public FileDatetimeInfo(string _filename, ServiceTools.DatetimeExtractionMethod method = ServiceTools.DatetimeExtractionMethod.Filename,
string _datetimeFilenameMask = "?????xxxxxxxxxxxxxxxxxxx*")
{
filename = _filename;
datetimeExtractionMethod = method;
datetimeFilenameMask = _datetimeFilenameMask;
switch (method)
{
case ServiceTools.DatetimeExtractionMethod.Filename:
{
try
{
string strDateOfFile =
String.Concat(
Path.GetFileName(filename)
.Zip(datetimeFilenameMask, (c, s) => (s == 'x') ? (c.ToString()) : (""))
.ToArray());
datetime = CommonTools.DateTimeOfString(strDateOfFile);
DateTime.SpecifyKind(datetime, DateTimeKind.Utc);
}
catch (Exception ex)
{
throw ex;
}
break;
}
case ServiceTools.DatetimeExtractionMethod.FileCreation:
{
datetime = new FileInfo(filename).CreationTimeUtc;
DateTime.SpecifyKind(datetime, DateTimeKind.Utc);
break;
}
case ServiceTools.DatetimeExtractionMethod.Content:
{
Dictionary <string, object> currDict = ServiceTools.ReadDictionaryFromXML(filename);
currDict.Add("XMLfileName", Path.GetFileName(filename));
ConcurrentData retVal = null;
try
{
retVal = new ConcurrentData(currDict);
datetime = retVal.datetimeUTC;
DateTime.SpecifyKind(datetime, DateTimeKind.Utc);
}
catch (Exception ex)
{
throw ex;
}
break;
}
default:
{
break;
}
}
}
public async Task<SunDiskCondition> CalcSDC_NN(string SDC_NNconfigFile, string SDC_NNtrainedParametersFile, string NormMeansFile, string NormRangeFile)
{
if (!File.Exists(SDC_NNconfigFile))
{
throw new FileNotFoundException("couldn`t find the file specified: " + SDC_NNconfigFile);
}
if (!File.Exists(SDC_NNtrainedParametersFile))
{
throw new FileNotFoundException("couldn`t find the file specified: " + SDC_NNtrainedParametersFile);
}
if (!File.Exists(NormMeansFile))
{
throw new FileNotFoundException("couldn`t find the file specified: " + NormMeansFile);
}
if (!File.Exists(NormRangeFile))
{
throw new FileNotFoundException("couldn`t find the file specified: " + NormRangeFile);
}
if ((ImagesRoundMasksXMLfilesMappingList == "") || (ImagesRoundMasksXMLfilesMappingList == null))
{
ImagesRoundMasksXMLfilesMappingList = Directory.GetCurrentDirectory() + Path.DirectorySeparatorChar + "settings" +
Path.DirectorySeparatorChar +
"ImagesRoundMasksXMLfilesMappingList.csv";
}
if (!File.Exists(ImagesRoundMasksXMLfilesMappingList))
{
throw new FileNotFoundException("couldn`t find the file specified: " + ImagesRoundMasksXMLfilesMappingList);
}
#region read or calculate GrIxYRGB stats
if (Directory.Exists(imageYRGBstatsXMLdataFilesDirectory))
{
List<string> foundXMLfiles = Directory.EnumerateFiles(imageYRGBstatsXMLdataFilesDirectory,
ConventionalTransitions.ImageGrIxYRGBstatsDataFileName(ImageFilename, "", false),
SearchOption.AllDirectories).ToList();
if (foundXMLfiles.Any())
{
currImageStatsData =
(SkyImageIndexesStatsData)
ServiceTools.ReadObjectFromXML(foundXMLfiles[0], typeof(SkyImageIndexesStatsData));
}
}
if (currImageStatsData == null)
{
Task<SkyImageIndexesStatsData> tskImageStatsCalculation = new Task<SkyImageIndexesStatsData>(() =>
{
Dictionary<string, object> optionalParameters = new Dictionary<string, object>();
optionalParameters.Add("ImagesRoundMasksXMLfilesMappingList", ImagesRoundMasksXMLfilesMappingList);
Stopwatch sw = new Stopwatch();
sw.Start();
optionalParameters.Add("Stopwatch", sw);
ImageStatsDataCalculationResult currImageProcessingResult = null;
try
{
currImageProcessingResult = ImageProcessing.CalculateImageStatsData(ImageFilename,
optionalParameters);
}
catch (Exception ex)
{
return null;
}
if (currImageProcessingResult == null)
{
return null;
}
else
{
string strImageGrIxYRGBDataFileName =
ConventionalTransitions.ImageGrIxYRGBstatsDataFileName(ImageFilename,
imageYRGBstatsXMLdataFilesDirectory);
