA Step 1](https://user-images.githubusercontent.com/279875/28242353-464c96bc-6977-11e7-9981-dc4e0789c7ba.png) - -Next we multiply the next number by alpha, multiply the current value by 1-alpha, and add -them to generate a new value. - -![EWMA Step 2](https://user-images.githubusercontent.com/279875/28242351-464abefa-6977-11e7-95d0-43900f29bef2.png) - -This continues until we are done. - -![EWMA Step N](https://user-images.githubusercontent.com/279875/28242352-464c58f0-6977-11e7-8cd0-e01e4efaac7f.png) - -Notice how each of the values in the series decays by half each time a new value -is added, and the top of the bars in the lower portion of the image represents the -size of the moving average. It is a smoothed, or low-pass, average of the original -series. - -For further reading, see [Exponentially weighted moving average](http://en.wikipedia.org/wiki/Moving_average#Exponential_moving_average) on wikipedia. - -### Choosing Alpha - -Consider a fixed-size sliding-window moving average (not an exponentially weighted moving average) -that averages over the previous N samples. What is the average age of each sample? It is N/2. - -Now suppose that you wish to construct a EWMA whose samples have the same average age. The formula -to compute the alpha required for this is: alpha = 2/(N+1). Proof is in the book -"Production and Operations Analysis" by Steven Nahmias. - -So, for example, if you have a time-series with samples once per second, and you want to get the -moving average over the previous minute, you should use an alpha of .032786885. This, by the way, -is the constant alpha used for this repository's SimpleEWMA. - -### Implementations - -This repository contains two implementations of the EWMA algorithm, with different properties. - -The implementations all conform to the MovingAverage interface, and the constructor returns -that type. - -Current implementations assume an implicit time interval of 1.0 between every sample added. -That is, the passage of time is treated as though it's the same as the arrival of samples. -If you need time-based decay when samples are not arriving precisely at set intervals, then -this package will not support your needs at present. - -#### SimpleEWMA - -A SimpleEWMA is designed for low CPU and memory consumption. It **will** have different behavior than the VariableEWMA -for multiple reasons. It has no warm-up period and it uses a constant -decay. These properties let it use less memory. It will also behave -differently when it's equal to zero, which is assumed to mean -uninitialized, so if a value is likely to actually become zero over time, -then any non-zero value will cause a sharp jump instead of a small change. - -#### VariableEWMA - -Unlike SimpleEWMA, this supports a custom age which must be stored, and thus uses more memory. -It also has a "warmup" time when you start adding values to it. It will report a value of 0.0 -until you have added the required number of samples to it. It uses some memory to store the -number of samples added to it. As a result it uses a little over twice the memory of SimpleEWMA. - -## Usage - -### API Documentation - -View the GoDoc generated documentation [here](http://godoc.org/github.com/VividCortex/ewma). - -```go -package main -import "github.com/VividCortex/ewma" - -func main() { - samples := [100]float64{ - 4599, 5711, 4746, 4621, 5037, 4218, 4925, 4281, 5207, 5203, 5594, 5149, - } - - e := ewma.NewMovingAverage() //=> Returns a SimpleEWMA if called without params - a := ewma.NewMovingAverage(5) //=> returns a VariableEWMA with a decay of 2 / (5 + 1) - - for _, f := range samples { - e.Add(f) - a.Add(f) - } - - e.Value() //=> 13.577404704631077 - a.Value() //=> 1.5806140565521463e-12 -} -``` - -## Contributing - -We only accept pull requests for minor fixes or improvements. This includes: - -* Small bug fixes -* Typos -* Documentation or comments - -Please open issues to discuss new features. Pull requests for new features will be rejected, -so we recommend forking the