package controllers

import (
	"errors"
	"fmt"
	"github.com/gin-gonic/gin"
	"github.com/sajari/regression"
	"gorm.io/gorm"
	"gorm.io/gorm/clause"
	"hr_receiver/config"
	"hr_receiver/models"
	"log"
	"math"
	"net/http"
	"strconv"
	"strings"
)

type StepTrainingController struct {
	DB *gorm.DB
}

func NewStepTrainingController() *StepTrainingController {
	return &StepTrainingController{DB: config.DB}
}

// 接收训练记录
func (tc *StepTrainingController) CreateTrainingRecord(c *gin.Context) {
	var record models.StepTrainRecord

	// 绑定并验证JSON数据
	if err := c.ShouldBindJSON(&record); err != nil {
		c.JSON(http.StatusBadRequest, gin.H{"error": err.Error()})
		return
	}

	// 使用事务保存数据[4](@ref)
	err := tc.DB.Transaction(func(tx *gorm.DB) error {
		// 保存主记录
		if err := tx.Clauses(clause.OnConflict{
			Columns: []clause.Column{{Name: "train_id"}}, // 指定冲突的列
			DoUpdates: clause.Assignments(map[string]interface{}{
				"max_heart_rate": record.MaxHeartRate,
				"start_time":     record.StartTime,
				"end_time":       record.EndTime,
				"duration":       record.Duration,
				"dead_zone":      record.DeadZone,
				"name":           record.Name,
				"evaluation":     record.Evaluation,
			}),
		}).Omit("HeartRates", "StrideFreqs").Create(&record).Error; err != nil {
			return err
		}

		// 保存关联的心率数据
		for i := range record.HeartRates {
			if err := tx.Clauses(
				clause.OnConflict{
					Columns:   []clause.Column{{Name: "identifier"}}, // 指定冲突的列
					DoUpdates: clause.Assignments(map[string]interface{}{"heart_rate_type": record.HeartRates[i].HeartRateType, "value": record.HeartRates[i].Value, "time": record.HeartRates[i].Time}),
				},
			).Create(&record.HeartRates[i]).Error; err != nil {

				return err
			}
		}
		for i := range record.StrideFreqs {
			if err := tx.Clauses(
				clause.OnConflict{
					Columns:   []clause.Column{{Name: "identifier"}}, // 指定冲突的列
					DoUpdates: clause.Assignments(map[string]interface{}{"value": record.StrideFreqs[i].Value, "time": record.StrideFreqs[i].Time}),
				},
			).Create(&record.StrideFreqs[i]).Error; err != nil {

				return err
			}
		}

		return nil
	})

	if err != nil {
		c.JSON(http.StatusInternalServerError, gin.H{"error": err.Error()})
		return
	}

	c.JSON(http.StatusCreated, gin.H{
		"message": "数据保存成功",
		"id":      record.TrainId,
	})
}

func (tc *StepTrainingController) GetTrainingRecords(c *gin.Context) {
	// 定义分页参数结构
	type PaginationParams struct {
		PageNum  int `form:"pageNum,default=1"`   // 页码，默认第一页
		PageSize int `form:"pageSize,default=10"` // 每页数量，默认10条
	}

	var params PaginationParams
	if err := c.ShouldBindQuery(&params); err != nil {
		c.JSON(http.StatusBadRequest, gin.H{"error": err.Error()})
		return
	}

	// 验证分页参数有效性
	if params.PageNum < 1 {
		params.PageNum = 1
	}
	if params.PageSize < 1 || params.PageSize > 100 {
		params.PageSize = 10
	}

	// 计算偏移量
	offset := (params.PageNum - 1) * params.PageSize

	var (
		records   []models.StepTrainRecord
		totalRows int64
	)

	// 获取总记录数
	if err := tc.DB.Model(&models.StepTrainRecord{}).Count(&totalRows).Error; err != nil {
		c.JSON(http.StatusInternalServerError, gin.H{"error": "获取记录总数失败"})
		return
	}

	// 查询分页数据（按开始时间倒序排列）
	result := tc.DB.
		Order("start_time DESC"). // 按开始时间倒序
		Offset(offset).
		Limit(params.PageSize).
		Find(&records)

	if result.Error != nil {
		c.JSON(http.StatusInternalServerError, gin.H{"error": result.Error.Error()})
		return
	}

