public cFeedBackMessage Run()
{
ToReturn = new cExtendedTable(this.Widht,this.Height,0);
RND = new Random(this.Seed);
if (this.DistributionType == eRandDistributionType.UNIFORM)
{
foreach (cExtendedList item in ToReturn)
for (int i = 0; i < item.Count; i++)
item[i] = RND.NextDouble() * (Max - Min) + Min;
}
else if (this.DistributionType == eRandDistributionType.GAUSSIAN)
{
foreach (cExtendedList item in ToReturn)
for (int i = 0; i < item.Count; i++)
{
double u1 = RND.NextDouble();
double u2 = RND.NextDouble();
double randStdNormal = Math.Sqrt(-2.0 * Math.Log(u1)) * Math.Sin(2.0 * Math.PI * u2);
item[i] = this.Mean + this.Stdev * randStdNormal;
}
}
else
{
FeedBackMessage.IsSucceed = false;
FeedBackMessage.Message = "Distribution Type not implemented";
}
return FeedBackMessage;
}
public cFeedBackMessage Run()
{
base.Start();
// Here is the right way for accessing the parameters...
// a little laborious, but that's the price to pay for a complete interface
object _firstValue = base.ListProperties.FindByName("First Paramater");
double firstValue = 0;
if (_firstValue == null)
{
base.GenerateError("First Paramater not found !");
return base.FeedBackMessage;
}
try
{
cProperty TmpProp = (cProperty)_firstValue;
firstValue = (double)TmpProp.GetValue();
}
catch (Exception)
{
base.GenerateError("First Paramater cast didn't work");
return base.FeedBackMessage;
}
ToReturn = new cExtendedTable((int)firstValue, (int)firstValue, 0);
base.End();
return FeedBackMessage;
}
public cFeedBackMessage Run()
{
if (this.Input == null)
{
FeedBackMessage.IsSucceed = false;
FeedBackMessage.Message = "No input data defined.";
return FeedBackMessage;
}
this.OutPut = new cExtendedTable(this.Input);
cRandomGenerator RG = new cRandomGenerator(this.Input.Count, this.Input[0].Count);
RG.Seed = (int)System.DateTime.Now.Ticks;
RG.Min = this.Min;
RG.Max = this.Max;
RG.Mean = this.Mean;
RG.Stdev = this.Stdv;
RG.DistributionType = this.DistributionType;
FeedBackMessage = RG.Run();
if (FeedBackMessage.IsSucceed == false)
{
return FeedBackMessage;
}
cArithmetic_DualOperator DO = new cArithmetic_DualOperator();
DO.SetInputData(this.Input, RG.GetOutPut());
DO.OperationType = eBinaryOperationType.ADD;
DO.Run();
this.OutPut = DO.GetOutPut();
this.OutPut.Name = "Add Noise(" + this.Input.Name + ")";
return FeedBackMessage;
}
public cFeedBackMessage Run()
{
if(this.Input==null)
{
FeedBackMessage.IsSucceed = false;
FeedBackMessage.Message = "No input data";
}
this.OutPut = new cExtendedTable(this.Input);
this.OutPut[0].Name = "X";
this.OutPut.Add(new cExtendedList());
this.OutPut[1].Name = "Sigmoid(X)";
foreach (var item in this.Input)
{
//double Value = Bottom +
// ( (Top - Bottom) / (1 + Math.Pow(10, (Math.Log10(EC50) - item) * Slope) ) );
double Value = Bottom + (Top - Bottom) / (1 + Math.Pow((EC50 / item),Slope));
this.OutPut[1].Add(Value);
/// (1 + Math.Pow((Math.Pow(10, EC50) / Math.Pow(10, /*ResultFit.GetLogConcentrations()*/item)), Slope)));
}
//for (int IdxConc = 0; IdxConc < this.Input[0].Count; IdxConc++)
// {
// Y_Estimated.Add(c[0] + (c[1] - c[0]) / (1 + Math.Pow((Math.Pow(10, c[2]) / Math.Pow(10, /*ResultFit.GetLogConcentrations()*/this.Input[0][IdxConc])), c[3])));
// // ResultFit.Y_Estimated.Add(c[0] + (c[1] - c[0]) / (1 + Math.Pow(((c[2]) / ResultFit.GetLogConcentrations()[IdxConc]), c[3])));
// }
return FeedBackMessage;
}
public cFeedBackMessage Run()
{
this.Output = new cExtendedTable();
this.Output.Tag = this.ListObjects.Tag;
this.Output.Name = "3D View Associated Data Table";
if(this.ListObjects.Name!=null)
this.Output.Name += this.ListObjects.Name;
this.Output.ListTags = new List<object>();
this.Output.ListRowNames = new List<string>();
this.Output.Add(new cExtendedList("X"));
