static void Main(string[] args)
{
#region Linear Regression
Console.WriteLine("Linear Regression");
double[] input = new double[] { -2, -1, 1, 4 };
double[] output = new double[] { -3, -1, 2, 3 };
LinearRegression linearRegression = new LinearRegression();
linearRegression.Training(input, output);
Console.WriteLine("Result: " + Math.Round(linearRegression.Run(0.5d),2));
Console.WriteLine("Coefficient Determination: " + Math.Round(linearRegression.CoefficientDetermination,2));
Console.WriteLine("--------------------------");
#endregion
#region Multiple Linear Regression
double[,] inputTrain = { { 2d, 3d }, { 2.5d, 2d }, { 1.8d, 4d } };
double[] outputTrain = { 5d, 6d, 4d };
MultipleLinearRegression mlr = new MultipleLinearRegression(inputTrain.GetLength(1), 0.5d);
mlr.Training(inputTrain, outputTrain);
Console.WriteLine("Multiple Linear Regression");
Console.WriteLine("Result: " + Math.Round(mlr.Run(new[] { 2.6d, 2.1d }), 2));
Console.WriteLine("--------------------------");
#endregion
#region Perceptron
Console.WriteLine("Perceptron");
#region AND Gate
double[,] inputAnd = new double[,] { { 1, 0 }, { 1, 1 }, { 0, 1 }, { 0, 0 } };
int[] outputAnd = new int[] { 0, 1, 0, 0 };
Perceptron p1 = new Perceptron();
p1.Training(inputAnd, outputAnd);
Console.WriteLine("AND Gate");
Console.WriteLine("Iteration of training: " + p1.Iteration);
Console.WriteLine("Test 1: " + p1.Run(new double[,] { { 1, 0 } }));
Console.WriteLine("Test 2: " + p1.Run(new double[,] { { 1, 1 } }));
#endregion
#region OR Gate
double[,] inputOr = new double[,] { { 1, 0 }, { 1, 1 }, { 0, 1 }, { 0, 0 } };
int[] outputOr = new int[] { 1, 1, 1, 0 };
Perceptron p2 = new Perceptron();
p2.Training(inputOr, outputOr);
Console.WriteLine("OR Gate");
Console.WriteLine("Iteration of training: " + p2.Iteration);
Console.WriteLine("Test 1: " + p2.Run(new double[,] { { 0, 1 } }));
Console.WriteLine("Test 2: " + p2.Run(new double[,] { { 0, 0 } }));
#endregion
Console.WriteLine("--------------------------");
#endregion
#region Multilayer Parceptron
Console.WriteLine("Multilayer Parceptron");
MultilayerPerceptron mlp = new MultilayerPerceptron(2, 5, 1);
mlp.Training(new double[,] { { 1, 1 }, { 1, 0 }, { 0, 0 }, { 0, 1 } }, new double[] { 1, 1, 0, 1 });
Console.WriteLine("OR Gate: " + Math.Round(mlp.Run(new double[] { 0, 1 }).FirstOrDefault(), 1));
Console.WriteLine("--------------------------");
#endregion
Console.ReadKey();
}
public override void Run()
{
var nt = new MultilayerPerceptron(
new PerceptronParameters {
LearningSpeed = 0.7, Moment = 0.3
},
new SigmoidActivation(4),
2, 2, 1);
//Helper.PrintMatrix(nt.Neurons);
/*nt.Weights = new double[2][][];
* nt.Weights[0] = new double[3][] {
* new double[] { 0.1, 0.2 },
* new double[] { 0.3, 0.4 },
* new double[] { 0.5, 0.6 },
* };
* nt.Weights[1] = new double[1][] {
* new double[] { 0.7, 0.9, 0.11 },
* //new double[] { 0.8, 0.14, 0.12 }
* };*/
Console.WriteLine("Weights: ");
//Helper.PrintMatrix(nt.Weights);
var learn = new double[12][] {
new [] { 0.018088, 0.01591 },
new [] { 0.0248, -0.00912 },
new [] { -0.013727, 0.00502 },
new [] { -0.023491, 0.007678 },
new [] { -0.011982, 0.025521 },
new [] { 0.00835, -0.0316 },
new [] { 0.041049, -0.041505 },
new [] { 0.050914, -0.046292 },
new [] { 0.076138, -0.106684 },
new [] { 0.131035, -0.092031 },
new [] { 0.206694, -0.209201 },
new [] { 0.168238, -0.211099 }
};
var middleError = 0.0;
for (var k = 1; k < 10000000; k++)
{
Vector result = null;
Vector error = null;
var st = new Stopwatch();
st.Start();
for (var j = 0; j < 1000; j++)
{
for (var i = 0; i < learn.Length - 1; i++)
{
(result, error) = nt.Learn(learn[i], new double[] { learn[i + 1][0] });
middleError += error[0];
//Console.WriteLine(result.Error[0] + "_");
}
}
st.Stop();
Console.Clear();
Console.WriteLine(st.Elapsed);
Console.WriteLine(middleError / nt.Epoch);
Console.WriteLine(error[0]);
for (var i = 0; i < learn.Length - 1; i++)
{
var res = nt.Run(learn[i]);
var result1 = nt.ConvertOutput(res);
Console.WriteLine(result1[0] + "\t" + learn[i + 1][0]);
}
}
middleError = middleError / (10000 * 11);
Console.WriteLine(middleError + "_");
}