public void GetArrayTest1()
{
double[] vector = new double[] { 1, 2, 3, 4 };
double[][] matrix = new double[][]
{
new double[] { 1, 0 },
new double[] { 2, 0 },
new double[] { 3, 0 },
new double[] { 4, 0 }
};
Assert.Equal(vector, EANN.GetArray(matrix));
}
public void Convert2JaggedTest()
{
double[] vectorIn = new double[] { 1, 2, 3, 4 };
double[][] matrixOut = new double[][]
{
new double[] { 1 },
new double[] { 2 },
new double[] { 3 },
new double[] { 4 }
};
Assert.Equal(matrixOut, EANN.ConvertToJagged(vectorIn));
}
static void SaveResults(EANN eo_ann, string filePath)
{
if (Equals(eo_ann, null))
{
return;
}
if (Equals(eo_ann.BestChart, null))
{
return;
}
if (Equals(eo_ann, null))
{
return;
}
using (FileStream fs = new FileStream(filePath, FileMode.OpenOrCreate))
{
using (StreamWriter sw = new StreamWriter(fs, System.Text.Encoding.UTF8))
{
System.Text.StringBuilder strb = new System.Text.StringBuilder();
strb.AppendLine("Best_Chart;");
for (int i = 0; i < eo_ann.BestChart.Count; i++)
{
strb.Append(eo_ann.BestChart[i]).AppendLine(";");
}
strb.AppendLine("Best Learning score;");
strb.Append(eo_ann.BestLearningScore).AppendLine(";");
strb.AppendLine("Best Testing Score;");
strb.Append(eo_ann.BestTestingScore).AppendLine(";");
strb.AppendLine("Best solution;");
for (int i = 0; i < eo_ann.BestSolution.Length; i++)
{
strb.Append(eo_ann.BestSolution[i]).AppendLine(";");
}
sw.Write(strb.ToString());
sw.Flush();
sw.Close();
fs.Close();
}
}
bool save = eo_ann.BestNeuralNetwork.SaveNeuralNetwork(string.Format("C:\\SSL\\The_Best_ANN_{0}.ann", fileName.Trim()));
Console.WriteLine("Save the best ANN : {0}", save);
}
public void GetArrayTest2()
{
double[] vector = new double[] { 0, 0, 0, 0, 0 };
double[][] matrix = new double[][]
{
new double[] { 0 },
new double[] { 0 },
new double[] { 0 },
new double[] { 0 },
new double[] { 0 }
};
Assert.Equal(vector, EANN.GetArray(matrix));
}
static void LaunchANNEO(DataFormater dfr, int n, int kmax, int learnAlgo)
{
int minHLNeurones = 1;
int maxHLNeurones = 15;
int kminL = 40;
int kmaxL = 200;
if (learnAlgo == 0)
{
kminL = 10000;
kmaxL = 21000;
}
var ranges = new List <MonoObjectiveEOALib.Range>
{
new MonoObjectiveEOALib.Range("Activation Function", 0.9, 2.1),
new MonoObjectiveEOALib.Range("Alpha of Activation Function", 0.1, 10),
new MonoObjectiveEOALib.Range("Learning rate", 0.05, 0.1),
new MonoObjectiveEOALib.Range("Momentum/Ajustement", 10, 12),
new MonoObjectiveEOALib.Range("Learning Err", 0.001, 0.01),
new MonoObjectiveEOALib.Range("Max Iteration (Kmax)", kminL, kmaxL),
new MonoObjectiveEOALib.Range("Hiden Layer Number", 1, 5),
new MonoObjectiveEOALib.Range("Layer 1 Nodes count", minHLNeurones, maxHLNeurones),
new MonoObjectiveEOALib.Range("Layer 2 Nodes count", minHLNeurones, maxHLNeurones),
new MonoObjectiveEOALib.Range("Layer 3 Nodes count", minHLNeurones, maxHLNeurones),
new MonoObjectiveEOALib.Range("Layer 4 Nodes count", minHLNeurones, maxHLNeurones),
new MonoObjectiveEOALib.Range("Layer 5 Nodes count", minHLNeurones, maxHLNeurones),
new MonoObjectiveEOALib.Range("Layer 6 Nodes count", minHLNeurones, maxHLNeurones)
};
EANN annEo = new EANN(dfr.TrainingInput, dfr.TrainingOutput, dfr.TestingInput, dfr.TestingOutput);
annEo.Learning_Algorithm = (LearningAlgorithmEnum)learnAlgo;// LearningAlgorithmEnum.LevenbergMarquardtLearning;
annEo.SearchRanges = ranges;
annEo.MaxOptimizationIterations = kmax;
annEo.PopulationSize = n;
annEo.LearnEO();
Console.WriteLine("EO-ANN :-> Best learning scroe = {0} ; EO-ANN :-> Best testing score = {1}", annEo.BestLearningScore, annEo.BestTestingScore);
foreach (var itm in annEo.BestChart)
{
Console.WriteLine(itm);
}
SaveResults(annEo, string.Format("C:\\SSL\\Results_ANN_{0}.txt", fileName.Trim()));
//double[][] xy = new double[][]
// {
// new double []{ 0.8},
// new double []{0.16},
// new double []{0.24},
// new double []{0.35},
// new double []{0.25}
// };
//double[][] xy = new double[][]
//{
// new double []{0.8, 0.12},
// new double []{0.16, 0.10},
// new double []{0.24, 0.26},
// new double []{0.35, 0.10},
// new double []{0.25, 0.12}
//};
//var z = annEo.Compute(xy);
//if (Equals(z, null)) { return; }
//foreach (double value in z)
//{
// Console.WriteLine("z = {0}", Math.Round(value, 3));
//}
}
public EANNTests()
{
_tst = new EANN();
}