ServiceTools.WriteObjectToXML(currImageProcessingResult.grixyrgbStatsData,
strImageGrIxYRGBDataFileName);
// currImageProcessingResult.stopwatch.Stop();
// string currentFullFileName = currImageProcessingResult.imgFilename;
// string strPerfCountersData = currentFullFileName + ";" +
// currImageProcessingResult.stopwatch.ElapsedMilliseconds + ";" +
// (currImageProcessingResult.procTotalProcessorTimeEnd -
// currImageProcessingResult.procTotalProcessorTimeStart)
// .TotalMilliseconds +
// Environment.NewLine;
// ServiceTools.logToTextFile(strPerformanceCountersStatsFile, strPerfCountersData, true);
currImageStatsData = currImageProcessingResult.grixyrgbStatsData;
return currImageProcessingResult.grixyrgbStatsData;
}
});
currImageStatsData = await tskImageStatsCalculation;
}
if (currImageStatsData == null)
{
// theLogWindow = ServiceTools.LogAText(theLogWindow, "ERROR searching concurrent data for image. Will not proceed.");
throw new Exception("ERROR searching concurrent data for image. Will not proceed.");
}
#endregion read or calculate GrIxYRGB stats
ConcurrentData nearestConcurrentData = null;
#region search for concurrent data
Task<ConcurrentData> tskImageConcurrentDataSearching = new Task<ConcurrentData>(() =>
{
string currImgFilename = Path.GetFileNameWithoutExtension(ImageFilename);
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);
List<Tuple<string, ConcurrentData>> lImagesConcurrentData =
new List<Tuple<string, ConcurrentData>>();
List<string> filesListConcurrentData =
Directory.EnumerateFiles(ConcurrentDataXMLfilesBasePath,
ConventionalTransitions.ImageConcurrentDataFilesNamesPattern(),
SearchOption.AllDirectories)
.ToList();
List<Tuple<string, DateTime>> XMLfilesListConcurrentData = filesListConcurrentData.ConvertAll(
strXMLfilename =>
{
string xmlFile1DateTimeString =
Path.GetFileNameWithoutExtension(strXMLfilename).Replace("data-", "").Substring(0, 19);
xmlFile1DateTimeString = xmlFile1DateTimeString.Substring(0, 11) + xmlFile1DateTimeString.Substring(11).Replace("-", ":");
DateTime dt1 = DateTime.Parse(xmlFile1DateTimeString);
return new Tuple<string, DateTime>(strXMLfilename, dt1);
});
string nearestConcurrentDataFileName = XMLfilesListConcurrentData.Aggregate((cDt1, cDt2) =>
{
TimeSpan tspan1 = new TimeSpan(Math.Abs((cDt1.Item2 - currImgDT).Ticks));
TimeSpan tspan2 = new TimeSpan(Math.Abs((cDt2.Item2 - currImgDT).Ticks));
return ((tspan1 <= tspan2) ? (cDt1) : (cDt2));
}).Item1;
Dictionary<string, object> currDict = ServiceTools.ReadDictionaryFromXML(nearestConcurrentDataFileName);
currDict.Add("XMLfileName", Path.GetFileName(nearestConcurrentDataFileName));
ConcurrentData nearestConcurrentDataObtained = new ConcurrentData(currDict);
if (new TimeSpan(Math.Abs((nearestConcurrentDataObtained.datetimeUTC - currImgDT).Ticks)) >=
new TimeSpan(0, 2, 0))
{
//theLogWindow = ServiceTools.LogAText(theLogWindow,
// "couldn`t find close enough concurrent data file for image:" + Environment.NewLine +
// bgwCurrImageFInfo.FullName + Environment.NewLine + "closest concurrent data file is:" +
// Environment.NewLine + nearestConcurrentData.filename + Environment.NewLine +
// "with date-time value " + nearestConcurrentData.datetimeUTC.ToString("o"));
nearestConcurrentDataObtained = null;
}
return nearestConcurrentDataObtained;
});
nearestConcurrentData = await tskImageConcurrentDataSearching;
if (nearestConcurrentData == null)
{
// theLogWindow = ServiceTools.LogAText(theLogWindow, "ERROR searching concurrent data for image. Will not proceed.");
throw new Exception("ERROR searching concurrent data for image. Will not proceed.");
}
#endregion search for concurrent data
DenseVector dvMeans = (DenseVector)((DenseMatrix)ServiceTools.ReadDataFromCSV(NormMeansFile, 0, ",")).Row(0);
