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.
go get github.com/sjwhitworth/golearn cd src/github.com/sjwhitworth/golearn go get ./...
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.
// 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. data, err := base.ParseCSVToInstances("datasets/iris_headers.csv", true) // Print a pleasant summary of your data. fmt.Println(data) // Split your dataframe into a training set, and a test set, with an 80/20 proportion. trainTest := base.InstancesTrainTestSplit(rawData, 0.8) trainData := trainTest testData := trainTest // Instantiate a new KNN classifier. Euclidean distance, with 2 neighbours. cls := knn.NewKnnClassifier("euclidean", 2) // Fit it on your training data. cls.Fit(trainData) // Get your predictions against test instances. predictions := cls.Predict(testData) // Print a confusion matrix with precision and recall metrics. confusionMat := evaluation.GetConfusionMatrix(testData, predictions) 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
GoLearn comes with practical examples. Dive in and see what is going on.
cd examples/ go run knnclassifier_iris.go go run instances.go
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Please send me a mail at stephen dot whitworth at hailocab dot com.
Package base provides base interfaces for GoLearn objects to implement.
|Package base provides base interfaces for GoLearn objects to implement.|
Package KNN implements a K Nearest Neighbors object, capable of both classification and regression.
|Package KNN implements a K Nearest Neighbors object, capable of both classification and regression.|
Package pairwise implements utilities to evaluate pairwise distances or inner product (via kernel).
|Package pairwise implements utilities to evaluate pairwise distances or inner product (via kernel).|
Package optimisation provides a number of optimisation functions.
|Package optimisation provides a number of optimisation functions.|
Package utilities implements a host of helpful miscellaneous functions to the library.
|Package utilities implements a host of helpful miscellaneous functions to the library.|