partial void test_ann_model_acyclicToolStripMenuItem_Click(object sender, EventArgs e)
{
string log = "";
string c = "";
c += "[neu=hiddenperceptron; act=tanh(a=0.5, b=0.03); fieldsize=2; nodes=2]";
c += "[neu=outputperceptron; act=tanh(a = 0.2, b = 1.786); nodes=2]";
Feedforward[] fnn = new Feedforward[2];
fnn[0] = new Feedforward();
fnn[0].Configure(c);
Node[] input = new Node[]
{
new Node(new double?[] { 0.05, 0.00 }),
new Node(new double?[] { 0.01, 0.00 })
};
fnn[0].Input = input;
double[][] dataSet = new double[2][];
dataSet[0] = new double[] { 0.05, 0.10 };
dataSet[1] = new double[] { 0.01, 0.99 };
richTextBox.Text = log;
}
partial void test_ann_trainer_deltaRuleToolStripMenuItem_Click(object sender, EventArgs e)
{
string log = "";
int epochs = 10;
double learningRate = 0.09, momentum = 0.6;
Node[] input = new Node[]
{
new Node(new double?[] { 0.00, null }),
new Node(new double?[] { 0.00, null })
};
string c = ""; // configuration string
c += "[neu=hiddenperceptron; act=tanh(a=1.7159, b=0.6667); fieldsize=2; nodes=2]";
c += "[neu=outputperceptron; act=tanh(a=1.7159, b=0.6667); nodes=1]";
Feedforward[] fnn = new Feedforward[2];
fnn[0] = new Feedforward();
fnn[0].Configure(c);
fnn[0].Input = input;
// create or data set
DataSet orDataSet = new DataSet(2, 1);
orDataSet.Add(new double[] { -1.0, -1.0 }, new double[] { -1.0 });
orDataSet.Add(new double[] { -1.0, +1.0 }, new double[] { +1.0 });
orDataSet.Add(new double[] { +1.0, -1.0 }, new double[] { +1.0 });
orDataSet.Add(new double[] { +1.0, +1.0 }, new double[] { +1.0 });
// instantiate trainer
DeltaRule dRule = new DeltaRule();
dRule.Configure(fnn[0], epochs, orDataSet.Data, learningRate, momentum);
//log += dRule.Next();
dRule.Next();
double[][] data = orDataSet.Data;
for (int i = 0; i < orDataSet.Count; i++)
{
fnn[0].SetInput(data[i * 2]);
fnn[0].PropagateSignal();
log += "\n\n" + fnn[0].ToString();
}
fnn[1] = new Feedforward();
fnn[1].Configure <Tanh>(new int[] { 2, 1 }, new double?[] { 1.7159, 0.6667 }, 2);
fnn[1].Input = input;
DataSet xrDataSet = new DataSet(2, 1);
xrDataSet.Add(new double[] { -1.0, -1.0 }, new double[] { -1.0 });
xrDataSet.Add(new double[] { -1.0, +1.0 }, new double[] { +1.0 });
xrDataSet.Add(new double[] { +1.0, -1.0 }, new double[] { +1.0 });
xrDataSet.Add(new double[] { +1.0, +1.0 }, new double[] { -1.0 });
epochs = 200;
learningRate = 0.1; momentum = 0.80;
dRule.Configure(fnn[1], epochs, xrDataSet.Data, learningRate, momentum);
//log += dRule.Next();
dRule.Next();
log += "\n\n////////////////////////////////////////////////////////////////////";
data = xrDataSet.Data;
for (int i = 0; i < xrDataSet.Count; i++)
{
fnn[1].SetInput(data[i * 2]);
fnn[1].PropagateSignal();
log += "\n\n" + fnn[1].ToString();
}
richTextBox.Text = log;
}
