public static Classificator Create(IReadOnlyList <Func <CurveBaseInfo, bool> > conditions)
{
conditions = conditions.Reverse().ToArray();
ClassifyingItem chain = null;
for (var i = 0; i < conditions.Count; i++)
{
var curveClass = new CurveClass()
{
ClassNumber = conditions.Count - i
};
chain = new ClassifyingItem(chain, conditions[i], curveClass);
}
return(new Classificator(chain));
}
public CurveClass Classify(CurveBaseInfo curveInfo)
{
var result = new CurveClass();
if (curveInfo.EdgeB <= Math.Pow(curveInfo.FieldOrder, 0.25))
{
result.ClassNumber = 1;
}
else if (curveInfo.EdgeB <= Math.Pow(curveInfo.FieldOrder, 0.33))
{
result.ClassNumber = 2;
}
else if (curveInfo.EdgeB <= Math.Pow(curveInfo.FieldOrder, 0.5))
{
result.ClassNumber = 3;
}
else
{
result.ClassNumber = 4;
}
return(result);
}
public CurveClass Classify(CurveBaseInfo curveInfo)
{
var result = new CurveClass();
if (curveInfo.EdgeB <= 100)
{
result.ClassNumber = 1;
}
else if (curveInfo.EdgeB <= 1000)
{
result.ClassNumber = 2;
}
else if (curveInfo.EdgeB <= 10000)
{
result.ClassNumber = 3;
}
else
{
result.ClassNumber = 4;
}
return(ClassifyingItem.Classify(curveInfo));
}
public ClassifyingItem(ClassifyingItem next, Func <CurveBaseInfo, bool> classifyFunc, CurveClass curveClass)
{
_next = next;
_classifyFunc = classifyFunc;
_curveClass = curveClass;
}
public static MetalTemperatureClass MultiMetalTemperatureCaculate(List <PValue> valueList, double LimitHH, double LimitH, double LimitRP, double LimitOO, double LimitRN, double LimitL, double LimitLL)
{
try
{
List <MetalTemperatureClass> returnClass = new List <MetalTemperatureClass>();
MetalTemperatureClass newClass = new MetalTemperatureClass();
int l = 1;
List <CurveClass> ccList = new List <CurveClass>();
foreach (PValue childItem in valueList)
{
CurveClass cc = new CurveClass();
cc.x = l;
cc.y = childItem.Value;
ccList.Add(cc);
l++;
}
double Min = ccList[0].y;
double MinN = 1;
double Max = ccList[0].y;
double MaxN = 1;
double Sum = 0;
double HHG = 0;
double HHHB = 0;
double HRPB = 0;
double RP0B = 0;
double RM0B = 0;
double RMLB = 0;
double LLLB = 0;
double LLL = 0;
double RPRMB = 0;
double HLB = 0;
double HHHLLLB = 0;
double HHLLGL = 0;
double HG = 0;
double LL = 0;
foreach (CurveClass childItem in ccList)
{
if (Min > childItem.y)
{
Min = childItem.y;
MinN = childItem.x;
}
if (Max < childItem.y)
{
Max = childItem.y;
MaxN = childItem.x;
}
Sum += childItem.y;
if (childItem.y > LimitHH)
{
HHG++;
}
if (childItem.y > LimitH)
{
HG++;
}
if (childItem.y <= LimitHH && childItem.y > LimitH)
{
HHHB++;
}
if (childItem.y <= LimitH && childItem.y > LimitRP)
{
HRPB++;
}
if (childItem.y <= LimitRP && childItem.y > LimitOO)
{
RP0B++;
}
if (childItem.y <= LimitOO && childItem.y > LimitRN)
{
RM0B++;
}
if (childItem.y <= LimitRN && childItem.y > LimitL)
{
RMLB++;
}
if (childItem.y <= LimitL && childItem.y > LimitLL)
{
LLLB++;
}
if (childItem.y <= LimitL)
{
LL++;
}
if (childItem.y <= LimitLL)
{
LLL++;
}
}
RPRMB = RP0B + RM0B;
HLB = HRPB + RP0B + RM0B + RMLB;
HHHLLLB = HHHB + LLLB;
HHLLGL = HHG + LLL;
newClass.Min = Min.ToString();
newClass.MinN = MinN.ToString();
newClass.Max = Max.ToString();
newClass.MaxN = MaxN.ToString();
double Avg = Math.Round(Sum / (double)(ccList.Count), 3);
newClass.Avg = Avg.ToString();
double xi = Math.Abs(Avg - ccList[0].y);
double AvgN = 1;
double[] xList = new double[ccList.Count];
double[] yList = new double[ccList.Count];
int n = 0;
foreach (CurveClass childItem in ccList)
{
