NeuralNetwork/neuralnetwork/teach/textdatareader.go

/*
 * MIT License
 *
 * Copyright (c) 2019 Alexey Edelev <semlanik@gmail.com>
 *
 * This file is part of NeuralNetwork project https://git.semlanik.org/semlanik/NeuralNetwork
 *
 * 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.
 */

package teach

import (
	"bufio"
	"fmt"
	"log"
	"os"
	"strconv"
	"strings"

	mat "gonum.org/v1/gonum/mat"
)

type TextDataReader struct {
	dataSet []*mat.Dense
	result  []*mat.Dense
	index   int
}

func NewTextDataReader(filename string) *TextDataReader {
	r := &TextDataReader{
		index: 0,
	}
	r.readData(filename)

	return r
}
func (r *TextDataReader) readData(filename string) {
	inputFile, err := os.Open(filename)
	if err != nil {
		log.Fatal(err)
	}

	defer inputFile.Close()

	scanner := bufio.NewScanner(inputFile)
	scanner.Split(bufio.ScanLines)

	max := []float64{0.0, 0.0, 0.0, 0.0}
	for scanner.Scan() {
		dataLine := scanner.Text()
		data := strings.Split(dataLine, ",")

		if len(data) < 5 {
			fmt.Printf("Garbage record: %s\n", dataLine)
			continue
		}

		var dataRaw []float64
		for i := 0; i < 4; i++ {
			val, err := strconv.ParseFloat(data[i], 64)
			if err != nil {
				break
			}
			dataRaw = append(dataRaw, val)

			if max[i] < val {
				max[i] = val
			}
		}

		if len(dataRaw) < 4 {
			fmt.Printf("Garbage record: %s\n", dataLine)
			continue
		}
		r.dataSet = append(r.dataSet, mat.NewDense(4, 1, dataRaw))

		switch data[4] {
		case "Iris-setosa":
			r.result = append(r.result, mat.NewDense(3, 1, []float64{1.0, 0.0, 0.0}))
		case "Iris-versicolor":
			r.result = append(r.result, mat.NewDense(3, 1, []float64{0.0, 1.0, 0.0}))
		case "Iris-virginica":
			r.result = append(r.result, mat.NewDense(3, 1, []float64{0.0, 0.0, 1.0}))
		}
	}

	//normalize
	for i := 0; i < len(r.dataSet); i++ {
		r.dataSet[i].Apply(func(r, _ int, val float64) float64 {
			return val / max[r]
		}, r.dataSet[i])
	}
}

func (r *TextDataReader) GetData() *mat.Dense {
	return r.dataSet[r.index]
}

func (r *TextDataReader) GetExpect() *mat.Dense {
	return r.result[r.index]
}

func (r *TextDataReader) Next() bool {
	r.index++
	if r.index >= len(r.result) {
		r.index = 0
		return false
	}

	return true
}

func (r *TextDataReader) Reset() {
	r.index = 0
}

func (r *TextDataReader) Index() int {
	return r.index
}