private static void LogEPV(ExperimentPredictiveValues ePV)
{
Console.Out.WriteLine("================================");
Console.Out.WriteLine($"ANN PPV LEARN PPV: {ePV.AnnPpvLearnPPV}");
Console.Out.WriteLine($"ANN PPV LEARN NPV: {ePV.AnnPpvLearnNPV}");
Console.Out.WriteLine($"ANN PPV VALIDATE PPV: {ePV.AnnPpvValidatePPV}");
Console.Out.WriteLine($"ANN PPV VALIDATE NPV: {ePV.AnnPpvValidateNPV}");
Console.Out.WriteLine($"ANN PPV TEST PPV: {ePV.AnnPpvTestPPV}");
Console.Out.WriteLine($"ANN PPV TEST NPV: {ePV.AnnPpvTestNPV}");
Console.Out.WriteLine($"ANN NPV LEARN PPV: {ePV.AnnNpvLearnPPV}");
Console.Out.WriteLine($"ANN NPV LEARN NPV: {ePV.AnnNpvLearnNPV}");
Console.Out.WriteLine($"ANN NPV VALIDATE PPV: {ePV.AnnNpvValidatePPV}");
Console.Out.WriteLine($"ANN NPV VALIDATE NPV: {ePV.AnnNpvValidateNPV}");
Console.Out.WriteLine($"ANN NPV TEST PPV: {ePV.AnnNpvTestPPV}");
Console.Out.WriteLine($"ANN NPV TEST NPV: {ePV.AnnNpvTestNPV}");
Console.Out.WriteLine("================================");
}
private static Experiment3Output Experiment3(int neuronCount, decimal minAnnPPV, decimal minAnnNPV, int treeCount, decimal minForestPPV, decimal minForestNPV)
{
var result = new Experiment3Output();
result.NeuronCount = neuronCount;
var metsRecords = LoadDataRecords(TRAINING_DATA_FILE, 0);
DateTime et = DateTime.Now;
var currentMinPPV = 0m;
var currentMinNPV = 0m;
Data data = null;
var learningData = new List<AnnLearningRecord>();
var annPV = new AlgoritamPredictiveValues();
var forestPV = new AlgoritamPredictiveValues();
while (currentMinPPV < minAnnPPV || currentMinNPV < minAnnNPV || currentMinPPV <= currentMinNPV || currentMinPPV < minForestPPV || currentMinNPV < minForestNPV)
{
var es = DateTime.Now;
data = CreateData(metsRecords, 0.8, 0.1);
result.Means = data.Means;
result.StandardDeviations = data.StandardDeviations;
Console.Out.WriteLine("================================");
Console.Out.WriteLine($" ANN PPV, NC:{neuronCount}");
Console.Out.WriteLine("================================");
learningData.Clear();
annPV = Ann(data, neuronCount, learningData, out var ann);
currentMinPPV = annPV.TestPPV;
currentMinNPV = annPV.TestNPV;
result.Ann = ann;
LogAPV(annPV);
LogMessage($"Experiment time: {DateTime.Now - es}, total time {DateTime.Now - et}");
}
var experimentPV = new ExperimentPredictiveValues()
{
AnnPpvLearnNPV = annPV.LearnNPV,
AnnPpvLearnPPV = annPV.LearnPPV,
AnnPpvValidateNPV = annPV.ValidateNPV,
AnnPpvValidatePPV = annPV.ValidatePPV,
AnnPpvTestNPV = annPV.TestNPV,
AnnPpvTestPPV = annPV.TestPPV,
};
using (var tw = File.CreateText($"d:\\mets_experiment_3_{neuronCount}_{DateTime.Now:yyyy_MM_dd_hh_mm}_rndfrst.csv"))
{
var csv = new CsvHelper.CsvWriter(tw);
csv.WriteHeader<AnnLearningRecord>();
csv.NextRecord();
csv.WriteRecords(learningData);
}