this.Output.Add(new cExtendedList("Y"));
this.Output.Add(new cExtendedList("Z"));
foreach (var item in this.ListObjects)
{
this.Output[0].Add(item.GetPosition().X);
this.Output[1].Add(item.GetPosition().Y);
this.Output[2].Add(item.GetPosition().Z);
this.Output.ListTags.Add(item.Tag);
this.Output.ListRowNames.Add(item.GetName());
}
return FeedBackMessage;
}
private cFeedBackMessage Process()
{
this.Output = new cExtendedTable(this.Input);
//int IDxCOl = 0;
// foreach (cExtendedList item in this.Output)
for (int IDxCOl = 0; IDxCOl < this.Output.Count; IDxCOl++)
{
this.Output[IDxCOl] = Input[IDxCOl].Normalize(this.NormalizationType);
}
switch (this.NormalizationType)
{
case eNormalizationType.STANDARDIZE :
this.Output.Name = "Standardize(" + this.Input.Name + ")";
break;
case eNormalizationType.MIN_MAX:
this.Output.Name = "MinMax(" + this.Input.Name + ")";
break;
case eNormalizationType.SHIFT_TO_MEAN:
this.Output.Name = "Shift To Mean(" + this.Input.Name + ")";
break;
case eNormalizationType.SHIFT_TO_MEDIAN:
this.Output.Name = "Shift To Median(" + this.Input.Name + ")";
break;
case eNormalizationType.SHIFT_TO_MIN:
this.Output.Name = "Shift To Min(" + this.Input.Name + ")";
break;
default:
break;
}
return base.FeedBackMessage;
}
public cFeedBackMessage Run()
{
if (this.Input == null)
{
FeedBackMessage.IsSucceed = false;
FeedBackMessage.Message = "No input data defined.";
return FeedBackMessage;
}
OutPutList = new cExtendedList();
OutPutList.ListTags = new List<object>();
for (int i = 0; i < this.Input.Count; i++)
{
for (int j = 0; j < this.Input[0].Count; j++)
{
OutPutList.Add(this.Input[i][j]);
}
if (this.Input[i].ListTags != null)
{
for (int j = 0; j < this.Input[i].Count; j++)
OutPutList.ListTags.Add(this.Input[i].ListTags[j]);
}
}
this.OutPut = new cExtendedTable(OutPutList);
this.OutPut.Name = "Linearize(" + this.Input.Name + ")";
return FeedBackMessage;
}
private void Process()
{
this.OutPut = new cExtendedTable();
double Value = double.NaN;
this.OutPut.ListRowNames = new List<string>();
this.OutPut.Name = "Z-Factor(s)";
for (int IdxCol = 0; IdxCol < this.Input.Count; IdxCol++)
{
this.OutPut.ListRowNames.Add(this.Input[IdxCol].Name);
cExtendedList NewResult = new cExtendedList();
NewResult.Name = this.Input[IdxCol].Name;
for (int IdxColBis = 0; IdxColBis < this.Input.Count; IdxColBis++)
{
if (IdxCol != IdxColBis)
Value = this.Input[IdxCol].ZFactor(this.Input[IdxColBis], this.IsRobust);
else
Value = double.NaN;
//if (double.IsInfinity(Value)) Value = 1;
NewResult.Add(Value);
}
this.OutPut.Add(NewResult);
}
base.Info = "Z-Factor (";
if (IsRobust) base.Info += "Robust) - ";
else
base.Info += "Regular) - ";
base.Info += this.Input[0].Count + " vs. " + this.Input[1].Count + " values.";
}
public cFeedBackMessage Run()
{
if(this.Input == null)
{
FeedBackMessage.IsSucceed = false;
FeedBackMessage.Message = "No input data defined.";
return FeedBackMessage;
}
OutPut = new cExtendedTable(this.Input);
this.OutPut.ListRowNames = null;
int NumberOfColumnsRemoved = 0;
int RealIdx = 0;
for (int i = 0; i < this.Input.Count; i++)
{
if ((this.Input[i].Count <= this.MinNumberOfData) || (this.Input[i].Count >= this.MaxNumberOfData))
{
this.OutPut.RemoveAt(RealIdx);
NumberOfColumnsRemoved++;
RealIdx--;
}
RealIdx++;
}
this.OutPut.Name = "Clean(" + this.Input.Name + ")";
FeedBackMessage.Message = NumberOfColumnsRemoved + " columns removed";
return FeedBackMessage;
}
public cFeedBackMessage Run()
{
if (this.Input == null)
{
FeedBackMessage.IsSucceed = false;
FeedBackMessage.Message = "No input data defined.";
return FeedBackMessage;
}
// ------------- now proceed -------------
this.OutPut = new cExtendedTable();
this.OutPut.ListRowNames = new List<string>();
