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golang-k-nn.go
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golang-k-nn.go
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package main
import (
"bytes"
"fmt"
"io/ioutil"
"strconv"
)
type LabelWithFeatures struct {
Label []byte
Features []float64
}
func NewLabelWithFeatures(parsedLine [][]byte) LabelWithFeatures {
label := parsedLine[0]
features := make([]float64, len(parsedLine)-1)
for i, feature := range parsedLine {
// skip label
if i == 0 {
continue
}
features[i-1] = byteSliceTofloat64(feature)
}
return LabelWithFeatures{label, features}
}
var newline = []byte("\n")
var comma = []byte(",")
func byteSliceTofloat64(b []byte) float64 {
x, _ := strconv.ParseFloat(string(b), 32)
return x
}
func parseCSVFile(filePath string) []LabelWithFeatures {
fileContent, _ := ioutil.ReadFile(filePath)
lines := bytes.Split(fileContent, newline)
numRows := len(lines)
labelsWithFeatures := make([]LabelWithFeatures, numRows-2)
for i, line := range lines {
// skip headers
if i == 0 || i == numRows-1 {
continue
}
labelsWithFeatures[i-1] = NewLabelWithFeatures(bytes.Split(line, comma))
}
return labelsWithFeatures
}
func squareDistance(features1, features2 []float64) (d float64) {
for i := 0; i < len(features1); i++ {
d += (features1[i] - features2[i]) * (features1[i] - features2[i])
}
return
}
var trainingSample = parseCSVFile("trainingsample.csv")
func classify(features []float64) (label []byte) {
label = trainingSample[0].Label
d := squareDistance(features, trainingSample[0].Features)
for _, row := range trainingSample {
dNew := squareDistance(features, row.Features)
if dNew < d {
label = row.Label
d = dNew
}
}
return
}
func main() {
validationSample := parseCSVFile("validationsample.csv")
totalCorrect := 0
for _, test := range validationSample {
if string(test.Label) == string(classify(test.Features)) {
totalCorrect++
}
}
fmt.Println(float64(totalCorrect) / float64(len(validationSample)))
}