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gateway-classification/src/classify/elmModel.go

@@ -0,0 +1,96 @@
+package classify
+
+import (
+	"errors"
+	"io/ioutil"
+	"strconv"
+	"strings"
+)
+
+type RealMatrix struct {
+	Rows int
+	Cols int
+	Data [][]float64
+}
+
+type Weight struct {
+	W  RealMatrix
+	BW RealMatrix
+}
+
+type ELMModel struct {
+	InputWeight     RealMatrix
+	BiasInputWeight RealMatrix
+	OutputWeight    RealMatrix
+	Separator       string
+}
+
+func NewELMModel(inputWeightFilePath, outputWeightFilePath string) (*ELMModel, error) {
+	var elmModel ELMModel
+
+	inputWeightFileBytes, err := ioutil.ReadFile(inputWeightFilePath)
+	if err != nil {
+		return nil, err
+	}
+
+	outputWeightFileBytes, err := ioutil.ReadFile(outputWeightFilePath)
+	if err != nil {
+		return nil, err
+	}
+
+	inputWeightFileLines := strings.Split(string(inputWeightFileBytes), "\n")
+	outputWeightFileLines := strings.Split(string(outputWeightFileBytes), "\n")
+
+	weight, err := convertListCsvTo2dArr(inputWeightFileLines, true)
+	if err != nil {
+		return nil, err
+	}
+	elmModel.InputWeight = weight.W
+	elmModel.BiasInputWeight = weight.BW
+
+	weight, err = convertListCsvTo2dArr(outputWeightFileLines, false)
+	if err != nil {
+		return nil, err
+	}
+	elmModel.OutputWeight = weight.W
+
+	return &elmModel, nil
+}
+
+func convertListCsvTo2dArr(input []string, useBias bool) (Weight, error) {
+	var weight Weight
+
+	listSize := len(input)
+	if listSize == 0 {
+		return weight, errors.New("empty input list")
+	}
+
+	csvElementSize := len(strings.Split(input[0], ","))
+	for _, line := range input {
+		if len(strings.Split(line, ",")) != csvElementSize {
+			return weight, errors.New("invalid CSV length")
+		}
+	}
+
+	weightData := make([][]float64, listSize)
+	biasWeightData := make([][]float64, listSize)
+
+	for i, line := range input {
+		splittedLine := strings.Split(line, ",")
+		temp := make([]float64, len(splittedLine))
+		for j, val := range splittedLine {
+			temp[j], _ = strconv.ParseFloat(strings.TrimSpace(val), 64)
+		}
+		if useBias {
+			weightData[i] = temp[:len(temp)-1]
+			biasWeightData[i] = []float64{temp[len(temp)-1]}
+		} else {
+			weightData[i] = temp
+		}
+	}
+
+	weight.W = RealMatrix{Rows: listSize, Cols: len(weightData[0]), Data: weightData}
+	weight.BW = RealMatrix{Rows: listSize, Cols: 1, Data: biasWeightData}
+
+	return weight, nil
+}