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@@ -0,0 +1,561 @@
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+package data
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+
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+import (
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+ "github.com/shopspring/decimal"
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+ "hongze/hongze_chart_lib/models"
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+ "hongze/hongze_chart_lib/utils"
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+ "time"
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+)
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+
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+// GetChartPredictEdbInfoDataListByRule1 根据规则1获取预测数据
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+func GetChartPredictEdbInfoDataListByRule1(edbInfoId int, dataValue float64, startDate, endDate time.Time, frequency string, predictEdbInfoData []*models.EdbDataList, existMap map[string]float64) (newPredictEdbInfoData []*models.EdbDataList) {
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+ newPredictEdbInfoData = predictEdbInfoData
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+ //获取后面的预测数据
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+ dayList := getPredictEdbDayList(startDate, endDate, frequency)
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+ predictEdbInfoData = make([]*models.EdbDataList, 0)
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+ for k, v := range dayList {
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+ newPredictEdbInfoData = append(newPredictEdbInfoData, &models.EdbDataList{
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+ EdbDataId: edbInfoId + 10000000000 + k,
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+ EdbInfoId: edbInfoId,
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+ DataTime: v.Format(utils.FormatDate),
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+ Value: dataValue,
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+ DataTimestamp: (v.UnixNano() / 1e6) + 1000, //前端需要让加1s，说是2022-09-01 00:00:00 这样的整点不合适
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+ })
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+ existMap[v.Format(utils.FormatDate)] = dataValue
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+ }
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+ return
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+}
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+
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+// GetChartPredictEdbInfoDataListByRuleTb 根据同比值规则获取预测数据
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+// 2.1 同比: 在未来某一个时间段内，给定一个固定的同比增速a，用去年同期值X乘以同比增速(1+a)，得到预测值Y=X(1+a)
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+// 例: 今年1-3月值，100，100，120。给定同比增速a=0.1，则明年1-3月预测值为: 100*1.1=110，100*1.1=110，120*1.1=132。
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+func GetChartPredictEdbInfoDataListByRuleTb(edbInfoId int, tbValue float64, startDate, endDate time.Time, frequency string, predictEdbInfoData []*models.EdbDataList, existMap map[string]float64) (newPredictEdbInfoData []*models.EdbDataList, minValue, maxValue float64) {
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+ newPredictEdbInfoData = predictEdbInfoData
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+ index := len(predictEdbInfoData)
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+ //获取后面的预测数据
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+ dayList := getPredictEdbDayList(startDate, endDate, frequency)
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+ predictEdbInfoData = make([]*models.EdbDataList, 0)
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+ for k, currentDate := range dayList {
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+
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+ tmpData := &models.EdbDataList{
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+ EdbDataId: edbInfoId + 10000000000 + index + k,
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+ EdbInfoId: edbInfoId,
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+ DataTime: currentDate.Format(utils.FormatDate),
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+ //Value: dataValue,
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+ DataTimestamp: (currentDate.UnixNano() / 1e6) + 1000, //前端需要让加1s，说是2022-09-01 00:00:00 这样的整点不合适
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+ }
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+
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+ var val float64
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+ var calculateStatus bool //计算结果
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+ //currentItem := existMap[av]
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+ //上一年的日期
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+ preDate := currentDate.AddDate(-1, 0, 0)
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+ preDateStr := preDate.Format(utils.FormatDate)
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+ if preValue, ok := existMap[preDateStr]; ok { //上一年同期找到
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+ val = TbzDiv(preValue, tbValue)
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+ calculateStatus = true
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+ } else {
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+ switch frequency {