this.OutPut.Name = "Student's t-Test";
if (FTestTails == eFTestTails.BOTH)
this.OutPut.Name += " (both tails)";
else if (FTestTails == eFTestTails.LEFT)
this.OutPut.Name += " (left tail)";
else if (FTestTails == eFTestTails.RIGHT)
this.OutPut.Name += " (right tail)";
for (int IdxCol = 0; IdxCol < this.Input.Count; IdxCol++)
{
this.OutPut.ListRowNames.Add(this.Input[IdxCol].Name);
cExtendedList NewResult = new cExtendedList();
NewResult.Name = this.Input[IdxCol].Name;
for (int IdxColBis = 0; IdxColBis < this.Input.Count; IdxColBis++)
{
double bothtails;
double lefttail;
double righttail;
if(this.IsUnequalVariance)
alglib.unequalvariancettest(this.Input[IdxCol].ToArray(), this.Input[IdxCol].Count, this.Input[IdxColBis].ToArray(), this.Input[IdxColBis].Count, out bothtails, out lefttail, out righttail);
else
alglib.studentttest2(this.Input[IdxCol].ToArray(), this.Input[IdxCol].Count, this.Input[IdxColBis].ToArray(), this.Input[IdxColBis].Count, out bothtails, out lefttail, out righttail);
//alglib.mannwhitneyutest(
if (FTestTails == eFTestTails.BOTH)
NewResult.Add(bothtails);
else if (FTestTails == eFTestTails.LEFT)
NewResult.Add(lefttail);
else if (FTestTails == eFTestTails.RIGHT)
NewResult.Add(righttail);
}
this.OutPut.Add(NewResult);
}
return FeedBackMessage;
}
public cFeedBackMessage Run()
{
if (this.Input == null)
{
FeedBackMessage.IsSucceed = false;
FeedBackMessage.Message = "No input defined.";
return FeedBackMessage;
}
if (this.ColumnIndexForSorting >= this.Input.Count)
{
FeedBackMessage.IsSucceed = false;
FeedBackMessage.Message = "Sorting column index exceed table size.";
return FeedBackMessage;
}
Process();
this.Output = new cExtendedTable(this.Input);
this.Output.Name = "Sorted "+this.Input.Name;
int IdxCol = 0;
foreach (var item in this.Input)
{
this.Output[IdxCol].Tag = item.Tag;
IdxCol++;
}
for (IdxCol = 0; IdxCol < this.Input.Count; IdxCol++)
{
for (int IdxRow = 0; IdxRow < RefList.Count; IdxRow++)
this.Output[IdxCol][IdxRow] = this.Input[IdxCol][RefList[IdxRow].Index];
if ((this.Input[IdxCol].ListTags != null) && (this.Input[IdxCol].ListTags.Count>0))
{
for (int IdxRow = 0; IdxRow < RefList.Count; IdxRow++)
this.Output[IdxCol].ListTags[IdxRow] = this.Input[IdxCol].ListTags[RefList[IdxRow].Index];
}
}
for (int IdxRow = 0; IdxRow < this.Input.ListRowNames.Count; IdxRow++)
this.Output.ListRowNames[IdxRow] = this.Input.ListRowNames[RefList[IdxRow].Index];
if (this.Input.ListTags != null)
{
for (int IdxRow = 0; IdxRow < this.Input.ListTags.Count; IdxRow++)
this.Output.ListTags[IdxRow] = this.Input.ListTags[RefList[IdxRow].Index];
}
return FeedBackMessage;
}
private void Project()
{
Matrix NewPt = new Matrix(this.DataToProject[0].Count, this.DataToProject.Count);
NewPt = DataToProject.CopyToWEKAMatrix().multiply(this.Basis.CopyToWEKAMatrix());
ProjectedData = new cExtendedTable(NewPt);
int Idx=0;
foreach (var item in DataToProject)
{
ProjectedData[Idx].Name = Basis.ListRowNames[Idx];
Idx++;
}
ProjectedData.ListRowNames.AddRange(DataToProject.ListRowNames);
}
public cFeedBackMessage Run()
{
if (this.Input == null)
{
FeedBackMessage.IsSucceed = false;
FeedBackMessage.Message = "No input data defined.";
return FeedBackMessage;
}
//if (Info != 1)
//{
// FeedBackMessage.IsSucceed = false;
// FeedBackMessage.Message = "Error while inversing the matrix";
// return FeedBackMessage;
//}
this.OutPut = new cExtendedTable(this.Input[0].Count, this.Input.Count, 0);
if (Input[0].ListTags != null)
{
foreach (var item in this.OutPut)
{
item.ListTags = new List<object>();
}
}
this.OutPut.ListRowNames = new List<string>();
for (int i = 0; i < this.Input.Count; i++)
{
this.OutPut.ListRowNames.Add(this.Input[i].Name);
