public void Learn(INeuralNetwork net, ILearningSequence<ISupervisedLearningVector> seq)
{
LearningStory = new List<NeuronState>();
bool isLearned = true;
do
{
StoreState(net);
isLearned = true;
IEnumerator<ISupervisedLearningVector> en = seq.Vectors.GetEnumerator();
while (en.MoveNext())
{
net.SetInput(en.Current.Data);
net.Compute();
double delta = en.Current.CorrectOutput - net[0];
if (delta != 0.0)
{
isLearned = false;
CorrectWeights(net, delta > 0 ? 1.0 : -1.0);
}
}
seq.Shuffle();
} while (!isLearned);
StoreState(net);
}
public void Learn(INeuralNetwork net, ILearningSequence<ISupervisedLearningVector> seq)
{
LearningStory = new List<NeuronState>();
bool isLearned = true;
int counter = 0;
do
{
StoreState(net);
isLearned = true;
IEnumerator<ISupervisedLearningVector> en = seq.Vectors.GetEnumerator();
while (en.MoveNext())
{
net.SetInput(en.Current.Data);
net.Compute();
CorrectWeights(net, en.Current.CorrectOutput - net.Layers[1][0].Potential);
}
en.Reset();
while (en.MoveNext())
{
net.SetInput(en.Current.Data);
net.Compute();
if (Math.Abs(en.Current.CorrectOutput - net[0]) > Epsilon)
{
isLearned = false;
break;
}
}
seq.Shuffle();
counter++;
} while (!isLearned && counter < EpochLimit);
StoreState(net);
Failed = !isLearned;
}
private void Form1_Load(object sender, EventArgs e)
{
learningSequence = new LearningSequence<ISupervisedLearningVector>();
learningSequenceDT = new DataTable();
dataGridView1.AutoGenerateColumns = true;
dataGridView1.DataSource = learningSequenceDT;
}
private void listBox1_SelectedIndexChanged_1(object sender, EventArgs e)
{
switch (listBox1.SelectedIndex)
{
case 0:
learningSequence = new LearningSequence<ISupervisedLearningVector>();
learningSequence.Add(new SupervisedLearningVector(new double[2] { -1, -1 }, -1));
learningSequence.Add(new SupervisedLearningVector(new double[2] { 1, -1 }, -1));
learningSequence.Add(new SupervisedLearningVector(new double[2] { -1, 1 }, -1));
learningSequence.Add(new SupervisedLearningVector(new double[2] { 1, 1 }, 1));
UpdateLearningSequenceDT();
break;
case 1:
learningSequence = new LearningSequence<ISupervisedLearningVector>();
learningSequence.Add(new SupervisedLearningVector(new double[2] { -1, -1 }, -1));
learningSequence.Add(new SupervisedLearningVector(new double[2] { 1, -1 }, 1));
learningSequence.Add(new SupervisedLearningVector(new double[2] { -1, 1 }, 1));
learningSequence.Add(new SupervisedLearningVector(new double[2] { 1, 1 }, 1));
UpdateLearningSequenceDT();
break;
case 2:
learningSequence = new LearningSequence<ISupervisedLearningVector>();
learningSequence.Add(new SupervisedLearningVector(new double[2] { 0, 0 }, 0));
learningSequence.Add(new SupervisedLearningVector(new double[2] { 1, 0 }, 0));
learningSequence.Add(new SupervisedLearningVector(new double[2] { 0, 1 }, 0));
learningSequence.Add(new SupervisedLearningVector(new double[2] { 1, 1 }, 1));
UpdateLearningSequenceDT();
break;
case 3:
learningSequence = new LearningSequence<ISupervisedLearningVector>();
learningSequence.Add(new SupervisedLearningVector(new double[2] { 0, 0 }, 0));
learningSequence.Add(new SupervisedLearningVector(new double[2] { 1, 0 }, 1));
learningSequence.Add(new SupervisedLearningVector(new double[2] { 0, 1 }, 1));
learningSequence.Add(new SupervisedLearningVector(new double[2] { 1, 1 }, 1));
UpdateLearningSequenceDT();
break;
}
}