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+ case "月度":
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+ //向上和向下，各找一个月
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+ nextDateDay := preDate
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+ preDateDay := preDate
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+ for i := 0; i <= 35; i++ {
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+ nextDateDayStr := nextDateDay.Format(utils.FormatDate)
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+ if preValue, ok := existMap[nextDateDayStr]; ok { //上一年同期->下一个月找到
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+ val = TbzDiv(preValue, tbValue)
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+ calculateStatus = true
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+ break
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+ } else {
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+ preDateDayStr := preDateDay.Format(utils.FormatDate)
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+ if preValue, ok := existMap[preDateDayStr]; ok { //上一年同期->上一个月找到
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+ val = TbzDiv(preValue, tbValue)
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+ calculateStatus = true
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+ break
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+ }
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+ }
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+ nextDateDay = nextDateDay.AddDate(0, 0, 1)
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+ preDateDay = preDateDay.AddDate(0, 0, -1)
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+ }
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+
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+ case "季度", "年度":
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+ if preValue, ok := existMap[preDateStr]; ok { //上一年同期->下一个月找到
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+ val = TbzDiv(preValue, tbValue)
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+ calculateStatus = true
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+ break
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+ }
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+ default:
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+ nextDateDay := preDate
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+ preDateDay := preDate
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+
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+ for i := 0; i < 35; i++ {
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+ nextDateDayStr := nextDateDay.Format(utils.FormatDate)
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+ if preValue, ok := existMap[nextDateDayStr]; ok { //上一年同期->下一个月找到
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+ val = TbzDiv(preValue, tbValue)
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+ calculateStatus = true
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+ break
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+ } else {
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+ preDateDayStr := preDateDay.Format(utils.FormatDate)
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+ if preValue, ok := existMap[preDateDayStr]; ok { //上一年同期->上一个月找到
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+ val = TbzDiv(preValue, tbValue)
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+ calculateStatus = true
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+ break
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+ } else {
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+ //fmt.Println("pre not find:", preDateStr, "i:", i)
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+ }
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+ }
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+ nextDateDay = nextDateDay.AddDate(0, 0, 1)
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+ preDateDay = preDateDay.AddDate(0, 0, -1)
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+ }
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+ }
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+ }
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+
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+ if calculateStatus {
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+ tmpData.Value = val
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+ newPredictEdbInfoData = append(newPredictEdbInfoData, tmpData)
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+
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+ // 最大最小值
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+ if val < minValue {
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+ minValue = val
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+ }
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+ if val < maxValue {
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+ maxValue = val
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+ }
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+ }
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+ }
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+ return
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+}
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+
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+// TbzDiv 同比值计算
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+// @params a float64 去年同期值
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+// @params b float64 固定同比增速