	// 计算总页数
	totalPages := int(math.Ceil(float64(totalRows) / float64(params.PageSize)))

	c.JSON(http.StatusOK, gin.H{
		"message": "查询成功",
		"data": gin.H{
			"list": records,
			"pagination": gin.H{
				"currentPage": params.PageNum,
				"pageSize":    params.PageSize,
				"totalPage":   totalPages,
				"totalList":   totalRows,
			},
		},
	})
}
func (tc *StepTrainingController) GetTrainingRecordByTrainId(c *gin.Context) {
	// 从URL路径参数获取trainId
	trainId := c.Param("trainId")
	if trainId == "" {
		c.JSON(http.StatusBadRequest, gin.H{"error": "训练ID不能为空"})
		return
	}

	// 将字符串trainId转换为uint类型
	tid, err := strconv.ParseInt(trainId, 10, 64)
	if err != nil {
		c.JSON(http.StatusBadRequest, gin.H{"error": "无效的训练ID格式"})
		return
	}

	var record models.StepTrainRecord

	// 查询主记录并预加载关联的心率和步频数据
	result := tc.DB.Where("train_id = ?", uint(tid)).
		Preload("HeartRates").
		Preload("StrideFreqs").
		First(&record)

	if result.Error != nil {
		if errors.Is(result.Error, gorm.ErrRecordNotFound) {
			c.JSON(http.StatusNotFound, gin.H{"error": "训练记录不存在"})
		} else {
			c.JSON(http.StatusInternalServerError, gin.H{"error": result.Error.Error()})
		}
		return
	}

	// 成功返回数据
	c.JSON(http.StatusOK, gin.H{
		"message": "查询成功",
		"data":    record,
	})
}

// 定义结构体
type SpeedSegment struct {
	Duration float64
	Speed    float64
}

// 实现线性回归算法
func performLinearRegression(averages []map[float64]float64) models.RegressionResult {
	if len(averages) == 0 {
		return models.RegressionResult{
			Equation: "无数据",
		}
	}

	// 收集数据点
	var points []struct{ x, y float64 }
	for _, m := range averages {
		for x, y := range m {
			points = append(points, struct{ x, y float64 }{x, y})
		}
	}

	// 使用回归库计算
	r := new(regression.Regression)
	r.SetObserved("y")
	r.SetVar(0, "x")

	for _, p := range points {
		r.Train(regression.DataPoint(p.y, []float64{p.x}))
	}

	if err := r.Run(); err != nil {
		log.Printf("线性回归计算失败: %v", err)
		return models.RegressionResult{
			Equation: "计算失败",
		}
	}

	// 创建结果
	slope := r.Coeff(1)
	intercept := r.Coeff(0)
	r2 := r.R2
	return models.RegressionResult{
		RegressionType: models.LinearRegression,
		Slope:          &slope,
		Intercept:      &intercept,
		RSquared:       &r2,
		Equation:       r.Formula,
	}
}

// 实现对数和二次回归算法
// 对数回归算法
func performLogarithmicRegression(averages []map[float64]float64) models.RegressionResult {
	if len(averages) == 0 {
		return models.RegressionResult{
			Equation: "无数据",
		}
	}

	// 收集数据点
	r := new(regression.Regression)
	r.SetObserved("y")
	r.SetVar(0, "log(x+1)")

	for _, m := range averages {
		for speed, hr := range m {
			logSpeed := math.Log(speed + 1)
			r.Train(regression.DataPoint(hr, []float64{logSpeed}))
		}
	}

	if err := r.Run(); err != nil {
		log.Printf("对数回归计算失败: %v", err)
		return models.RegressionResult{
			Equation: "计算失败",
		}
	}

	// 创建结果
	logA := r.Coeff(1)
	logB := r.Coeff(0)
	r2 := r.R2
	return models.RegressionResult{
		RegressionType: models.LogarithmicRegression,
		LogA:           &logA,
		LogB:           &logB,
		RSquared:       &r2,
		Equation:       r.Formula,
	}
}

