//https://blog.csdn.net/red_stone1/article/details/80491895
public PerceptronLearning(ActivationNetwork network)
{
if (network.Layers.Length != 1)
{
throw new ArgumentException("无效的神经网络，它应该只有一层。");
}
_network = network;
}
public SlowLearning(ActivationNetwork network)
{
_network = network;
}
static void Main(string[] args)
{
double[][] inputs = new double[4][];
double[][] outputs = new double[4][];
//(0,0);(0,1);(1,0)
inputs[0] = new double[] { 0, 0 };
inputs[1] = new double[] { 0, 1 };
inputs[2] = new double[] { 1, 0 };
outputs[0] = new double[] { 0 };
outputs[1] = new double[] { 0 };
outputs[2] = new double[] { 0 };
//(1,1)
inputs[3] = new double[] { 1, 1 };
outputs[3] = new double[] { 1 };
ActivationNetwork network = new ActivationNetwork(
new ThresholdFunction(), 2, 1);
SlowLearning teacher = new SlowLearning(network);
int iteration = 1;
while (true)
{
double error = teacher.RunEpoch(inputs, outputs);
Console.WriteLine($"迭代次数:{iteration},总体误差:{error} ");
if (error == 0)
{
break;
}
iteration++;
}
Console.WriteLine();
//double[][] inputs = new double[4][];
//double[][] outputs = new double[4][];
//(0, 0); (0, 1); (1, 0)
//inputs[0] = new double[] { 0, 0 };
//inputs[1] = new double[] { 0, 1 };
//inputs[2] = new double[] { 1, 0 };
//outputs[0] = new double[] { 0 };
//outputs[1] = new double[] { 0 };
//outputs[2] = new double[] { 0 };
//(1, 1)
//inputs[3] = new double[] { 1, 1 };
//outputs[3] = new double[] { 1 };
//ActivationNetwork network = new ActivationNetwork(
// new ThresholdFunction(), 2, 1);
//PerceptronLearning teacher = new PerceptronLearning(network);
//teacher.LearningRate = 0.1;
//int iteration = 1;
//while (true)
//{
// double error = teacher.RunEpoch(inputs, outputs);
// Console.WriteLine(@"迭代次数:{0},总体误差:{1}", iteration, error);
// if (error == 0)
// break;
// iteration++;
//}
//Console.WriteLine();
//ActivationNeuron neuron = network.Layers[0].Neurons[0] as ActivationNeuron;
//Console.WriteLine(@"Weight 1:{0}", neuron.Weights[0].ToString("F3"));
//Console.WriteLine(@"Weight 2:{0}", neuron.Weights[1].ToString("F3"));
//Console.WriteLine(@"Threshold:{0}", neuron.Threshold.ToString("F3"));
//Console.Read();
//double[][] inputs = new double[4][];
//double[][] outputs = new double[4][];
////(0,0)
//inputs[0] = new double[] { 0, 0 };
//outputs[0] = new double[] { 0 };
////(1,1);(0,1);(1,0)
//inputs[1] = new double[] { 0, 1 };
//inputs[2] = new double[] { 1, 0 };
//inputs[3] = new double[] { 1, 1 };
//outputs[1] = new double[] { 1 };
//outputs[2] = new double[] { 1 };
//outputs[3] = new double[] { 1 };
//ActivationNetwork network = new ActivationNetwork(new ThresholdFunction(), 2, 1);
//SlowLearning teacher = new SlowLearning(network);
//int iteration = 1;
//while (true)
//{
// double error = teacher.RunEpoch(inputs, outputs);
// Console.WriteLine(@"迭代次数:{0},总体误差:{1}", iteration, error);
// if (error == 0)
// break;
// iteration++;
//}
//Console.WriteLine();
//ActivationNeuron neuron = network.Layers[0].Neurons[0] as ActivationNeuron;
//Console.WriteLine(@"Weight 1:{0}", neuron.Weights[0].ToString("F3"));
//Console.WriteLine(@"Weight 2:{0}", neuron.Weights[1].ToString("F3"));
//Console.WriteLine(@"Threshold:{0}", neuron.Threshold.ToString("F3"));
//double[][] inputs = new double[4][];
//double[][] outputs = new double[4][];
////(0,0)
//inputs[0] = new double[] { 0, 0 };
//outputs[0] = new double[] { 0 };
////(1,1);(0,1);(1,0)
//inputs[1] = new double[] { 0, 1 };
//inputs[2] = new double[] { 1, 0 };
//inputs[3] = new double[] { 1, 1 };
//outputs[1] = new double[] { 1 };
//outputs[2] = new double[] { 1 };
//outputs[3] = new double[] { 1 };
//ActivationNetwork network = new ActivationNetwork(
// new ThresholdFunction(), 2, 1);
//PerceptronLearning teacher = new PerceptronLearning(network);
//teacher.LearningRate = 0.1;
//int iteration = 1;
//while (true)
//{
// double error = teacher.RunEpoch(inputs, outputs);
// Console.WriteLine(@"迭代次数:{0},总体误差:{1}", iteration, error);
// if (error == 0)
// break;
// iteration++;
//}
//Console.WriteLine();