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+func TbzDiv(a, b float64) (result float64) {
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+ if b != 0 {
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+ // 去年同期值
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+ af := decimal.NewFromFloat(a)
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+
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+ // 同比增速
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+ bf := decimal.NewFromFloat(b)
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+
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+ // 默认1
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+ cf := decimal.NewFromFloat(1)
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+
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+ // 总增速
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+ val := bf.Add(cf)
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+
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+ // 计算
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+ result, _ = val.Mul(af).RoundCeil(4).Float64()
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+ } else {
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+ result = 0
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+ }
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+ return
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+}
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+
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+// GetChartPredictEdbInfoDataListByRuleTc 根据同差值规则获取预测数据
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+// 2.2 同差: 在未来某一个时间段内，给定一个固定的同比增加值a，用去年同期值X加上同比增加值A，得到预测值Y=X+a
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+// 例: 今年1-3月值，100，100，120。给定同比增加值a=10，则明年1-3月预测值为: 100+10=110，100+10=110，120+10=130
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+func GetChartPredictEdbInfoDataListByRuleTc(edbInfoId int, tcValue float64, startDate, endDate time.Time, frequency string, predictEdbInfoData []*models.EdbDataList, existMap map[string]float64) (newPredictEdbInfoData []*models.EdbDataList, minValue, maxValue float64) {
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+ newPredictEdbInfoData = predictEdbInfoData
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+ index := len(predictEdbInfoData)
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+ //获取后面的预测数据
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+ dayList := getPredictEdbDayList(startDate, endDate, frequency)
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+ predictEdbInfoData = make([]*models.EdbDataList, 0)
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+ for k, currentDate := range dayList {
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+
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+ tmpData := &models.EdbDataList{
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+ EdbDataId: edbInfoId + 10000000000 + index + k,
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+ EdbInfoId: edbInfoId,
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+ DataTime: currentDate.Format(utils.FormatDate),
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+ //Value: dataValue,
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+ DataTimestamp: (currentDate.UnixNano() / 1e6) + 1000, //前端需要让加1s，说是2022-09-01 00:00:00 这样的整点不合适
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+ }
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+
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+ var val float64
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+ var calculateStatus bool //计算结果
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+ //currentItem := existMap[av]
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+ //上一年的日期
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+ preDate := currentDate.AddDate(-1, 0, 0)
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+ preDateStr := preDate.Format(utils.FormatDate)
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+ if preValue, ok := existMap[preDateStr]; ok { //上一年同期找到
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+ val = TczDiv(preValue, tcValue)
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+ calculateStatus = true
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+ } else {
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+ switch frequency {
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+ case "月度":
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+ //向上和向下，各找一个月
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+ nextDateDay := preDate
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+ preDateDay := preDate
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+ for i := 0; i <= 35; i++ {
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+ nextDateDayStr := nextDateDay.Format(utils.FormatDate)
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+ if preValue, ok := existMap[nextDateDayStr]; ok { //上一年同期->下一个月找到
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+ val = TczDiv(preValue, tcValue)
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+ calculateStatus = true
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+ break
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+ } else {
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+ preDateDayStr := preDateDay.Format(utils.FormatDate)
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+ if preValue, ok := existMap[preDateDayStr]; ok { //上一年同期->上一个月找到
