private void PerformDAlg(InputSectionDataAlg inSectionData)
{
ValidateConditions.Validate(inSectionData, true);
realizat_label.Text = "".ToString(CultureInfo.InvariantCulture);
lossesOne_label.Text = "".ToString(CultureInfo.InvariantCulture);
resultDynam_label.Text = "".ToString(CultureInfo.InvariantCulture);
resultDynam_label.Text = string.Format(MessagesText.ResultDynamicProgram);
var flatCount = inSectionData.ListLenOneFlat.Count + inSectionData.ListLenTwoFlat.Count;
realizat_label.Text += string.Format(MessagesText.RealizationForRectangles, flatCount).ToString(CultureInfo.InvariantCulture);
// lossesOne_label.Text += string.Format(MessagesText.SummarizeAdditionLengthForH, inData.SumDelta.ToString(CultureInfo.InvariantCulture));
var dataAlg = new DataPerformAlgorithm();
var resultDataAfterGrouping = GroupingOnTheFloors.GroupingFlat(inSectionData);
dataAlg.ListLenOneFlat =
PreparationSquares.FlatsWithTheAdditiveLength(resultDataAfterGrouping.ListResultOneFlat);
dataAlg.ListLenTwoFlat =
PreparationSquares.FlatsWithTheAdditiveLength(resultDataAfterGrouping.ListResultTwoFlat);
var myStopWatchDynamic = new Stopwatch();
myStopWatchDynamic.Start();
// Create solution-tree
var resultListDynM =
DynamicMethodeSect.DynamicMethode(new DataMethode(dataAlg.ListLenOneFlat, dataAlg.ListLenTwoFlat,
inSectionData.OptCountFlatOnFloor));
// Backtracking for optimal solution
var backTrackingResult = BackTrackForDynPr.BackTracking(resultListDynM);
// Print Result ( штраф по этажам и от группировки считаем внутри печати, а также приведение площадей)
PrintResult.DynamicProgrammingPrintResult(backTrackingResult, inSectionData.CountFloor, resultDataAfterGrouping, resultDynam_label);
myStopWatchDynamic.Stop();
resultDynam_label.Text +=
string.Format(MessagesText.WorkTimeDynamicProgram,
myStopWatchDynamic.ElapsedMilliseconds / 1000.0).ToString(CultureInfo.InvariantCulture);
}
private void PerformComprehensiveSearch(InputSectionDataAlg inSectionData)
{
ValidateConditions.Validate(inSectionData, false);
realizat_label.Text = "".ToString(CultureInfo.InvariantCulture);
lossesOne_label.Text = "".ToString(CultureInfo.InvariantCulture);
resultFullSearch_label.Text = "".ToString(CultureInfo.InvariantCulture);
var flatCount = inSectionData.ListLenOneFlat.Count + inSectionData.ListLenTwoFlat.Count;
realizat_label.Text += string.Format(MessagesText.RealizationForRectangles, flatCount).ToString(CultureInfo.InvariantCulture);
// lossesOne_label.Text += string.Format(MessagesText.SummarizeAdditionLengthForH, inData.SumDelta.ToString(CultureInfo.InvariantCulture));
var dataAlg = new DataPerformAlgorithm();
var resultDataAfterGrouping = GroupingOnTheFloors.GroupingFlat(inSectionData);
dataAlg.ListLenOneFlat = PreparationSquares.FlatsWithTheAdditiveLength(resultDataAfterGrouping.ListResultOneFlat);
dataAlg.ListLenTwoFlat = PreparationSquares.FlatsWithTheAdditiveLength(resultDataAfterGrouping.ListResultTwoFlat);
dataAlg.FineAfterGrouping = resultDataAfterGrouping.Fine;
var myStopWatch = new Stopwatch();
myStopWatch.Start();
var fullSearch = MethodeFullSearch.FullSearch(dataAlg, inSectionData.OptCountFlatOnFloor);
fullSearch.Fine = Math.Round(fullSearch.Fine * inSectionData.CountFloor, 1);
fullSearch.Fine = Math.Round(fullSearch.Fine + dataAlg.FineAfterGrouping, 1);
fullSearch.ListExcessOneFlat = resultDataAfterGrouping.ListExcessOneFlat;
fullSearch.ListExcessTwoFlat = resultDataAfterGrouping.ListExcessTwoFlat;
PrintResult.FullSearchPrintResult(fullSearch, inSectionData.CountFloor, resultFullSearch_label);
myStopWatch.Stop();
resultFullSearch_label.Text += MessagesText.NextLine;
var timeFullSearch = string.Format(MessagesText.WorkTimeComprehensiveSearch,