for (int j = 0; j < this.Input[i].Count; j++)
{
this.OutPut[j][i] = this.Input[i][j];
if (this.Input[i].ListTags != null) this.OutPut[j].ListTags.Add(this.Input[i].ListTags[j]);
}
}
if (this.Input.ListRowNames != null)
{
for (int i = 0; i < this.Input.ListRowNames.Count; i++)
this.OutPut[i].Name = this.Input.ListRowNames[i];
}
this.OutPut.Name = "Transpose(" + this.Input.Name + ")";
return FeedBackMessage;
}
public cExtendedTable GetPropertyTable()
{
cExtendedTable ToReturn = new cExtendedTable();
ToReturn.Name = this.Name + " - Properties";
cExtendedList Properties = new cExtendedList("Properties");
ToReturn.Add(Properties);
ToReturn.ListRowNames = new List<string>();
ToReturn.ListRowNames.Add("Dimension X");
Properties.Add(this.Width);
ToReturn.ListRowNames.Add("Dimension Y");
Properties.Add(this.Height);
ToReturn.ListRowNames.Add("Dimension Z");
Properties.Add(this.Depth);
ToReturn.ListRowNames.Add("Voxel Size X");
Properties.Add(this.Resolution.X);
ToReturn.ListRowNames.Add("Voxel Size Y");
Properties.Add(this.Resolution.Y);
ToReturn.ListRowNames.Add("Voxel Size Z");
Properties.Add(this.Resolution.Z);
ToReturn.ListRowNames.Add("Position X");
Properties.Add(this.Position.X);
ToReturn.ListRowNames.Add("Position Y");
Properties.Add(this.Position.Y);
ToReturn.ListRowNames.Add("Position Z");
Properties.Add(this.Position.Z);
ToReturn.ListRowNames.Add("Channel Numbers");
Properties.Add(this.GetNumChannels());
for (int i = 0; i < this.GetNumChannels(); i++)
{
ToReturn.ListRowNames.Add(this.SingleChannelImage[i].Name + " - Wavelenght");
Properties.Add(i);
}
return ToReturn;
}
public cFeedBackMessage Run()
{
if (this.Input == null)
{
FeedBackMessage.IsSucceed = false;
FeedBackMessage.Message = "No input data defined.";
return FeedBackMessage;
}
if (this.Input.Count != this.Input[0].Count)
{
FeedBackMessage.IsSucceed = false;
FeedBackMessage.Message = "A square matrix is required for this operation";
return FeedBackMessage;
}
// ------------- now proceed -------------
int Info;
double[,] Mat = this.Input.CopyToArray();
alglib.rmatrixinverse(ref Mat, out Info, out Report);
if (Info != 1)
{
FeedBackMessage.IsSucceed = false;
FeedBackMessage.Message = "Error while inversing the matrix";
return FeedBackMessage;
}
this.OutPut = new cExtendedTable(Mat);
if (this.Input.ListRowNames != null)
{
this.OutPut.ListRowNames = new List<string>();
this.OutPut.ListRowNames.AddRange(this.Input.ListRowNames.ToArray());
}
int IdxCOl=0;
foreach (var item in this.Input)
{
this.OutPut[IdxCOl++].Name = item.Name;
}
this.OutPut.Name = "Inverse(" + this.Input.Name + ")";
return FeedBackMessage;
}
private cFeedBackMessage Process()
{
cExtendedTable TmpTable = new cExtendedTable(this.Input[this.IdxColumnToProcess]);
cSort S = new cSort();
S.SetInputData(TmpTable);
base.FeedBackMessage = S.Run();
if (base.FeedBackMessage.IsSucceed == false) return base.FeedBackMessage;
cNormalize N = new cNormalize();
N.NormalizationType = eNormalizationType.STANDARDIZE;
N.SetInputData(S.GetOutPut());
base.FeedBackMessage = N.Run();
if (base.FeedBackMessage.IsSucceed == false) return base.FeedBackMessage;
int Num = N.GetOutPut()[0].Count;
double[] CumulativeProba = new double[Num];
for (int i = 1; i < Num - 1; i++)
CumulativeProba[i] = (i - 0.3175) / (Num + 0.365);
CumulativeProba[Num - 1] = Math.Pow(0.5, 1.0 / Num);
CumulativeProba[0] = 1 - CumulativeProba[Num - 1];
double[] PercentPointFunction = new double[Num];
for (int i = 0; i < Num; i++)
PercentPointFunction[i] = alglib.normaldistr.invnormaldistribution(CumulativeProba[i]);
this.Output = new cExtendedTable(new cExtendedList(PercentPointFunction));
this.Output.Add(new cExtendedList());
this.Output.ListTags = new List<object>();