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+ val = TczDiv(preValue, tcValue)
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+ calculateStatus = true
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+ break
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+ }
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+ }
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+ nextDateDay = nextDateDay.AddDate(0, 0, 1)
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+ preDateDay = preDateDay.AddDate(0, 0, -1)
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+ }
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+
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+ case "季度", "年度":
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+ if preValue, ok := existMap[preDateStr]; ok { //上一年同期->下一个月找到
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+ val = TczDiv(preValue, tcValue)
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+ calculateStatus = true
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+ break
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+ }
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+ default:
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+ nextDateDay := preDate
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+ preDateDay := preDate
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+
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+ for i := 0; i < 35; i++ {
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+ nextDateDayStr := nextDateDay.Format(utils.FormatDate)
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+ if preValue, ok := existMap[nextDateDayStr]; ok { //上一年同期->下一个月找到
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+ val = TczDiv(preValue, tcValue)
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+ calculateStatus = true
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+ break
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+ } else {
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+ preDateDayStr := preDateDay.Format(utils.FormatDate)
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+ if preValue, ok := existMap[preDateDayStr]; ok { //上一年同期->上一个月找到
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+ val = TczDiv(preValue, tcValue)
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+ calculateStatus = true
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+ break
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+ } else {
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+ //fmt.Println("pre not find:", preDateStr, "i:", i)
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+ }
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+ }
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+ nextDateDay = nextDateDay.AddDate(0, 0, 1)
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+ preDateDay = preDateDay.AddDate(0, 0, -1)
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+ }
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+ }
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+ }
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+
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+ if calculateStatus {
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+ tmpData.Value = val
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+ newPredictEdbInfoData = append(newPredictEdbInfoData, tmpData)
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+
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+ // 最大最小值
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+ if val < minValue {
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+ minValue = val
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+ }
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+ if val < maxValue {
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+ maxValue = val
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+ }
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+ }
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+ }
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+ return
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+}
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+
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+// TczDiv 环差值计算
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+// @params a float64 上一期值
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+// @params b float64 固定的环比增加值
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+func TczDiv(a, b float64) (result float64) {
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+ if b != 0 {
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+ // 上一期值
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+ af := decimal.NewFromFloat(a)
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+
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+ // 固定的环比增加值
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+ bf := decimal.NewFromFloat(b)
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+
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+ // 计算
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+ result, _ = af.Add(bf).RoundCeil(4).Float64()
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+ } else {
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+ result = 0
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+ }
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+ return
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+}
