public static LangleyAlgorithm SelectState(LangleyExperimentTable let)
{
LangleyDistributionSelection distributionSelection = null;
LangleyMethodStandardSelection langleyMethod = null;
if (let.let_DistributionState == 0)
{
distributionSelection = new Normal();
}
else if (let.let_DistributionState == 1)
{
distributionSelection = new Logistic();
}
if (let.let_StandardState == 0)
{
langleyMethod = new Standard();
}
else if (let.let_StandardState == 1)
{
langleyMethod = new Ln();
}
else if (let.let_StandardState == 2)
{
langleyMethod = new Log();
}
else if (let.let_StandardState == 3)
{
langleyMethod = new Pow(double.Parse(let.let_Power));
}
return(new LangleyAlgorithm(distributionSelection, langleyMethod));;
}
public override bool Insert(LangleyExperimentTable let)
{
try
{
db.LangleyExperimentTable.Add(let);
db.SaveChanges();
}
catch (Exception)
{
return(false);
}
return(true);
}
public ActionResult ResponsePointIntervalEstimate(double reponseProbability2, double confidenceLevel2, double cjl, double favg, double fsigma, int langlryExpTableId)
{
LangleyExperimentTable langlryExpTable = dbDrive.GetLangleyExperimentTable(langlryExpTableId);
List <LangleyDataTable> ldts = dbDrive.GetAllLangleyDataTable(langlryExpTable.let_Id);
ldts.RemoveRange(ldts.Count - 1, 1);
var xOrVArray = LangleyPublic.XAndVArrays(ldts);
xOrVArray.vArray = LangleyPublic.IsFlipTheResponse(langlryExpTable, xOrVArray.vArray);
var lr = LangleyPublic.SelectState(langlryExpTable);
var ies = lr.ResponsePointIntervalEstimate(xOrVArray.xArray, xOrVArray.vArray, reponseProbability2, confidenceLevel2, cjl, favg, fsigma);
string[] value = { "(" + ies[0].Confidence.Down.ToString("f6") + "," + ies[0].Confidence.Up.ToString("f6") + ")", "(" + ies[0].Mu.Down.ToString("f6") + "," + ies[0].Mu.Up.ToString("f6") + ")", "(" + ies[0].Sigma.Down.ToString("f6") + "," + ies[0].Sigma.Up.ToString("f6") + ")", "(" + ies[1].Confidence.Down.ToString("f6") + "," + ies[1].Confidence.Up.ToString("f6") + ")", "(" + ies[1].Mu.Down.ToString("f6") + "," + ies[1].Mu.Up.ToString("f6") + ")", "(" + ies[1].Sigma.Down.ToString("f6") + "," + ies[1].Sigma.Up.ToString("f6") + ")" };
return(Json(value));
}
private static Langley_list GetLangley_lists(LangleyExperimentTable let)
{
Langley_list langley_List = new Langley_list();
langley_List.Id = let.let_Id;
langley_List.PrecisionInstruments = let.let_PrecisionInstruments;
langley_List.StimulusQuantityFloor = let.let_StimulusQuantityFloor;
langley_List.StimulusQuantityCeiling = let.let_StimulusQuantityCeiling;
langley_List.DistributionState = DistributionState(let);
langley_List.Correction = let.let_Correction;
langley_List.FlipTheResponse = let.let_FlipTheResponse;
langley_List.ExperimentalDate = let.let_ExperimentalDate.ToString();
langley_List.projectname = let.let_ProductName;
return(langley_List);
}
public static int[] IsFlipTheResponse(LangleyExperimentTable let, int[] vArray)
{
if (let.let_FlipTheResponse == 1)
{
for (int i = 0; i < vArray.Length; i++)
{
if (vArray[i] == 0)
{
vArray[i] = 1;
}
else
{
vArray[i] = 0;
}
}
}
return(vArray);
}
public JsonResult LanglieParameterSettingsJson()
{
double[] xArray = new double[] { };
int[] vArray = new int[] { };
var str = new StreamReader(Request.InputStream);
var stream = str.ReadToEnd();
JavaScriptSerializer js = new JavaScriptSerializer();
LangleyExperimentTable let = js.Deserialize <LangleyExperimentTable>(stream);