DenseVector dvRanges = (DenseVector)((DenseMatrix)ServiceTools.ReadDataFromCSV(NormRangeFile, 0, ",")).Row(0);
DenseVector dvThetaValues = (DenseVector)ServiceTools.ReadDataFromCSV(SDC_NNtrainedParametersFile, 0, ",");
List<int> NNlayersConfig =
new List<double>(((DenseMatrix)ServiceTools.ReadDataFromCSV(SDC_NNconfigFile, 0, ",")).Row(0)).ConvertAll
(dVal => Convert.ToInt32(dVal));
List<double> decisionProbabilities = new List<double>();
return PredictSDC_NN(currImageStatsData, nearestConcurrentData, NNlayersConfig, dvThetaValues, dvMeans,
dvRanges, out decisionProbabilities);
}
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 SunDiskCondition CalcSDC_NN(string statsXMLfile, string concurrentDataXMLfile, string SDC_NNconfigFile,
string SDC_NNtrainedParametersFile, string NormMeansFile, string NormRangeFile, out List<double> decisionProbabilities)
{
if (!File.Exists(SDC_NNconfigFile))
{
throw new FileNotFoundException("couldn`t find the file specified: " + SDC_NNconfigFile);
}
if (!File.Exists(SDC_NNtrainedParametersFile))
{
throw new FileNotFoundException("couldn`t find the file specified: " + SDC_NNtrainedParametersFile);
}
if (!File.Exists(NormMeansFile))
{
throw new FileNotFoundException("couldn`t find the file specified: " + NormMeansFile);
}
if (!File.Exists(NormRangeFile))
{
throw new FileNotFoundException("couldn`t find the file specified: " + NormRangeFile);
}
if (!File.Exists(statsXMLfile))
{
throw new FileNotFoundException("couldn`t find the file specified: " + statsXMLfile);
}
if (!File.Exists(concurrentDataXMLfile))
{
throw new FileNotFoundException("couldn`t find the file specified: " + concurrentDataXMLfile);
}
#region read GrIxYRGB stats
SkyImageIndexesStatsData currImageStatsData = null;
try
{
currImageStatsData =
(SkyImageIndexesStatsData)
ServiceTools.ReadObjectFromXML(statsXMLfile, typeof(SkyImageIndexesStatsData));
}
catch (Exception ex)
{
throw ex;
}
if (currImageStatsData == null)
{
throw new Exception("ERROR reading stats data for image. Will not proceed.");
}
#endregion read or calculate GrIxYRGB stats
#region search for concurrent data
ConcurrentData nearestConcurrentData = null;
try
{
Dictionary<string, object> currDict = ServiceTools.ReadDictionaryFromXML(concurrentDataXMLfile);
currDict.Add("XMLfileName", Path.GetFileName(concurrentDataXMLfile));
nearestConcurrentData = new ConcurrentData(currDict);
}
catch (Exception)
{
throw new Exception("ERROR reading concurrent data for image. Will not proceed."); ;
}
if (nearestConcurrentData == null)
{
// theLogWindow = ServiceTools.LogAText(theLogWindow, "ERROR searching concurrent data for image. Will not proceed.");
throw new Exception("ERROR searching concurrent data for image. Will not proceed.");
}
#endregion search for concurrent data
DenseVector dvMeans = (DenseVector)((DenseMatrix)ServiceTools.ReadDataFromCSV(NormMeansFile, 0, ",")).Row(0);
DenseVector dvRanges = (DenseVector)((DenseMatrix)ServiceTools.ReadDataFromCSV(NormRangeFile, 0, ",")).Row(0);
DenseVector dvThetaValues = (DenseVector)ServiceTools.ReadDataFromCSV(SDC_NNtrainedParametersFile, 0, ",");
List<int> NNlayersConfig =
new List<double>(((DenseMatrix)ServiceTools.ReadDataFromCSV(SDC_NNconfigFile, 0, ",")).Row(0)).ConvertAll
(dVal => Convert.ToInt32(dVal));
return PredictSDC_NN(currImageStatsData, nearestConcurrentData, NNlayersConfig, dvThetaValues, dvMeans,
dvRanges, out decisionProbabilities);
}
public async Task <int> CalcTCC_NN(string SDC_NNconfigFile, string SDC_NNtrainedParametersFile,
string SDC_NormMeansFile,
string SDC_NormRangeFile, string TCC_NNconfigFile, string TCC_NNtrainedParametersFile,
string TCC_NormMeansFile, string TCC_NormRangeFile, string TCC_ExcludingVarsFile)
