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https://github.com/sajari/regression
Multivariable regression library in Go
https://github.com/sajari/regression
go linear-regression regression
Last synced: 2 days ago
JSON representation
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 (7 months ago)
- Last Synced: 2024-10-28T08:41:43.529Z (11 days ago)
- Topics: go, linear-regression, regression
- Language: Go
- Homepage:
- Size: 56.6 KB
- Stars: 400
- 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
=======
[](https://godoc.org/github.com/sajari/regression)
[](https://goreportcard.com/report/sajari/regression)
[](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()
```