实施梯度下降

时间：2019-11-13 08:30:52

标签： go machine-learning gradient-descent

我正在尝试在Go中实现梯度下降。我的目标是根据行驶里程来预测汽车的成本。 这是我的数据集：

km,price
240000,3650
139800,3800
150500,4400
185530,4450
176000,5250
114800,5350
166800,5800
89000,5990
144500,5999
84000,6200
82029,6390
63060,6390
74000,6600
97500,6800
67000,6800
76025,6900
48235,6900
93000,6990
60949,7490
65674,7555
54000,7990
68500,7990
22899,7990
61789,8290

我尝试了各种方法，例如规范化数据集，不规范化数据集，保留thetas不变，对thetas进行非规范化……但是我无法获得正确的结果。 我的数学一定不在某个地方，但我不知道在哪里。 我想要得到的结果应该是大约t0 = 8500，t1 = -0.02 我的实现如下：

package main

import (
    "encoding/csv"
    "fmt"
    "log"
    "math"
    "os"
    "strconv"
)

const (
    dataFile     = "data.csv"
    iterations   = 20000
    learningRate = 0.1
)

type dataSet [][]float64

var minKm, maxKm, minPrice, maxPrice float64

func (d dataSet) getExtremes(column int) (float64, float64) {

    min := math.Inf(1)
    max := math.Inf(-1)
    for _, row := range d {
        item := row[column]
        if item > max {
            max = item
        }
        if item < min {
            min = item
        }
    }

    return min, max
}

func normalizeItem(item, min, max float64) float64 {

    return (item - min) / (max - min)
}

func (d *dataSet) normalize() {

    minKm, maxKm = d.getExtremes(0)
    minPrice, maxPrice = d.getExtremes(1)
    for _, row := range *d {
        row[0], row[1] = normalizeItem(row[0], minKm, maxKm), normalizeItem(row[1], minPrice, maxPrice)
    }
}

func processEntry(entry []string) []float64 {

    if len(entry) != 2 {
        log.Fatalln("expected two fields")
    }
    km, err := strconv.ParseFloat(entry[0], 64)
    if err != nil {
        log.Fatalln(err)
    }
    price, err := strconv.ParseFloat(entry[1], 64)
    if err != nil {
        log.Fatalln(err)
    }
    return []float64{km, price}
}

func getData() dataSet {

    file, err := os.Open(dataFile)
    if err != nil {
        log.Fatalln(err)
    }
    reader := csv.NewReader(file)
    entries, err := reader.ReadAll()
    if err != nil {
        log.Fatalln(err)
    }
    entries = entries[1:]

    data := make(dataSet, len(entries))
    for k, entry := range entries {
        data[k] = processEntry(entry)
    }
    return data
}

func outputResult(theta0, theta1 float64) {
    file, err := os.OpenFile("weights.csv", os.O_WRONLY, 0644)
    if err != nil {
        log.Fatalln(err)
    }
    defer file.Close()
    file.Truncate(0)
    file.Seek(0, 0)
    file.WriteString(fmt.Sprintf("theta0,%.6f\ntheta1,%.6f\n", theta0, theta1))
}

func estimatePrice(theta0, theta1, mileage float64) float64 {

    return theta0 + theta1*mileage
}

func (d dataSet) computeThetas(theta0, theta1 float64) (float64, float64) {

    dataSize := float64(len(d))
    t0sum, t1sum := 0.0, 0.0
    for _, it := range d {
        mileage := it[0]
        price := it[1]
        err := estimatePrice(theta0, theta1, mileage) - price
        t0sum += err
        t1sum += err * mileage
    }

    return theta0 - (t0sum / dataSize * learningRate), theta1 - (t1sum / dataSize * learningRate)
}

func denormalize(theta, min, max float64) float64 {

    return theta*(max-min) + min
}

func main() {

    data := getData()
    data.normalize()
    theta0, theta1 := 0.0, 0.0
    for k := 0; k < iterations; k++ {
        theta0, theta1 = data.computeThetas(theta0, theta1)
    }
    theta0 = denormalize(theta0, minKm, maxKm)
    theta1 = denormalize(theta1, minPrice, maxPrice)
    outputResult(theta0, theta1)
}