// 二次回归算法
//func performQuadraticRegression(averages []map[float64]float64) models.RegressionResult {
//	if len(averages) == 0 {
//		return models.RegressionResult{
//			Equation: "无数据",
//		}
//	}
//
//	// 收集数据点
//	r := new(regression.Regression)
//	r.SetObserved("y")
//	r.SetVar(0, "x")
//	r.SetVar(1, "x²")
//
//	for _, m := range averages {
//		for speed, hr := range m {
//			speedSq := math.Pow(speed, 2)
//			r.Train(regression.DataPoint(hr, []float64{speed, speedSq}))
//		}
//	}
//
//	if err := r.Run(); err != nil {
//		log.Printf("二次回归计算失败: %v", err)
//		return models.RegressionResult{
//			Equation: "计算失败",
//		}
//	}
//
//	// 创建结果
//	a := r.Coeff(2)
//	b := r.Coeff(1)
//	c := r.Coeff(0)
//	r2 := r.R2
//	return models.RegressionResult{
//		RegressionType: models.QuadraticRegression,
//		QuadraticA:     &a,
//		QuadraticB:     &b,
//		QuadraticC:     &c,
//		RSquared:       &r2,
//		Equation:       r.Formula,
//	}
//}

func performQuadraticRegression(averages []map[float64]float64) models.RegressionResult {
	if len(averages) == 0 {
		return models.RegressionResult{
			Equation: "无数据",
		}
	}

	// 步骤1：收集所有数据点（与Flutter一致）
	var xValues []float64
	var yValues []float64
	for _, m := range averages {
		for speed, hr := range m {
			xValues = append(xValues, speed)
			yValues = append(yValues, hr)
		}
	}
	n := float64(len(xValues))

	// 步骤2：计算各项和（完全匹配Flutter的计算）
	var sumX, sumY, sumX2, sumX3, sumX4, sumXY, sumX2Y float64
	for i := 0; i < len(xValues); i++ {
		x := xValues[i]
		y := yValues[i]
		x2 := x * x
		x3 := x2 * x
		x4 := x3 * x

		sumX += x
		sumY += y
		sumX2 += x2
		sumX3 += x3
		sumX4 += x4
		sumXY += x * y
		sumX2Y += x2 * y
	}

	// 步骤3：构建正规方程矩阵（与Flutter完全一致）
	matrix := [3][3]float64{
		{n, sumX, sumX2},
		{sumX, sumX2, sumX3},
		{sumX2, sumX3, sumX4},
	}
	vector := []float64{sumY, sumXY, sumX2Y}

	// 步骤4：计算矩阵行列式（复制Flutter的determinant3x3逻辑）
	det := matrix[0][0]*(matrix[1][1]*matrix[2][2]-matrix[1][2]*matrix[2][1]) -
		matrix[0][1]*(matrix[1][0]*matrix[2][2]-matrix[1][2]*matrix[2][0]) +
		matrix[0][2]*(matrix[1][0]*matrix[2][1]-matrix[1][1]*matrix[2][0])

	if det == 0 {
		return models.RegressionResult{
			Equation: "无法拟合",
		}
	}

	// 步骤5：克莱姆法则求解系数（顺序与Flutter一致）
	// 注意：最终系数顺序 a=二次项, b=一次项, c=常数项
	c := det3x3([3][3]float64{
		{vector[0], matrix[0][1], matrix[0][2]},
		{vector[1], matrix[1][1], matrix[1][2]},
		{vector[2], matrix[2][1], matrix[2][2]},
	}) / det

	b := det3x3([3][3]float64{
		{matrix[0][0], vector[0], matrix[0][2]},
		{matrix[1][0], vector[1], matrix[1][2]},
		{matrix[2][0], vector[2], matrix[2][2]},
	}) / det

	a := det3x3([3][3]float64{
		{matrix[0][0], matrix[0][1], vector[0]},
		{matrix[1][0], matrix[1][1], vector[1]},
		{matrix[2][0], matrix[2][1], vector[2]},
	}) / det

	// 步骤6：计算R平方（完全复制Flutter的计算逻辑）
	var ssRes, ssTot float64
	meanY := sumY / n
	for i := 0; i < len(xValues); i++ {
		x := xValues[i]
		y := yValues[i]
		yPred := a*x*x + b*x + c
		ssRes += math.Pow(y-yPred, 2)
		ssTot += math.Pow(y-meanY, 2)
	}
	rSquared := 0.0
	if ssTot != 0 {
		rSquared = 1 - ssRes/ssTot
	}

