feat: regression
This commit is contained in:
@ -2,14 +2,19 @@ package controllers
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import (
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"errors"
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"fmt"
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"github.com/gin-gonic/gin"
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"github.com/sajari/regression"
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"gorm.io/gorm"
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"gorm.io/gorm/clause"
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"hr_receiver/config"
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"hr_receiver/models"
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"log"
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"math"
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"net/http"
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"sort"
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"strconv"
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"strings"
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)
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type StepTrainingController struct {
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@ -187,3 +192,615 @@ func (tc *StepTrainingController) GetTrainingRecordByTrainId(c *gin.Context) {
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"data": record,
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})
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}
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// 定义结构体
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type SpeedSegment struct {
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Duration float64
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Speed float64
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}
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// 实现线性回归算法
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func performLinearRegression(averages []map[float64]float64) models.RegressionResult {
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if len(averages) == 0 {
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return models.RegressionResult{
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Equation: "无数据",
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}
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}
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// 收集数据点
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var points []struct{ x, y float64 }
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for _, m := range averages {
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for x, y := range m {
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points = append(points, struct{ x, y float64 }{x, y})
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}
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}
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// 使用回归库计算
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r := new(regression.Regression)
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r.SetObserved("y")
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r.SetVar(0, "x")
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for _, p := range points {
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r.Train(regression.DataPoint(p.y, []float64{p.x}))
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}
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if err := r.Run(); err != nil {
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log.Printf("线性回归计算失败: %v", err)
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return models.RegressionResult{
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Equation: "计算失败",
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}
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}
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// 创建结果
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slope := r.Coeff(0)
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intercept := r.Coeff(1)
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r2 := r.R2
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return models.RegressionResult{
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RegressionType: models.LinearRegression,
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Slope: &slope,
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Intercept: &intercept,
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RSquared: &r2,
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Equation: r.Formula,
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}
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}
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// 实现对数和二次回归算法
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// 对数回归算法
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func performLogarithmicRegression(averages []map[float64]float64) models.RegressionResult {
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if len(averages) == 0 {
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return models.RegressionResult{
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Equation: "无数据",
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}
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}
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// 收集数据点
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r := new(regression.Regression)
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r.SetObserved("y")
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r.SetVar(0, "log(x+1)")
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for _, m := range averages {
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for speed, hr := range m {
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logSpeed := math.Log(speed + 1)
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r.Train(regression.DataPoint(hr, []float64{logSpeed}))
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}
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}
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if err := r.Run(); err != nil {
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log.Printf("对数回归计算失败: %v", err)
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return models.RegressionResult{
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Equation: "计算失败",
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}
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}
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// 创建结果
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logA := r.Coeff(1)
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logB := r.Coeff(0)
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r2 := r.R2
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return models.RegressionResult{
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RegressionType: models.LogarithmicRegression,
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LogA: &logA,
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LogB: &logB,
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RSquared: &r2,
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Equation: r.Formula,
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}
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}
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// 二次回归算法
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//func performQuadraticRegression(averages []map[float64]float64) models.RegressionResult {
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// if len(averages) == 0 {
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// return models.RegressionResult{
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// Equation: "无数据",
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// }
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// }
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//
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// // 收集数据点
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// r := new(regression.Regression)
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// r.SetObserved("y")
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// r.SetVar(0, "x")
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// r.SetVar(1, "x²")
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//
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// for _, m := range averages {
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// for speed, hr := range m {
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// speedSq := math.Pow(speed, 2)
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// r.Train(regression.DataPoint(hr, []float64{speed, speedSq}))
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// }
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// }
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//
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// if err := r.Run(); err != nil {
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// log.Printf("二次回归计算失败: %v", err)
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// return models.RegressionResult{
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// Equation: "计算失败",
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// }
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// }
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//
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// // 创建结果
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// a := r.Coeff(2)
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// b := r.Coeff(1)
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// c := r.Coeff(0)
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// r2 := r.R2
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// return models.RegressionResult{
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// RegressionType: models.QuadraticRegression,
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// QuadraticA: &a,
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// QuadraticB: &b,
