From 9490957edfc694a0378ee52af47e921b8ac44e7a Mon Sep 17 00:00:00 2001
From: qin <a@163.com>
Date: 星期二, 04 六月 2024 09:12:11 +0800
Subject: [PATCH] 冲突
---
Hydraulic/Hydro.CommonBase/Helper/CurveFitHelper.cs | 170 ++++++++++++++++++++++++++++++++++++++++++++++----------
1 files changed, 138 insertions(+), 32 deletions(-)
diff --git a/Hydraulic/Hydro.CommonBase/Helper/CurveFitHelper.cs b/Hydraulic/Hydro.CommonBase/Helper/CurveFitHelper.cs
index b2e0f49..eedff37 100644
--- a/Hydraulic/Hydro.CommonBase/Helper/CurveFitHelper.cs
+++ b/Hydraulic/Hydro.CommonBase/Helper/CurveFitHelper.cs
@@ -7,6 +7,7 @@
using System.Linq;
using System.Text;
using System.Threading.Tasks;
+using System.Windows.Forms.DataVisualization.Charting;
namespace Hydro.CommonBase
{
@@ -71,7 +72,7 @@
var list = data.FindAll(p => x - (xMax - xMin) / count / 2 <= p.X && p.X < x + (xMax - xMin) / count/2).Select(p => (double)p.Y).ToList();
if (list.Count >= 3)
{
- var Bounds = ConfidenceRangeHelper.GetConfidenceRange(list, 95);
+ var Bounds = GetConfidenceRange(list, 95);
var x_avg = x;
if (Bounds[0] == double.NaN || Bounds[1] == double.NaN) continue;
down_list.Add(new PointF((float)x_avg, (float)Bounds[0]));
@@ -115,33 +116,29 @@
//var curve1 = h1.GetFitCurve(count);
return new List<List<PointF>> { down_list, up_list, avg_list };
}
- }
-
- public static class ConfidenceRangeHelper
- {
- public static double[] GetConfidenceRange(List<double> data, double confidenceLevel = 90,bool mode=true)//true鏄腑浣嶆暟锛岀疆淇″尯闂存槸false
+ public static double[] GetConfidenceRange(List<double> data, double confidenceLevel = 90, bool mode = true)//true鏄腑浣嶆暟锛岀疆淇″尯闂存槸false
{
//濡傛灉model==true,杩欒绠椾腑浣嶆暟,姣斾緥涓篶onfidenceLevel
if (mode)
{
-
+
data.Sort();
double lowerBound = 0;
double upperBound = 0;
//鍙栫櫨鍒嗕綅confidenceLevel/100鍜�1-confidenceLevel/100.0鐨勬暟
- int upperIndex = (int)Math.Round( data.Count*confidenceLevel/100.0);
- int lowerIndex = (int)Math.Round(data.Count * (1-confidenceLevel / 100.0));
+ int upperIndex = (int)Math.Round(data.Count * confidenceLevel / 100.0);
+ int lowerIndex = (int)Math.Round(data.Count * (1 - confidenceLevel / 100.0));
if (upperIndex == data.Count)
{
upperIndex = data.Count - 1;
}
- if (lowerIndex <0)
+ if (lowerIndex < 0)
{
lowerIndex = 0;
}
return new double[] { data[lowerIndex], data[upperIndex] };
-
+
}
@@ -162,35 +159,35 @@
}
}
- // 浣跨敤寰幆璁＄畻鏍锋湰鍧囧��
- double sum = 0;
- foreach (double value in data)
- {
- sum += value;
- }
- double mean = sum / data.Count;
+ // 浣跨敤寰幆璁＄畻鏍锋湰鍧囧��
+ double sum = 0;
+ foreach (double value in data)
+ {
+ sum += value;
+ }
+ double mean = sum / data.Count;
- // 浣跨敤寰幆璁＄畻鏍囧噯宸�
- double sumOfSquares = 0;
- foreach (double value in data)
- {
- sumOfSquares += Math.Pow(value - mean, 2);
- }
- double stdDev = Math.Sqrt(sumOfSquares / (data.Count - 1));
+ // 浣跨敤寰幆璁＄畻鏍囧噯宸�
+ double sumOfSquares = 0;
+ foreach (double value in data)
+ {
+ sumOfSquares += Math.Pow(value - mean, 2);
+ }
+ double stdDev = Math.Sqrt(sumOfSquares / (data.Count - 1));
- double zValue = 0;
- double alpha = 1 - confidenceLevel / 100.0;
+ double zValue = 0;
+ double alpha = 1 - confidenceLevel / 100.0;
- double zValueOneTail = GetZValueOneTail(alpha / 2);
- double marginOfError = zValueOneTail * stdDev;
- double lowerBound = mean - marginOfError;
