//private void ImageProcessing(ImagesProcessingData ipdt)
//{
//}
private void EnumerateFilesToProcess()
{
string directory = Path.GetDirectoryName(inputBasePath);
string filemask = "*.jpg";
List <string> filesList =
new List <string>(Directory.EnumerateFiles(directory, filemask,
bEnumerateFilesRecursively ? SearchOption.AllDirectories : SearchOption.TopDirectoryOnly));
#region filter by camID
//...devID1.jpg
string ptrnCamID = "devid" + CamIDtoProcess + ".jpg";
filesList = filesList.Where(fname => fname.ToLower().Contains(ptrnCamID)).ToList();
#endregion
Console.WriteLine("found " + filesList.Count + " images.");
#region list, read and map image stats files
Console.WriteLine("filtering by ready-to-use GrIxYRGB XML files...");
List <string> statsFilesList =
new List <string>(Directory.EnumerateFiles(imageYRGBstatsXMLdataFilesDirectory, ConventionalTransitions.ImageGrIxYRGBstatsFileNamesPattern(),
bEnumerateFilesRecursively ? SearchOption.AllDirectories : SearchOption.TopDirectoryOnly));
List <string> statsFilesListWOpath = statsFilesList.ConvertAll(Path.GetFileName);
Console.WriteLine("found " + statsFilesList.Count + " XML stats files in directory " + Environment.NewLine +
imageYRGBstatsXMLdataFilesDirectory + Environment.NewLine + "by mask " +
Environment.NewLine + ConventionalTransitions.ImageGrIxYRGBstatsFileNamesPattern());
int removed =
filesList.RemoveAll(
fname =>
!statsFilesListWOpath.Contains(ConventionalTransitions.ImageGrIxYRGBstatsDataFileName(fname, "",
false)));
Console.WriteLine("removed " + removed + " items (couldn`t find stats data files). Remains " + filesList.Count + " to process.");
if (!filesList.Any())
{
Console.WriteLine("There is no " + filemask + " files that sutisfy settings specified. Processing will not be started.");
return;
}
lStatsProcessing = filesList.ConvertAll(strImgFname =>
{
ImagesProcessingData retVal = new ImagesProcessingData()
{
filename = strImgFname
};
return(retVal);
});
#if DEBUG
//lStatsProcessing = lStatsProcessing.Where((ipd, ind) => ind < 10).ToList();
#endif
Console.WriteLine("started reading and mapping stats data");
int totalFilesCountToRead = lStatsProcessing.Count;
int filesRead = 0;
int currProgressPerc = 0;
foreach (ImagesProcessingData ipdt in lStatsProcessing)
{
ipdt.grixyrgbStatsXMLfile =
statsFilesList.First(
statsFname =>
statsFname.Contains(ConventionalTransitions.ImageGrIxYRGBstatsDataFileName(ipdt.filename, "",
false)));
ipdt.grixyrgbStats =
ServiceTools.ReadObjectFromXML(ipdt.grixyrgbStatsXMLfile, typeof(SkyImageIndexesStatsData)) as
SkyImageIndexesStatsData;
#region calculate and report progress
filesRead++;
double progress = 100.0d * (double)filesRead / (double)totalFilesCountToRead;
if (progress - (double)currProgressPerc > 1.0d)
{
currProgressPerc = Convert.ToInt32(progress);
Console.WriteLine("read " + currProgressPerc + "%");
}
#endregion calculate and report progress
}
#endregion
#region list, read and map concurrent data
List <string> concurrentDataFilesList =
Directory.EnumerateFiles(ConcurrentDataXMLfilesDirectory,
ConventionalTransitions.ImageConcurrentDataFilesNamesPattern(),