let.let_RecordEmployeeId = LangleyPublic.adminId;
let.let_ExperimentalDate = DateTime.Now;
dbDrive.Insert(let);
double sq = LangleyPublic.SelectState(let).CalculateStimulusQuantity(xArray, vArray, let.let_StimulusQuantityCeiling, let.let_StimulusQuantityFloor, let.let_PrecisionInstruments);
bool isTure = dbDrive.Insert(LangleyPublic.LangleyDataTables(let.let_Id, dbDrive, sq));
string name = let.let_ProductName;
string[] value = { isTure.ToString(), let.let_Id.ToString(), name };
return(Json(value));
}
public override bool Update(LangleyExperimentTable let)
{
try
{
var entry = db.Set <LangleyExperimentTable>().Find(let.let_Id);
if (entry != null)
{
db.Entry <LangleyExperimentTable>(entry).State = EntityState.Detached; //这个是在同一个上下文能修改的关键
}
db.LangleyExperimentTable.Attach(let);
db.Entry(let).State = EntityState.Modified;
db.SaveChanges();
}
catch (Exception)
{
return(false);
}
return(true);
}
public override bool Delete(LangleyExperimentTable let)
{
LangleyExperimentTable modle = db.LangleyExperimentTable.FirstOrDefault(m => m.let_Id == let.let_Id);
if (modle == null)
{
return(false);
}
try
{
db.LangleyExperimentTable.Remove(modle);
db.SaveChanges();
}
catch (Exception)
{
return(false);
}
return(true);
}
public static List <Langley> Langleys(LangleyExperimentTable langlryExpTable, List <LangleyDataTable> ldts, int first, int count)
{
List <Langley> langleys = new List <Langley>();
for (int i = ldts.Count - 1; i >= 0; i--)
{
Langley langley = new Langley();
langley.ldt_Id = ldts[i].ldt_Id;
langley.ldt_Number = ldts[i].ldt_Number;
langley.ldt_StimulusQuantity = ldts[i].ldt_StimulusQuantity;
langley.ldt_Response = ldts[i].ldt_Response;
langley.ldt_Mean = ldts[i].ldt_Mean;
langley.ldt_MeanVariance = ldts[i].ldt_MeanVariance;
langley.ldt_StandardDeviation = ldts[i].ldt_StandardDeviation;
langley.ldt_StandardDeviationVariance = ldts[i].ldt_StandardDeviationVariance;
langley.ldt_Covmusigma = ldts[i].ldt_Covmusigma;
langley.number = count;
langleys.Add(langley);
}
return(langleys);
}
//修改数据值
public static LangleyDataTable UpdateLangleyDataTable(LangleyExperimentTable langlryExpTable, LangleyAlgorithm langleyAlgorithm, double[] xArray, int[] vArray, LangleyDataTable ldt)
{
ldt.ldt_Response = vArray[vArray.Length - 1];
ldt.ldt_StimulusQuantity = xArray[xArray.Length - 1];
vArray = IsFlipTheResponse(langlryExpTable, vArray);
var pointCalculateValue = langleyAlgorithm.GetResult(xArray, vArray);
ldt.ldt_Mean = double.Parse(pointCalculateValue.μ0_final.ToString("f13"));
if (langlryExpTable.let_Correction == 0)
{
pointCalculateValue.σ0_final = langleyAlgorithm.CorrectionAlgorithm(pointCalculateValue.σ0_final, xArray.Length);
}
ldt.ldt_StandardDeviation = pointCalculateValue.σ0_final;
if (double.IsNaN(pointCalculateValue.varmu))
{
ldt.ldt_MeanVariance = 0;
}
else
{
ldt.ldt_MeanVariance = pointCalculateValue.varmu;
}
if (double.IsNaN(pointCalculateValue.varsigma))
{
ldt.ldt_StandardDeviationVariance = 0;
}
else
{
ldt.ldt_StandardDeviationVariance = pointCalculateValue.varsigma;
}
if (double.IsNaN(pointCalculateValue.covmusigma))
{
ldt.ldt_Covmusigma = 0;
}
else
{
ldt.ldt_Covmusigma = pointCalculateValue.covmusigma;
}
return(ldt);
}
//修改分析参数
public JsonResult UpdateParameter()
{
var str = new StreamReader(Request.InputStream);
var stream = str.ReadToEnd();
JavaScriptSerializer js = new JavaScriptSerializer();