for (int i = 0; i < N.GetOutPut()[0].Count; i++)
{
this.Output[1].Add(N.GetOutPut()[0][i]);
if(S.GetOutPut()[0].ListTags!=null)
this.Output.ListTags.Add(S.GetOutPut()[0].ListTags[i]);
}
this.Output[0].Name = "";
this.Output[1].Name = "Normalized Data";
return base.FeedBackMessage;
}
cFeedBackMessage Process()
{
this.Output = new cExtendedTable();
this.Output.Name = "Histogram (" + this.Input.Name + ")";
List<double[]> Res = null;
if (IsBinNumberMode)
{
if (double.IsNaN(this.Min) || double.IsNaN(this.Max))
Res = Input[0].CreateHistogram(this.BinNumber, this.IsNormalized);
else
Res = Input[0].CreateHistogram(this.Min, this.Max, (int)this.BinNumber, this.IsNormalized);
}
else
{
//if (double.IsNaN(this.Min) || double.IsNaN(this.Max))
// Res = Input[0].CreateHistogram(this.BinNumber, this.IsNormalized);
//else
Res = Input[0].CreateHistogram(this.Min, this.Max, this.BinSize);
}
if (Res.Count == 0)
{
base.FeedBackMessage.IsSucceed = false;
base.FeedBackMessage.Message = "Histogram failure";
return base.FeedBackMessage;
}
string ColName = "Frequency";
if (IsNormalized) ColName = "Normalized " + ColName;
cExtendedList ListY = new cExtendedList(ColName);
if (this.Input[0].Name == "") ColName = "Value";
else
ColName = this.Input[0].Name;
cExtendedList ListX = new cExtendedList(ColName);
ListX.AddRange(Res[0]);
ListY.AddRange(Res[1]);
this.Output.Add(ListX);
this.Output.Add(ListY);
return base.FeedBackMessage;
}
private void Process()
{
this.OutPut = new cExtendedTable();
double Value = double.NaN;
this.OutPut.ListRowNames = new List<string>();
this.OutPut.Name = "Covariance Matrix";
for (int IdxCol = 0; IdxCol < this.Input.Count; IdxCol++)
{
this.OutPut.ListRowNames.Add(this.Input[IdxCol].Name);
cExtendedList NewResult = new cExtendedList();
NewResult.Name = this.Input[IdxCol].Name;
for (int IdxColBis = 0; IdxColBis < this.Input.Count; IdxColBis++)
{
Value = alglib.cov2(this.Input[IdxCol].ToArray(), this.Input[IdxColBis].ToArray());
NewResult.Add(Value);
}
this.OutPut.Add(NewResult);
}
}
private void Process()
{
this.OutPut = new cExtendedTable();
double Value = double.NaN;
this.OutPut.ListRowNames = new List<string>();
this.OutPut.Name = "Mahalanobis";
for (int IdxCol = 0; IdxCol < this.Input.Count; IdxCol++)
{
this.OutPut.ListRowNames.Add(this.Input[IdxCol].Name);
cExtendedList NewResult = new cExtendedList();
NewResult.Name = this.Input[IdxCol].Name;
for (int IdxColBis = 0; IdxColBis < this.Input.Count; IdxColBis++)
{
Value = this.Input[IdxCol].Dist_Euclidean(this.Input[IdxColBis]);
NewResult.Add(Value);
}
this.OutPut.Add(NewResult);
}
}
public cExtendedTable GetLiveListValues()
{
cExtendedTable CET = new cExtendedTable();
CET.Name = Input.Name;
//foreach (DataGridViewColumn item in GridView.SelectedColumns)
for (int ColumnSelected = 0; ColumnSelected < GridView.SelectedColumns.Count; ColumnSelected++)
{
CET.Add(new cExtendedList());
for (int i = 0; i < GridView.Rows.Count - 1; i++)
{
CET[ColumnSelected].Add(double.Parse(GridView[GridView.SelectedColumns[ColumnSelected].Index, i].Value.ToString()));
}
//GridView[item.Index,
//CET.Add();
}
//GridView.SelectedColumns
return CET;
}
void Process()
{
// here is the core of the meta component ...
// just a list of Component steps
foreach (cWell item in Input)
{
item.AssociatedPlate.DBConnection = new cDBConnection(item.AssociatedPlate, item.SQLTableName);
cListSingleBiologicalObjects ListPhenotypes = item.AssociatedPlate.DBConnection.GetBiologicalPhenotypes(item);
cExtendedTable ET = item.AssociatedPlate.DBConnection.GetWellValues(item, cGlobalInfo.CurrentScreening.ListDescriptors.GetActiveDescriptors());
item.AssociatedPlate.DBConnection.CloseConnection();
ET.ListTags = new List<object>();
