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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def predict(self, features):
 """Dot-product the features and current weights and return the best label."""

        scores = defaultdict(float)
        for feat in features:
            if feat not in self.weights:
                continue
            weights = self.weights[feat]
            for label, weight in weights.items():
                scores[label] += weight
        # Do a secondary alphabetic sort, for stability
        return max(self.classes, key=lambda label: (scores[label], label))