要正确实施梯度下降，我应该解决什么？

1 个答案:

答案 0 :(得分：2)

Linear Regression非常简单：

// yi = alpha + beta*xi + ei
func linearRegression(x, y []float64) (float64, float64) {
    EX := expected(x)
    EY := expected(y)
    EXY := expectedXY(x, y)
    EXX := expectedXY(x, x)

    covariance := EXY - EX*EY
    variance := EXX - EX*EX
    beta := covariance / variance
    alpha := EY - beta*EX
    return alpha, beta
}

尝试here，输出：

8499.599649933218 -0.021448963591702314 396270.87871142407

代码：

package main

import (
    "encoding/csv"
    "fmt"
    "strconv"
    "strings"
)

func main() {
    x, y := readXY(`data.csv`)
    alpha, beta := linearRegression(x, y)
    fmt.Println(alpha, beta, -alpha/beta) // 8499.599649933218 -0.021448963591702314 396270.87871142407
}

// https://en.wikipedia.org/wiki/Ordinary_least_squares#Simple_linear_regression_model
// yi = alpha + beta*xi + ei
func linearRegression(x, y []float64) (float64, float64) {
    EX := expected(x)
    EY := expected(y)
    EXY := expectedXY(x, y)
    EXX := expectedXY(x, x)

    covariance := EXY - EX*EY
    variance := EXX - EX*EX
    beta := covariance / variance
    alpha := EY - beta*EX
    return alpha, beta
}

// E[X]
func expected(x []float64) float64 {
    sum := 0.0
    for _, v := range x {
        sum += v
    }
    return sum / float64(len(x))
}

// E[XY]
func expectedXY(x, y []float64) float64 {
    sum := 0.0
    for i, v := range x {
        sum += v * y[i]
    }
    return sum / float64(len(x))
}

func readXY(filename string) ([]float64, []float64) {
    // file, err := os.Open(filename)
    // if err != nil {
    //  panic(err)
    // }
    // defer file.Close()
    file := strings.NewReader(data)

    reader := csv.NewReader(file)
    records, err := reader.ReadAll()
    if err != nil {
        panic(err)
    }
    records = records[1:]
    size := len(records)
    x := make([]float64, size)
    y := make([]float64, size)
    for i, v := range records {
        val, err := strconv.ParseFloat(v[0], 64)
        if err != nil {
            panic(err)
        }
        x[i] = val
        val, err = strconv.ParseFloat(v[1], 64)
        if err != nil {
            panic(err)
        }
        y[i] = val
    }
    return x, y
}

var data = `km,price
240000,3650
139800,3800
150500,4400
185530,4450
176000,5250
114800,5350
166800,5800
89000,5990
144500,5999
84000,6200
82029,6390
63060,6390
74000,6600
97500,6800
67000,6800
76025,6900
48235,6900
93000,6990
60949,7490
65674,7555
54000,7990
68500,7990
22899,7990
61789,8290`

Gradient descent基于以下观察结果：如果定义了多变量函数F(x)并且在点a的附近是可微的，则F（x）最快下降从a朝F，a处-∇F(a)的负梯度方向移动，例如：

// F(x)
f := func(x float64) float64 {
    return alpha + beta*x // write your target function here
}

微分函数：

h := 0.000001
// Derivative function ∇F(x)
df := func(x float64) float64 {
    return (f(x+h) - f(x-h)) / (2 * h) // write your target function derivative here
}

搜索：

minimunAt := 1.0       // We start the search here
gamma := 0.01          // Step size multiplier
precision := 0.0000001 // Desired precision of result
max := 100000          // Maximum number of iterations
currentX := 0.0
step := 0.0
for i := 0; i < max; i++ {
    currentX = minimunAt
    minimunAt = currentX - gamma*df(currentX)
    step = minimunAt - currentX
    if math.Abs(step) <= precision {
        break
    }
}

fmt.Printf("Minimum at %.8f value: %v\n", minimunAt, f(minimunAt))
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