데이터 마이닝 및 분석 개발을 위해 Go 언어를 사용하는 방법

package main

import (
    "fmt"
    "io/ioutil"
    "net/http"
)

func main() {
    resp, err := http.Get("http://api.example.com/data")
    if err != nil {
        fmt.Println("获取数据失败: ", err)
        return
    }

    defer resp.Body.Close()
    body, err := ioutil.ReadAll(resp.Body)
    if err != nil {
        fmt.Println("读取响应失败: ", err)
        return
    }

    fmt.Println(string(body))
}

package main

import (
    "encoding/json"
    "fmt"
)

type Data struct {
    Name  string  `json:"name"`
    Age   int     `json:"age"`
    Email string  `json:"email"`
    Score float64 `json:"score"`
}

func main() {
    jsonData := `
        {
            "name": "Alice",
            "age": 25,
            "email": "alice@example.com",
            "score": 88.5
        }
    `

    var data Data
    err := json.Unmarshal([]byte(jsonData), &data)
    if err != nil {
        fmt.Println("解析JSON失败: ", err)
        return
    }

    fmt.Println(data)
}

package main

import (
    "fmt"
    "gonum.org/v1/gonum/mat"
    "gonum.org/v1/gonum/stat/regression"
)

func main() {
    // 构造特征矩阵和目标向量
    features := mat.NewDense(4, 2, []float64{
        1, 1,
        1, 2,
        1, 3,
        1, 4,
    })
    targets := mat.NewVecDense(4, []float64{3, 4, 5, 6})

    // 构造线性回归模型
    model := new(regression.Linear)
    model.Fit(features, targets)

    // 预测新数据
    newData := mat.NewVecDense(2, []float64{1, 5})
    prediction, _ := model.Predict(newData)

    fmt.Println(prediction)
}