{
#region check files existence
if (!File.Exists(SDC_NNconfigFile))
{
throw new FileNotFoundException("couldn`t find the file specified: " + SDC_NNconfigFile);
}
if (!File.Exists(SDC_NNtrainedParametersFile))
{
throw new FileNotFoundException("couldn`t find the file specified: " + SDC_NNtrainedParametersFile);
}
if (!File.Exists(SDC_NormMeansFile))
{
throw new FileNotFoundException("couldn`t find the file specified: " + SDC_NormMeansFile);
}
if (!File.Exists(SDC_NormRangeFile))
{
throw new FileNotFoundException("couldn`t find the file specified: " + SDC_NormRangeFile);
}
if (!File.Exists(TCC_NNconfigFile))
{
throw new FileNotFoundException("couldn`t find the file specified: " + TCC_NNconfigFile);
}
if (!File.Exists(TCC_NNtrainedParametersFile))
{
throw new FileNotFoundException("couldn`t find the file specified: " + TCC_NNtrainedParametersFile);
}
if (!File.Exists(TCC_NormMeansFile))
{
throw new FileNotFoundException("couldn`t find the file specified: " + TCC_NormMeansFile);
}
if (!File.Exists(TCC_NormRangeFile))
{
throw new FileNotFoundException("couldn`t find the file specified: " + TCC_NormRangeFile);
}
if (!File.Exists(TCC_ExcludingVarsFile))
{
throw new FileNotFoundException("couldn`t find the file specified: " + TCC_ExcludingVarsFile);
}
if ((ImagesRoundMasksXMLfilesMappingList == "") || (ImagesRoundMasksXMLfilesMappingList == null))
{
ImagesRoundMasksXMLfilesMappingList = Directory.GetCurrentDirectory() + Path.DirectorySeparatorChar + "settings" +
Path.DirectorySeparatorChar +
"ImagesRoundMasksXMLfilesMappingList.csv";
}
if (!File.Exists(ImagesRoundMasksXMLfilesMappingList))
{
throw new FileNotFoundException("couldn`t find the file specified: " + ImagesRoundMasksXMLfilesMappingList);
}
#endregion check files existence
#region read or calculate GrIxYRGB stats
if (Directory.Exists(imageYRGBstatsXMLdataFilesDirectory))
{
List <string> foundXMLfiles = Directory.EnumerateFiles(imageYRGBstatsXMLdataFilesDirectory,
ConventionalTransitions.ImageGrIxYRGBstatsDataFileName(ImageFilename, "", false),
SearchOption.AllDirectories).ToList();
if (foundXMLfiles.Any())
{
currImageStatsData =
(SkyImageIndexesStatsData)
ServiceTools.ReadObjectFromXML(foundXMLfiles[0], typeof(SkyImageIndexesStatsData));
}
}
if (currImageStatsData == null)
{
Task <SkyImageIndexesStatsData> tskImageStatsCalculation = new Task <SkyImageIndexesStatsData>(() =>
{
Dictionary <string, object> optionalParameters = new Dictionary <string, object>();
optionalParameters.Add("ImagesRoundMasksXMLfilesMappingList", ImagesRoundMasksXMLfilesMappingList);
Stopwatch sw = new Stopwatch();
sw.Start();
optionalParameters.Add("Stopwatch", sw);
ImageStatsDataCalculationResult currImageProcessingResult = null;
try
{
currImageProcessingResult = ImageProcessing.CalculateImageStatsData(ImageFilename,
optionalParameters);
}
catch (Exception ex)
{
return(null);
}
if (currImageProcessingResult == null)
{
return(null);
}
else
{
string strImageGrIxYRGBDataFileName =
ConventionalTransitions.ImageGrIxYRGBstatsDataFileName(ImageFilename,
imageYRGBstatsXMLdataFilesDirectory);
ServiceTools.WriteObjectToXML(currImageProcessingResult.grixyrgbStatsData,
strImageGrIxYRGBDataFileName);
currImageStatsData = currImageProcessingResult.grixyrgbStatsData;
return(currImageProcessingResult.grixyrgbStatsData);
}
});
currImageStatsData = await tskImageStatsCalculation;
}
if (currImageStatsData == null)
{
// theLogWindow = ServiceTools.LogAText(theLogWindow, "ERROR searching concurrent data for image. Will not proceed.");
throw new Exception("ERROR searching concurrent data for image. Will not proceed.");
}
#endregion read or calculate GrIxYRGB stats
ConcurrentData nearestConcurrentData = null;