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// QuadraticC: &c,
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// RSquared: &r2,
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// Equation: r.Formula,
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// }
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//}
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func performQuadraticRegression(averages []map[float64]float64) models.RegressionResult {
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if len(averages) == 0 {
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return models.RegressionResult{
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Equation: "无数据",
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}
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}
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// 步骤1：收集所有数据点（与Flutter一致）
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var xValues []float64
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var yValues []float64
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for _, m := range averages {
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for speed, hr := range m {
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xValues = append(xValues, speed)
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yValues = append(yValues, hr)
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}
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}
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n := float64(len(xValues))
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// 步骤2：计算各项和（完全匹配Flutter的计算）
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var sumX, sumY, sumX2, sumX3, sumX4, sumXY, sumX2Y float64
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for i := 0; i < len(xValues); i++ {
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x := xValues[i]
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y := yValues[i]
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x2 := x * x
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x3 := x2 * x
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x4 := x3 * x
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sumX += x
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sumY += y
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sumX2 += x2
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sumX3 += x3
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sumX4 += x4
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sumXY += x * y
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sumX2Y += x2 * y
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}
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// 步骤3：构建正规方程矩阵（与Flutter完全一致）
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matrix := [3][3]float64{
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{n, sumX, sumX2},
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{sumX, sumX2, sumX3},
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{sumX2, sumX3, sumX4},
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}
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vector := []float64{sumY, sumXY, sumX2Y}
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// 步骤4：计算矩阵行列式（复制Flutter的determinant3x3逻辑）
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det := matrix[0][0]*(matrix[1][1]*matrix[2][2]-matrix[1][2]*matrix[2][1]) -
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matrix[0][1]*(matrix[1][0]*matrix[2][2]-matrix[1][2]*matrix[2][0]) +
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matrix[0][2]*(matrix[1][0]*matrix[2][1]-matrix[1][1]*matrix[2][0])
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if det == 0 {
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return models.RegressionResult{
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Equation: "无法拟合",
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}
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}
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// 步骤5：克莱姆法则求解系数（顺序与Flutter一致）
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// 注意：最终系数顺序 a=二次项, b=一次项, c=常数项
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c := det3x3([3][3]float64{
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{vector[0], matrix[0][1], matrix[0][2]},
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{vector[1], matrix[1][1], matrix[1][2]},
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{vector[2], matrix[2][1], matrix[2][2]},
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}) / det
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b := det3x3([3][3]float64{
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{matrix[0][0], vector[0], matrix[0][2]},
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{matrix[1][0], vector[1], matrix[1][2]},
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{matrix[2][0], vector[2], matrix[2][2]},
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}) / det
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a := det3x3([3][3]float64{
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{matrix[0][0], matrix[0][1], vector[0]},
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{matrix[1][0], matrix[1][1], vector[1]},
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{matrix[2][0], matrix[2][1], vector[2]},
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}) / det
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// 步骤6：计算R平方（完全复制Flutter的计算逻辑）
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var ssRes, ssTot float64
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meanY := sumY / n
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for i := 0; i < len(xValues); i++ {
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x := xValues[i]
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y := yValues[i]
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yPred := a*x*x + b*x + c
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ssRes += math.Pow(y-yPred, 2)
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ssTot += math.Pow(y-meanY, 2)
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}
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rSquared := 0.0
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if ssTot != 0 {
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rSquared = 1 - ssRes/ssTot
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}
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// 步骤7：格式化公式字符串（与Flutter格式完全一致）
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equation := formatEquation(a, b, c, rSquared)
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return models.RegressionResult{
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RegressionType: models.QuadraticRegression,
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QuadraticA: &a,
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QuadraticB: &b,
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QuadraticC: &c,
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RSquared: &rSquared,
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Equation: equation,
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}
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}
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// 3x3行列式计算（与Flutter实现相同）
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func det3x3(m [3][3]float64) float64 {
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return m[0][0]*(m[1][1]*m[2][2]-m[1][2]*m[2][1]) -
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m[0][1]*(m[1][0]*m[2][2]-m[1][2]*m[2][0]) +
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m[0][2]*(m[1][0]*m[2][1]-m[1][1]*m[2][0])
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}
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// 公式格式化（完全匹配Flutter格式）
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func formatEquation(a, b, c, r2 float64) string {
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// 保留4位小数
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aStr := fmt.Sprintf("%.4f", a)
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bStr := fmt.Sprintf("%.4f", b)
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cStr := fmt.Sprintf("%.4f", c)
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r2Str := fmt.Sprintf("%.4f", r2)
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builder := strings.Builder{}
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builder.WriteString("y = ")
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// 处理二次项
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if a >= 0 {
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builder.WriteString(aStr + " x²")
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} else {
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builder.WriteString("-" + strings.TrimPrefix(aStr, "-") + " x²")
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}
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// 处理一次项
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if b >= 0 {
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builder.WriteString(" + " + bStr + " x")
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} else {
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builder.WriteString(" - " + strings.TrimPrefix(bStr, "-") + " x")
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}
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// 处理常数项
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if c >= 0 {
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builder.WriteString(" + " + cStr)
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} else {
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builder.WriteString(" - " + strings.TrimPrefix(cStr, "-"))
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}
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builder.WriteString(" (R² = " + r2Str + ")")
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return builder.String()
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}
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// 步频数据转换为速度段
