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Add generic support; CopyFrom(), Init() methods #19

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c9d4254
Add go module, add simple sampler helper
Nov 3, 2019
865dd6a
NewVaropt->New
Nov 8, 2019
3dfa2d1
Add testable examples
Nov 8, 2019
2be048e
Update README with examples and godoc link
Nov 8, 2019
08e548b
Update
Nov 8, 2019
0f1df4e
Merge pull request #1 from lightstep/jmacd/update_readme
jmacd Nov 8, 2019
43a69f4
Use a deterministic random source; name examples for godoc
Nov 8, 2019
68f1fab
Add comments
Nov 8, 2019
bef2465
Merge pull request #2 from lightstep/jmacd/godocex
jmacd Nov 8, 2019
5c2a41c
Add circle config
Nov 8, 2019
d93f23f
Merge pull request #3 from lightstep/jmacd/add_circleci
jmacd Nov 8, 2019
cd73568
Move simple into subpackage
Nov 8, 2019
8fa9c70
Merge pull request #6 from lightstep/jmacd/move_simple
jmacd Nov 8, 2019
2afca79
Restore the simple test
Nov 8, 2019
08dc129
Merge pull request #7 from lightstep/jmacd/typos_missing_files
jmacd Nov 8, 2019
7ec92c7
Add simple/doc.go
Nov 8, 2019
58f77e4
Merge pull request #8 from lightstep/jmacd/more_docs
jmacd Nov 8, 2019
4b69751
Rephrase and fix typos
Nov 9, 2019
9305cbe
Merge pull request #9 from lightstep/jmacd/more_typos
jmacd Nov 9, 2019
4062031
Add a benchmark
Nov 9, 2019
ba028d8
Add benchmark notes
Nov 9, 2019
f865a35
Merge pull request #10 from lightstep/jmacd/benchmark
jmacd Nov 9, 2019
db2f575
Check for NaN weight
Nov 13, 2019
5cd2650
Return an error, don't panic
Nov 14, 2019
b92f087
Inline the large-weight heap to avoid interface conversions
Nov 15, 2019
b00b2fa
Add a test
Nov 15, 2019
cd12c03
Merge upstream
Nov 15, 2019
358db24
Merge pull request #12 from lightstep/jmacd/nan_check
jmacd Nov 15, 2019
b0dd5c7
Merge upstream
Nov 15, 2019
f53b1a8
Move heap code to internal for better testing
Nov 16, 2019
879f6b8
Merge pull request #11 from lightstep/jmacd/inline_heap
jmacd Nov 16, 2019
4b2b9c3
Add a Reset method
Nov 23, 2019
d2bfbc8
Return the ejected sample from Add
Nov 23, 2019
be3c37a
Typo
Nov 23, 2019
fcf92a6
Merge pull request #13 from lightstep/jmacd/optimize
jmacd Nov 23, 2019
30965f6
Pre-allocate main buffers
Nov 25, 2019
3a82c69
Merge pull request #14 from lightstep/jmacd/pre_alloc
jmacd Nov 25, 2019
8de72e7
Check for infinity, it creates trouble (#15)
jmacd Jul 13, 2021
69a5c9f
Use Apache Software Licence v2 (#17)
jmacd Jul 13, 2021
cc90130
Remove a test-only method (#16)
jmacd Jul 14, 2021
ef41899
Make this package generic
jmacd Nov 20, 2023
4cad612
remove this
jmacd Nov 20, 2023
b627cc4
test copyfrom; go work sync
jmacd Nov 23, 2023
8f3da1f
add github dir
jmacd Nov 27, 2023
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20 changes: 20 additions & 0 deletions .github/ISSUE_TEMPLATE/bug_report.md
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---
name: Bug report
about: Create a report to help us improve
title: ''
labels: ''
assignees: ''

---

**Describe the bug**
A clear and concise description of what the bug is.

**To Reproduce**
Steps to reproduce the behavior. Include code samples here when possible.

**Expected behavior**
A clear and concise description of what you expected to happen.

**Additional context**
Add any other context about the problem here.
20 changes: 20 additions & 0 deletions .github/ISSUE_TEMPLATE/feature_request.md
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---
name: Feature request
about: Suggest an idea for this project
title: ''
labels: ''
assignees: ''

---

**Is your feature request related to a problem? Please describe.**
A clear and concise description of what the problem is. Ex. I'm always frustrated when [...]

**Describe the solution you'd like**
A clear and concise description of what you want to happen.

**Describe alternatives you've considered**
A clear and concise description of any alternative solutions or features you've considered.