repository and making changes in your fork for your use case. - -## License - -This repository is Copyright (c) 2013 VividCortex, Inc. All rights reserved. -It is licensed under the MIT license. Please see the LICENSE file for applicable license terms. diff --git a/vendor/github.com/VividCortex/ewma/ewma.go b/vendor/github.com/VividCortex/ewma/ewma.go deleted file mode 100644 index 44d5d53e3..000000000 --- a/vendor/github.com/VividCortex/ewma/ewma.go +++ /dev/null @@ -1,126 +0,0 @@ -// Package ewma implements exponentially weighted moving averages. -package ewma - -// Copyright (c) 2013 VividCortex, Inc. All rights reserved. -// Please see the LICENSE file for applicable license terms. - -const ( - // By default, we average over a one-minute period, which means the average - // age of the metrics in the period is 30 seconds. - AVG_METRIC_AGE float64 = 30.0 - - // The formula for computing the decay factor from the average age comes - // from "Production and Operations Analysis" by Steven Nahmias. - DECAY float64 = 2 / (float64(AVG_METRIC_AGE) + 1) - - // For best results, the moving average should not be initialized to the - // samples it sees immediately. The book "Production and Operations - // Analysis" by Steven Nahmias suggests initializing the moving average to - // the mean of the first 10 samples. Until the VariableEwma has seen this - // many samples, it is not "ready" to be queried for the value of the - // moving average. This adds some memory cost. - WARMUP_SAMPLES uint8 = 10 -) - -// MovingAverage is the interface that computes a moving average over a time- -// series stream of numbers. The average may be over a window or exponentially -// decaying. -type MovingAverage interface { - Add(float64) - Value() float64 - Set(float64) -} - -// NewMovingAverage constructs a MovingAverage that computes an average with the -// desired characteristics in the moving window or exponential decay. If no -// age is given, it constructs a default exponentially weighted implementation -// that consumes minimal memory. The age is related to the decay factor alpha -// by the formula given for the DECAY constant. It signifies the average age -// of the samples as time goes to infinity. -func NewMovingAverage(age ...float64) MovingAverage { - if len(age) == 0 || age[0] == AVG_METRIC_AGE { - return new(SimpleEWMA) - } - return &VariableEWMA{ - decay: 2 / (age[0] + 1), - } -} - -// A SimpleEWMA represents the exponentially weighted moving average of a -// series of numbers. It WILL have different behavior than the VariableEWMA -// for multiple reasons. It has no warm-up period and it uses a constant -// decay. These properties let it use less memory. It will also behave -// differently when it's equal to zero, which is assumed to mean -// uninitialized, so if a value is likely to actually become zero over time, -// then any non-zero value will cause a sharp jump instead of a small change. -// However, note that this takes a long time, and the value may just -// decays to a stable value that's close to zero, but which won't be mistaken -// for uninitialized. See http://play.golang.org/p/litxBDr_RC for example. -type SimpleEWMA struct { - // The current value of the average. After adding with Add(), this is - // updated to reflect the average of all values seen thus far. - value float64 -} - -// Add adds a value to the series and updates the moving average. -func (e *SimpleEWMA) Add(value float64) { - if e.value == 0 { // this is a proxy for "uninitialized" - e.value = value - } else { - e.value = (value * DECAY) + (e.value * (1 - DECAY)) - } -} - -// Value returns the current value of the moving average. -func (e *SimpleEWMA) Value() float64 { - return e.value -} - -// Set sets the EWMA's value. -func (e *SimpleEWMA) Set(value float64) { - e.value = value -} - -// VariableEWMA represents the exponentially weighted moving average of a series of -// numbers. Unlike SimpleEWMA, it supports a custom age, and thus uses more memory. -type VariableEWMA struct { - // The