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func convertStrideFrequencyToSegments(steps []models.StepStrideFreq) []SpeedSegment {
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if len(steps) == 0 {
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return []SpeedSegment{}
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}
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// 过滤零值并排序
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validSteps := make([]models.StepStrideFreq, 0, len(steps))
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for _, s := range steps {
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if s.Value > 0 {
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validSteps = append(validSteps, s)
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}
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}
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if len(validSteps) == 0 {
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return []SpeedSegment{}
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}
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// 按时间排序
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for i := 0; i < len(validSteps)-1; i++ {
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for j := i + 1; j < len(validSteps); j++ {
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if validSteps[i].Time > validSteps[j].Time {
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validSteps[i], validSteps[j] = validSteps[j], validSteps[i]
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}
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}
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}
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// 创建速度段
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segments := make([]SpeedSegment, 0)
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startTime := validSteps[0].Time
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currentValue := validSteps[0].Value
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for i := 1; i < len(validSteps); i++ {
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if validSteps[i].Value != currentValue {
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duration := float64(validSteps[i].Time-startTime) / 1000.0
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if duration > 0 {
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segments = append(segments, SpeedSegment{
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Duration: duration,
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Speed: float64(currentValue),
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})
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}
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startTime = validSteps[i].Time
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currentValue = validSteps[i].Value
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}
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}
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// 添加最后一个段
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if len(validSteps) > 0 {
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duration := float64(validSteps[len(validSteps)-1].Time-startTime) / 1000.0
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if duration > 0 {
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segments = append(segments, SpeedSegment{
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Duration: duration,
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Speed: float64(currentValue),
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})
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}
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}
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return segments
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}
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// 计算区段平均值
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func calculateSegmentAverages(heartRates []models.StepHeartRate, segments []SpeedSegment, errorThreshold int) []map[float64]float64 {
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currentTime := 0.0
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results := make([]map[float64]float64, 0)
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for _, seg := range segments {
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minRequired := 60 + (60 - float64(errorThreshold))
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// 跳过不满足条件的区段
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if seg.Duration < minRequired {
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currentTime += seg.Duration
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continue
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}
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// 计算时间窗口
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startSec := currentTime + 60
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endSec := currentTime
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if seg.Duration >= 120 {
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endSec = currentTime + 120
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} else {
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endSec = currentTime + 120 - float64(errorThreshold)
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}
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// 收集该区段的心率数据
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sum, count := 0, 0
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for _, hr := range heartRates {
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sec := float64(hr.Time) / 1000.0
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if sec >= startSec && sec <= endSec {
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sum += hr.Value
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count++
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}
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}
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// 计算平均值
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if count > 0 {
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avg := float64(sum) / float64(count)
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results = append(results, map[float64]float64{seg.Speed: avg})
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}
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currentTime += seg.Duration
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}
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return results
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}
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// 计算步频区段的心率平均值
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func CalculateSegmentAveragesByRealStep(heartRates []models.StepHeartRate, steps []models.StepStrideFreq) []map[float64]float64 {
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segments := convertStrideFrequencyToSegments(steps)
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return calculateSegmentAverages(heartRates, segments, 15) // 默认5秒误差阈值
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}
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// 存储回归结果到数据库
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func (tc *StepTrainingController) SaveRegressionResult(trainId uint, result models.RegressionResult) error {
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result.TrainId = trainId
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return tc.DB.Clauses(clause.OnConflict{
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Columns: []clause.Column{{Name: "id"}},
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DoUpdates: clause.Assignments(map[string]interface{}{
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"equation": result.Equation,
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"slope": result.Slope,
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"intercept": result.Intercept,
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"log_a": result.LogA,
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"log_b": result.LogB,
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"quadratic_a": result.QuadraticA,
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"quadratic_b": result.QuadraticB,
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"quadratic_c": result.QuadraticC,
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"r_squared": result.RSquared,
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"updated_at": gorm.Expr("CURRENT_TIMESTAMP"),
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}),
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}).Create(&result).Error
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}
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// 获取或计算回归结果
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func (tc *StepTrainingController) GetOrCalculateRegression(trainId uint) (models.RegressionResult, error) {
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// 首先尝试从数据库获取
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var result models.RegressionResult
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err := tc.DB.Where("train_id = ?", trainId).First(&result).Error
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// 如果找到记录，直接返回
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if err == nil {
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return result, nil
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}
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// 如果错误不是记录不存在，返回错误
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if !errors.Is(err, gorm.ErrRecordNotFound) {
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return models.RegressionResult{}, err
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}
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// 查询训练记录及相关数据
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var record models.StepTrainRecord
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if err := tc.DB.