**Additional context**
Add any other context or screenshots about the feature request here.
9 changes: 9 additions & 0 deletions .github/pull_request_template.md
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**Description:** <Describe what has changed.
Ex. Fixing a bug - Describe the bug and how this fixes the issue.
Ex. Adding a feature - Explain what this achieves.>

**Link to tracking Issue:** <Issue number if applicable>

**Testing:** < Describe what testing was performed and which tests were added.>

**Documentation:** < Describe the documentation added.>
32 changes: 32 additions & 0 deletions .github/workflows/build.yml
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name: build

on:
push:
branches: [ main ]
pull_request:
branches: [ main ]

jobs:

build:
strategy:
matrix:
go-version: [1.21.3]
name: Build
runs-on: ubuntu-latest
steps:

- name: Set up Go 1.x
uses: actions/setup-go@v2
with:
go-version: ${{ matrix.go-version }}
id: go

- name: Check out code into the Go module directory
uses: actions/checkout@v2

- name: Build
run: make ci

- name: Code coverage
run: bash <(curl -s https://codecov.io/bash)
38 changes: 38 additions & 0 deletions .github/workflows/build.yml~
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name: build

on:
push:
branches: [ main ]
pull_request:
branches: [ main ]

jobs:

build:
strategy:
matrix:
go-version: [1.21.3]
name: Build
runs-on: ubuntu-latest
steps:

- name: Set up Go 1.x
uses: actions/setup-go@v2
with:
go-version: ${{ matrix.go-version }}
id: go

- name: Check out code into the Go module directory
uses: actions/checkout@v2

- name: Build
run: make ci

- name: Code coverage
run: bash <(curl -s https://codecov.io/bash)

- name: Send telemetry to Lightstep
uses: codeboten/github-action-to-otlp@v1
with:
endpoint: "ingest.lightstep.com:443"
headers: "lightstep-access-token=${{ secrets.ACCESS_TOKEN }}"
3 changes: 3 additions & 0 deletions .gitignore
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go.work
go.work.sum

201 changes: 201 additions & 0 deletions LICENSE
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57 changes: 56 additions & 1 deletion README.md
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[![Docs](https://godoc.org/github.com/lightstep/varopt?status.svg)](https://godoc.org/github.com/lightstep/varopt)

# VarOpt Sampling Algorithm

This is an implementation of VarOpt, an unbiased weighted sampling
algorithm described in the paper [Stream sampling for variance-optimal
estimation of subset sums](https://arxiv.org/pdf/0803.0473.pdf).
estimation of subset sums](https://arxiv.org/pdf/0803.0473.pdf) (2008)
by Edith Cohen, Nick Duffield, Haim Kaplan, Carsten Lund, and Mikkel
Thorup.

VarOpt is a reservoir-type sampler that maintains a fixed-size sample
and provides a mechanism for merging unequal-weight samples.

This repository also includes a simple reservoir sampling algorithm,
often useful in conjunction with weighed reservoir sampling, that
implements Algorithm R from [Random sampling with a
reservoir](https://en.wikipedia.org/wiki/Reservoir_sampling#Algorithm_R)
(1985) by Jeffrey Vitter.

## Usage: Natural Weights

A typical use of VarOpt sampling is to estimate network flows using
sample packets. In this use-case, the weight applied to each sample
is the size of the packet. Because VarOpt computes an unbiased
sample, sample data points can be summarized along secondary
dimensions. For example, we can select a subset of sampled packets
according to a secondary attribute, sum the sample weights, and the
result is expected to equal the size of packets corresponding to the
secondary attribute from the original population.

See [weighted_test.go](https://github.com/lightstep/varopt/blob/master/weighted_test.go) for an example.

## Usage: Inverse-probability Weights

Another use for VarOpt sampling uses inverse-probability weights to
estimate frequencies while simultaneously controlling sample
diversity. Suppose a sequence of observations can be naturally
categorized into N different buckets. The goal in this case is to
compute a sample where each bucket is well represented, while
maintaining frequency estimates.

In this use-case, the weight assigned to each observation is the
inverse probability of the bucket it belongs to. The result of
weighted sampling with inverse-probability weights is a uniform
expectated value; in this example we expect an equal number of
observations falling into each bucket. Each observation represents a
frequency of its sample weight (computed by VarOpt) divided by its
original weight (the inverse-probability).

See [frequency_test.go](https://github.com/lightstep/varopt/blob/master/frequency_test.go) for an example.

## Usage: Merging Samples

VarOpt supports merging independently collected samples one
observation at a time. This is useful for building distributed
sampling schemes. In this use-case, each node in a distributed system
computes a weighted sample. To combine samples, simply input all the
observations and their corresponding weights into a new VarOpt sample.
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