for (int i = 0; i < ET.Count; i++)
{
ET[i].ListTags = new List<object>();
for (int j = 0; j < ListPhenotypes.Count; j++)
ET[i].ListTags.Add(ListPhenotypes[j]);
}
for (int j = 0; j < ListPhenotypes.Count; j++)
ET.ListTags.Add(ListPhenotypes[j]);
if (Output == null)
Output = new cExtendedTable(ET);
else
{
cMerge M = new cMerge();
M.IsHorizontal = false;
M.SetInputData(Output, ET);
M.Run();
Output = M.GetOutPut();
}
}
}
private void Process()
{
this.OutPut = new cExtendedTable();
double Value = double.NaN;
this.OutPut.ListRowNames = new List<string>();
this.OutPut.Name = "dot product(s)";
for (int IdxCol = 0; IdxCol < this.Input.Count; IdxCol++)
{
this.OutPut.ListRowNames.Add(this.Input[IdxCol].Name);
cExtendedList NewResult = new cExtendedList();
NewResult.Name = this.Input[IdxCol].Name;
for (int IdxColBis = 0; IdxColBis < this.Input.Count; IdxColBis++)
{
//if (IdxCol != IdxColBis)
Value = this.Input[IdxCol].DotProduct(this.Input[IdxColBis], IsNormalized);
NewResult.Add(Value);
}
this.OutPut.Add(NewResult);
}
}
public cFeedBackMessage Run()
{
if (this.Input == null)
{
FeedBackMessage.IsSucceed = false;
FeedBackMessage.Message = "No input data";
}
this.OutPut = new cExtendedTable();
this.OutPut.Add(new cExtendedList());
for (int i = 0; i < NumberOfPoint; i++)
{
if (i == 0)
this.OutPut[0].Add(Start);
else
this.OutPut[0].Add(this.OutPut[0][this.OutPut[0].Count - 1] / this.DilutionFactor);
}
double End = this.OutPut[0][this.OutPut[0].Count - 1];
this.OutPut[0].Name = "Titration[" + Start + "..." + End + ":" + DilutionFactor + "]";
return FeedBackMessage;
}
public void SetInputData(cExtendedTable Input)
{
this.Input = Input;
}
void Refresh()
{
base.CurrentSeries.Clear();
double GlobalMinX = 0;
double GlobalMaxX = 0;
if (this.SliderForMarkerSizeBinSize.radioButtonMinMaxAutomated.Checked)
{
GlobalMinX = this.InputSimpleData.Min();
GlobalMaxX = this.InputSimpleData.Max();
}
else
{
GlobalMinX = (double)this.SliderForMarkerSizeBinSize.numericUpDownMin.Value;
GlobalMaxX = (double)this.SliderForMarkerSizeBinSize.numericUpDownMax.Value;
}
for (int IdxSerie = 0; IdxSerie < InputSimpleData.Count; IdxSerie++)
{
if (InputSimpleData[IdxSerie].Count == 0) continue;
Series NewSerie = new System.Windows.Forms.DataVisualization.Charting.Series(base.InputSimpleData[IdxSerie].Name + "_Histogram");
NewSerie.Tag = base.InputSimpleData[IdxSerie].Tag;
cHistogramBuilder HB = new cHistogramBuilder();
HB.SetInputData(new cExtendedTable(InputSimpleData[IdxSerie]));
if (this.SliderForMarkerSizeBinSize.radioButtonMinMaxAutomated.Checked)
{
HB.Min = GlobalMinX;
HB.Max = GlobalMaxX;
}
else
{
HB.Min = (double)this.SliderForMarkerSizeBinSize.numericUpDownMin.Value;
HB.Max = (double)this.SliderForMarkerSizeBinSize.numericUpDownMax.Value;
}
HB.IsBinNumberMode = this.SliderForMarkerSizeBinSize.radioButtonBinNumber.Checked;
HB.BinSize = (double)this.SliderForMarkerSizeBinSize.numericUpDownBinSize.Value;
if(this.BinNumber==-1)
HB.BinNumber = HB.Max - HB.Min+1;
else
HB.BinNumber = this.BinNumber;
HB.Run();
CurrentHistogram = HB.GetOutPut();
//if (ISPoint)
// NewSerie.ChartType = SeriesChartType.Point;
//if (IsLine)
// NewSerie.ChartType = SeriesChartType.Line;
//if (IsBar)
// NewSerie.ChartType = SeriesChartType.Column;
//NewSerie.ChartType = SeriesChartType.StackedColumn;
if (this.Is100)
{
NewSerie.ChartType = SeriesChartType.StackedColumn100;
base.LabelAxisY = "Frequency %";
base.CurrentChartArea.AxisY.Minimum = 0;
base.CurrentChartArea.AxisY.Maximum = 100;
}
else
{
NewSerie.ChartType = SeriesChartType.StackedColumn;
base.LabelAxisY = "Frequency";
base.CurrentChartArea.AxisY.Minimum = Double.NaN;
base.CurrentChartArea.AxisY.Maximum = Double.NaN;
// base.CurrentChartArea.AxisY.
