Metalearning Algorithm

class MetaLearningAlgorithm:
    def __init__(self):
        self.supervised_model = SupervisedModel()
        self.unsupervised_model = UnsupervisedModel()
        self.reinforcement_model = ReinforcementModel()
        self.balance_weights = [1/3, 1/3, 1/3]  # Initial equal weighting

    def train(self, data):
        # Train each model
        self.supervised_model.train(data.supervised)
        self.unsupervised_model.train(data.unsupervised)
        self.reinforcement_model.train(data.reinforcement)

        # Update balance weights based on performance
        self.update_balance_weights()

    def update_balance_weights(self):
        # Pseudo-code for updating weights based on performance metrics
        supervised_performance = self.supervised_model.evaluate(data.validation)
        unsupervised_performance = self.unsupervised_model.evaluate(data.validation)
        reinforcement_performance = self.reinforcement_model.evaluate(data.validation)

        total_performance = supervised_performance + unsupervised_performance + reinforcement_performance
        self.balance_weights = [
            supervised_performance / total_performance,
            unsupervised_performance / total_performance,
            reinforcement_performance / total_performance
        ]

    def predict(self, input_data):
        # Combine predictions from all models based on balance weights
        supervised_pred = self.supervised_model.predict(input_data)
        unsupervised_pred = self.unsupervised_model.predict(input_data)
        reinforcement_pred = self.reinforcement_model.predict(input_data)

        combined_prediction = (
            self.balance_weights[0] * supervised_pred +
            self.balance_weights[1] * unsupervised_pred +
            self.balance_weights[2] * reinforcement_pred
        )
        return combined_prediction

# Example usage
metalearner = MetaLearningAlgorithm()
metalearner.train(training_data)
prediction = metalearner.predict(new_data)