public void ThresholdOutput_ReturnsStepValue()
{
ActivationFunction thresholdFunction = new ThresholdFunction();
double input1 = -1;
double input2 = 0;
double input3 = 1;
double expected1 = 0;
double expected2 = 1;
double expected3 = 1;
double actual1 = thresholdFunction.Output(input1);
double actual2 = thresholdFunction.Output(input2);
double actual3 = thresholdFunction.Output(input3);
Assert.AreEqual(expected1, actual1, 0.0001, "Invalid threshold output");
Assert.AreEqual(expected2, actual2, 0.0001, "Invalid threshold output");
Assert.AreEqual(expected3, actual3, 0.0001, "Invalid threshold output");
}
static void Main(string[] args)
{
double[][] inputs = { new double[] { 0, 0 }, new double[] { 0, 1 }, new double[] { 1, 0 }, new double[] { 1, 1 } };
double[] targets = { 0, 1, 1, 1 };
double error = 1;
int epoch = 0;
ActivationFunction function = new ThresholdFunction();
Neuron neuron = new Neuron(2, function);
LearningStrategy learning = new PerceptronLearning(neuron, 0.25);
while(error > 0)
{
error = learning.RunEpoch(inputs, targets);
epoch++;
logger.InfoFormat("Iteration {0} error: {1}", epoch, error);
}
logger.InfoFormat("Training complete after {0} epochs using the Perceptron learning regime.", epoch);
}