using (var tw = File.CreateText($"d:\\mets_experiment_3_PV_{neuronCount}_{DateTime.Now:yyyy_MM_dd_hh_mm}_rndfrst.csv"))
{
var csv = new CsvHelper.CsvWriter(tw);
csv.WriteHeader<ExperimentPredictiveValues>();
csv.NextRecord();
csv.WriteRecords(new ExperimentPredictiveValues[] { experimentPV });
}
return result;
}
private static void Experiment2(int neuronCountPPV, int neuronCountNPV, int repeats)
{
var metsRecords = LoadDataRecords(TRAINING_DATA_FILE, 0);
var results = new List<ExperimentPredictiveValues>();
DateTime et = DateTime.Now;
for (int i = 0; i < repeats; i++)
{
var es = DateTime.Now;
var data = CreateData(metsRecords, 0.8, 0.1);
Console.Out.WriteLine("================================");
Console.Out.WriteLine($" EXPERIMENT #{i:000}");
Console.Out.WriteLine("================================");
Console.Out.WriteLine();
Console.Out.WriteLine("================================");
Console.Out.WriteLine($" ANN PPV, NC:{neuronCountPPV}");
Console.Out.WriteLine("================================");
var ann_PPV_PV = Ann(data, neuronCountPPV, null, out var ann);
LogAPV(ann_PPV_PV);
var ann_NPV_PV = new AlgoritamPredictiveValues();
if (neuronCountNPV != neuronCountPPV)
{
Console.Out.WriteLine("================================");
Console.Out.WriteLine($" ANN NPV, NC:{neuronCountNPV}");
Console.Out.WriteLine("================================");
ann_NPV_PV = Ann(data, neuronCountNPV, null, out ann);
LogAPV(ann_NPV_PV);
}
var experimentPV = new ExperimentPredictiveValues()
{
AnnPpvLearnNPV = ann_PPV_PV.LearnNPV,
AnnPpvLearnPPV = ann_PPV_PV.LearnPPV,
AnnPpvLearnSENS = ann_PPV_PV.LearnSENS,
AnnPpvLearnSPEC = ann_PPV_PV.LearnSPEC,
AnnPpvValidateNPV = ann_PPV_PV.ValidateNPV,
AnnPpvValidatePPV = ann_PPV_PV.ValidatePPV,
AnnPpvValidateSENS = ann_PPV_PV.ValidateSENS,
AnnPpvValidateSPEC = ann_PPV_PV.ValidateSPEC,
AnnPpvTestNPV = ann_PPV_PV.TestNPV,
AnnPpvTestPPV = ann_PPV_PV.TestPPV,
AnnPpvTestSENS = ann_PPV_PV.TestSENS,
AnnPpvTestSPEC = ann_PPV_PV.TestSPEC,
AnnNpvLearnNPV = ann_NPV_PV.LearnNPV,
AnnNpvLearnPPV = ann_NPV_PV.LearnPPV,
AnnNpvLearnSENS = ann_NPV_PV.LearnSENS,
AnnNpvLearnSPEC = ann_NPV_PV.LearnSPEC,
AnnNpvValidateNPV = ann_NPV_PV.ValidateNPV,
AnnNpvValidatePPV = ann_NPV_PV.ValidatePPV,
AnnNpvValidateSENS = ann_NPV_PV.ValidateSENS,
AnnNpvValidateSPEC = ann_NPV_PV.ValidateSPEC,
AnnNpvTestNPV = ann_NPV_PV.TestNPV,
AnnNpvTestPPV = ann_NPV_PV.TestPPV,
AnnNpvTestSENS = ann_NPV_PV.TestSENS,
AnnNpvTestSPEC = ann_NPV_PV.TestSPEC,
};
LogEPV(experimentPV);
results.Add(experimentPV);
LogMessage($"Experiment time: {DateTime.Now - es}, total time {DateTime.Now - et}");
}
LogAvgEPV(results);
using (var tw = File.CreateText($"d:\\mets_experiment_2_{neuronCountPPV}_{neuronCountNPV}_R{repeats}{DateTime.Now:yyyy_MM_dd_hh_mm}.csv"))
{
var csv = new CsvHelper.CsvWriter(tw);
csv.WriteHeader<ExperimentPredictiveValues>();
csv.NextRecord();
csv.WriteRecords(results);
}
}