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]);
}