myStopWatch.ElapsedMilliseconds / 1000.0).ToString(CultureInfo.InvariantCulture);
resultFullSearch_label.Text += timeFullSearch.ToString(CultureInfo.InvariantCulture);
}
private void PerformHAlg(InputSectionDataAlg inSectionData)
{
ValidateConditions.Validate(inSectionData, true);
realizat_label.Text = "".ToString(CultureInfo.InvariantCulture);
lossesOne_label.Text = "".ToString(CultureInfo.InvariantCulture);
resultGreedy_label.Text = "".ToString(CultureInfo.InvariantCulture);
var flatCount = inSectionData.ListLenOneFlat.Count + inSectionData.ListLenTwoFlat.Count;
realizat_label.Text +=
string.Format(MessagesText.RealizationForRectangles, flatCount).ToString(CultureInfo.InvariantCulture);
// lossesOne_label.Text += string.Format(MessagesText.SummarizeAdditionLengthForH, inData.SumDelta.ToString(CultureInfo.InvariantCulture));
var dataAlg = new DataPerformAlgorithm();
var resultDataAfterGrouping = GroupingOnTheFloors.GroupingFlat(inSectionData);
dataAlg.ListLenOneFlat =
PreparationSquares.FlatsWithTheAdditiveLength(resultDataAfterGrouping.ListResultOneFlat);
dataAlg.ListLenTwoFlat =
PreparationSquares.FlatsWithTheAdditiveLength(resultDataAfterGrouping.ListResultTwoFlat);
dataAlg.FineAfterGrouping = resultDataAfterGrouping.Fine;
//for print in txt-file
/*
* var str1 = new StringBuilder();
* var str2 = new StringBuilder();
* foreach (var elem in dataAlg.ListLenOneFlat)
* {
* str1.Append(elem + " ");
* }
*
* foreach (var elem in dataAlg.ListLenTwoFlat)
* {
* str2.Append(elem + " ");
* }
*
* File.WriteAllText(@"C:\Users\marchenko.a\Downloads\Модифицированная Статья\ExampleTwoFirstList.txt", str1.ToString());
* File.WriteAllText(@"C:\Users\marchenko.a\Downloads\Модифицированная Статья\ExampleTwoSecondList.txt", str2.ToString());
*/
var myStopWatchGreedy = new Stopwatch();
myStopWatchGreedy.Start();
var resultList = new ResultListGreedyMethode();
foreach (var flag in resultList.PositionStart)
{
var allIterationsResult = new ResultGreedyMethode[Constraints.NumberOfIteration];
var totalOptimalResult = new ResultGreedyMethode(double.MaxValue);
var firstOneFlat = 0.0; // в RGreedyMethode
var numberIteration = 1;
while (numberIteration <= Constraints.NumberOfIteration)
{
var resultGreedyIter =
GreedyMethodeSect.GreedyMethode(
new DataMethode(dataAlg.ListLenOneFlat, dataAlg.ListLenTwoFlat, inSectionData.OptCountFlatOnFloor),
firstOneFlat, flag);
firstOneFlat = resultGreedyIter.NewFirstOneFlat;
resultGreedyIter.NumIter = numberIteration;
resultGreedyIter.Fine = Math.Round(resultGreedyIter.Fine * inSectionData.CountFloor, 1);
resultGreedyIter.Fine = Math.Round(resultGreedyIter.Fine + dataAlg.FineAfterGrouping, 1);
resultGreedyIter.ListLenExceedOneFlat = resultDataAfterGrouping.ListExcessOneFlat;
resultGreedyIter.ListLenExceedTwoFlat = resultDataAfterGrouping.ListExcessTwoFlat;
allIterationsResult[numberIteration - 1] = resultGreedyIter;
if (resultGreedyIter.Fine < totalOptimalResult.Fine)
{
totalOptimalResult = resultGreedyIter;
}
//Вывод результата по итерациям
//PrintResult.GreedyIterationPrintResult(resultGreedyIter, inData.CountFloor, true, resultGreedy_label);
numberIteration++;
if (numberIteration > Constraints.NumberOfIteration)
{
resultList.Results.Add(totalOptimalResult);
}
}
}
myStopWatchGreedy.Stop();
PrintResult.GreedyIterationPrintResult(resultList.Results.OrderBy(a => a.Fine).First(), inSectionData.CountFloor,
false, resultGreedy_label);
resultGreedy_label.Text +=
string.Format(MessagesText.WorkTimeHeuristicAlgoruthm,
myStopWatchGreedy.ElapsedMilliseconds / 1000.0).ToString(CultureInfo.InvariantCulture).ToString(CultureInfo.InvariantCulture);
}