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+
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+// GetChartPredictEdbInfoDataListByRuleHb 根据环比值规则获取预测数据
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+// 环比：在未来某一个时间段内，给定一个固定的环比增速a，用上一期值X乘以环比增速(1+a)，得到预测值Y=X(1+a)
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+// 例: 最近1期值为100，给定环比增速a=0.2，则未来3期预测值为: 100*1.2=120，120*1.2=144，144*1.2=172.8
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+func GetChartPredictEdbInfoDataListByRuleHb(edbInfoId int, hbValue float64, startDate, endDate time.Time, frequency string, predictEdbInfoData []*models.EdbDataList, existMap map[string]float64) (newPredictEdbInfoData []*models.EdbDataList, minValue, maxValue float64) {
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+ newPredictEdbInfoData = predictEdbInfoData
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+ index := len(predictEdbInfoData)
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+ //获取后面的预测数据
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+ dayList := getPredictEdbDayList(startDate, endDate, frequency)
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+ for k, currentDate := range dayList {
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+ tmpK := index + k - 1 //上1期的值
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+
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+ // 环比值计算
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+ val := HbzDiv(newPredictEdbInfoData[tmpK].Value, hbValue)
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+
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+ currentDateStr := currentDate.Format(utils.FormatDate)
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+ newPredictEdbInfoData = append(newPredictEdbInfoData, &models.EdbDataList{
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+ EdbDataId: edbInfoId + 10000000000 + index + k,
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+ EdbInfoId: edbInfoId,
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+ DataTime: currentDateStr,
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+ Value: val,
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+ DataTimestamp: (currentDate.UnixNano() / 1e6) + 1000, //前端需要让加1s，说是2022-09-01 00:00:00 这样的整点不合适
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+ })
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+ existMap[currentDateStr] = val
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+
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+ // 最大最小值
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+ if val < minValue {
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+ minValue = val
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+ }
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+ if val < maxValue {
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+ maxValue = val
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+ }
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+ }
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+ return
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+}
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+
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+// HbzDiv 环比值计算
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+// @params a float64 上一期值
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+// @params b float64 固定的环比增速
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+func HbzDiv(a, b float64) (result float64) {
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+ if b != 0 {
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+ // 上一期值
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+ af := decimal.NewFromFloat(a)
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+
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+ // 固定的环比增速
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+ bf := decimal.NewFromFloat(b)
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+
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+ // 默认1
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+ cf := decimal.NewFromFloat(1)
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+
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+ // 总增速
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+ val := bf.Add(cf)
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+
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+ // 计算
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+ result, _ = val.Mul(af).RoundCeil(4).Float64()
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+ } else {
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+ result = 0
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+ }
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+ return
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+}
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+
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+// GetChartPredictEdbInfoDataListByRuleHc 根据环差值规则获取预测数据
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+// 2.4 环差：在未来某一个时间段内，给定一个固定的环比增加值a，用上一期值X加上环比增加值a，得到预测值Y=X+a
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+// 例: 最近1期值为100，给定环比增加值a=10，则未来3期预测值为: 100+10=110，110+10=120，120+10=130
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+func GetChartPredictEdbInfoDataListByRuleHc(edbInfoId int, hcValue float64, startDate, endDate time.Time, frequency string, predictEdbInfoData []*models.EdbDataList, existMap map[string]float64) (newPredictEdbInfoData []*models.EdbDataList, minValue, maxValue float64) {