bEnumerateFilesRecursively ? SearchOption.AllDirectories : SearchOption.TopDirectoryOnly).ToList();
List <ConcurrentData> lConcurrentData = null;
#region reading
Console.WriteLine("started concurrent data reading");
totalFilesCountToRead = concurrentDataFilesList.Count;
filesRead = 0;
currProgressPerc = 0;
List <Dictionary <string, object> > lDictionariesConcurrentData =
new List <Dictionary <string, object> >();
foreach (string strConcDataXMLFile in concurrentDataFilesList)
{
Dictionary <string, object> currDict = ServiceTools.ReadDictionaryFromXML(strConcDataXMLFile);
currDict.Add("XMLfileName", Path.GetFileName(strConcDataXMLFile));
lDictionariesConcurrentData.Add(currDict);
#region calculate and report progress
filesRead++;
double progress = 100.0d * (double)filesRead / (double)totalFilesCountToRead;
if (progress - (double)currProgressPerc > 1.0d)
{
currProgressPerc = Convert.ToInt32(progress);
Console.WriteLine("read " + currProgressPerc + "%");
}
#endregion calculate and report progress
}
lDictionariesConcurrentData.RemoveAll(dict => dict == null);
lConcurrentData =
lDictionariesConcurrentData.ConvertAll <ConcurrentData>(dict =>
{
ConcurrentData retVal = null;
try
{
retVal = new ConcurrentData(dict);
}
catch (Exception ex)
{
string strError = "couldn`t parse XML file " + dict["XMLfileName"] + " : " +
Environment.NewLine + ex.Message;
Console.WriteLine(strError);
}
return(retVal);
});
lConcurrentData.RemoveAll(val => val == null);
#endregion reading
#region mapping
// map obtained concurrent data to images by its datetime
Console.WriteLine("concurrent data mapping started");
lStatsProcessing = lStatsProcessing.ConvertAll(ipdt =>
{
string currImgFilename = ipdt.filename;
currImgFilename = Path.GetFileNameWithoutExtension(currImgFilename);
string ptrn = @"(devID\d)";
Regex rgxp = new Regex(ptrn, RegexOptions.IgnoreCase);
string strCurrImgDT = rgxp.Replace(currImgFilename.Substring(4), "");
//2015-12-16T06-01-38
strCurrImgDT = strCurrImgDT.Substring(0, 11) + strCurrImgDT.Substring(11).Replace("-", ":");
DateTime currImgDT = DateTime.Parse(strCurrImgDT, null,
System.Globalization.DateTimeStyles.AdjustToUniversal);
ConcurrentData nearestConcurrentData = lConcurrentData.Aggregate((cDt1, cDt2) =>
{
TimeSpan tspan1 = new TimeSpan(Math.Abs((cDt1.datetimeUTC - currImgDT).Ticks));
TimeSpan tspan2 = new TimeSpan(Math.Abs((cDt2.datetimeUTC - currImgDT).Ticks));
return((tspan1 <= tspan2) ? (cDt1) : (cDt2));
});
if (new TimeSpan(Math.Abs((nearestConcurrentData.datetimeUTC - currImgDT).Ticks)) >=
TimeSpanForConcurrentDataMappingTolerance)
{
string strError = "couldn`t find close enough concurrent data file for image:" + Environment.NewLine +
currImgFilename + Environment.NewLine + "closest concurrent data file is:" +
Environment.NewLine + nearestConcurrentData.filename + Environment.NewLine +
"with date-time value " + nearestConcurrentData.datetimeUTC.ToString("o");
Console.WriteLine(strError);
nearestConcurrentData = null;
}
ipdt.concurrentData = nearestConcurrentData;
if (nearestConcurrentData != null)
{
ipdt.concurrentDataXMLfile = nearestConcurrentData.filename;