}
//SeriesPos[i].ChartType = SeriesChartType.StackedColumn;
// double Step = (GlobalMaxX - GlobalMinX + 1) / CurrentHistogram[0].Count;
for (int j = 0; j < CurrentHistogram[0].Count; j++)
{
double[] Value = new double[1];
Value[0] = CurrentHistogram[1][j];
DataPoint DP = new DataPoint();
DP.SetValueXY(/*Step * j + GlobalMinX*/ CurrentHistogram[0][j], Value[0]);
DP.Tag = InputSimpleData[IdxSerie].Tag;
if (IsBorder)
{
DP.MarkerBorderColor = Color.Black;
DP.MarkerBorderWidth = 1;
DP.BorderColor = Color.Black;
DP.BorderWidth = 1;
}
else
{
DP.BorderWidth = 0;
DP.MarkerBorderWidth = 0;
}
if (InputSimpleData[IdxSerie].Tag != null)
{
if (InputSimpleData[IdxSerie].Tag.GetType() == typeof(cWellClassType))
{
DP.Color = ((cWellClassType)(InputSimpleData[IdxSerie].Tag)).ColourForDisplay;
DP.ToolTip = ((cWellClassType)(InputSimpleData[IdxSerie].Tag)).Name + "\n";
}
if (InputSimpleData[IdxSerie].Tag.GetType() == typeof(cCellularPhenotype))
{
DP.Color = ((cCellularPhenotype)(InputSimpleData[IdxSerie].Tag)).ColourForDisplay;
DP.ToolTip = ((cCellularPhenotype)(InputSimpleData[IdxSerie].Tag)).Name + "\n";
}
}
DP.ToolTip += DP.XValue.ToString("N2") + " :\n" + DP.YValues[0];
NewSerie.Points.Add(DP);
}
base.CurrentSeries.Add(NewSerie);
}
// base.LabelAxisY = "Frequency";
base.Update();
}
public void Set_Input(cExtendedTable DataToProject)
{
this.DataToProject = DataToProject;
}
public void Set_Basis(cExtendedTable Basis)
{
this.Basis = Basis;
}
private void Process()
{
double[,] DataForLDA = Input.CopyToArray();
//double[,] Basis;
//double[] s2;
int Info;
/*************************************************************************
Linear least squares fitting.
QR decomposition is used to reduce task to MxM, then triangular solver or
SVD-based solver is used depending on condition number of the system. It
allows to maximize speed and retain decent accuracy.
INPUT PARAMETERS:
Y - array[0..N-1] Function values in N points.
FMatrix - a table of basis functions values, array[0..N-1, 0..M-1].
FMatrix[I, J] - value of J-th basis function in I-th point.
N - number of points used. N>=1.
M - number of basis functions, M>=1.
OUTPUT PARAMETERS:
Info - error code:
* -4 internal SVD decomposition subroutine failed (very
rare and for degenerate systems only)
* 1 task is solved
C - decomposition coefficients, array[0..M-1]
Rep - fitting report. Following fields are set:
* Rep.TaskRCond reciprocal of condition number
* R2 non-adjusted coefficient of determination
(non-weighted)
* RMSError rms error on the (X,Y).
* AvgError average error on the (X,Y).
* AvgRelError average relative error on the non-zero Y
* MaxError maximum error
NON-WEIGHTED ERRORS ARE CALCULATED
ERRORS IN PARAMETERS
This solver also calculates different kinds of errors in parameters and
fills corresponding fields of report:
* Rep.CovPar covariance matrix for parameters, array[K,K].
* Rep.ErrPar errors in parameters, array[K],
errpar = sqrt(diag(CovPar))
* Rep.ErrCurve vector of fit errors - standard deviations of empirical
best-fit curve from "ideal" best-fit curve built with
infinite number of samples, array[N].
errcurve = sqrt(diag(F*CovPar*F')),
where F is functions matrix.
* Rep.Noise vector of per-point estimates of noise, array[N]
NOTE: noise in the data is estimated as follows:
* for fitting without user-supplied weights all points are
assumed to have same level of noise, which is estimated from
the data
* for fitting with user-supplied weights we assume that noise
level in I-th point is inversely proportional to Ith weight.
Coefficient of proportionality is estimated from the data.
NOTE: we apply small amount of regularization when we invert squared
Jacobian and calculate covariance matrix. It guarantees that
algorithm won't divide by zero during inversion, but skews
error estimates a bit (fractional error is about 10^-9).
However, we believe that this difference is insignificant for
all practical purposes except for the situation when you want
to compare ALGLIB results with "reference" implementation up
to the last significant digit.
-- ALGLIB --
Copyright 17.08.2009 by Bochkanov Sergey
*************************************************************************/
double[] weights = null;
int fitResult = 0;
double[] resultData = new double[this.Input[0].Count];
//alglib.lsfit.lsfitreport rep = new alglib.lsfit.lsfitreport();
//alglib.lsfit.lsfitlinear(resultData, DataForLDA, this.Input[0].Count, this.Input.Count, ref fitResult, ref weights, rep);
//alglib.lsfitstate state;
int info;
//double[] c = new double[] { 0, 0, 0, 0 };
