fix:修复沿程损失计算异常的问题

cloudflight

2024-07-27 49eb2a09634e439090d4a2b13ec7d6f911d3deaf

 Hydraulic/Hydro.CommonBase/Helper/CurveFitHelper.cs

@@ -28,8 +28,29 @@
            this.data = data;

            double[] xlist = data.Select(p => (double)p.X).ToArray();
            coefficients = Fit.Polynomial(xlist, data.Select(p => (double)p.Y).ToArray(), 2);
            isFitted = true;
            if (data.Count>2)
            {
                coefficients = Fit.Polynomial(xlist, data.Select(p => (double)p.Y).ToArray(), 2);
                isFitted = true;
            }
            else if(data.Count==2)
            {
                //使用两个点的线性拟合
                var tuple = Fit.Line(xlist, data.Select(p => (double)p.Y).ToArray());
                coefficients= new DenseVector(new double[] { tuple.Item1, tuple.Item2 });
                isFitted = true;
            }
            else if (data.Count==1)
            {
                coefficients = new DenseVector(new double[] { data[0].Y });
                isFitted = true;
            }
            else
            {
                coefficients = new DenseVector(new double[] { 0});
                isFitted = true;
            }
            
            var list = xlist.ToList();
            xMax = list.Max();
            xMin = list.Min();
@@ -37,7 +58,7 @@

        public double Evaluate(double x)
        {
            while (!isFitted) ;
            if (!isFitted) return double.NaN;
            double y = 0;
            for (int i = 0; i < coefficients.Count; i++)
            {
@@ -50,7 +71,8 @@
        public List<PointF> GetFitCurve(int count)
        {
            List<PointF> curve = new List<PointF>();
            for (double x = xMin; x < xMax; x += (xMax - xMin) / count)
            
            for (double x = xMin; x <= xMax && curve.Count<count; x += (xMax - xMin) / count)
            {
                double y = Evaluate(x);
                curve.Add(new PointF((float)x, (float)y));
@@ -248,14 +270,14 @@
        /// <param name="numIntervals"></param>
        /// <param name="filterNum">以最小值作为过滤的系数</param>
        /// <returns></returns>
        public static List<DataPoint> GetFrequencyDistribution(List<double> data, out List<double>[] datapoints, int numIntervals = 10,double filterNum=5 )
        public static List<DataPoint> GetFrequencyDistribution(List<Result> data, out List<DataPoint>[] datapoints, int numIntervals = 10,double filterNum=5 )
        {

            List<DataPoint> resultPoints = new List<DataPoint>();
            datapoints= new List<double>[numIntervals];
            datapoints= new List<DataPoint>[numIntervals];
            for(int i=0;i<numIntervals;i++)
            {
                datapoints[i]= new List<double>();
                datapoints[i]= new List<DataPoint>();
            }
            // 设置置信水平

@@ -269,8 +291,8 @@


            // 计算数据的最小值和最大值
            double minValue = data.Min();
            double maxValue = data.Max();
            double minValue = data.Min(x=>x.ObjFunctionValue);
            double maxValue = data.Max(x => x.ObjFunctionValue);

            // 计算区间宽度
            double intervalWidth = (maxValue - minValue) / numIntervals;
@@ -279,23 +301,24 @@
            int[] freqDist = new int[numIntervals];

            // 计算各个区间的频率
            foreach (double x in data)
            foreach (var xd in data)
            {
                var x = xd.ObjFunctionValue;
                int index = (int)((x - minValue) / intervalWidth);
                if (index < 0) // 数据小于最小值
                {
                    freqDist[0]++;
                    datapoints[0].Add(x);
                    datapoints[0].Add(new DataPoint() {XValue= x,Tag=xd });
                }
                else if (index >= numIntervals) // 数据大于最大值
                {
                    freqDist[numIntervals - 1]++;
                    datapoints[numIntervals - 1].Add(x);
                    datapoints[numIntervals - 1].Add(new DataPoint() { XValue = x, Tag = xd });
                }
                else // 数据在区间内
                {
                    freqDist[index]++;
                    datapoints[index].Add(x);
                    datapoints[index].Add(new DataPoint() { XValue = x, Tag = xd });
                }
            }
            double[] freqDistPersent =new double[numIntervals];