}
return(ipdt);
});
#endregion mapping
removed = lStatsProcessing.RemoveAll(ipdt => ipdt.concurrentData == null);
Console.WriteLine("removed " + removed + " items (couldn`t find concurrent data). " + lStatsProcessing.Count + " files remains to process.");
#endregion list, read and map concurrent data
if (!lStatsProcessing.Any())
{
Console.WriteLine("There is no files that sutisfy settings specified and have all required concurrent data (stats or GPS etc.). Processing will not be proceeded.");
return;
}
#region Predict SDC values using pre-trained NN parameters
string csvHeader = lStatsProcessing[0].grixyrgbStats.CSVHeader() +
",SunElevationDeg,SunAzimuthDeg,sunDiskCondition";
List <string> lCSVheader = csvHeader.Split(',').ToList();
List <int> columnsToDelete =
lCSVheader.Select((str, idx) => new Tuple <int, string>(idx, str))
.Where(tpl => tpl.Item2.ToLower().Contains("filename")).ToList().ConvertAll(tpl => tpl.Item1);
List <List <string> > lCalculatedData = lStatsProcessing.ConvertAll(dt =>
{
string currImageALLstatsDataCSVWithConcurrentData = dt.grixyrgbStats.ToCSV() + "," +
dt.concurrentData.gps.SunZenithAzimuth()
.ElevationAngle.ToString()
.Replace(",", ".") + "," +
dt.concurrentData.gps.SunZenithAzimuth()
.Azimuth.ToString()
.Replace(",", ".");
List <string> retVal = currImageALLstatsDataCSVWithConcurrentData.Split(',').ToList();
retVal = retVal.Where((str, idx) => !columnsToDelete.Contains(idx)).ToList();
return(retVal);
});
List <DenseVector> lDV_objects_features =
lCalculatedData.ConvertAll(
list =>
DenseVector.OfEnumerable(list.ConvertAll <double>(str => Convert.ToDouble(str.Replace(".", ",")))));
DenseVector dvMeans = (DenseVector)((DenseMatrix)ServiceTools.ReadDataFromCSV(NormMeansFile, 0, ",")).Row(0);
DenseVector dvRanges = (DenseVector)((DenseMatrix)ServiceTools.ReadDataFromCSV(NormRangeFile, 0, ",")).Row(0);
lDV_objects_features = lDV_objects_features.ConvertAll(dv =>
{
DenseVector dvShifted = dv - dvMeans;
DenseVector dvNormed = (DenseVector)dvShifted.PointwiseDivide(dvRanges);
return(dvNormed);
});
DenseMatrix dmObjectsFeatures = DenseMatrix.OfRowVectors(lDV_objects_features);
DenseVector dvThetaValues = (DenseVector)ServiceTools.ReadDataFromCSV(NNtrainedParametersFile, 0, ",");
List <int> NNlayersConfig =
new List <double>(((DenseMatrix)ServiceTools.ReadDataFromCSV(NNconfigFile, 0, ",")).Row(0)).ConvertAll
(dVal => Convert.ToInt32(dVal));
List <List <double> > lDecisionProbabilities = null;
List <SunDiskCondition> predictedSDClist =
NNclassificatorPredictor <SunDiskCondition> .NNpredict(dmObjectsFeatures, dvThetaValues, NNlayersConfig,
out lDecisionProbabilities, SunDiskConditionData.MatlabEnumeratedSDCorderedList()).ToList();
//List<SunDiskCondition> predictedSDClist = predictedSDC.ConvertAll(sdcInt =>
//{
// switch (sdcInt)
// {
// case 4:
// return SunDiskCondition.NoSun;
// break;
// case 1:
// return SunDiskCondition.Sun0;
// break;
// case 2:
// return SunDiskCondition.Sun1;
// break;
// case 3:
// return SunDiskCondition.Sun2;
// break;
// default:
// return SunDiskCondition.Defect;
// }
//});