- double upperBound = mean + marginOfError;
+ double zValueOneTail = GetZValueOneTail(alpha / 2);
+ double marginOfError = zValueOneTail * stdDev;
+ double lowerBound = mean - marginOfError;
+ double upperBound = mean + marginOfError;
return new double[] { lowerBound, upperBound };
}
-
+
}
static double[,] zTable = {
{0.0000, 0.0039, 0.0078, 0.0117, 0.0156, 0.0195, 0.0234, 0.0274, 0.0314, 0.0353},
@@ -243,6 +240,115 @@
return zValue;
}
+ //鏁版嵁棰戠巼鍒嗗竷鍥剧殑鑻辨枃鎬庝箞璇�,
+ /// <summary>
+ /// 鑾峰彇鏁版嵁棰戠巼鐨勫垎甯冨浘
+ /// </summary>
+ /// <param name="data"></param>
+ /// <param name="numIntervals"></param>
+ /// <param name="filterNum">浠ユ渶灏忓�间綔涓鸿繃婊ょ殑绯绘暟</param>
+ /// <returns></returns>
+ public static List<DataPoint> GetFrequencyDistribution(List<double> data, out List<double>[] datapoints, int numIntervals = 10,double filterNum=5 )
+ {
+
+ List<DataPoint> resultPoints = new List<DataPoint>();
+ datapoints= new List<double>[numIntervals];
+ for(int i=0;i<numIntervals;i++)
+ {
+ datapoints[i]= new List<double>();
+ }
+ // 璁剧疆缃俊姘村钩
+
+
+ // 璁剧疆鍖洪棿鏁伴噺
+
+
+ ////鍦╠ata涓繃婊ゅ拰鏈�澶х殑5%鐨勬暟鎹�
+ //data = data.Where(d => d <= data.Min() * filterNum).ToList();
+
+
+
+ // 璁＄畻鏁版嵁鐨勬渶灏忓�煎拰鏈�澶у��
+ double minValue = data.Min();
+ double maxValue = data.Max();
+
+ // 璁＄畻鍖洪棿瀹藉害
+ double intervalWidth = (maxValue - minValue) / numIntervals;
+
+ // 鍒濆鍖栭鐜囧垎甯冩暟缁�
+ int[] freqDist = new int[numIntervals];
+
+ // 璁＄畻鍚勪釜鍖洪棿鐨勯鐜�
+ foreach (double x in data)
+ {
+ int index = (int)((x - minValue) / intervalWidth);
+ if (index < 0) // 鏁版嵁灏忎簬鏈�灏忓��
+ {
+ freqDist[0]++;
+ datapoints[0].Add(x);
+ }
+ else if (index >= numIntervals) // 鏁版嵁澶т簬鏈�澶у��
+ {
+ freqDist[numIntervals - 1]++;
+ datapoints[numIntervals - 1].Add(x);
+ }
+ else // 鏁版嵁鍦ㄥ尯闂村唴
+ {
+ freqDist[index]++;
+ datapoints[index].Add(x);
+ }
+ }
+ double[] freqDistPersent =new double[numIntervals];
+ double sum = freqDist.Sum();
+ //璁＄畻freqDist鐨勯鐜囩櫨鍒嗘瘮
+ for (int i = 0; i < numIntervals; i++)
+ {
+ freqDistPersent[i] = (freqDist[i] / (double)sum );
+ }
+
+ //// 璁＄畻鏍锋湰鍧囧�煎拰鏍囧噯宸�
+ //double mean = data.Average();
+ //double stdDev = Math.Sqrt(data.Select(x => (x - mean) * (x - mean)).Sum() / (data.Length - 1));
+
+ //double zValue = 0;
+ //double alpha = 1 - confidenceLevel / 100.0;
+
+ //double zValueOneTail = GetZValueOneTail(alpha / 2);
+
+ ////zValue = mean + zValueOneTail * stdDev / Math.Sqrt(data.Length);
+ //// 璁＄畻缃俊鍖洪棿鐨勮寖鍥�
+ ////double z = GetZValue(confidenceLevel); //chart1.DataManipulator.Statistics.InverseNormalDistribution(1 - confidenceLevel / 200.0); // 鏍规嵁缃俊姘村钩鏌ユ壘鏍囧噯姝ｆ�佸垎甯冪殑涓寸晫鍊�
+ //double marginOfError = zValueOneTail * stdDev;/// Math.Sqrt(data.Length);
+ //double lowerBound = mean - marginOfError;
+ //double upperBound = mean + marginOfError;
+ for (int i = 0; i < numIntervals; i++)
+ {
+ double xValue = minValue + i * intervalWidth + intervalWidth / 2;
+ double yValue = freqDistPersent[i];
+ if (isValid(xValue) && isValid(yValue))
+ resultPoints.Add(new DataPoint(xValue, yValue));
+ }
+
+
+ return resultPoints;
+
+
+
+ }
+
+ static bool isValid(double value)
+ {
+ if (value == (int)value && value < 0) return false;
+ return !Double.IsNaN(value) && !Double.IsInfinity(value);
+ }
+
+
}
+
+ //public static class ConfidenceRangeHelper
+ //{
+
+
+ //}
}
--
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