Documentation

The perceptron model.

The classes that the perceptron assigns are represnted with a newtype-wrapped String.

Eventually, I think this should become a typeclass, so the classes can be defined by the users of the Perceptron (such as custom POS tag ADTs, or more complex classes).

Typedef for doubles to make the code easier to read, and to make this simple to change if necessary.

An empty perceptron, used to start training.

Predict a class given a feature vector.

Ported from python:

def predict(self, features):
    '''Dot-product the features and current weights and return the best label.'''
    scores = defaultdict(float)
    for feat, value in features.items():
        if feat not in self.weights or value == 0:
            continue
        weights = self.weights[feat]
        for label, weight in weights.items():
            scores[label] += value * weight
    # Do a secondary alphabetic sort, for stability
    return max(self.classes, key=lambda label: (scores[label], label))

Update the perceptron with a new example.

update(self, truth, guess, features)
   ...
        self.i += 1
        if truth == guess:
            return None
        for f in features:
            weights = self.weights.setdefault(f, {}) -- setdefault is Map.findWithDefault, and destructive.
            upd_feat(truth, f, weights.get(truth, 0.0), 1.0)
            upd_feat(guess, f, weights.get(guess, 0.0), -1.0)
        return None

Average the weights

Ported from Python:

def average_weights(self):
    for feat, weights in self.weights.items():
        new_feat_weights = {}
        for clas, weight in weights.items():
            param = (feat, clas)
            total = self._totals[param]
            total += (self.i - self._tstamps[param]) * weight
            averaged = round(total / float(self.i), 3)
            if averaged:
                new_feat_weights[clas] = averaged
        self.weights[feat] = new_feat_weights
    return None