https://github.com/sajari/regression

Multivariable regression library in Go
https://github.com/sajari/regression

go linear-regression regression

Last synced: 5 days ago
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Multivariable regression library in Go

• Host: GitHub
• URL: https://github.com/sajari/regression
• Owner: sajari
• License: mit
• Created: 2013-07-10T00:28:14.000Z (over 11 years ago)
• Default Branch: master
• Last Pushed: 2024-04-23T19:49:24.000Z (8 months ago)
• Last Synced: 2024-11-20T07:19:02.173Z (23 days ago)
• Topics: go, linear-regression, regression
• Language: Go
• Homepage:
• Size: 56.6 KB
• Stars: 401
• Watchers: 17
• Forks: 68
• Open Issues: 8
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Awesome Lists containing this project

• awesome-golang-ai - regression

README

        
regression

=======

[![GoDoc](https://godoc.org/github.com/sajari/regression?status.svg)](https://godoc.org/github.com/sajari/regression)

[![Go Report Card](https://goreportcard.com/badge/sajari/regression)](https://goreportcard.com/report/sajari/regression)

[![Build Status](https://travis-ci.org/sajari/regression.svg?branch=master)](https://travis-ci.org/sajari/regression)

[![License][license-image]][license-url]

[license-image]: http://img.shields.io/badge/license-MIT-green.svg?style=flat-square

[license-url]: LICENSE.txt

Multivariable Linear Regression in Go (golang)

installation

------------

    $ go get github.com/sajari/regression

Supports Go 1.8+

example usage

-------------

Import the package, create a regression and add data to it. You can use as many variables as you like, in the below example there are 3 variables for each observation.

```go

package main

import (

	"fmt"

	"github.com/sajari/regression"

)

func main() {

	r := new(regression.Regression)

	r.SetObserved("Murders per annum per 1,000,000 inhabitants")

	r.SetVar(0, "Inhabitants")

	r.SetVar(1, "Percent with incomes below $5000")

	r.SetVar(2, "Percent unemployed")

	r.Train(

		regression.DataPoint(11.2, []float64{587000, 16.5, 6.2}),

		regression.DataPoint(13.4, []float64{643000, 20.5, 6.4}),

		regression.DataPoint(40.7, []float64{635000, 26.3, 9.3}),

		regression.DataPoint(5.3, []float64{692000, 16.5, 5.3}),

		regression.DataPoint(24.8, []float64{1248000, 19.2, 7.3}),

		regression.DataPoint(12.7, []float64{643000, 16.5, 5.9}),

		regression.DataPoint(20.9, []float64{1964000, 20.2, 6.4}),

		regression.DataPoint(35.7, []float64{1531000, 21.3, 7.6}),

		regression.DataPoint(8.7, []float64{713000, 17.2, 4.9}),

		regression.DataPoint(9.6, []float64{749000, 14.3, 6.4}),

		regression.DataPoint(14.5, []float64{7895000, 18.1, 6}),

		regression.DataPoint(26.9, []float64{762000, 23.1, 7.4}),

		regression.DataPoint(15.7, []float64{2793000, 19.1, 5.8}),

		regression.DataPoint(36.2, []float64{741000, 24.7, 8.6}),

		regression.DataPoint(18.1, []float64{625000, 18.6, 6.5}),

		regression.DataPoint(28.9, []float64{854000, 24.9, 8.3}),

		regression.DataPoint(14.9, []float64{716000, 17.9, 6.7}),

		regression.DataPoint(25.8, []float64{921000, 22.4, 8.6}),

		regression.DataPoint(21.7, []float64{595000, 20.2, 8.4}),

		regression.DataPoint(25.7, []float64{3353000, 16.9, 6.7}),

	)

	r.Run()

	fmt.Printf("Regression formula:\n%v\n", r.Formula)

	fmt.Printf("Regression:\n%s\n", r)

}

```

Note: You can also add data points one by one.

Once calculated you can print the data, look at the R^2, Variance, residuals, etc. You can also access the coefficients directly to use elsewhere, e.g.

```go

// Get the coefficient for the "Inhabitants" variable 0:

c := r.Coeff(0)

```

You can also use the model to predict new data points

```go

prediction, err := r.Predict([]float64{587000, 16.5, 6.2})

```

Feature crosses are supported so your model can capture fixed non-linear relationships

```go

r.Train(

  regression.DataPoint(11.2, []float64{587000, 16.5, 6.2}),

)

//Add a new feature which is the first variable (index 0) to the power of 2

r.AddCross(PowCross(0, 2))

r.Run()

```