!7

cmodule PerceptronSpike > DynPrintLog {
  sclass Example {
    double[] inputs;
    int answer;

    *(double x, double y, int a) {
      inputs = new double[] {x, y, 1};
      answer = a;
    }
  }

  int nExamples = 200;
  L<Example> examples;
  double[] weights;
  double c = 0.00001;

  start-thread {
    if (empty(examples))
      setField(examples := repF(nExamples, () -> {
        int x = rand(640), y = rand(360);
        ret new Example(x, y, y < f(x) ? -1 : 1);
      }));
      
    weights = new double[l(first(examples).inputs)];
    for i over weights: weights[i] = random(-1.0, 1.0);

    for i to 100:
      print("Error: " + train());
  }
  
  double train() {
    double error = 0;
    for (Example e : examples) error += train(e);
    ret error/l(examples);
  }
  
  // function to be learned
  private double f(double x) {
    ret x * 0.7 + 40;
  }

  int feedForward(double[] inputs) {
    double sum = 0;
    for i over weights: sum += inputs[i] * weights[i];
    ret activate(sum);
  }

  int activate(double s) {
    return s > 0 ? 1 : -1;
  }

  double train(Example e) {
    int guess = feedForward(e.inputs);
    double error = e.answer - guess;
    for i over weights: weights[i] += c * error * e.inputs[i];
    ret error;
  }
}