string strToShow = "SDC values probabilities: " + Environment.NewLine +
"| No Sun | Sun_0 | Sun_1 | Sun_2 | Detected |" + Environment.NewLine;
foreach (List <double> lDecisionProbability in lDecisionProbabilities)
{
strToShow += "| " + lDecisionProbability[3].ToString("F4") +
" | " + lDecisionProbability[0].ToString("F4") +
" | " + lDecisionProbability[1].ToString("F4") +
" | " + lDecisionProbability[2].ToString("F4") + " |" +
predictedSDClist[lDecisionProbabilities.IndexOf(lDecisionProbability)] + "|" +
Environment.NewLine;
}
ServiceTools.logToTextFile(errorLogFilename, strToShow, true, false);
#endregion
//lStatsProcessing =
// lStatsProcessing.Where((ipd, idx) => predictedSDClist[idx] == SunDiskCondition.Sun2).ToList();
lStatsProcessing =
lStatsProcessing.Where((ipd, idx) => predictedSDClist[idx] == sdcFilter).ToList();
Console.WriteLine("Detected " + lStatsProcessing.Count + " images with SDC = " + sdcFilter.ToString());
if (!lStatsProcessing.Any())
{
Console.WriteLine("There is no files with SDC = Sun2. Processing will not be proceeded.");
return;
}
Console.WriteLine("finished enumerating and filtering files. Files to process: " + lStatsProcessing.Count);
}
public void Start(string[] args)
{
readDefaultProperties();
List <string> argsList = new List <string>(args);
string filename = argsList.Last();
if (!File.Exists(filename))
{
Console.WriteLine("Couldn`t find input file \"" + filename + "\"");
return;
}
CommonTools.PrintDictionaryToConsole(defaultProperties,
"Default settings specified in file \"" + defaultPropertiesXMLfileName + "\"");
if (!File.Exists(NNconfigFile) || !File.Exists(NNtrainedParametersFile) || !File.Exists(NormMeansFile) || !File.Exists(NormRangeFile))
{
Console.WriteLine(
"couldn`t find at least one of pre-calculated NN parameters file specified in settings: ");
return;
}
List <List <string> > csvFileContentStrings = ServiceTools.ReadDataFromCSV(filename, 0, true, ",");
List <string> csvFileHeader = csvFileContentStrings[0];
csvFileContentStrings = csvFileContentStrings.Where((list, idx) => idx > 0).ToList();
List <int> columnsToDelete =
csvFileHeader.Select((str, idx) => new Tuple <int, string>(idx, str))
.Where(tpl => tpl.Item2.ToLower().Contains("filename")).ToList().ConvertAll(tpl => tpl.Item1);
List <List <string> > csvFileContentStringsFiltered = new List <List <string> >();
foreach (List <string> listDataStrings in csvFileContentStrings)
{
csvFileContentStringsFiltered.Add(
listDataStrings.Where((str, idx) => !columnsToDelete.Contains(idx)).ToList());
}
List <SunDiskCondition> trueAnswers =
csvFileContentStringsFiltered.ConvertAll(
lstr => (SunDiskCondition)Enum.Parse(typeof(SunDiskCondition), lstr.Last()));
//List<int> trueAnswersInt = trueAnswers.ConvertAll(sdc => SunDiskConditionData.MatlabNumeralSDC(sdc));
List <List <string> > csvFileContentStringsFiltered_wo_sdc =
csvFileContentStringsFiltered.ConvertAll(list => list.Where((val, idx) => idx < list.Count - 1).ToList());
List <DenseVector> lDV_objects_features =
csvFileContentStringsFiltered_wo_sdc.ConvertAll(
list =>
DenseVector.OfEnumerable(list.ConvertAll <double>(str => Convert.ToDouble(str.Replace(".", ",")))));
