/// <summary>
/// Compute the output for this network.
/// </summary>
///
/// <param name="input">The input.</param>
/// <param name="output">The output.</param>
public void Compute(double[] input, double[] output)
{
var input2 = new BasicMLData(input);
IMLData output2 = Compute(input2);
output2.CopyTo(output, 0, output2.Count);
}
public virtual void Compute(IMLData input, double[] output)
{
int sourceIndex = _layerOutput.Length
- _layerCounts[_layerCounts.Length - 1];
input.CopyTo(_layerOutput, sourceIndex, _inputCount);
InnerCompute(output);
}
/// <summary>
/// Note: for Boltzmann networks, you will usually want to call the "run"
/// method to compute the output.
/// This method can be used to copy the input data to the current state. A
/// single iteration is then run, and the new current state is returned.
/// </summary>
///
/// <param name="input">The input pattern.</param>
/// <returns>The new current state.</returns>
public override sealed IMLData Compute(IMLData input)
{
var result = new BiPolarMLData(input.Count);
input.CopyTo(CurrentState.Data, 0, input.Count);
Run();
EngineArray.ArrayCopy(CurrentState.Data, result.Data);
return(result);
}
private char Denormalize(IMLData value)
{
var query = new double[value.Count];
value.CopyTo(query, 0, value.Count);
var index = query.ToList().IndexOf(1D);
return(_alphas[index]);
}
/// <summary>
/// Note: for Hopfield networks, you will usually want to call the "run"
/// method to compute the output.
/// This method can be used to copy the input data to the current state. A
/// single iteration is then run, and the new current state is returned.
/// </summary>
///
/// <param name="input">The input pattern.</param>
/// <returns>The new current state.</returns>
public override sealed IMLData Compute(IMLData input)
{
var result = new BiPolarMLData(input.Count);
input.CopyTo(CurrentState.Data, 0, input.Count);
Run();
for (int i = 0; i < CurrentState.Count; i++)
{
result.SetBoolean(i,
BiPolarUtil.Double2bipolar(CurrentState[i]));
}
EngineArray.ArrayCopy(CurrentState.Data, result.Data);
return(result);
}
private void button2_Click(object sender, EventArgs e)
{
double[][] x = { new double[] { 0.1, 0.2 },
new double[] { 0.4, 0.3 } };
double[][] y = { new double[] { 0.3 },
new double[] { 0.7 } };
BasicMLDataSet dataset = new BasicMLDataSet(x, y);
BasicNetwork rede = new BasicNetwork();
rede.AddLayer(new BasicLayer(new ActivationSigmoid(), true, 2));
rede.AddLayer(new BasicLayer(new ActivationSigmoid(), true, 3));
rede.AddLayer(new BasicLayer(new ActivationSigmoid(), true, 1));
rede.Structure.FinalizeStructure();
rede.Reset();
Backpropagation train = new Backpropagation(rede, dataset, 0.5, 0.1);
int epoch = 0;
do
{
train.Iteration();
if (epoch % 100 == 0)
{
richTextBox2.AppendText("Época " + epoch.ToString() + " Erro " + train.Error + Environment.NewLine);
}
epoch++;
} while (epoch < 3000);
for (double t = 0.0; t <= 5; t += 0.05)
{
double[] d = new double[] { t, t };
IMLData input = new BasicMLData(d);
IMLData output = rede.Compute(input);
double[] result = new double[output.Count];
output.CopyTo(result, 0, output.Count);
richTextBox2.AppendText(" " + t + "+" + t + "=" + result[0] + Environment.NewLine);
}
}
private double MaxInArray(IMLData result)
{
var array = new double[Network.OutputSize];
result.CopyTo(array, 0, Network.OutputSize - 1);
var maxValue = array.Max();
var maxIndex = array.ToList().IndexOf(maxValue);
return maxIndex + 1;
}
public virtual void Compute(IMLData input, double[] output)
{
int sourceIndex = _layerOutput.Length
- _layerCounts[_layerCounts.Length - 1];
input.CopyTo(_layerOutput, sourceIndex, _inputCount);
InnerCompute(output);
}
/// <summary>
/// Note: for Boltzmann networks, you will usually want to call the "run"
/// method to compute the output.
/// This method can be used to copy the input data to the current state. A
/// single iteration is then run, and the new current state is returned.
/// </summary>
///
/// <param name="input">The input pattern.</param>
/// <returns>The new current state.</returns>
public override sealed IMLData Compute(IMLData input)
{
var result = new BiPolarMLData(input.Count);
input.CopyTo(CurrentState.Data, 0, input.Count);
Run();
EngineArray.ArrayCopy(CurrentState.Data, result.Data);
return result;
}
/// <summary>
/// Note: for Hopfield networks, you will usually want to call the "run"
/// method to compute the output.
/// This method can be used to copy the input data to the current state. A
/// single iteration is then run, and the new current state is returned.
/// </summary>
///
/// <param name="input">The input pattern.</param>
/// <returns>The new current state.</returns>
public override sealed IMLData Compute(IMLData input)
{
var result = new BiPolarMLData(input.Count);
input.CopyTo(CurrentState.Data, 0, input.Count);
Run();
for (int i = 0; i < CurrentState.Count; i++)
{
result.SetBoolean(i,
BiPolarUtil.Double2bipolar(CurrentState[i]));
}
EngineArray.ArrayCopy(CurrentState.Data, result.Data);
return result;
}