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Where("train_id = ?", uint(trainId)).
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Preload("HeartRates", "heart_rate_type = ?", 1).
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Preload("StrideFreqs", "predict_value = ?", 1).
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First(&record).Error; err != nil {
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return models.RegressionResult{}, err
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}
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// 计算心率平均值（模仿Flutter的calculateSegmentAveragesByRealStep）
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averages := CalculateSegmentAveragesByRealStep(record.HeartRates, record.StrideFreqs)
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if len(averages) == 0 {
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return models.RegressionResult{}, errors.New("无足够数据进行回归计算")
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}
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// 计算三种回归
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result = models.RegressionResult{
|
||||
TrainId: trainId,
|
||||
}
|
||||
|
||||
// 线性回归
|
||||
linearRes := performLinearRegression(averages)
|
||||
result.Slope = linearRes.Slope
|
||||
result.Intercept = linearRes.Intercept
|
||||
result.RSquared = linearRes.RSquared
|
||||
result.Equation = "线性回归: " + linearRes.Equation
|
||||
|
||||
// 对数回归
|
||||
logRes := performLogarithmicRegression(averages)
|
||||
result.LogA = logRes.LogA
|
||||
result.LogB = logRes.LogB
|
||||
if result.Equation != "" {
|
||||
result.Equation += "\n"
|
||||
}
|
||||
result.Equation += "对数回归: " + logRes.Equation
|
||||
|
||||
// 二次回归
|
||||
quadRes := performQuadraticRegression(averages)
|
||||
result.QuadraticA = quadRes.QuadraticA
|
||||
result.QuadraticB = quadRes.QuadraticB
|
||||
result.QuadraticC = quadRes.QuadraticC
|
||||
if result.Equation != "" {
|
||||
result.Equation += "\n"
|
||||
}
|
||||
result.Equation += "二次回归: " + quadRes.Equation
|
||||
|
||||
// 保存计算结果到数据库
|
||||
if err := tc.SaveRegressionResult(trainId, result); err != nil {
|
||||
log.Printf("保存回归结果失败: %v", err)
|
||||
}
|
||||
|
||||
return result, nil
|
||||
}
|
||||
|
||||
// 新增接口：获取回归结果
|
||||
func (tc *StepTrainingController) GetRegressionResult(c *gin.Context) {
|
||||
trainIdStr := c.Param("trainId")
|
||||
tid, err := strconv.ParseUint(trainIdStr, 10, 32)
|
||||
if err != nil {
|
||||
c.JSON(http.StatusBadRequest, gin.H{"error": "无效的训练ID"})
|
||||
return
|
||||
}
|
||||
|
||||
result, err := tc.GetOrCalculateRegression(uint(tid))
|
||||
if err != nil {
|
||||
c.JSON(http.StatusInternalServerError, gin.H{"error": err.Error()})
|
||||
return
|
||||
}
|
||||
|
||||
c.JSON(http.StatusOK, gin.H{
|
||||
"message": "获取成功",
|
||||
"data": result,
|
||||
})
|
||||
}
|
||||
|
||||
// 获取训练记录的排名
|
||||
func (tc *StepTrainingController) GetTrainingRank(c *gin.Context) {
|
||||
// 解析参数
|
||||
trainIdStr := c.Param("trainId")
|
||||
|
||||
regressionTypeStr := c.Query("type")