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+ newPredictEdbInfoData = predictEdbInfoData
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+ index := len(predictEdbInfoData)
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+ //获取后面的预测数据
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+ dayList := getPredictEdbDayList(startDate, endDate, frequency)
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+ for k, currentDate := range dayList {
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+ tmpK := index + k - 1 //上1期的值
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+
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+ // 环差别值计算
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+ val := HczDiv(newPredictEdbInfoData[tmpK].Value, hcValue)
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+
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+ currentDateStr := currentDate.Format(utils.FormatDate)
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+ newPredictEdbInfoData = append(newPredictEdbInfoData, &models.EdbDataList{
|
|
|
+ EdbDataId: edbInfoId + 10000000000 + index + k,
|
|
|
+ EdbInfoId: edbInfoId,
|
|
|
+ DataTime: currentDateStr,
|
|
|
+ Value: val,
|
|
|
+ DataTimestamp: (currentDate.UnixNano() / 1e6) + 1000, //前端需要让加1s，说是2022-09-01 00:00:00 这样的整点不合适
|
|
|
+ })
|
|
|
+ existMap[currentDateStr] = val
|
|
|
+
|
|
|
+ // 最大最小值
|
|
|
+ if val < minValue {
|
|
|
+ minValue = val
|
|
|
+ }
|
|
|
+ if val < maxValue {
|
|
|
+ maxValue = val
|
|
|
+ }
|
|
|
+ }
|
|
|
+ return
|
|
|
+}
|
|
|
+
|
|
|
+// HczDiv 环差值计算
|
|
|
+// @params a float64 上一期值
|
|
|
+// @params b float64 固定的环比增加值
|
|
|
+func HczDiv(a, b float64) (result float64) {
|
|
|
+ if b != 0 {
|
|
|
+ // 上一期值
|
|
|
+ af := decimal.NewFromFloat(a)
|
|
|
+
|
|
|
+ // 固定的环比增加值
|
|
|
+ bf := decimal.NewFromFloat(b)
|
|
|
+
|
|
|
+ // 计算
|
|
|
+ result, _ = af.Add(bf).RoundCeil(4).Float64()
|
|
|
+ } else {
|
|
|
+ result = 0
|
|
|
+ }
|
|
|
+ return
|
|
|
+}
|
|
|
+
|
|
|
+// GetChartPredictEdbInfoDataListByRuleNMoveMeanValue 根据N期移动均值规则获取预测数据
|
|
|
+// 2.5 N期移动均值：在未来某一个时间段内，下一期值等于过去N期值得平均值。
|
|
|
+// 例：最近3期值(N=3),为95，98，105则未来第1期值为 1/3*(95+98+105)=99.33, 未来第2期值为 1/3*(98+105+99.33)=100.78依次类推。
|
|
|
+func GetChartPredictEdbInfoDataListByRuleNMoveMeanValue(edbInfoId int, nValue int, startDate, endDate time.Time, frequency string, realPredictEdbInfoData, predictEdbInfoData []*models.EdbDataList, existMap map[string]float64) (newPredictEdbInfoData []*models.EdbDataList, minValue, maxValue float64) {
|
|
|
+ allDataList := make([]*models.EdbDataList, 0)
|
|
|
+ allDataList = append(allDataList, realPredictEdbInfoData...)
|
|
|
+ allDataList = append(allDataList, predictEdbInfoData...)
|
|
|
+
|
|
|
+ newPredictEdbInfoData = predictEdbInfoData
|
|
|
+
|
|
|
+ lenAllData := len(allDataList)
|
|
|
+ if lenAllData < nValue || lenAllData <= 0 {
|
|
|
+ return
|
|
|
+ }
|
|
|
+ if nValue <= 0 {
|
|
|
+ return
|
|
|
+ }
|
|
|
+ // 分母
|
|
|
+ decimalN := decimal.NewFromInt(int64(nValue))
|
|
|
+
|
|
|
+ //获取后面的预测数据
|
|
|
+ dayList := getPredictEdbDayList(startDate, endDate, frequency)
|
|
|
+ for k, currentDate := range dayList {
|
|
|
+ tmpIndex := lenAllData + k - 1 //上1期的值
|
|
|
+
|
|
|
+ // 数据集合中的最后一个数据
|
|
|
+ tmpDecimalVal := decimal.NewFromFloat(allDataList[tmpIndex].Value)
|
|
|
+ for tmpK := 2; tmpK <= nValue; tmpK++ {
|
|
|
+ tmpIndex2 := tmpIndex - tmpK //上N期的值
|
|
|
+ tmpDecimalVal2 := decimal.NewFromFloat(allDataList[tmpIndex2].Value)
|
|
|
+ tmpDecimalVal = tmpDecimalVal.Add(tmpDecimalVal2)
|
|
|
+ }
|
|
|
+
|
|
|
+ // N期移动均值计算
|
|
|
+ val, _ := tmpDecimalVal.Div(decimalN).RoundCeil(4).Float64()
|
|
|
+
|
|
|
+ currentDateStr := currentDate.Format(utils.FormatDate)
|
|
|
+ tmpData := &models.EdbDataList{
|
|
|
+ EdbDataId: edbInfoId + 10000000000 + lenAllData + k,
|
|
|
+ EdbInfoId: edbInfoId,
|
|
|
+ DataTime: currentDateStr,
|
|
|
+ Value: val,
|
|
|
+ DataTimestamp: (currentDate.UnixNano() / 1e6) + 1000, //前端需要让加1s，说是2022-09-01 00:00:00 这样的整点不合适
|
|
|
+ }
|
|
|
+ newPredictEdbInfoData = append(newPredictEdbInfoData, tmpData)
|
|
|
+ allDataList = append(allDataList, tmpData)
|
|
|
+ existMap[currentDateStr] = val
|
|
|
+
|
|
|
+ // 最大最小值
|
|
|
+ if val < minValue {
|
|
|
+ minValue = val
|
|
|
+ }
|
|
|
+ if val < maxValue {
|
|
|
+ maxValue = val
|
|
|
+ }
|
|
|
+ }
|
|
|
+ return
|
|
|
+}
|
|
|
+
|
|
|
+// GetChartPredictEdbInfoDataListByRuleNLinearRegression 根据N期移动均值规则获取预测数据
|
|
|
+// 2.6N期段线性外推值：给出过去N期值所确定的线性回归方程(Y=aX+b)在未来一段时间内的推算值。回归方程虽然比较复杂，但各种编程语言应该都有现成的模块或函数，应该无需自己编写。
|
|
|
+// 例1：过去5期值(N=5)分别为：3，5，7，9，11（每两期值之间的时间间隔相等）。那么按照线性回归方程推算，未来三期的预测值是：13，15，17。
|
|
|
+//
|
|
|
+// 例2：过去6期值(N=6)分别为：3，3，5，7，9，11（每两期值之间的时间间隔相等）。那么按照线性回归方程推算，未来三期的预测值是：12.33，14.05，15.76。例1和例2的区别在于，多加了一期数据，导致回归方程发生改变，从而预测值不同。
|
|
|
+func GetChartPredictEdbInfoDataListByRuleNLinearRegression(edbInfoId int, nValue int, startDate, endDate time.Time, frequency string, realPredictEdbInfoData, predictEdbInfoData []*models.EdbDataList, existMap map[string]float64) (newPredictEdbInfoData []*models.EdbDataList, minValue, maxValue float64) {
|
|
|
+ //var errMsg string
|
|
|
+ //defer func() {
|
|
|
+ // if errMsg != `` {
|
|
|
+ // go alarm_msg.SendAlarmMsg("更新上海的token失败；ERR："+err.Error(), 3)
|
|
|
+ // }
|
|
|
+ //}()
|
|
|
+ allDataList := make([]*models.EdbDataList, 0)
|
|
|
+ allDataList = append(allDataList, realPredictEdbInfoData...)
|
|
|
+ allDataList = append(allDataList, predictEdbInfoData...)