//ActivationNeuron neuron = network.Layers[0].Neurons[0] as ActivationNeuron;
//Console.WriteLine(@"Weight 1:{0}", neuron.Weights[0].ToString("F3"));
//Console.WriteLine(@"Weight 2:{0}", neuron.Weights[1].ToString("F3"));
//Console.WriteLine(@"Threshold:{0}", neuron.Threshold.ToString("F3"));
//double[][] inputs = new double[15][];
//double[][] outputs = new double[15][];
////(0.1,0.1);(0.2,0.3);(0.3,0.4);(0.1,0.3);(0.2,0.5)
//inputs[0] = new double[] { 0.1, 0.1 };
//inputs[1] = new double[] { 0.2, 0.3 };
//inputs[2] = new double[] { 0.3, 0.4 };
//inputs[3] = new double[] { 0.1, 0.3 };
//inputs[4] = new double[] { 0.2, 0.5 };
//outputs[0] = new double[] { 1, 0, 0 };
//outputs[1] = new double[] { 1, 0, 0 };
//outputs[2] = new double[] { 1, 0, 0 };
//outputs[3] = new double[] { 1, 0, 0 };
//outputs[4] = new double[] { 1, 0, 0 };
////(0.1,1.0);(0.2,1.1);(0.3,0.9);(0.4,0.8);(0.2,0.9)
//inputs[5] = new double[] { 0.1, 1.0 };
//inputs[6] = new double[] { 0.2, 1.1 };
//inputs[7] = new double[] { 0.3, 0.9 };
//inputs[8] = new double[] { 0.4, 0.8 };
//inputs[9] = new double[] { 0.2, 0.9 };
//outputs[5] = new double[] { 0, 1, 0 };
//outputs[6] = new double[] { 0, 1, 0 };
//outputs[7] = new double[] { 0, 1, 0 };
//outputs[8] = new double[] { 0, 1, 0 };
//outputs[9] = new double[] { 0, 1, 0 };
////(1.0,0.4);(0.9,0.5);(0.8,0.6);(0.9,0.4);(1.0,0.5)
//inputs[10] = new double[] { 1.0, 0.4 };
//inputs[11] = new double[] { 0.9, 0.5 };
//inputs[12] = new double[] { 0.8, 0.6 };
//inputs[13] = new double[] { 0.9, 0.4 };
//inputs[14] = new double[] { 1.0, 0.5 };
//outputs[10] = new double[] { 0, 0, 1 };
//outputs[11] = new double[] { 0, 0, 1 };
//outputs[12] = new double[] { 0, 0, 1 };
//outputs[13] = new double[] { 0, 0, 1 };
//outputs[14] = new double[] { 0, 0, 1 };
//ActivationNetwork network = new ActivationNetwork(new ThresholdFunction(), 2, 3);
//SlowLearning teacher = new SlowLearning(network);
//int iteration = 1;
//while (true)
//{
// double error = teacher.RunEpoch(inputs, outputs);
// Console.WriteLine(@"迭代次数:{0},总体误差:{1}", iteration, error);
// if (error == 0)
// break;
// iteration++;
//}
//double[][] inputs = new double[15][];
//double[][] outputs = new double[15][];
////(0.1,0.1);(0.2,0.3);(0.3,0.4);(0.1,0.3);(0.2,0.5)
//inputs[0] = new double[] { 0.1, 0.1 };
//inputs[1] = new double[] { 0.2, 0.3 };
//inputs[2] = new double[] { 0.3, 0.4 };
//inputs[3] = new double[] { 0.1, 0.3 };
//inputs[4] = new double[] { 0.2, 0.5 };
//outputs[0] = new double[] { 1, 0, 0 };
//outputs[1] = new double[] { 1, 0, 0 };
//outputs[2] = new double[] { 1, 0, 0 };
//outputs[3] = new double[] { 1, 0, 0 };
//outputs[4] = new double[] { 1, 0, 0 };
////(0.1,1.0);(0.2,1.1);(0.3,0.9);(0.4,0.8);(0.2,0.9)
//inputs[5] = new double[] { 0.1, 1.0 };
//inputs[6] = new double[] { 0.2, 1.1 };
//inputs[7] = new double[] { 0.3, 0.9 };
//inputs[8] = new double[] { 0.4, 0.8 };
//inputs[9] = new double[] { 0.2, 0.9 };
//outputs[5] = new double[] { 0, 1, 0 };
//outputs[6] = new double[] { 0, 1, 0 };
//outputs[7] = new double[] { 0, 1, 0 };
//outputs[8] = new double[] { 0, 1, 0 };
//outputs[9] = new double[] { 0, 1, 0 };
////(1.0,0.4);(0.9,0.5);(0.8,0.6);(0.9,0.4);(1.0,0.5)
//inputs[10] = new double[] { 1.0, 0.4 };
//inputs[11] = new double[] { 0.9, 0.5 };
//inputs[12] = new double[] { 0.8, 0.6 };
//inputs[13] = new double[] { 0.9, 0.4 };
//inputs[14] = new double[] { 1.0, 0.5 };
//outputs[10] = new double[] { 0, 0, 1 };
//outputs[11] = new double[] { 0, 0, 1 };
//outputs[12] = new double[] { 0, 0, 1 };
//outputs[13] = new double[] { 0, 0, 1 };
//outputs[14] = new double[] { 0, 0, 1 };
//ActivationNetwork network = new ActivationNetwork(new ThresholdFunction(), 2, 3);
//PerceptronLearning teacher = new PerceptronLearning(network);
//teacher.LearningRate = 0.1;
//int iteration = 1;
//while (true)
//{
// double error = teacher.RunEpoch(inputs, outputs);
// Console.WriteLine(@"迭代次数:{0},总体误差:{1}", iteration, error);
// if (error == 0)
// break;
// iteration++;
//}
Console.Read();
}