DenseVector dvMeans = (DenseVector)((DenseMatrix)ServiceTools.ReadDataFromCSV(NormMeansFile, 0, ",")).Row(0);
DenseVector dvRanges = (DenseVector)((DenseMatrix)ServiceTools.ReadDataFromCSV(NormRangeFile, 0, ",")).Row(0);
lDV_objects_features = lDV_objects_features.ConvertAll(dv =>
{
DenseVector dvShifted = dv - dvMeans;
DenseVector dvNormed = (DenseVector)dvShifted.PointwiseDivide(dvRanges);
return(dvNormed);
});
DenseMatrix dmObjectsFeatures = DenseMatrix.OfRowVectors(lDV_objects_features);
DenseVector dvThetaValues = (DenseVector)ServiceTools.ReadDataFromCSV(NNtrainedParametersFile, 0, ",");
List <int> NNlayersConfig =
new List <double>(((DenseMatrix)ServiceTools.ReadDataFromCSV(NNconfigFile, 0, ",")).Row(0)).ConvertAll
(dVal => Convert.ToInt32(dVal));
List <SunDiskCondition> predictedSDC =
NNclassificatorPredictor <SunDiskCondition> .NNpredict(dmObjectsFeatures, dvThetaValues, NNlayersConfig,
SunDiskConditionData.MatlabEnumeratedSDCorderedList()).ToList();
List <Tuple <SunDiskCondition, SunDiskCondition> > PredictedVStrue = predictedSDC.Zip(trueAnswers,
(predVal, trueVal) => new Tuple <SunDiskCondition, SunDiskCondition>(predVal, trueVal)).ToList();
Console.WriteLine("=== Prediction result vs true ===");
foreach (Tuple <SunDiskCondition, SunDiskCondition> tpl in PredictedVStrue)
{
Console.WriteLine("pred: " + tpl.Item1.ToString() + ":" + tpl.Item2.ToString() + " :true");
}
double accuracy = 100.0d * ((double)PredictedVStrue.Count(tpl => tpl.Item1 == tpl.Item2)) /
(double)PredictedVStrue.Count();
Console.WriteLine("accuracy: " + accuracy);
Console.WriteLine("Finished. Press any key...");
Console.ReadKey();
}
public static List<SunDiskCondition> PredictSDC_NN(
List<Tuple<SkyImageIndexesStatsData, ConcurrentData>> lTplInputData, IEnumerable<int> SDC_NNconfig,
IEnumerable<double> SDC_NNtrainedParameters,
IEnumerable<double> NNfeturesNormMeans, IEnumerable<double> NNfeaturesNormRange,
out List<List<double>> decisionProbabilitiesList)
{
List<string> lImagesALLstatsDataCSVWithConcurrentData = lTplInputData.ConvertAll(tpl =>
{
SkyImageIndexesStatsData imageStats = tpl.Item1;
ConcurrentData snapshotConcurrentData = tpl.Item2;
return imageStats.ToCSV() + "," +
snapshotConcurrentData.gps.SunZenithAzimuth().ElevationAngle.ToString().Replace(",", ".") + "," +
snapshotConcurrentData.gps.SunZenithAzimuth().Azimuth.ToString().Replace(",", ".");
});
// string currImageALLstatsDataCSVWithConcurrentData =
string csvHeader = lTplInputData.First().Item1.CSVHeader() +
",SunElevationDeg,SunAzimuthDeg,sunDiskCondition";
List<string> lCalculatedData = new List<string>();
// lCalculatedData.Add(currImageALLstatsDataCSVWithConcurrentData);
lCalculatedData.AddRange(lImagesALLstatsDataCSVWithConcurrentData);
List<List<string>> csvFileContentStrings =
lCalculatedData.ConvertAll(str => str.Split(',').ToList()).ToList();
List<string> lCSVheader = csvHeader.Split(',').ToList();
List<int> columnsToDelete =
lCSVheader.Select((str, idx) => new Tuple<int, string>(idx, str))
.Where(tpl => tpl.Item2.ToLower().Contains("filename")).ToList().ConvertAll(tpl => tpl.Item1);
