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sclass Perceptron {
  srecord Example(double[] inputs, bool answer) {}

  new L<Example> examples;
  double c = 1;
  bool randomizeC = true, startWithRandomWeights = false;
  long trainingRound;
  double error = -1; // -1 = not trained yet
  double[] weights;
  
  *() {}
  *(L<Example> examples) {}

  Iterator<Double> trainingIterator() {
    if (empty(examples)) fail("No examples");
    int n = l(first(examples).inputs);
    if (weights == null) {
      weights = new double[n];
      if (startWithRandomWeights)
        for i over weights: weights[i] = random(-1.0, 1.0);
    }
    ret iff(() -> {
      if (error == 0) ret endMarker();
      ++trainingRound;
      double lastError = error;
      error = trainARound();
      ret error != lastError ? error : null;
    });
  }
  
  double trainARound() {
    double error = 0;
    for (Example e : examples) error += abs(train(e));
    ret error/l(examples);
  }
  
  bool feedForward(double[] inputs) {
    double sum = last(weights);
    for i over inputs: sum += inputs[i] * weights[i];
    ret activate(sum);
  }

  bool activate(double s) {
    return s > 0;
  }

  double train(Example e) {
    int guess = boolToInt(feedForward(e.inputs));
    double error = boolToInt(e.answer) - guess;
    double cc = c/l(examples);
    for i over weights:
      weights[i] += (randomizeC ? rand(cc) : cc) * error * e.inputs[i];
    ret error;
  }
  
  void printWithWeights {
    print("Error: " + error + ", round " + trainingRound + ", weights: " + sfu(weights));
  }
}