multiplier factor by which the previous samples decay. - decay float64 - // The current value of the average. - value float64 - // The number of samples added to this instance. - count uint8 -} - -// Add adds a value to the series and updates the moving average. -func (e *VariableEWMA) Add(value float64) { - switch { - case e.count < WARMUP_SAMPLES: - e.count++ - e.value += value - case e.count == WARMUP_SAMPLES: - e.count++ - e.value = e.value / float64(WARMUP_SAMPLES) - e.value = (value * e.decay) + (e.value * (1 - e.decay)) - default: - e.value = (value * e.decay) + (e.value * (1 - e.decay)) - } -} - -// Value returns the current value of the average, or 0.0 if the series hasn't -// warmed up yet. -func (e *VariableEWMA) Value() float64 { - if e.count <= WARMUP_SAMPLES { - return 0.0 - } - - return e.value -} - -// Set sets the EWMA's value. -func (e *VariableEWMA) Set(value float64) { - e.value = value - if e.count <= WARMUP_SAMPLES { - e.count = WARMUP_SAMPLES + 1 - } -} diff --git a/vendor/github.com/VividCortex/ewma/ewma_test.go b/vendor/github.com/VividCortex/ewma/ewma_test.go deleted file mode 100644 index 8060a859f..000000000 --- a/vendor/github.com/VividCortex/ewma/ewma_test.go +++ /dev/null @@ -1,103 +0,0 @@ -package ewma - -// Copyright (c) 2013 VividCortex, Inc. All rights reserved. -// Please see the LICENSE file for applicable license terms. - -import ( - "math" - "testing" -) - -const testMargin = 0.00000001 - -var samples = [100]float64{ - 4599, 5711, 4746, 4621, 5037, 4218, 4925, 4281, 5207, 5203, 5594, 5149, - 4948, 4994, 6056, 4417, 4973, 4714, 4964, 5280, 5074, 4913, 4119, 4522, - 4631, 4341, 4909, 4750, 4663, 5167, 3683, 4964, 5151, 4892, 4171, 5097, - 3546, 4144, 4551, 6557, 4234, 5026, 5220, 4144, 5547, 4747, 4732, 5327, - 5442, 4176, 4907, 3570, 4684, 4161, 5206, 4952, 4317, 4819, 4668, 4603, - 4885, 4645, 4401, 4362, 5035, 3954, 4738, 4545, 5433, 6326, 5927, 4983, - 5364, 4598, 5071, 5231, 5250, 4621, 4269, 3953, 3308, 3623, 5264, 5322, - 5395, 4753, 4936, 5315, 5243, 5060, 4989, 4921, 4480, 3426, 3687, 4220, - 3197, 5139, 6101, 5279, -} - -func withinMargin(a, b float64) bool { - return math.Abs(a-b) <= testMargin -} - -func TestSimpleEWMA(t *testing.T) { - var e SimpleEWMA - for _, f := range samples { - e.Add(f) - } - if !withinMargin(e.Value(), 4734.500946466118) { - t.Errorf("e.Value() is %v, wanted %v", e.Value(), 4734.500946466118) - } - e.Set(1.0) - if e.Value() != 1.0 { - t.Errorf("e.Value() is %d", e.Value()) - } -} - -func TestVariableEWMA(t *testing.T) { - e := NewMovingAverage(30) - for _, f := range samples { - e.Add(f) - } - if !withinMargin(e.Value(), 4734.500946466118) { - t.Errorf("e.Value() is %v, wanted %v", e.Value(), 4734.500946466118) - } - e.Set(1.0) - if e.Value() != 1.0 { - t.Errorf("e.Value() is %d", e.Value()) - } -} - -func TestVariableEWMA2(t *testing.T) { - e := NewMovingAverage(5) - for _, f := range samples { - e.Add(f) - } - if !withinMargin(e.Value(), 5015.397367486725) { - t.Errorf("e.Value() is %v, wanted %v", e.Value(), 5015.397367486725) - } -} - -func TestVariableEWMAWarmup(t *testing.T) { - e := NewMovingAverage(5) - for i, f := range samples { - e.Add(f) - - // all values returned during warmup should be 0.0 - if uint8(i) < WARMUP_SAMPLES { - if e.Value() != 0.0 { - t.Errorf("e.Value() is %v, expected %v", e.Value(), 0.0) - } - } - } - e = NewMovingAverage(5) - e.Set(5) - e.Add(1) - if e.Value() >= 5 { - t.Errorf("e.Value() is %d, expected it to decay towards 0", e.Value()) - } -} - -func TestVariableEWMAWarmup2(t *testing.T) { - e := NewMovingAverage(5) - testSamples := [12]float64{1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 10000, 1} - for i, f := range testSamples { - e.Add(f) - - // all values returned during warmup should be 0.0 - if uint8(i) < WARMUP_SAMPLES { - if e.Value() != 0.0 { - t.Errorf("e.Value() is %v, expected %v", e.Value(), 0.0) - } - } - } - if val := e.Value(); val == 1.0 { - t.Errorf("e.Value() is expected to be greater than %v", 1.0) - } -}