	// 步骤7：格式化公式字符串（与Flutter格式完全一致）
	equation := formatEquation(a, b, c, rSquared)

	return models.RegressionResult{
		RegressionType: models.QuadraticRegression,
		QuadraticA:     &a,
		QuadraticB:     &b,
		QuadraticC:     &c,
		RSquared:       &rSquared,
		Equation:       equation,
	}
}

// 3x3行列式计算（与Flutter实现相同）
func det3x3(m [3][3]float64) float64 {
	return m[0][0]*(m[1][1]*m[2][2]-m[1][2]*m[2][1]) -
		m[0][1]*(m[1][0]*m[2][2]-m[1][2]*m[2][0]) +
		m[0][2]*(m[1][0]*m[2][1]-m[1][1]*m[2][0])
}

// 公式格式化（完全匹配Flutter格式）
func formatEquation(a, b, c, r2 float64) string {
	// 保留4位小数
	aStr := fmt.Sprintf("%.4f", a)
	bStr := fmt.Sprintf("%.4f", b)
	cStr := fmt.Sprintf("%.4f", c)
	r2Str := fmt.Sprintf("%.4f", r2)

	builder := strings.Builder{}
	builder.WriteString("y = ")

	// 处理二次项
	if a >= 0 {
		builder.WriteString(aStr + " x²")
	} else {
		builder.WriteString("-" + strings.TrimPrefix(aStr, "-") + " x²")
	}

	// 处理一次项
	if b >= 0 {
		builder.WriteString(" + " + bStr + " x")
	} else {
		builder.WriteString(" - " + strings.TrimPrefix(bStr, "-") + " x")
	}

	// 处理常数项
	if c >= 0 {
		builder.WriteString(" + " + cStr)
	} else {
		builder.WriteString(" - " + strings.TrimPrefix(cStr, "-"))
	}

	builder.WriteString(" (R² = " + r2Str + ")")
	return builder.String()
}

// 步频数据转换为速度段
func convertStrideFrequencyToSegments(steps []models.StepStrideFreq) []SpeedSegment {
	if len(steps) == 0 {
		return []SpeedSegment{}
	}

	// 过滤零值并排序
	validSteps := make([]models.StepStrideFreq, 0, len(steps))
	for _, s := range steps {
		if s.Value > 0 {
			validSteps = append(validSteps, s)
		}
	}

	if len(validSteps) == 0 {
		return []SpeedSegment{}
	}

	// 按时间排序
	for i := 0; i < len(validSteps)-1; i++ {
		for j := i + 1; j < len(validSteps); j++ {
			if validSteps[i].Time > validSteps[j].Time {
				validSteps[i], validSteps[j] = validSteps[j], validSteps[i]
			}
		}
	}

	// 创建速度段
	segments := make([]SpeedSegment, 0)
	startTime := validSteps[0].Time
	currentValue := validSteps[0].Value

	for i := 1; i < len(validSteps); i++ {
		if validSteps[i].Value != currentValue {
			duration := float64(validSteps[i].Time-startTime) / 1000.0
			if duration > 0 {
				segments = append(segments, SpeedSegment{
					Duration: duration,
					Speed:    float64(currentValue),
				})
			}
			startTime = validSteps[i].Time
			currentValue = validSteps[i].Value
		}
	}

	// 添加最后一个段
	if len(validSteps) > 0 {
		duration := float64(validSteps[len(validSteps)-1].Time-startTime) / 1000.0
		if duration > 0 {
			segments = append(segments, SpeedSegment{
				Duration: duration,
				Speed:    float64(currentValue),
			})
		}
	}

	return segments
}

// 计算区段平均值
func calculateSegmentAverages(heartRates []models.StepHeartRate, segments []SpeedSegment, errorThreshold int) []map[float64]float64 {
	currentTime := 0.0
	results := make([]map[float64]float64, 0)

	for _, seg := range segments {
		minRequired := 60 + (60 - float64(errorThreshold))

		// 跳过不满足条件的区段
		if seg.Duration < minRequired {
			currentTime += seg.Duration
			continue
		}