List<List<string>> csvFileContentStringsFiltered = new List<List<string>>();
foreach (List<string> listDataStrings in csvFileContentStrings)
{
csvFileContentStringsFiltered.Add(
listDataStrings.Where((str, idx) => !columnsToDelete.Contains(idx)).ToList());
}
List<List<string>> csvFileContentStringsFiltered_wo_sdc = csvFileContentStringsFiltered;
List<DenseVector> lDV_objects_features =
csvFileContentStringsFiltered_wo_sdc.ConvertAll(
list =>
DenseVector.OfEnumerable(list.ConvertAll<double>(str => Convert.ToDouble(str.Replace(".", ",")))));
DenseVector dvMeans = DenseVector.OfEnumerable(NNfeturesNormMeans);
DenseVector dvRanges = DenseVector.OfEnumerable(NNfeaturesNormRange);
DenseVector dvThetaValues = DenseVector.OfEnumerable(SDC_NNtrainedParameters);
List<int> NNlayersConfig = SDC_NNconfig.ToList();
lDV_objects_features = lDV_objects_features.ConvertAll(dv =>
{
DenseVector dvShifted = dv - dvMeans;
DenseVector dvNormed = (DenseVector)dvShifted.PointwiseDivide(dvRanges);
return dvNormed;
});
DenseMatrix dmObjectsFeatures = DenseMatrix.OfRowVectors(lDV_objects_features);
List<List<double>> lDecisionProbabilities = null;
List<SunDiskCondition> predictedSDClist =
NNclassificatorPredictor<SunDiskCondition>.NNpredict(dmObjectsFeatures, dvThetaValues, NNlayersConfig,
out lDecisionProbabilities, SunDiskConditionData.MatlabEnumeratedSDCorderedList()).ToList();
//List<SunDiskCondition> predictedSDClist =
// predictedSDC.ConvertAll(sdcInt => SunDiskConditionData.MatlabSDCenum(sdcInt));
decisionProbabilitiesList = lDecisionProbabilities;
return predictedSDClist;
}
public static int PredictTCC_NN(SkyImageIndexesStatsData imageStats, ConcurrentData snapshotConcurrentData,
IEnumerable <int> SDC_NNconfig, IEnumerable <double> SDC_NNtrainedParameters,
IEnumerable <double> SDC_NNfeturesNormMeans,
IEnumerable <double> SDC_NNfeaturesNormRange, IEnumerable <int> TCCnnLayersConfig,
IEnumerable <double> TCC_NNtrainedParameters, IEnumerable <double> TCC_NNfeturesNormMeans,
IEnumerable <double> TCC_NNfeaturesNormRange, IEnumerable <int> TCCnnConfigVarsToExclude,
out List <double> TCCdecisionProbabilities)
{
string currImageALLstatsDataCSVWithConcurrentData = imageStats.ToCSV() + "," +
snapshotConcurrentData.gps.SunZenithAzimuth().ElevationAngle.ToString().Replace(",", ".") + "," +
snapshotConcurrentData.gps.SunZenithAzimuth().Azimuth.ToString().Replace(",", ".");
List <string> lCalculatedData = new List <string>();
lCalculatedData.Add(currImageALLstatsDataCSVWithConcurrentData);
string csvHeader = imageStats.CSVHeader();
List <string> lCSVheader = csvHeader.Split(',').ToList();
List <int> columnsToDelete =
lCSVheader.Select((str, idx) => new Tuple <int, string>(idx, str))
.Where(tpl => tpl.Item2.ToLower().Contains("filename")).ToList().ConvertAll(tpl => tpl.Item1);
List <List <string> > csvFileContentStrings =
lCalculatedData.ConvertAll(str => str.Split(',').ToList()).ToList();
List <List <string> > csvFileContentStringsFiltered = new List <List <string> >();
foreach (List <string> listDataStrings in csvFileContentStrings)
{
csvFileContentStringsFiltered.Add(