LangleyExperimentTable let = dbDrive.GetLangleyExperimentTable(js.Deserialize <LangleyExperimentTable>(stream).let_Id);
let.let_DistributionState = js.Deserialize <LangleyExperimentTable>(stream).let_DistributionState;
let.let_Correction = js.Deserialize <LangleyExperimentTable>(stream).let_Correction;
dbDrive.Update(let);
List <LangleyDataTable> ldts = dbDrive.GetAllLangleyDataTable(let.let_Id);
ldts.RemoveRange(ldts.Count - 1, 1);
var xOrVArray = LangleyPublic.XAndVArrays(ldts);
var lr = LangleyPublic.SelectState(let);
LangleyDataTable langleyDataTable = new LangleyDataTable();
bool isTure = false;
for (int i = 1; i <= ldts.Count; i++)
{
double[] xArray = new double[i];
int[] vArray = new int[i];
for (int j = 0; j < i; j++)
{
xArray[j] = xOrVArray.xArray[j];
vArray[j] = xOrVArray.vArray[j];
}
langleyDataTable = LangleyPublic.UpdateLangleyDataTable(let, lr, xArray, vArray, ldts[i - 1]);
isTure = dbDrive.Update(langleyDataTable);
if (isTure == false)
{
break;
}
}
string[] value = { isTure.ToString(), lr.Precs(langleyDataTable.ldt_Mean, langleyDataTable.ldt_StandardDeviation)[0].ToString("f6"), lr.Precs(langleyDataTable.ldt_Mean, langleyDataTable.ldt_StandardDeviation)[1].ToString("f6"), langleyDataTable.ldt_Mean.ToString("f6"), langleyDataTable.ldt_StandardDeviation.ToString("f6") };
return(Json(value));
}
public static string LangleyFreeSpireExcel(LangleyExperimentTable langlryExpTable, List <LangleyDataTable> ldts)
{
var lr = LangleyPublic.SelectState(langlryExpTable);
ldts.RemoveRange(ldts.Count - 1, 1);
var xOrVArray = LangleyPublic.XAndVArrays(ldts);
Workbook book = new Workbook();
Worksheet sheet = book.Worksheets[0];
var iCellcount = 1;
//1.设置表头
sheet.Range[1, iCellcount++].Text = "兰利法感度试验数据记录及处理结果";
sheet.Range["A1:H1"].Merge();
sheet.Range["A1:H1"].Style.HorizontalAlignment = HorizontalAlignType.Center;
sheet.Range["E2"].Text = "打印时间";
sheet.Range["F2:H2"].Text = DateTime.Now.ToString("yyyy-MM-dd");
sheet.Range["F2:H2"].Merge();
sheet.Range["A3"].Text = "样本名称";
sheet.Range["B3:D3"].Text = langlryExpTable.let_ProductName;
sheet.Range["B3:D3"].Merge();
sheet.Range["E3"].Text = "试验时间";
sheet.Range["F3:H3"].Text = langlryExpTable.let_ExperimentalDate.ToString("yyyy-MM-dd HH:mm");
sheet.Range["F3:H3"].Merge();
sheet.Range["A4"].Text = "实验数量";
sheet.Range["B4"].Text = ldts.Count.ToString();
sheet.Range["C4"].Text = "分辨率";
sheet.Range["D4"].Text = langlryExpTable.let_PrecisionInstruments.ToString();
sheet.Range["E4"].Text = "发布选择";
sheet.Range["F4:H4"].Text = LangleyPublic.DistributionState(langlryExpTable);
sheet.Range["F4:H4"].Merge();
sheet.Range["A5"].Text = "刺激量上限";
sheet.Range["B5"].Text = langlryExpTable.let_StimulusQuantityCeiling.ToString();
sheet.Range["C5"].Text = "刺激量下限";
sheet.Range["D5"].Text = langlryExpTable.let_StimulusQuantityFloor.ToString();
sheet.Range["E5"].Text = "标准差修正";
sheet.Range["F5"].Text = langlryExpTable.let_Correction == 0 ? "是" : "否";
sheet.Range["G5"].Text = "翻转响应";
sheet.Range["H5"].Text = langlryExpTable.let_FlipTheResponse == 1 ? "是" : "否";
sheet.Range["A6"].Text = "技术条件";
sheet.Range["B6:H6"].Text = langlryExpTable.let_TechnicalConditions;
sheet.Range["B6:H6"].Merge();
if (langlryExpTable.let_FlipTheResponse == 0)
{
if (langlryExpTable.let_Correction == 1)
{
sheet.Range["A7:H7"].Text = "标记：发火：“1”，不发火：“0” 点估计标准差计算结果为最大拟然估计结果";
}
else
{
sheet.Range["A7:H7"].Text = "标记：发火：“1”，不发火：“0” 点估计标准差计算结果为按照GJB377修正结果";
}
}
else