		// 计算时间窗口
		startSec := currentTime + 60
		endSec := currentTime
		if seg.Duration >= 120 {
			endSec = currentTime + 120
		} else {
			endSec = currentTime + 120 - float64(errorThreshold)
		}

		// 收集该区段的心率数据
		sum, count := 0, 0
		for _, hr := range heartRates {
			sec := float64(hr.Time) / 1000.0
			if sec >= startSec && sec <= endSec {
				sum += hr.Value
				count++
			}
		}

		// 计算平均值
		if count > 0 {
			avg := float64(sum) / float64(count)
			results = append(results, map[float64]float64{seg.Speed: avg})
		}

		currentTime += seg.Duration
	}

	return results
}

// 计算步频区段的心率平均值
func CalculateSegmentAveragesByRealStep(heartRates []models.StepHeartRate, steps []models.StepStrideFreq) []map[float64]float64 {
	segments := convertStrideFrequencyToSegments(steps)
	return calculateSegmentAverages(heartRates, segments, 15) // 默认5秒误差阈值
}

// 存储回归结果到数据库（支持多种回归类型）
func (tc *StepTrainingController) SaveRegressionResults(trainId uint, results []models.RegressionResult) error {
	return tc.DB.Transaction(func(tx *gorm.DB) error {
		for i := range results {
			results[i].TrainId = trainId
			// 使用复合唯一约束确保每种回归类型只存储一条记录
			err := tx.Clauses(clause.OnConflict{
				Columns: []clause.Column{
					{Name: "id"},
				},
				DoUpdates: clause.Assignments(map[string]interface{}{
					"equation":    results[i].Equation,
					"slope":       results[i].Slope,
					"intercept":   results[i].Intercept,
					"log_a":       results[i].LogA,
					"log_b":       results[i].LogB,
					"quadratic_a": results[i].QuadraticA,
					"quadratic_b": results[i].QuadraticB,
					"quadratic_c": results[i].QuadraticC,
					"r_squared":   results[i].RSquared,
					"updated_at":  gorm.Expr("CURRENT_TIMESTAMP"),
				}),
			}).Create(&results[i]).Error

			if err != nil {
				return err
			}
		}
		return nil
	})
}

// 获取或计算回归结果（返回多种回归类型列表）
func (tc *StepTrainingController) GetOrCalculateRegression(trainId uint) ([]models.RegressionResult, error) {
	// 尝试从数据库获取所有类型的回归结果
	var results []models.RegressionResult
	err := tc.DB.Where("train_id = ?", trainId).Find(&results).Error

	// 如果已存在三种类型的结果，直接返回
	if err == nil && len(results) >= 3 {
		return results, nil
	}

	// 查询训练记录及相关数据
	var record models.StepTrainRecord
	if err := tc.DB.
		Where("train_id = ?", uint(trainId)).
		Preload("HeartRates", "heart_rate_type = ?", 1).
		Preload("StrideFreqs", "predict_value = ?", 1).
		First(&record).Error; err != nil {
		return nil, err
	}

	// 计算心率平均值
	averages := CalculateSegmentAveragesByRealStep(record.HeartRates, record.StrideFreqs)
	if len(averages) == 0 {
		return nil, errors.New("无足够数据进行回归计算")
	}

	// 创建三种回归类型的结果
	results = make([]models.RegressionResult, 3)

	// 线性回归
	linearRes := performLinearRegression(averages)
	results[0] = models.RegressionResult{
		RegressionType: models.LinearRegression,
		TrainId:        trainId,
		Equation:       linearRes.Equation,
		Slope:          linearRes.Slope,
		Intercept:      linearRes.Intercept,
		RSquared:       linearRes.RSquared,
	}

	// 对数回归
	logRes := performLogarithmicRegression(averages)
	results[1] = models.RegressionResult{
		RegressionType: models.LogarithmicRegression,
		TrainId:        trainId,
		Equation:       logRes.Equation,
		LogA:           logRes.LogA,
		LogB:           logRes.LogB,
		RSquared:       logRes.RSquared,
	}

	// 二次回归
	quadRes := performQuadraticRegression(averages)
	results[2] = models.RegressionResult{
		RegressionType: models.QuadraticRegression,
		TrainId:        trainId,
		Equation:       quadRes.Equation,
		QuadraticA:     quadRes.QuadraticA,
		QuadraticB:     quadRes.QuadraticB,
		QuadraticC:     quadRes.QuadraticC,
		RSquared:       quadRes.RSquared,
	}

