public void TestLogicalOr()
{
var perceptron = new Perceptron(2);
perceptron.ActivationFunction = x => x >= 1 ? 1 : 0;
perceptron.Bias = 0;
perceptron.Weights[0] = 1;
perceptron.Weights[1] = 1;
// 0 & 0 -> 0
perceptron.Inputs[0] = 0;
perceptron.Inputs[1] = 0;
Assert.AreEqual(0, perceptron.Output);
// 0 & 1 -> 0
perceptron.Inputs[0] = 0;
perceptron.Inputs[1] = 1;
Assert.AreEqual(1, perceptron.Output);
// 1 & 0 -> 0
perceptron.Inputs[0] = 1;
perceptron.Inputs[1] = 0;
Assert.AreEqual(1, perceptron.Output);
// 1 & 1 -> 0
perceptron.Inputs[0] = 1;
perceptron.Inputs[1] = 1;
Assert.AreEqual(1, perceptron.Output);
}
public void TestTrainingBounce()
{
var perceptron = new Perceptron(2);
perceptron.ActivationFunction = x => x >= 1 ? 1 : 0;
var trainer = new PerceptronTrainer(perceptron);
var rows = new []
{
new PerceptronTrainingRow ( new [] { 0.0, 1.0 }, 0 ),
new PerceptronTrainingRow ( new [] { 0.5, 1.0 }, 0 ),
new PerceptronTrainingRow ( new [] { 1.0, 0.0 }, 0 ),
new PerceptronTrainingRow ( new [] { 1.5, 5.0 }, 0 ),
new PerceptronTrainingRow ( new [] { 3.0, 3.0 }, 0 ),
new PerceptronTrainingRow ( new [] { 3.5, 0.0 }, 0 ),
new PerceptronTrainingRow ( new [] { 1.0, 6.0 }, 1 ),
new PerceptronTrainingRow ( new [] { 2.0, 9.0 }, 1 ),
new PerceptronTrainingRow ( new [] { 4.0, 6.0 }, 1 ),
new PerceptronTrainingRow ( new [] { 5.5, 1.0 }, 1 ),
new PerceptronTrainingRow ( new [] { 6.0, 4.0 }, 1 ),
new PerceptronTrainingRow ( new [] { 9.0, 3.0 }, 1 ),
};
trainer.Train(rows, 0.01, 500);
perceptron.Inputs[0] = 2.0;
perceptron.Inputs[1] = 2.0;
Assert.AreEqual(0, perceptron.Output);
perceptron.Inputs[0] = 7.0;
perceptron.Inputs[1] = 6.0;
Assert.AreEqual(1, perceptron.Output);
}
public static DotNeuralNet.Perceptrons.Perceptron CreateAndTrainPerceptron(Scenario scenario, Func <double, double> activationFunction,
double adjust, int rounds)
{
var perceptron = new DotNeuralNet.Perceptrons.Perceptron(scenario.InputNodeCount);
perceptron.ActivationFunction = activationFunction;
var trainer = new PerceptronTrainer(perceptron);
trainer.Train(GetTrainingRows(scenario).ToArray(), adjust, rounds);
return(perceptron);
}
public PerceptronTrainer(Perceptron perceptron)
{
_perceptron = perceptron;
}
public PerceptronTrainer(Perceptron perceptron)
{
_perceptron = perceptron;
}
