README
¶
GoLearn
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
Install
See here 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.
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", false)
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.
cd $GOPATH/src/github.com/sjwhitworth/golearn/examples/knnclassifier
go run knnclassifier_iris.go
cd $GOPATH/src/github.com/sjwhitworth/golearn/examples/instances
go run instances.go
cd $GOPATH/src/github.com/sjwhitworth/golearn/examples/trees
go run trees.go
Docs
Join the team
Please send me a mail at stephenjameswhitworth@gmail.com
Documentation
Source Files
Directories
¶
| Path | Synopsis |
|---|---|
|
Package base provides base interfaces for GoLearn objects to implement.
|
Package base provides base interfaces for GoLearn objects to implement. |
|
This package implements clustering algorithms
|
This package implements clustering algorithms |
|
examples
|
|
|
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 linear_models implements linear and logistic regression models.
|
Package linear_models implements linear and logistic regression models. |
|
metrics
|
|
|
pairwise
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 neural contains Neural Network functions.
|
Package neural contains Neural Network functions. |
|
Package optimisation provides a number of optimisation functions.
|
Package optimisation provides a number of optimisation functions. |
|
Implementation of Principal Component Analysis(PCA) with SVD
|
Implementation of Principal Component Analysis(PCA) with SVD |
|
Package utilities implements a host of helpful miscellaneous functions to the library.
|
Package utilities implements a host of helpful miscellaneous functions to the library. |