	// 批量保存结果到数据库
	if err := tc.SaveRegressionResults(trainId, results); err != nil {
		log.Printf("保存回归结果失败: %v", err)
		return nil, err
	}

	return results, 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,
	})
}

func (tc *StepTrainingController) GetTrainingRank(c *gin.Context) {
	// 参数解析
	trainIdStr := c.Param("trainId")
	regressionTypeStr := c.Query("type")
	regressionType, err := strconv.Atoi(regressionTypeStr)
	if err != nil {
		c.JSON(http.StatusBadRequest, gin.H{"error": "参数type必须为整数"})
		return
	}

	// 验证回归类型
	regType := models.RegressionType(regressionType)
	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
	}

	// 转换训练ID
	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
	}
	// 获取指定训练的基准值
	var baseValue float64
	baseQuery := tc.DB.Model(&models.RegressionResult{}).
		Select(getValueColumn(regType)).
		Where("train_id = ?", trainId)

	if err := baseQuery.Row().Scan(&baseValue); err != nil {
		if errors.Is(err, gorm.ErrRecordNotFound) {
			c.JSON(http.StatusNotFound, gin.H{"error": "指定的训练数据不存在"})
			return
		}
		c.JSON(http.StatusInternalServerError, gin.H{"error": "获取基准数据失败"})
		return
	}

	// 在数据库中进行排名计算
	var rank struct {
		BetterCount int64
		Total       int64
	}

	// 动态生成比较条件
	betterCondition := fmt.Sprintf("%s %s ?",
		getValueColumn(regType),
		getComparisonOperator(regType))

	totalQuery := tc.DB.Model(&models.RegressionResult{}).
		Where(getTypeCondition(regType))

	if err := totalQuery.Count(&rank.Total).Error; err != nil {
		c.JSON(http.StatusInternalServerError, gin.H{"error": "统计总数失败"})
		return
	}

	if err := tc.DB.Model(&models.RegressionResult{}).
		Where(getTypeCondition(regType)).
		Where(betterCondition, baseValue).
		Count(&rank.BetterCount).Error; err != nil {
		c.JSON(http.StatusInternalServerError, gin.H{"error": "计算排名失败"})
		return
	}

	// 计算实际排名 (并列排名)
	currentRank := rank.BetterCount + 1

	// 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":    currentRank,
			"total":   rank.Total,
		},
	})
}

// 辅助函数：获取排序字段名
func getValueColumn(regType models.RegressionType) string {
	switch regType {
	case models.LinearRegression:
		return "slope"
	case models.QuadraticRegression:
		return "ABS(quadratic_a)" // 计算绝对值
	default:
		return ""
	}
}

// 辅助函数：获取比较操作符
func getComparisonOperator(regType models.RegressionType) string {
	switch regType {
	case models.LinearRegression:
		return "<" // 线性回归：值越小越好
	case models.QuadraticRegression:
		return ">" // 二次回归：绝对值越大越好
	default:
		return ""
	}
}

// 辅助函数：获取类型条件
func getTypeCondition(regType models.RegressionType) string {
	switch regType {
	case models.LinearRegression:
		return "slope IS NOT NULL"
	case models.QuadraticRegression:
		return "quadratic_a IS NOT NULL AND quadratic_a < 0" // 确保是负值
	default:
		return ""
	}
}

// 辅助函数：比较浮点指针（用于线性回归）
func compareFloatPtr(a, b *float64, ascending bool) bool {
	if a == nil && b == nil {
		return false
	}
	if a == nil {
		return false // 空值排最后
	}
	if b == nil {
		return true // 非空值排前
	}

	if ascending {
		return *a < *b
	}
	return *a > *b
}

// 辅助函数：比较二次项系数（用于二次回归）
func compareQuadraticA(a, b *float64) bool {
	if a == nil && b == nil {
		return false
	}
	if a == nil {
		return false
	}
	if b == nil {
		return true
	}

	// 比较绝对值（a和b都是负值，所以取绝对值后大的排前面）
	return math.Abs(*a) > math.Abs(*b)
}