Golang技术在机器学习中遇到的挑战和解决方案

import (
    "fmt"
    "image"
    "image/jpeg"
    "log"
    "os"
    "time"

    "github.com/gonum/gonum/mat"
)

func main() {
    // 加载图像
    file, err := os.Open("image.jpg")
    if err != nil {
        log.Fatal(err)
    }
    defer file.Close()

    img, err := jpeg.Decode(file)
    if err != nil {
        log.Fatal(err)
    }

    // 转换为灰度图像
    bounds := img.Bounds()
    gray := image.NewGray(bounds)
    for y := bounds.Min.Y; y < bounds.Max.Y; y++ {
        for x := bounds.Min.X; x < bounds.Max.X; x++ {
            gray.Set(x, y, img.At(x, y))
        }
    }

    // 转换为矩阵
    data := make([]float64, bounds.Max.X*bounds.Max.Y)
    for y := bounds.Min.Y; y < bounds.Max.Y; y++ {
        for x := bounds.Min.X; x < bounds.Max.X; x++ {
            data[y*bounds.Max.X+x] = float64(gray.At(x, y).Y)
        }
    }
    dataMat := mat.NewDense(bounds.Max.Y, bounds.Max.X, data)

    // 训练模型
    model := LogisticRegression{}
    start := time.Now()
    model.Train(dataMat, labels)
    fmt.Printf("训练时间：%s", time.Since(start))

    // 评估模型
    start = time.Now()
    accuracy := model.Evaluate(dataMat, labels)
    fmt.Printf("评估时间：%s\n", time.Since(start))
    fmt.Printf("准确率：%.2f%%\n", accuracy*100)
}