//alglib.lsfitreport Nrep;
//alglib.lsfitresults(state, out info, out c, out Nrep);
// alglib.fisherldan(DataForLDA, this.Input[0].Count, this.Input.Count - 1, (int)this.Input[this.Input.Count - 1].Max() + 1, out Info, out Basis);
//Output = new cExtendedTable(Basis);
alglib.linearmodel LM = null;
alglib.lrreport Lreport = null;
alglib.lrbuild(DataForLDA, this.Input[0].Count, this.Input.Count-1, out info, out LM, out Lreport);
//RelativeError = rep.avgrelerror;
//LM.innerobj.w[0] = 1;
double[] Coeff = null;
int NVars;
alglib.lrunpack(LM, out Coeff, out NVars);
cExtendedList CL = new cExtendedList();
CL.AddRange(Coeff);
CL.Name = "Coefficients";
this.Output = new cExtendedTable(CL);
this.Output.ListRowNames = new List<string>();
for (int i = 0; i < this.Output[0].Count; i++)
this.Output.ListRowNames.Add("Coeff_" + i);
double RelativeError = Lreport.avgrelerror;
this.Output.ListRowNames.Add("Relative Error");
CL.Add(RelativeError);
Output.Name = "Linear regression coeff. of (" + this.Input.Name + ")";
//foreach (var item in Output)
//{
// item.ListTags = new List<object>();
// for (int i = 0; i < Output[0].Count; i++)
// item.ListTags.Add(this.Input[i].Tag);
//}
//for (int IdxLDA = 0; IdxLDA < Output.Count; IdxLDA++)
//{
// Output[IdxLDA].Name = "LDA_" + (IdxLDA + 1);
// Output.ListRowNames.Add(this.Input[IdxLDA].Name);
//}
}
private void createAveragePlateToolStripMenuItem_Click(object sender, EventArgs e)
{
FormForPlateAveraging FFPA = new FormForPlateAveraging();
PanelForPlatesSelection PlatesSelectionPanel = new PanelForPlatesSelection(true, null, true);
PlatesSelectionPanel.Height = FFPA.panelForPlateList.Height;
FFPA.panelForPlateList.Controls.Add(PlatesSelectionPanel);
if (FFPA.ShowDialog() != System.Windows.Forms.DialogResult.OK) return;
cListPlates LP = PlatesSelectionPanel.GetListSelectedPlates();
cPlate NewPlate = new cPlate("Average Plate", cGlobalInfo.CurrentScreening);
cWell TmpWell;
#region QC report
cExtendedList TmpReportForQuality = new cExtendedList("Weight");
TmpReportForQuality.ListTags = new List<object>();
cExtendedTable TableForQCReport = new cExtendedTable(TmpReportForQuality);
TableForQCReport.ListRowNames = new List<string>();
TableForQCReport.ListTags = new List<object>();
foreach (cPlate TmpPlate in LP)
{
object QualityValue = TmpPlate.ListProperties.FindValueByName("Quality");
if ((QualityValue != null) && (FFPA.checkBoxWeightedSum.Checked))
TableForQCReport[0].Add((double)QualityValue);
else
{
this.richTextBoxConsole.AppendText(TmpPlate.GetName() + " - Quality is NULL. Weight set to 1.\n");
TableForQCReport[0].Add(1);
}
TableForQCReport.ListRowNames.Add(TmpPlate.GetName());
TableForQCReport.ListTags.Add(TmpPlate);
TableForQCReport[0].ListTags.Add(TmpPlate);
}
cDisplayExtendedTable DET = new cDisplayExtendedTable();
DET.SetInputData(TableForQCReport);
DET.Title = "QC report";
TableForQCReport.Name = "QC report";
DET.Run();
#endregion
for (int X = 0; X < cGlobalInfo.CurrentScreening.Columns; X++)
for (int Y = 0; Y < cGlobalInfo.CurrentScreening.Rows; Y++)
{
cListSignature LDesc = new cListSignature();
for (int i = 0; i < cGlobalInfo.CurrentScreening.ListDescriptors.Count; i++)
{
double Value = 0;
double NormFactor = 0;
foreach (cPlate TmpPlate in LP)
{
double Weight = 1;
object QualityValue = TmpPlate.ListProperties.FindValueByName("Quality");
if ((QualityValue != null) && (FFPA.checkBoxWeightedSum.Checked))
Weight = (double)QualityValue;
NormFactor += Weight;
TmpWell = TmpPlate.GetWell(X, Y, false);
if (TmpWell != null)
{
Value += Weight * TmpWell.ListSignatures[i].GetValue();
}
}
if (NormFactor > 0)
{
cSignature Desc = new cSignature(Value / NormFactor, cGlobalInfo.CurrentScreening.ListDescriptors[i], cGlobalInfo.CurrentScreening);
LDesc.Add(Desc);
}
}
cWell NewWell = new cWell(LDesc, X + 1, Y + 1, cGlobalInfo.CurrentScreening, NewPlate);
NewWell.SetCpdName("Average Well [" + (X + 1) + ":" + (Y + 1) + "]");
NewPlate.AddWell(NewWell);
}
cGlobalInfo.CurrentScreening.AddPlate(NewPlate);
cGlobalInfo.CurrentScreening.ListPlatesActive.Add(NewPlate);
toolStripcomboBoxPlateList.Items.Add(NewPlate.GetName());
cGlobalInfo.CurrentScreening.ListPlatesActive[cGlobalInfo.CurrentScreening.ListPlatesActive.Count - 1].UpDataMinMax();
cGlobalInfo.CurrentScreening.CurrentDisplayPlateIdx = 0;
cGlobalInfo.CurrentScreening.GetCurrentDisplayPlate().DisplayDistribution(cGlobalInfo.CurrentScreening.ListDescriptors.GetActiveDescriptor(), true);
}
public void SetInputData(cExtendedTable MyData)
{
this.Input1 = MyData;
}
