Merge pull request #1 from anyappinc/renovate-entire-project

Renovate the entire project

Author: usk83

.travis.yml

@@ -1,5 +1,31 @@
 The MIT License (MIT)
 
+Copyright (c) 2021 株式会社エニーアップ
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in
+all copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
+THE SOFTWARE.
+
+The original design and implementation are distributed by Sajari Pty Ltd
+under the same license, available at https://github.com/sajari/regression
+The following is the original copyright.
+
+The MIT License (MIT)
+
 Copyright (c) 2014 Sajari Pty Ltd
 
 Permission is hereby granted, free of charge, to any person obtaining a copy
@@ -18,4 +44,4 @@ FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
 AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
 LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
 OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
-THE SOFTWARE.
+THE SOFTWARE.

LICENSE

@@ -1,21 +1,19 @@
 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
+[license-url]: LICENSE
 
+Goで重回帰分析するライブラリ  
 Multivariable Linear Regression in Go (golang)
 
+*NOTE: This project is originally based on https://github.com/sajari/regression*
+
 installation
 ------------
 
-    $ go get github.com/sajari/regression
-
-Supports Go 1.8+
+    $ go get github.com/anyappinc/regression
 
 example usage
 -------------
@@ -28,68 +26,57 @@ package main
 import (
 	"fmt"
 
-	"github.com/sajari/regression"
+	"github.com/anyappinc/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 := regression.NewRegression()
+	r.SetObjectiveVariableLabel("Murders per annum per 1,000,000 inhabitants")
+	r.SetExplanatoryVariableLabel(0, "Inhabitants")
+	r.SetExplanatoryVariableLabel(1, "Percent with incomes below $5000")
+	r.SetExplanatoryVariableLabel(2, "Percent unemployed")
+	r.AddObservations(
+		regression.NewObservation(11.2, []float64{587000, 16.5, 6.2}),
+		regression.NewObservation(13.4, []float64{643000, 20.5, 6.4}),
+		regression.NewObservation(40.7, []float64{635000, 26.3, 9.3}),
+		regression.NewObservation(5.3, []float64{692000, 16.5, 5.3}),
+		regression.NewObservation(24.8, []float64{1248000, 19.2, 7.3}),
+		regression.NewObservation(12.7, []float64{643000, 16.5, 5.9}),
+		regression.NewObservation(20.9, []float64{1964000, 20.2, 6.4}),
+		regression.NewObservation(35.7, []float64{1531000, 21.3, 7.6}),
+		regression.NewObservation(8.7, []float64{713000, 17.2, 4.9}),
+		regression.NewObservation(9.6, []float64{749000, 14.3, 6.4}),
+		regression.NewObservation(14.5, []float64{7895000, 18.1, 6}),
+		regression.NewObservation(26.9, []float64{762000, 23.1, 7.4}),
+		regression.NewObservation(15.7, []float64{2793000, 19.1, 5.8}),
+		regression.NewObservation(36.2, []float64{741000, 24.7, 8.6}),
+		regression.NewObservation(18.1, []float64{625000, 18.6, 6.5}),
+		regression.NewObservation(28.9, []float64{854000, 24.9, 8.3}),
+		regression.NewObservation(14.9, []float64{716000, 17.9, 6.7}),
+		regression.NewObservation(25.8, []float64{921000, 22.4, 8.6}),
+		regression.NewObservation(21.7, []float64{595000, 20.2, 8.4}),
+		regression.NewObservation(25.7, []float64{3353000, 16.9, 6.7}),
 	)
-	r.Run()
+	model, err := r.Run()
+	if err != nil {
+		log.Fatalln(err)
+	}
 
-	fmt.Printf("Regression formula:\n%v\n", r.Formula)
-	fmt.Printf("Regression:\n%s\n", r)
+	fmt.Printf("Regression formula:\n%v\n", model.FormulaString())
 }
 ```
 
-Note: You can also add data points one by one.
+Note: You can also add observations 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.
+Once calculated, you can look at the R^2, Standard Error, ANOVA, Coefficients, etc. e.g.
 
 ```go
 // Get the coefficient for the "Inhabitants" variable 0:
-c := r.Coeff(0)
+c := model.ExplanatoryVars[0].Coeff
 ```
 
-You can also use the model to predict new data points
+You can also use the model to predict new observation
 
 ```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()
-
+prediction, err := model.Predict([]float64{587000, 16.5, 6.2})
 ```