private static double ExecuteAlgorithmForSpecifiedData(SingleLayerPerceptron singleLayerPerceptron, string treningDataPath, string testDataPath)
{
char separator = '\t';
char decimalPoint = '.';
var winner = singleLayerPerceptron.Teach(treningDataPath, separator, decimalPoint, 1000, 0.2);
return(singleLayerPerceptron.Test(testDataPath, separator, decimalPoint, winner.Weights, winner.Bias));
}
public void Create(SingleLayerPerceptron p)
{
for (int i = 0, length = this.inputs.GetLength(1); i < length; i++)
{
this.inputs[i, 0] = SingleLayerPerceptron.rng.NextFloat(-11);
this.inputs[i, 1] = SingleLayerPerceptron.rng.NextFloat(-11);
this.targets[i, 0] = p.IsAboveLine(new float[] { this.inputs[i, 0], this.inputs[i, 1] });
}
}
private static void ProgramFormula(int nInput, int nOutput, int neuronsPerLayers)
{
SingleLayerPerceptron slp = new SingleLayerPerceptron(nInput, neuronsPerLayers, nOutput);
for (int i = 0; i < 1000000; i++)
{
Random random = new Random(Guid.NewGuid().GetHashCode());
double value1 = random.NextDouble();
random = new Random(Guid.NewGuid().GetHashCode());
double value2 = random.NextDouble();
double y = Formula(value1);
double result = value2 > y ? 1 : 0;
slp.Train(new List <double>()
{
value1, value2
}, new List <double> {
result
});
}
double n = 0;
for (int i = 0; i < 200; i++)
{
Random random = new Random(Guid.NewGuid().GetHashCode());
double value1 = random.NextDouble();
random = new Random(Guid.NewGuid().GetHashCode());
double value2 = random.NextDouble();
double y = Formula(value1);
double result = value2 > y ? 1 : 0;
var guess = slp.Predict(new List <double>()
{
value1, value2
});
double proj = guess[0] > 0.5 ? 1 : 0;
bool correct = proj == result;
Console.WriteLine("result : " + result);
Console.WriteLine("guess : " + guess[0]);
Console.WriteLine("correct : " + correct);
Console.WriteLine("--");
if (correct)
{
n++;
}
}
Console.WriteLine(n / 200);
}
static void Main(string[] args)
{
var slp = new SingleLayerPerceptron();
Console.WriteLine("Accuracy in A: {0}", ExecuteAlgorithmForSpecifiedData(slp, "Data/iris_2vs3_A_tr.txt", "Data/iris_2vs3_A_te.txt"));
Console.WriteLine("Accuracy in B: {0}", ExecuteAlgorithmForSpecifiedData(slp, "Data/iris_2vs3_B_tr.txt", "Data/iris_2vs3_B_te.txt"));
Console.WriteLine("Accuracy in C: {0}", ExecuteAlgorithmForSpecifiedData(slp, "Data/iris_2vs3_C_tr.txt", "Data/iris_2vs3_C_te.txt"));
Console.WriteLine("Accuracy in D: {0}", ExecuteAlgorithmForSpecifiedData(slp, "Data/iris_2vs3_D_tr.txt", "Data/iris_2vs3_D_te.txt"));
Console.WriteLine("Accuracy in E: {0}", ExecuteAlgorithmForSpecifiedData(slp, "Data/iris_2vs3_E_tr.txt", "Data/iris_2vs3_E_te.txt"));
Console.WriteLine("Accuracy in AND: {0}", ExecuteAlgorithmForSpecifiedData(slp, "Data/iris_and.txt", "Data/iris_and.txt"));
Console.ReadKey();
}
private static SingleLayerPerceptron SingleLayerPerceptronXORProgram(int nInput, int nOutput, int neuronsPerLayers)
{
SingleLayerPerceptron slp = new SingleLayerPerceptron(nInput, neuronsPerLayers, nOutput);
for (int i = 0; i < 100000; i++)
{
Random random = new Random(Guid.NewGuid().GetHashCode());
int value1 = random.Next() % 2;
random = new Random(Guid.NewGuid().GetHashCode());
int value2 = random.Next() % 2;
int xor = XOR(value1, value2);
slp.Train(new List <double>()
{
value1, value2
}, new List <double>()
{
xor
});
}
double n = 0;
for (int i = 0; i < 2000; i++)
{
Random random = new Random(Guid.NewGuid().GetHashCode());
int value1 = random.Next() % 2;
random = new Random(Guid.NewGuid().GetHashCode());
int value2 = random.Next() % 2;
int xor = XOR(value1, value2);
var guess = slp.Predict(new List <double>()
{
value1, value2
});
double result = guess[0] > 0.5 ? 1 : 0;
bool correct = result == xor;
Console.WriteLine("xor : " + xor);
Console.WriteLine("guess : " + guess[0]);
Console.WriteLine("--");
if (correct)
{
n++;
}
}
Console.WriteLine(n / 2000);
Console.Read();
return(slp);
}