|
|
|
+
|
|
|
+ newPredictEdbInfoData = predictEdbInfoData
|
|
|
+
|
|
|
+ lenAllData := len(allDataList)
|
|
|
+ if lenAllData < nValue || lenAllData <= 0 {
|
|
|
+ return
|
|
|
+ }
|
|
|
+
|
|
|
+ if nValue <= 0 {
|
|
|
+ return
|
|
|
+ }
|
|
|
+
|
|
|
+ //获取后面的预测数据
|
|
|
+ // 获取线性方程公式的a、b的值
|
|
|
+ coordinateData := make([]Coordinate, 0)
|
|
|
+ for tmpK := nValue; tmpK > 0; tmpK-- {
|
|
|
+ tmpIndex2 := lenAllData - tmpK //上N期的值
|
|
|
+ tmpCoordinate := Coordinate{
|
|
|
+ X: float64(nValue - tmpK + 1),
|
|
|
+ Y: allDataList[tmpIndex2].Value,
|
|
|
+ }
|
|
|
+ coordinateData = append(coordinateData, tmpCoordinate)
|
|
|
+ }
|
|
|
+ a, b := getLinearResult(coordinateData)
|
|
|
+ //fmt.Println("a:", a, ";======b:", b)
|
|
|
+
|
|
|
+ dayList := getPredictEdbDayList(startDate, endDate, frequency)
|
|
|
+ for k, currentDate := range dayList {
|
|
|
+ tmpK := nValue + k + 1
|
|
|
+
|
|
|
+ aDecimal := decimal.NewFromFloat(a)
|
|
|
+ xDecimal := decimal.NewFromInt(int64(tmpK))
|
|
|
+ bDecimal := decimal.NewFromFloat(b)
|
|
|
+
|
|
|
+ val, _ := aDecimal.Mul(xDecimal).Add(bDecimal).RoundCeil(4).Float64()
|
|
|
+
|
|
|
+ currentDateStr := currentDate.Format(utils.FormatDate)
|
|
|
+ tmpData := &models.EdbDataList{
|
|
|
+ EdbDataId: edbInfoId + 10000000000 + lenAllData + k,
|
|
|
+ EdbInfoId: edbInfoId,
|
|
|
+ DataTime: currentDateStr,
|
|
|
+ Value: val,
|
|
|
+ DataTimestamp: (currentDate.UnixNano() / 1e6) + 1000, //前端需要让加1s，说是2022-09-01 00:00:00 这样的整点不合适
|
|
|
+ }
|
|
|
+ newPredictEdbInfoData = append(newPredictEdbInfoData, tmpData)
|
|
|
+ allDataList = append(allDataList, tmpData)
|
|
|
+ existMap[currentDateStr] = val
|
|
|
+
|
|
|
+ // 最大最小值
|
|
|
+ if val < minValue {
|
|
|
+ minValue = val
|
|
|
+ }
|
|
|
+ if val < maxValue {
|
|
|
+ maxValue = val
|
|
|
+ }
|
|
|
+ }
|
|
|
+ return
|
|
|
+}
|
|
|
+
|
|
|
+// Series is a container for a series of data
|
|
|
+type Series []Coordinate
|
|
|
+
|
|
|
+// Coordinate holds the data in a series
|
|
|
+type Coordinate struct {
|
|
|
+ X, Y float64
|
|
|
+}
|
|
|
+
|
|
|
+func getLinearResult(s []Coordinate) (gradient, intercept float64) {
|
|
|
+ if len(s) == 0 {
|
|
|
+ return
|
|
|
+ }
|
|
|
+
|
|
|
+ // Placeholder for the math to be done
|
|
|
+ var sum [5]float64
|
|
|
+
|
|
|
+ // Loop over data keeping index in place
|
|
|
+ i := 0
|
|
|
+ for ; i < len(s); i++ {
|
|
|
+ sum[0] += s[i].X
|
|
|
+ sum[1] += s[i].Y
|
|
|
+ sum[2] += s[i].X * s[i].X
|
|
|
+ sum[3] += s[i].X * s[i].Y
|
|
|
+ sum[4] += s[i].Y * s[i].Y
|
|
|
+ }
|
|
|
+
|
|
|
+ // Find gradient and intercept
|
|
|
+ f := float64(i)
|
|
|
+ gradient = (f*sum[3] - sum[0]*sum[1]) / (f*sum[2] - sum[0]*sum[0])
|
|
|
+ intercept = (sum[1] / f) - (gradient * sum[0] / f)
|
|
|
+
|
|
|
+ //fmt.Println("gradient:", gradient, ";intercept:", intercept)
|
|
|
+ // Create the new regression series
|
|
|
+ //for j := 0; j < len(s); j++ {
|
|
|
+ // regressions = append(regressions, Coordinate{
|
|
|
+ // X: s[j].X,
|
|
|
+ // Y: s[j].X*gradient + intercept,
|
|
|
+ // })
|
|
|
+ //}
|
|
|
+
|
|
|
+ return
|
|
|
+}
|