xList[n] = childItem.x;
yList[n] = childItem.y;
if (xi > Math.Abs(Avg - childItem.y))
{
xi = Math.Abs(Avg - childItem.y);
AvgN = childItem.x;
}
n++;
}
double dMaxB = ccList[1].y - ccList[0].y;
double dMaxBN = 1;
for (int i = 1; i < ccList.Count - 1; i++)
{
if (dMaxB < ccList[i].y - ccList[i - 1].y)
{
dMaxB = ccList[i].y - ccList[i - 1].y;
dMaxBN = ccList[i - 1].x;
}
}
newClass.AvgN = AvgN.ToString();
newClass.dX = (Max - Min).ToString();
newClass.dXNR = Math.Round((MaxN - MinN) / (double)(ccList.Count), 3).ToString();
newClass.dMaxB = AvgN.ToString();
newClass.dMaxBN = AvgN.ToString();
newClass.sigma = AlgorithmHelper.StandardDeviationSolve(ccList).ToString();
double lk = 0;
double lb = 0;
AlgorithmHelper.LinearRegressionSolve(ccList, ref lk, ref lb);
newClass.lk = lk.ToString();
newClass.lb = lb.ToString();
List <double> lY = new List <double>();
List <double> lX = new List <double>();
foreach (CurveClass lcItem in ccList)
{
lY.Add(lcItem.y);
lX.Add(lcItem.x);
}
List <double> lYTest = new List <double>();
foreach (double fdItem in lX)
{
lYTest.Add(lk * fdItem + lb);
}
double lR = GoodnessOfFit.RSquared(lY, lYTest);
newClass.lr = lR.ToString();
double[] res = Fit.Polynomial(xList, yList, 2);
newClass.qa = Math.Round(res[2], 3).ToString();
newClass.qb = Math.Round(res[1], 3).ToString();
newClass.qc = Math.Round(res[0], 3).ToString();
List <double> qYTest = new List <double>();
foreach (double qdItem in xList)
{
qYTest.Add(res[2] * Math.Pow(qdItem, 2) + res[1] * qdItem + res[0]);
}
double qR = GoodnessOfFit.RSquared(yList, qYTest);
newClass.qr = qR.ToString();
double Bulge = (ccList[1].y - ccList[0].y) * 2;
double BulgeN = 1;
double Cave = (ccList[1].y - ccList[0].y) * 2;
double CaveN = 1;
for (int i = 1; i < ccList.Count - 1; i++)
{
double b = 0;
if (i == ccList.Count - 1)
{
b = (ccList[i].y - ccList[i - 1].y) * 2;
}
else
{
b = ccList[i].y * 2 - ccList[i - 1].y - ccList[i + 1].y;
}
if (Bulge < b)
{
Bulge = b;
BulgeN = ccList[i].x;
}
if (Cave > b)
{
Cave = b;
CaveN = ccList[i].x;
}
}
newClass.Bulge = Bulge.ToString();
newClass.BulgeN = BulgeN.ToString();
newClass.Cave = Cave.ToString();
newClass.CaveN = CaveN.ToString();
newClass.HHG = Math.Round(HHG / (double)(ccList.Count), 3).ToString();
newClass.HHHB = Math.Round(HHHB / (double)(ccList.Count), 3).ToString();
newClass.HRPB = Math.Round(HRPB / (double)(ccList.Count), 3).ToString();
newClass.RP0B = Math.Round(RP0B / (double)(ccList.Count), 3).ToString();
newClass.RM0B = Math.Round(RM0B / (double)(ccList.Count), 3).ToString();
newClass.RMLB = Math.Round(RMLB / (double)(ccList.Count), 3).ToString();
newClass.LLLB = Math.Round(LLLB / (double)(ccList.Count), 3).ToString();
newClass.LLL = Math.Round(LLL / (double)(ccList.Count), 3).ToString();
newClass.RPRMB = Math.Round(RPRMB / (double)(ccList.Count), 3).ToString();
newClass.HLB = Math.Round(HLB / (double)(ccList.Count), 3).ToString();
newClass.HHHLLLB = Math.Round(HHHLLLB / (double)(ccList.Count), 3).ToString();
newClass.HHLLGL = Math.Round(HHLLGL / (double)(ccList.Count), 3).ToString();
newClass.HG = Math.Round(HG / (double)(ccList.Count), 3).ToString();
newClass.LL = Math.Round(LL / (double)(ccList.Count), 3).ToString();
//returnClass.Add(newClass);
return(newClass);
}
catch (Exception ex)
{
throw ex;
}
}
public static Results Calcu(List <PValue>[] inputs, CalcuInfo calcuinfo)
{
//公用变量
bool _errorFlag = false;
string _errorInfo = "";
bool _warningFlag = false;
string _warningInfo = "";
bool _fatalFlag = false;