listDataStrings.Where((str, idx) => !columnsToDelete.Contains(idx)).ToList());
}
#region SDC prediction
List <List <string> > csvFileContentStringsFiltered_wo_CC = csvFileContentStringsFiltered;
List <DenseVector> lDV_objects_features =
csvFileContentStringsFiltered_wo_CC.ConvertAll(
list =>
DenseVector.OfEnumerable(list.ConvertAll <double>(CommonTools.ParseDouble)));
DenseMatrix dmSDCpredictionObjectsFeatures = DenseMatrix.OfRows(lDV_objects_features);
// DenseVector dvMeans = DenseVector.OfEnumerable(SDC_NNfeturesNormMeans);
// DenseVector dvRanges = DenseVector.OfEnumerable(SDC_NNfeaturesNormRange);
DenseMatrix dmSDCpredictionObjectsFeaturesNormed =
ANNservice.FeatureNormalization(dmSDCpredictionObjectsFeatures, SDC_NNfeturesNormMeans,
SDC_NNfeaturesNormRange);
#region Predict SDC
List <int> sdcMatlabValues = new List <int>();
List <double> lSDCpredictionProbabilities = new List <double>();
SunDiskCondition sdc = SDCpredictorNN.PredictSDC_NN(imageStats, snapshotConcurrentData, SDC_NNconfig,
SDC_NNtrainedParameters, SDC_NNfeturesNormMeans, SDC_NNfeaturesNormRange, out lSDCpredictionProbabilities);
sdcMatlabValues.Add(SunDiskConditionData.MatlabNumeralSDC(sdc));
//lDV_objects_features = lDV_objects_features.Zip(sdcMatlabValues, (dv, intSDC) =>
//{
// List<double> lFeaturesWithSDCdata = lDV_objects_features[0].ToList();
// lFeaturesWithSDCdata.Add((double)intSDC);
// return DenseVector.OfEnumerable(lFeaturesWithSDCdata);
//}).ToList();
#endregion Predict SDC
#endregion SDC prediction
DenseMatrix dmTCCpredictionObjectsFeatures = dmSDCpredictionObjectsFeatures.Copy();
List <List <double> > rowsSDCprobabilitiesPerObject = new List <List <double> >();
rowsSDCprobabilitiesPerObject.Add(lSDCpredictionProbabilities);
DenseMatrix dmToAppend = DenseMatrix.OfRows(rowsSDCprobabilitiesPerObject);
dmTCCpredictionObjectsFeatures = (DenseMatrix)dmTCCpredictionObjectsFeatures.Append(dmToAppend);
// remove vars listed in TCCnnConfigVarsToExclude
List <int> TCCnnConfigVarsToExcludeIndexes = TCCnnConfigVarsToExclude.ToList();
TCCnnConfigVarsToExcludeIndexes = TCCnnConfigVarsToExcludeIndexes.ConvertAll(i => i - 1); // based_1 indexes to based_0
DenseMatrix dmTCCpredictionObjectsFeatures_RemovedExcludingFeatures =
dmTCCpredictionObjectsFeatures.RemoveColumns(TCCnnConfigVarsToExcludeIndexes);
DenseMatrix dmTCCpredictionObjectsFeatures_RemovedExcludingFeatures_Normed =
ANNservice.FeatureNormalization(dmTCCpredictionObjectsFeatures_RemovedExcludingFeatures,
TCC_NNfeturesNormMeans,
TCC_NNfeaturesNormRange);
List <List <double> > lTCCdecisionProbabilities = null;
List <int> TCCvaluesSet = new List <int>();
for (int i = 0; i < 9; i++)
{
TCCvaluesSet.Add(i);
}
List <int> predictedTCC =
NNclassificatorPredictor <int> .NNpredict(dmTCCpredictionObjectsFeatures_RemovedExcludingFeatures_Normed,
TCC_NNtrainedParameters, TCCnnLayersConfig, out lTCCdecisionProbabilities, TCCvaluesSet).ToList();
// Matlab trained TCC model: classes 1-9
//predictedTCC = predictedTCC.ConvertAll(iVal => iVal - 1);
TCCdecisionProbabilities = lTCCdecisionProbabilities[0];
return(predictedTCC[0]);
}
