淘汰Stdev的高精度模式

datalist.go

@@ -71,6 +71,7 @@ type IDataList interface {
 	Upper() *DataList
 	Lower() *DataList
 	Capitalize() *DataList
+
 	// Statistics
 	Sum() float64
 	Max() float64
@@ -81,7 +82,7 @@ type IDataList interface {
 	Median() interface{}
 	Mode() interface{}
 	MAD() interface{}
-	Stdev(highPrecision ...bool) interface{}
+	Stdev() interface{}
 	StdevP(highPrecision ...bool) interface{}
 	Var(highPrecision ...bool) interface{}
 	VarP(highPrecision ...bool) interface{}
@@ -409,7 +410,7 @@ func (dl *DataList) ReplaceAll(oldValue, newValue interface{}) {
 // ReplaceOutliers replaces outliers in the DataList with the specified replacement value (e.g., mean, median).
 func (dl *DataList) ReplaceOutliers(stdDevs float64, replacement float64) *DataList {
 	mean := dl.Mean()
-	stddev := dl.Stdev(false).(float64)
+	stddev := dl.Stdev().(float64)
 	threshold := stdDevs * stddev
 
 	for i, v := range dl.Data() {
@@ -695,7 +696,7 @@ func (dl *DataList) ClearOutliers(stdDevs float64) *DataList {
 	}()
 
 	mean := dl.Mean()
-	stddev := dl.Stdev(false).(float64)
+	stddev := dl.Stdev().(float64)
 	threshold := stdDevs * stddev
 
 	// 打印調試信息，確保計算值與 R 相同
@@ -752,7 +753,7 @@ func (dl *DataList) Standardize() *DataList {
 		go dl.updateTimestamp()
 	}()
 	mean := dl.Mean()
-	stddev := dl.Stdev(false).(float64)
+	stddev := dl.Stdev().(float64)
 	dl.mu.Lock()
 	for i, v := range dl.Data() {
 		vfloat := conv.ParseF64(v)
@@ -882,7 +883,7 @@ func (dl *DataList) MovingStdev(windowSize int) *DataList {
 	movingStdDevData := make([]float64, dl.Len()-windowSize+1)
 	for i := 0; i < len(movingStdDevData); i++ {
 		window := NewDataList(dl.Data()[i : i+windowSize])
-		movingStdDevData[i] = window.Stdev(false).(float64)
+		movingStdDevData[i] = window.Stdev().(float64)
 	}
 	return NewDataList(movingStdDevData)
 }
@@ -1356,38 +1357,19 @@ func (dl *DataList) MAD() interface{} {
 // Returns the standard deviation.
 // Returns nil if the DataList is empty.
 // Stdev returns the standard deviation of the DataList.
-func (dl *DataList) Stdev(highPrecision ...bool) interface{} {
+func (dl *DataList) Stdev() interface{} {
 	if len(dl.data) == 0 {
 		LogWarning("DataList.Stdev(): DataList is empty, returning nil.")
 		return nil
 	}
 
-	// 判斷是否使用高精度模式
-	useHighPrecision := len(highPrecision) == 1 && highPrecision[0]
-	if len(highPrecision) > 1 {
-		LogWarning("DataList.Stdev(): Too many arguments, returning nil.")
-		return nil
-	}
-
-	var variance interface{}
-	if useHighPrecision {
-		variance = dl.Var(true)
-	} else {
-		variance = dl.Var(false)
-	}
+	variance := dl.Var(false)
 
 	if variance == nil {
 		LogWarning("DataList.Stdev(): Variance calculation failed, returning nil.")
 		return nil
 	}
 
-	if useHighPrecision {
-		// 高精度模式下使用 SqrtRat 進行開方運算
-		varianceRat := variance.(*big.Rat)
-		sqrtVariance := SqrtRat(varianceRat)
-		return sqrtVariance
-	}
-
 	// 普通模式下使用 float64 計算
 	return math.Sqrt(ToFloat64(variance))
 }

stats/correlation.go

@@ -91,8 +91,8 @@ func pearsonCorrelation(dlX, dlY insyra.IDataList) *big.Rat {
 	cov := Covariance(dlX, dlY)
 
 	// 計算標準差
-	stdX := dlX.Stdev(true).(*big.Rat)
-	stdY := dlY.Stdev(true).(*big.Rat)
+	stdX := new(big.Rat).SetFloat64(dlX.Stdev().(float64))
+	stdY := new(big.Rat).SetFloat64(dlY.Stdev().(float64))
 
 	// 防止除以0
 	if stdX.Sign() == 0 || stdY.Sign() == 0 {

stats/ttest.go

@@ -29,7 +29,7 @@ func SingleSampleTTest(data insyra.IDataList, mu float64) *TTestResult {
 	mean := data.Mean()
 
 	// 計算標準差和標準誤差
-	stddev := data.Stdev(false).(float64)
+	stddev := data.Stdev().(float64)
 	standardError := stddev / math.Sqrt(float64(n))
 
 	// 計算 t 值
@@ -60,8 +60,8 @@ func TwoSampleTTest(data1, data2 insyra.IDataList, equalVariance bool) *TTestRes
 	mean2 := data2.Mean()
 
 	// 計算兩個樣本的標準差
-	stddev1 := data1.Stdev(false).(float64)
-	stddev2 := data2.Stdev(false).(float64)
+	stddev1 := data1.Stdev().(float64)
+	stddev2 := data2.Stdev().(float64)
 
 	var standardError float64
 	var df int
@@ -110,7 +110,7 @@ func PairedTTest(data1, data2 insyra.IDataList) *TTestResult {
 
 	// 計算差值的均值和標準差
 	meanDiff := insyra.NewDataList(diffs).Mean()
-	stddevDiff := insyra.NewDataList(diffs).Stdev(false).(float64)
+	stddevDiff := insyra.NewDataList(diffs).Stdev().(float64)
 
 	// 計算 t 值
 	standardError := stddevDiff / math.Sqrt(float64(n))