mutiple_linear_regration.go

package main

import (
	"encoding/csv"
	"fmt"
	"github.com/sajari/regression"
	"log"
	"math"
	"os"
	"strconv"
)

func main() {
	// Open the training dataset file.
	f, err := os.Open("training.csv")
	if err != nil {
		log.Fatal(err)
	}
	defer f.Close()

	// Create a new CSV reader reading from the opened file.
	reader := csv.NewReader(f)

	// Read in all of the CSV records
	reader.FieldsPerRecord = 4
	trainingData, err := reader.ReadAll()
	if err != nil {
		log.Fatal(err)
	}

	// In this case we are going to try and model our Sales
	// by the TV and Radio features plus an intercept.
	var r regression.Regression
	r.SetObserved("Sales")
	r.SetVar(0, "TV")
	r.SetVar(1, "Radio")

	// Loop over the CSV records adding the training data.
	for i, record := range trainingData {

		// Skip the header.
		if i == 0 {
			continue
		}

		// Parse the Sales.
		yVal, err := strconv.ParseFloat(record[3], 64)
		if err != nil {
			log.Fatal(err)
		}

		// Parse the TV value.
		tvVal, err := strconv.ParseFloat(record[0], 64)
		if err != nil {
			log.Fatal(err)
		}

		// Parse the Radio value.
		radioVal, err := strconv.ParseFloat(record[1], 64)
		if err != nil {
			log.Fatal(err)
		}

		// Add these points to the regression value.
		r.Train(regression.DataPoint(yVal, []float64{tvVal, radioVal}))
	}

	// Train/fit the regression model.
	r.Run()

	// Output the trained model parameters.
	fmt.Printf("\nRegression Formula:\n%v\n\n", r.Formula)

	// Open the test dataset file.
	f, err = os.Open("test.csv")
	if err != nil {
		log.Fatal(err)
	}
	defer f.Close()

	// Create a CSV reader reading from the opened file.
	reader = csv.NewReader(f)

	// Read in all of the CSV records
	reader.FieldsPerRecord = 4
	testData, err := reader.ReadAll()
	if err != nil {
		log.Fatal(err)
	}

	// Loop over the test data predicting y and evaluating the prediction
	// with the mean absolute error.
	var mAE float64
	for i, record := range testData {

		// Skip the header.
		if i == 0 {
			continue
		}

		// Parse the Sales.
		yObserved, err := strconv.ParseFloat(record[3], 64)
		if err != nil {
			log.Fatal(err)
		}

		// Parse the TV value.
		tvVal, err := strconv.ParseFloat(record[0], 64)
		if err != nil {
			log.Fatal(err)
		}

		// Parse the Radio value.
		radioVal, err := strconv.ParseFloat(record[1], 64)
		if err != nil {
			log.Fatal(err)
		}

		// Predict y with our trained model.
		yPredicted, err := r.Predict([]float64{tvVal, radioVal})

		// Add the to the mean absolute error.
		mAE += math.Abs(yObserved-yPredicted) / float64(len(testData))
	}

	// Output the MAE to standard out.
	fmt.Printf("MAE = %0.2f\n\n", mAE)
}