/// <summary>
/// Gets (and removes) a random example from the 'presentlyProcessed'
/// </summary>
/// <param name="index"></param>
/// <returns></returns>
public NeuralNetworkExample GetExample(int index)
{
NeuralNetworkExample obj = presentlyProcessed.Get(index);
presentlyProcessed.Remove(obj);
return(obj);
}
/// <summary>
/// Induces the layer of this perceptron from the specified set of examples
/// </summary>
/// <param name="innds">a set of training examples for constructing the layer of this perceptron.</param>
/// <param name="numberofEpochs">the number of training epochs to be used.</param>
public void TrainOn(NeuralNetworkDataSet innds, int numberofEpochs)
{
for (int i = 0; i < numberofEpochs; ++i)
{
innds.RefreshDataset();
while (innds.HasMoreExamples())
{
NeuralNetworkExample nne = innds.GetExampleAtRandom();
ProcessInput(nne.GetInput());
Vector error = layer.ErrorVectorFrom(nne.GetTarget());
ProcessError(error);
}
}
}
/// <summary>
/// Returns the accuracy of the hypothesis on the specified set of examples
/// </summary>
/// <param name="nnds">the neural network data set to be tested on.</param>
/// <returns>the accuracy of the hypothesis on the specified set of examples</returns>
public int[] TestOnDataSet(NeuralNetworkDataSet nnds)
{
int[] result = new int[] { 0, 0 };
nnds.RefreshDataset();
while (nnds.HasMoreExamples())
{
NeuralNetworkExample nne = nnds.GetExampleAtRandom();
Vector prediction = Predict(nne);
if (nne.IsCorrect(prediction))
{
result[0] = result[0] + 1;
}
else
{
result[1] = result[1] + 1;
}
}
return(result);
}
/// <summary>
/// Returns the outcome predicted for the specified example
/// </summary>
/// <param name="nne">an example</param>
/// <returns>the outcome predicted for the specified example</returns>
public Vector Predict(NeuralNetworkExample nne)
{
return(ProcessInput(nne.GetInput()));
}
