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115 lines
3.0 KiB
115 lines
3.0 KiB
package common_calc
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import (
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"crypto/md5"
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"github.com/google/uuid"
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"log"
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"math"
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"sort"
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"strconv"
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)
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// Decimal float64 保留小数点后位数
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// value float64 浮点数
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// prec int 需保留小数点后的位数
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func Decimal(value float64, prec int) float64 {
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value, _ = strconv.ParseFloat(strconv.FormatFloat(value, 'f', prec, 64), 64)
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return value
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}
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// 和scala 计算不一致 注意！
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func UUIDFromString(raw string) string {
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bys := []byte(raw)
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uid := uuid.NewMD5(uuid.Nil, bys)
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log.Println(uid.String(), uid.Version())
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uid = uuid.NewMD5(uuid.NameSpaceURL, bys)
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log.Println(uid.String(), uid.Version())
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uid = uuid.NewMD5(uuid.NameSpaceX500, bys)
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log.Println(uid.String(), uid.Version())
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uid = uuid.NewMD5(uuid.NameSpaceDNS, bys)
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log.Println(uid.String(), uid.Version())
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uid = uuid.NewMD5(uuid.NameSpaceOID, bys)
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log.Println(uid.String(), uid.Version())
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return uid.String()
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}
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func NameUUIDFromString(raw string) string {
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sb := md5.Sum([]byte(raw))
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sb[6] &= 0x0f
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sb[6] |= 0x30
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sb[8] &= 0x3f
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sb[8] |= 0x80
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uid, err := uuid.FromBytes(sb[:])
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if err != nil {
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log.Printf("uid 错误异常=%x", err.Error())
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}
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return uid.String()
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}
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func MinMax(input []float64) (float64, float64) {
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sort.Float64s(input)
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return input[0], input[len(input)-1]
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}
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func AbsMax(input []float64) float64 {
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sort.Float64s(input)
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return max(math.Abs(input[0]), math.Abs(input[len(input)-1]))
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}
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func MeanSqrt(data []float64) float64 {
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var sumSquares float64
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for _, v := range data {
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sumSquares += v * v
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}
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return math.Sqrt(sumSquares / float64(len(data)))
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}
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func GetAvg(dataArray []float64) float64 {
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sum := 0.0
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for _, f := range dataArray {
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sum += f
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}
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return sum / float64(len(dataArray))
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}
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func GetMedian(dataArray []float64) float64 {
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n := len(dataArray)
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sort.Float64s(dataArray)
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if n%2 == 1 {
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// 如果切片元素个数为奇数，中位数即为中间的元素
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return dataArray[n/2]
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} else {
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// 如果元素个数为偶数，中位数为中间两个元素的平均值
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return (dataArray[n/2-1] + dataArray[n/2]) / 2.0
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}
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}
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// GetVariance calculates the unbiased population variance from the provided samples as an unsorted array.
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// On a dataset of size N, it will use an N-1 normalizer (Bessel's correction).
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// Returns NaN if data has less than two entries or if any entry is NaN.
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// 方差
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// Translated from Math.Net
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// Source code at https://github.com/mathnet/mathnet-numerics/blob/master/src/Numerics/Statistics/ArrayStatistics.cs
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func GetVariance(samples []float64) float64 {
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if len(samples) <= 1 {
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return math.NaN()
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}
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var variance float64
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t := samples[0]
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for i := 1; i < len(samples); i++ {
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t += samples[i]
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diff := float64((i+1)*int(samples[i])) - t
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variance += (diff * diff) / (float64(i+1) * float64(i))
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}
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return variance / float64(len(samples)-1)
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}
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func StandardDeviation(samples []float64) float64 {
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return math.Sqrt(GetVariance(samples))
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}
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func MeanStandardDeviation(samples []float64) (float64, float64) {
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return GetAvg(samples), StandardDeviation(samples)
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}
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