} // End run_training
/*******************************************************************
*
* Running the Perceptron
*
*******************************************************************/
/// <summary>
/// Allows the user to input values and see what the output is based on the given perceptron
/// </summary>
/// <param name="perceptron">The perceptron for the user to check inputs and their respective outputs</param>
public void run()
{
do
{
foreach (Neuron temp in perceptron.Value)
{
Console.WriteLine("Give an Input of 0, 1, true, or false: ");
temp.UpdateInputValues(DataValidation.get_boolean());
}
Console.WriteLine("Output value of: " + perceptron.Key.Calculate());
Console.WriteLine("Continue? y/n");
} while (DataValidation.yes_no());
}
/*******************************************************************
*
* Perceptron Training Methods
*
*******************************************************************/
/// <summary>
/// Suser built build the training set method
/// </summary>
/// <returns>The trained perceptront</returns>
public void user_built_perceptron()
{
// Initialize training set
List <KeyValuePair <Boolean, Boolean[]> > training_set = new List <KeyValuePair <bool, bool[]> >();
Console.WriteLine("How many neurons are in the input?");
int size = 0;
size = DataValidation.get_int();
do
{
Boolean[] inputs = new Boolean[size];
Boolean output;
Console.WriteLine("All inputs should either be 0, 1, true, or false");
for (int i = 0; i < size; ++i)
{
Console.WriteLine("Input for " + i + ": ");
inputs[i] = DataValidation.get_boolean();
}
Console.WriteLine("Expected output: ");
output = DataValidation.get_boolean();
training_set.Add(new KeyValuePair <bool, bool[]>(output, inputs));
Console.WriteLine("Add another training set?\ny/n");
} while (DataValidation.yes_no());
Console.WriteLine("What is the output neuron's activation threshold?");
Double thresh = Double.Parse(Console.ReadLine());
perceptron = run_training(training_set, thresh);
} // End user_built_perceptron