#region search for concurrent data
Task <ConcurrentData> tskImageConcurrentDataSearching = new Task <ConcurrentData>(() =>
{
string currImgFilename = Path.GetFileNameWithoutExtension(ImageFilename);
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);
List <Tuple <string, ConcurrentData> > lImagesConcurrentData =
new List <Tuple <string, ConcurrentData> >();
List <string> filesListConcurrentData =
Directory.EnumerateFiles(ConcurrentDataXMLfilesBasePath,
ConventionalTransitions.ImageConcurrentDataFilesNamesPattern(),
SearchOption.AllDirectories)
.ToList();
List <Tuple <string, DateTime> > XMLfilesListConcurrentData = filesListConcurrentData.ConvertAll(
strXMLfilename =>
{
string xmlFile1DateTimeString =
Path.GetFileNameWithoutExtension(strXMLfilename).Replace("data-", "").Substring(0, 19);
xmlFile1DateTimeString = xmlFile1DateTimeString.Substring(0, 11) + xmlFile1DateTimeString.Substring(11).Replace("-", ":");
DateTime dt1 = DateTime.Parse(xmlFile1DateTimeString);
return(new Tuple <string, DateTime>(strXMLfilename, dt1));
});
string nearestConcurrentDataFileName = XMLfilesListConcurrentData.Aggregate((cDt1, cDt2) =>
{
TimeSpan tspan1 = new TimeSpan(Math.Abs((cDt1.Item2 - currImgDT).Ticks));
TimeSpan tspan2 = new TimeSpan(Math.Abs((cDt2.Item2 - currImgDT).Ticks));
return((tspan1 <= tspan2) ? (cDt1) : (cDt2));
}).Item1;
Dictionary <string, object> currDict = ServiceTools.ReadDictionaryFromXML(nearestConcurrentDataFileName);
currDict.Add("XMLfileName", Path.GetFileName(nearestConcurrentDataFileName));
ConcurrentData nearestConcurrentDataObtained = new ConcurrentData(currDict);
if (new TimeSpan(Math.Abs((nearestConcurrentDataObtained.datetimeUTC - currImgDT).Ticks)) >=
new TimeSpan(0, 2, 0))
{
nearestConcurrentDataObtained = null;
}
return(nearestConcurrentDataObtained);
});
nearestConcurrentData = await tskImageConcurrentDataSearching;
if (nearestConcurrentData == null)
{
// theLogWindow = ServiceTools.LogAText(theLogWindow, "ERROR searching concurrent data for image. Will not proceed.");
throw new Exception("ERROR searching concurrent data for image. Will not proceed.");
}
#endregion search for concurrent data
DenseVector dvSDCmeans = (DenseVector)((DenseMatrix)ServiceTools.ReadDataFromCSV(SDC_NormMeansFile, 0, ",")).Row(0);
DenseVector dvSDCranges = (DenseVector)((DenseMatrix)ServiceTools.ReadDataFromCSV(SDC_NormRangeFile, 0, ",")).Row(0);
DenseVector dvSDCthetaValues = (DenseVector)ServiceTools.ReadDataFromCSV(SDC_NNtrainedParametersFile, 0, ",");
List <int> SDCnnLayersConfig =
new List <double>(((DenseMatrix)ServiceTools.ReadDataFromCSV(SDC_NNconfigFile, 0, ",")).Row(0)).ConvertAll
(dVal => Convert.ToInt32(dVal));
DenseVector dvTCCmeans = (DenseVector)((DenseMatrix)ServiceTools.ReadDataFromCSV(TCC_NormMeansFile, 0, ",")).Row(0);
DenseVector dvTCCranges = (DenseVector)((DenseMatrix)ServiceTools.ReadDataFromCSV(TCC_NormRangeFile, 0, ",")).Row(0);
DenseVector dvTCCthetaValues = (DenseVector)ServiceTools.ReadDataFromCSV(TCC_NNtrainedParametersFile, 0, ",");
List <int> TCCnnLayersConfig =
new List <double>(((DenseMatrix)ServiceTools.ReadDataFromCSV(TCC_NNconfigFile, 0, ",")).Row(0)).ConvertAll
(dVal => Convert.ToInt32(dVal));
List <int> TCCnnConfigVarsToExclude =
new List <double>(((DenseMatrix)ServiceTools.ReadDataFromCSV(TCC_ExcludingVarsFile, 0, ",")).Row(0)).ConvertAll
(dVal => Convert.ToInt32(dVal));
List <double> TCCdecisionProbabilities = null;
return(PredictTCC_NN(currImageStatsData, nearestConcurrentData, SDCnnLayersConfig, dvSDCthetaValues,
dvSDCmeans,
dvSDCranges, TCCnnLayersConfig, dvTCCthetaValues, dvTCCmeans, dvTCCranges, TCCnnConfigVarsToExclude,
out TCCdecisionProbabilities));
}
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]);
}