|
||||
regressionType, err := strconv.Atoi(regressionTypeStr) // 字符串转整型
|
||||
if err != nil {
|
||||
// 转换失败时返回400错误
|
||||
c.JSON(http.StatusBadRequest, gin.H{"error": "参数type必须为整数"})
|
||||
return
|
||||
}
|
||||
|
||||
regType := models.RegressionType(regressionType)
|
||||
|
||||
// 验证回归类型
|
||||
if regType != models.LinearRegression && regType != models.QuadraticRegression {
|
||||
c.JSON(http.StatusBadRequest, gin.H{"error": "无效的回归类型，必须是'linear'或'quadratic'"})
|
||||
return
|
||||
}
|
||||
|
||||
// 转换trainId
|
||||
tid, err := strconv.ParseUint(trainIdStr, 10, 64)
|
||||
if err != nil {
|
||||
c.JSON(http.StatusBadRequest, gin.H{"error": "无效的训练ID"})
|
||||
return
|
||||
}
|
||||
trainId := uint(tid)
|
||||
|
||||
// 确保回归结果存在
|
||||
if _, err := tc.GetOrCalculateRegression(trainId); err != nil {
|
||||
c.JSON(http.StatusInternalServerError, gin.H{"error": "获取回归结果失败：" + err.Error()})
|
||||
return
|
||||
}
|
||||
|
||||
// 获取所有记录用于排名
|
||||
var records []models.RegressionResult
|
||||
query := tc.DB.Model(&models.RegressionResult{})
|
||||
switch regType {
|
||||
case models.LinearRegression:
|
||||
query = query.Where("slope IS NOT NULL").Select("train_id, slope AS metric")
|
||||
case models.QuadraticRegression:
|
||||
query = query.Where("quadratic_a IS NOT NULL").
|
||||
Select("train_id, ABS(quadratic_a) AS metric")
|
||||
}
|
||||
if err := query.Find(&records).Error; err != nil {
|
||||
c.JSON(http.StatusInternalServerError, gin.H{"error": "查询排名数据失败"})
|
||||
return
|
||||
}
|
||||
|
||||
// 处理无数据情况
|
||||
if len(records) == 0 {
|
||||
c.JSON(http.StatusNotFound, gin.H{"error": "无可用数据计算排名"})
|
||||
return
|
||||
}
|
||||
|
||||
// 排序逻辑
|
||||
sort.Slice(records, func(i, j int) bool {
|
||||
if regType == models.LinearRegression {
|
||||
return *records[i].Slope < *records[j].Slope
|
||||
}
|
||||
return math.Abs(*records[i].QuadraticA) > math.Abs(*records[j].QuadraticA) // 二次回归按绝对值降序
|
||||
})
|
||||
|
||||
// 计算排名（处理并列）
|
||||
currentRank := 1
|
||||
rankMap := make(map[uint]int)
|
||||
for i, record := range records {
|
||||
// 处理第一个记录
|
||||
if i == 0 {
|
||||
rankMap[record.TrainId] = currentRank
|
||||
continue
|
||||
}
|
||||
|
||||
if regType == models.LinearRegression {
|
||||
if records[i].Slope != records[i-1].Slope {
|
||||
currentRank = i + 1 // 值变化时，当前排名 = 索引 + 1
|
||||
}
|
||||
rankMap[record.TrainId] = currentRank
|
||||
} else {
|
||||
if records[i].QuadraticA != records[i-1].QuadraticA {
|
||||
currentRank = i + 1 // 值变化时，当前排名 = 索引 + 1
|
||||
}
|
||||
rankMap[record.TrainId] = currentRank
|
||||
}
|
||||
// 检测值是否变化
|
||||
}
|
||||
|
||||
// 获取当前训练记录的排名
|
||||
rank, exists := rankMap[trainId]
|
||||
if !exists {
|
||||
c.JSON(http.StatusInternalServerError, gin.H{"error": "训练记录未包含在排名中"})
|
||||
return
|
||||
}
|
||||
|
||||
// 返回响应
|
||||
c.JSON(http.StatusOK, gin.H{
|
||||
"message": "排名查询成功",
|
||||
"data": gin.H{
|
||||
"trainId": trainId,
|
||||
"type": regressionType,
|
||||
"rank": rank,
|
||||
"total": len(records),
|
||||
},
|
||||
})
|
||||
}
|
||||
|
||||
Reference in New Issue
Block a user