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);
}
public void TestFullSearch8()
{
const int optCountFlat = 4;
const double optFine = 3.6;
var list1 = new List <double> {
3.9, 6.6, 6.6, 7.2
};
var list2 = new List <double> {
7.5, 7.5, 7.8, 8.1
};
var optItem1 = new List <double> {
6.0, 6.6, 6.6, 7.2
};
var optItem2 = new List <double> {
8.1, 8.1, 8.7, 7.5
};
var result = MethodeFullSearch.FullSearch(new DataPerformAlgorithm(list1, list2), optCountFlat);
Assert.AreEqual(result.ListResultOneFlat.Except(optItem1).ToList().Count, 0);
Assert.AreEqual(result.ListResultTwoFlat.Except(optItem2).ToList().Count, 0);
Assert.AreEqual(result.Fine, optFine);
}
