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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
| Date | Stars |
|---|---|
| 2026-07-31 | 9439 |
| 2026-08-01 | 9439 |
| 2026-08-04 | 9440 |
| 2026-08-06 | 9439 |
Today
-1 stars today
This week
— stars this week
This month
— stars this month
Momentum
0.0
growth rate 0.00%/day
GoLearn
=======
<img src="http://talks.golang.org/2013/advconc/gopherhat.jpg" width=125><br>
[](https://godoc.org/github.com/sjwhitworth/golearn)
[](https://travis-ci.org/sjwhitworth/golearn)<br>
[](https://codecov.io/gh/sjwhitworth/golearn)
[](https://www.gittip.com/sjwhitworth/)
GoLearn is a 'batteries included' machine learning library for Go. **Simplicity**, paired with customisability, is the goal.
We are in active development, and would love comments from users out in the wild. Drop us a line on Twitter.
twitter: [@golearn_ml](http://www.twitter.com/golearn_ml)
Install
=======
See [here](https://github.com/sjwhitworth/golearn/wiki/Installation) for installation instructions.
Getting Started
=======
Data are loaded in as Instances. You can then perform matrix like operations on them, and pass them to estimators.
GoLearn implements the scikit-learn interface of Fit/Predict, so you can easily swap out estimators for trial and error.
GoLearn also includes helper functions for data, like cross validation, and train and test splitting.
```go
package main
import (
"fmt"
"github.com/sjwhitworth/golearn/base"
"github.com/sjwhitworth/golearn/evaluation"
"github.com/sjwhitworth/golearn/knn"
)
func main() {
// Load in a dataset, with headers. Header attributes will be stored.
// Think of instances as a Data Frame structure in R or Pandas.
// You can also create instances from scratch.
rawData, err := base.ParseCSVToInstances("datasets/iris.csv", true)
if err != nil {
panic(err)
}
// Print a pleasant summary of your data.
fmt.Println(rawData)
//Initialises a new KNN classifier
cls := knn.NewKnnClassifier("euclidean", "linear", 2)
//Do a training-test split
trainData, testData := base.InstancesTrainTestSplit(rawData, 0.50)
cls.Fit(trainData)
//Calculates the Euclidean distance and returns the most popular label
predictions, err := cls.Predict(testData)
if err != nil {
panic(err)
}
// Prints precision/recall metrics
confusionMat, err := evaluation.GetConfusionMatrix(testData, predictions)
if err != nil {
panic(fmt.Sprintf("Unable to get confusion matrix: %s", err.Error()))
}
fmt.Println(evaluation.GetSummary(confusionMat))
}
```
```
Iris-virginica 28 2 56 0.9333 0.9333 0.9333
Iris-setosa 29 0 59 1.0000 1.0000 1.0000
Iris-versicolor 27 2 57 0.9310 0.9310 0.9310
Overall accuracy: 0.9545
```
Examples
========
GoLearn comes with practical examples. Dive in and see what is going on.
```bash
cd $GOPATH/src/github.com/sjwhitworth/golearn/examples/knnclassifier
go run knnclassifier_iris.go
```
```bash
cd $GOPATH/src/github.com/sjwhitworth/golearn/examples/instances
go run instances.go
```
```bash
cd $GOPATH/src/github.com/sjwhitworth/golearn/examples/trees
go run trees.go
```
Docs
====
* [English](https://github.com/sjwhitworth/golearn/wiki)
* [中文文档(简体)](doc/zh_CN/Home.md)
* [中文文档(繁体)](doc/zh_TW/Home.md)
Join the team
=============
Please send me a mail at [email protected]
Excerpt of 3,319 characters
Read on GitHub
177
Stephen Whitworth · @monzo · United Kingdom
67
42
Yi-Hsien Chen · Taiwan
42
28
23
23
12
9
6
5
5
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4
Alexey Palazhchenko · Armenia
3
3
3
Pekka Enberg · Finland
2
2
2
Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:f13681d5dca0f985, llm:Repository description: "Machine Learning for Go" (language: Go). Known as a Go ML library providing algorithms, datasets, and utilities for ML in Go.
matched fp:f13681d5dca0f985, llm:Repository description: "Machine Learning for Go" (language: Go). Known as a Go ML library providing algorithms, datasets, and utilities for ML in Go.
matched fp:f13681d5dca0f985, llm:Repository description: "Machine Learning for Go" (language: Go). Known as a Go ML library providing algorithms, datasets, and utilities for ML in Go.