string _fatalInfo = "";
int i;
//0输出初始化：该算法如果没有有效输入值（inputs为null）或者输入值得有效值为null，给出的计算结果。值为0，计算标志位为StatusConst.InputIsNull
List <PValue>[] results = new List <PValue> [1];
for (i = 0; i < results.Length; i++)
{
results[i] = new List <PValue>();
}
try
{
//读取参数
string mode;
string[] paras = calcuinfo.fparas.Split(';');
mode = paras[0];
double equSigma = 0;
int equSigmaMaxP = 0;
int equSigmaMinP = 0;
ArrayList equSigmaList = new ArrayList();
if (mode == "S")
{
int l = 1;
List <CurveClass> MessageInList = new List <CurveClass>();
int point = 1;
foreach (List <PValue> item in inputs)
{
List <CurveClass> childList = new List <CurveClass>();
int childL = 1;
foreach (PValue childItem in item)
{
CurveClass cClass = new CurveClass();
cClass.x = l;
cClass.y = childItem.Value;
MessageInList.Add(cClass);
CurveClass childClass = new CurveClass();
childClass.x = childL++;
childClass.y = childItem.Value;
childList.Add(childClass);
l++;
}
equSigmaList.Add(new DictionaryEntry(point++, AlgorithmHelper.StandardDeviationSolve(childList)));
}
equSigma = AlgorithmHelper.StandardDeviationSolve(MessageInList);
equSigmaMaxP = Convert.ToInt32(((DictionaryEntry)equSigmaList[0]).Key);
equSigmaMinP = Convert.ToInt32(((DictionaryEntry)equSigmaList[0]).Key);
double equSigmaMax = Convert.ToDouble(((DictionaryEntry)equSigmaList[0]).Value);
double equSigmaMin = Convert.ToDouble(((DictionaryEntry)equSigmaList[0]).Value);
foreach (var item in equSigmaList)
{
if (equSigmaMax < Convert.ToDouble(((DictionaryEntry)item).Value))
{
equSigmaMax = Convert.ToDouble(((DictionaryEntry)item).Value);
equSigmaMaxP = Convert.ToInt32(((DictionaryEntry)item).Key);
}
if (equSigmaMin > Convert.ToDouble(((DictionaryEntry)item).Value))
{
equSigmaMin = Convert.ToDouble(((DictionaryEntry)item).Value);
equSigmaMinP = Convert.ToInt32(((DictionaryEntry)item).Key);
}
}
}
else
{
double maxSigma = inputs[0][0].Value;
List <int> equSigmaMaxPList = new List <int>();
List <int> equSigmaMinPList = new List <int>();
foreach (List <PValue> item in inputs)
{
equSigmaMaxPList.Add(Convert.ToInt32(item[1].Value));
equSigmaMinPList.Add(Convert.ToInt32(item[2].Value));
foreach (PValue childItem in item)
{
if (maxSigma < childItem.Value)
{
maxSigma = childItem.Value;
}
}
}
equSigma = maxSigma * 0.75;
equSigmaMaxP = equSigmaMaxPList.GroupBy(x => x).OrderBy(y => y.Count()).First().Key;
equSigmaMinP = equSigmaMinPList.GroupBy(x => x).OrderBy(y => y.Count()).First().Key;
}
results[0].Add(new PValue(equSigma, calcuinfo.fstarttime, calcuinfo.fendtime, 0));
results[0].Add(new PValue(equSigmaMaxP, calcuinfo.fstarttime, calcuinfo.fendtime, 0));
results[0].Add(new PValue(equSigmaMinP, calcuinfo.fstarttime, calcuinfo.fendtime, 0));
return(new Results(results, _errorFlag, _errorInfo, _warningFlag, _warningInfo, _fatalFlag, _fatalInfo));
}
catch (Exception ex)
{
//计算中出任何错误，则需要记录log
//LogHelper.Write(LogType.Error, "计算模块错误!");
//记录计算模块的名称、当前标签、起始时间、结束时间
//string moduleInfo = string.Format("——计算模块的名称是：{0}，当前计算源标签是：{1}，计算起始时间是：{2}，计算结束时间是：{3}。", calcuInfo.fmodulename, calcuInfo.sourcetagname, calcuinfo.fstarttime.ToString(), calcuinfo.fendtime.ToString());
//LogHelper.Write(LogType.Error, moduleInfo);
//计算引擎报错具体信息
//string errInfo = string.Format("——具体报错信息：{0}。", ex.ToString());
//LogHelper.Write(LogType.Error, errInfo);
//返回null供计算引擎处理
_fatalFlag = true;
_fatalInfo = ex.ToString();
return(new Results(results, _errorFlag, _errorInfo, _warningFlag, _warningInfo, _fatalFlag, _fatalInfo));
}
}
