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109 lines
3.3 KiB
Markdown
109 lines
3.3 KiB
Markdown
GoLearn
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=======
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<img src="http://talks.golang.org/2013/advconc/gopherhat.jpg" width=125><br>
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[](https://godoc.org/github.com/sjwhitworth/golearn)
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[](https://travis-ci.org/sjwhitworth/golearn)<br>
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[](https://codecov.io/gh/sjwhitworth/golearn)
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[](https://www.gittip.com/sjwhitworth/)
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GoLearn is a 'batteries included' machine learning library for Go. **Simplicity**, paired with customisability, is the goal.
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We are in active development, and would love comments from users out in the wild. Drop us a line on Twitter.
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twitter: [@golearn_ml](http://www.twitter.com/golearn_ml)
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Install
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=======
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See [here](https://github.com/sjwhitworth/golearn/wiki/Installation) for installation instructions.
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Getting Started
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=======
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Data are loaded in as Instances. You can then perform matrix like operations on them, and pass them to estimators.
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GoLearn implements the scikit-learn interface of Fit/Predict, so you can easily swap out estimators for trial and error.
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GoLearn also includes helper functions for data, like cross validation, and train and test splitting.
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```go
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package main
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import (
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"fmt"
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"github.com/sjwhitworth/golearn/base"
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"github.com/sjwhitworth/golearn/evaluation"
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"github.com/sjwhitworth/golearn/knn"
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)
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func main() {
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// Load in a dataset, with headers. Header attributes will be stored.
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// Think of instances as a Data Frame structure in R or Pandas.
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// You can also create instances from scratch.
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rawData, err := base.ParseCSVToInstances("datasets/iris.csv", false)
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if err != nil {
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panic(err)
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}
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// Print a pleasant summary of your data.
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fmt.Println(rawData)
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//Initialises a new KNN classifier
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cls := knn.NewKnnClassifier("euclidean", "linear", 2)
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//Do a training-test split
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trainData, testData := base.InstancesTrainTestSplit(rawData, 0.50)
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cls.Fit(trainData)
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//Calculates the Euclidean distance and returns the most popular label
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predictions, err := cls.Predict(testData)
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if err != nil {
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panic(err)
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}
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// Prints precision/recall metrics
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confusionMat, err := evaluation.GetConfusionMatrix(testData, predictions)
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if err != nil {
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panic(fmt.Sprintf("Unable to get confusion matrix: %s", err.Error()))
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}
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fmt.Println(evaluation.GetSummary(confusionMat))
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}
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```
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```
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Iris-virginica 28 2 56 0.9333 0.9333 0.9333
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Iris-setosa 29 0 59 1.0000 1.0000 1.0000
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Iris-versicolor 27 2 57 0.9310 0.9310 0.9310
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Overall accuracy: 0.9545
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```
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Examples
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========
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GoLearn comes with practical examples. Dive in and see what is going on.
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```bash
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cd $GOPATH/src/github.com/sjwhitworth/golearn/examples/knnclassifier
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go run knnclassifier_iris.go
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```
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```bash
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cd $GOPATH/src/github.com/sjwhitworth/golearn/examples/instances
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go run instances.go
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```
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```bash
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cd $GOPATH/src/github.com/sjwhitworth/golearn/examples/trees
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go run trees.go
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```
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Docs
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====
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* [English](https://github.com/sjwhitworth/golearn/wiki)
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* [中文文档(简体)](doc/zh_CN/Home.md)
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* [中文文档(繁体)](doc/zh_TW/Home.md)
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Join the team
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=============
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Please send me a mail at stephenjameswhitworth@gmail.com
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