if (langlryExpTable.let_Correction == 1)
{
sheet.Range["A7:H7"].Text = "标记：发火：“0”，不发火：“1” 点估计标准差计算结果为最大拟然估计结果";
}
else
{
sheet.Range["A7:H7"].Text = "标记：发火：“0”，不发火：“1” 点估计标准差计算结果为按照GJB377修正结果";
}
TableHead(sheet);
int count = 9;
for (int i = 0; i < ldts.Count; i++)
{
sheet.Range["A" + count + ""].Text = (i + 1).ToString();
sheet.Range["B" + count + ":C" + count + ""].Text = ldts[i].ldt_StimulusQuantity.ToString();
sheet.Range["B" + count + ":C" + count + ""].Merge();
sheet.Range["D" + count + ""].Text = ldts[i].ldt_Response.ToString();
sheet.Range["E" + count + ":F" + count + ""].Text = ldts[i].ldt_Mean.ToString();
sheet.Range["E" + count + ":F" + count + ""].Merge();
sheet.Range["G" + count + ":H" + count + ""].Text = ldts[i].ldt_StandardDeviation.ToString();
sheet.Range["G" + count + ":H" + count + ""].Merge();
count++;
}
sheet.Range["A" + count + ""].Text = "点估计：";
sheet.Range["B" + count + ":D" + count + ""].Text = "均值：" + ldts[ldts.Count - 1].ldt_Mean + "";
sheet.Range["B" + count + ":D" + count + ""].Merge();
sheet.Range["E" + count + ":H" + count + ""].Text = "标准差：" + ldts[ldts.Count - 1].ldt_StandardDeviation + "";
sheet.Range["E" + count + ":H" + count + ""].Merge();
count++;
var ignition99 = LangleyIgnition(ldts, lr, 0.99);
var ignition1 = LangleyIgnition(ldts, lr, 0.01);
var ignition999 = LangleyIgnition(ldts, lr, 0.999);
var ignition01 = LangleyIgnition(ldts, lr, 0.001);
var ignition9999 = LangleyIgnition(ldts, lr, 0.9999);
var ignition001 = LangleyIgnition(ldts, lr, 0.0001);
count = PointCalculation(count, ignition99, ignition1, ignition999, ignition01, ignition9999, ignition001, sheet);
var ie = lr.GetIntervalEstimationValue(lr.DoubleSideEstimation(xOrVArray.xArray, xOrVArray.vArray, 0.999, 0.8));
var ie01 = lr.GetIntervalEstimationValue(lr.DoubleSideEstimation(xOrVArray.xArray, xOrVArray.vArray, 0.001, 0.8));
count = IntervalEstimation(count, sheet, ie, ie01, 0.8);
ie = lr.GetIntervalEstimationValue(lr.DoubleSideEstimation(xOrVArray.xArray, xOrVArray.vArray, 0.999, 0.95));
ie01 = lr.GetIntervalEstimationValue(lr.DoubleSideEstimation(xOrVArray.xArray, xOrVArray.vArray, 0.001, 0.95));
count = IntervalEstimation(count, sheet, ie, ie01, 0.95);
ie = lr.GetIntervalEstimationValue(lr.DoubleSideEstimation(xOrVArray.xArray, xOrVArray.vArray, 0.999, 0.99));
ie01 = lr.GetIntervalEstimationValue(lr.DoubleSideEstimation(xOrVArray.xArray, xOrVArray.vArray, 0.001, 0.99));
count = IntervalEstimation(count, sheet, ie, ie01, 0.99);
sheet.Range["A3:H" + count + ""].BorderInside(LineStyleType.Thin, Color.Black);
sheet.Range["A3:H" + count + ""].BorderAround(LineStyleType.Medium, Color.Black);
count++;
sheet.Range["B" + count + ""].Text = "复查人";
sheet.Range["F" + count + ""].Text = "试验人";
//设置行宽
sheet.Range["A1:H1"].RowHeight = 20;
var strFullName = @"C:\兰利法\" + "兰利法" + DateTime.Now.ToString("yyyy-MM-dd-HH-mm-ss") + ".xlsx";
book.SaveToFile(strFullName, ExcelVersion.Version2010);
return(strFullName);
}
public override LangleyExperimentTable GetLangleyExperimentTable(int let_id)
{
LangleyExperimentTable let = db.LangleyExperimentTable.Where(m => m.let_Id == let_id).First();
return(let);
}
public static string DistributionState(LangleyExperimentTable let)
{
LangleyAlgorithm lr = SelectState(let);
return(lr.Discription());
}
public abstract bool Update(LangleyExperimentTable let);
public abstract bool Insert(LangleyExperimentTable let);
