private async Task <StringBuilder> Predict(List <List <string> > trainData,
List <List <string> > rowNames, List <List <string> > testData)
{
StringBuilder sb = null;
try
{
//ml algo rule: iterations can also set rowcount and -1 for mlinstructs removed
int iRowCount = (Shared.GetRowCount(_iterations, trainData.Count) - 1);
//columns of data used and returned in DataResults
_actualColNames = Shared.GetActualColNames(_colNames, _depColNames).ToArray();
//ml instructions associated with actual colNames
List <string> normTypes = Shared.GetNormTypes(trainData[0], _colNames, _depColNames);
//instructions in both row names and datasets
List <string> actualMLInstructs = Shared.GetAlgoInstructs(rowNames);
actualMLInstructs.AddRange(normTypes);
// error allowance
double dbPlusorMinus
= CalculatorHelpers.ConvertStringToDouble(actualMLInstructs[0]);
//converts rows to columns with normalized data
List <List <double> > trainDB = Shared.GetNormalizedDData(trainData,
this.IndicatorQT, _colNames, _depColNames, normTypes, "F2");
List <List <double> > testDB = Shared.GetNormalizedDData(testData,
this.IndicatorQT, _colNames, _depColNames, normTypes, "F2");
//make a new list with same matrix, to be replaced with results
int iColCount = testDB.Count;
if (_subalgorithm == MATHML_SUBTYPES.subalgorithm_03.ToString().ToString())
{
//subalgo02 needs qtm and percent probability of accuracy, qtm, low ci, high ci
iColCount = testDB.Count + 5;
//normtypes need full columns before insertion
normTypes = Shared.FixNormTypes(normTypes, iColCount);
}
//row count comes from original testdata to account for the instructions row
DataResults = CalculatorHelpers.GetList(testData.Count, iColCount);
DataResults[0] = normTypes;
//dep var output count
int numOutput = 1;
//less col[0]
int numInput = trainDB.Count - 1;
int numHidden = 12;
//can truncate the data to iRowCount
double[][] trainInputs = Shared.MakeInputDData(trainDB, iRowCount, this.IndicatorQT,
numInput);
//build a neural network
NeuralNetwork2 nn2 = new NeuralNetwork2(numInput, numHidden, numOutput);
int maxEpochs = iRowCount;
double learnRate = 0.001;
//train nn2
double[] wts = nn2.Train(trainInputs, maxEpochs, learnRate, sb);
//mean squared error
double trainErr = nn2.Error(trainInputs);
//final model accuracy
double trainAcc = nn2.Accuracy(trainInputs, dbPlusorMinus);
//add classified test data to DataResults
bool bHasNewClassifs = await AddNewClassifications(nn2, testDB,
trainAcc, trainErr, iRowCount, dbPlusorMinus, _ciLevel);
}
catch (Exception ex)
{
IndicatorQT.ErrorMessage = ex.Message;
}
return(sb);
}
