Metalearning Algorithm

    import numpy as np
import logging
from abc import ABC, abstractmethod
from typing import Dict

# Configure logging for visibility into training and meta-learning processes
logging.basicConfig(level=logging.INFO, format='%(asctime)s [%(levelname)s] %(message)s')

class BaseModel(ABC):
    """Abstract base class for all learning models."""
    def __init__(self, name: str):
        self.name = name

    @abstractmethod
    def train(self, data: np.ndarray) -> None:
        """Train the model on data."""
        pass

    @abstractmethod
    def evaluate(self, data: np.ndarray) -> float:
        """Evaluate the model and return a performance score."""
        pass

    @abstractmethod
    def predict(self, data: np.ndarray) -> np.ndarray:
        """Make predictions with the model."""
        pass

class SupervisedModel(BaseModel):
    def __init__(self):
        super().__init__('Supervised')

    def train(self, data: np.ndarray) -> None:
        logging.debug(f"{self.name}: training on {data.shape} samples.")
        # Placeholder for supervised training logic

    def evaluate(self, data: np.ndarray) -> float:
        perf = float(np.random.rand())
        logging.debug(f"{self.name}: evaluation performance {perf:.4f}")
        return perf

    def predict(self, data: np.ndarray) -> np.ndarray:
        return np.random.rand(*data.shape)

class UnsupervisedModel(BaseModel):
    def __init__(self):
        super().__init__('Unsupervised')

    def train(self, data: np.ndarray) -> None:
        logging.debug(f"{self.name}: training on {data.shape} samples.")
        # Placeholder for unsupervised training logic

    def evaluate(self, data: np.ndarray) -> float:
        perf = float(np.random.rand())
        logging.debug(f"{self.name}: evaluation performance {perf:.4f}")
        return perf

    def predict(self, data: np.ndarray) -> np.ndarray:
        return np.random.rand(*data.shape)

class ReinforcementModel(BaseModel):
    def __init__(self):
        super().__init__('Reinforcement')

    def train(self, data: np.ndarray) -> None:
        logging.debug(f"{self.name}: training on {data.shape} samples.")
        # Placeholder for reinforcement learning logic

    def evaluate(self, data: np.ndarray) -> float:
        perf = float(np.random.rand())
        logging.debug(f"{self.name}: evaluation performance {perf:.4f}")
        return perf

    def predict(self, data: np.ndarray) -> np.ndarray:
        return np.random.rand(*data.shape)

class RunawayMetaLearner:
    """Meta-learning ensemble that can 'run away' if meta-weights diverge."""
    def __init__(self):
        # Initialize base models
        self.models = [
            SupervisedModel(),
            UnsupervisedModel(),
            ReinforcementModel(),
        ]
        # Start with equal ensemble weights
        self.weights = np.array([1/3] * len(self.models), dtype=float)
        # Meta-model weights for non-linear adjustment
        self.meta_weights = np.random.randn(len(self.models))
        # Learning hyperparameters
        self.learning_rate = 0.01
        self.runaway_threshold = 10.0

    def train(self, data: Dict[str, np.ndarray]) -> None:
        """Train each base model and update ensemble/meta weights."""
        keys = ['supervised', 'unsupervised', 'reinforcement']
        for model, key in zip(self.models, keys):
            model.train(data[key])
        self._update_weights(data['validation'])
        self._check_runaway()

    def _update_weights(self, validation_data: np.ndarray) -> None:
        # Evaluate each model
        performances = np.array([m.evaluate(validation_data) for m in self.models])
        total = performances.sum() + 1e-8  # Prevent division by zero
        # Update ensemble weights proportionally
        self.weights = performances / total
        # Meta-learning gradient step
        gradient = performances - self.weights
        self.meta_weights += self.learning_rate * gradient
        logging.info(f"Updated ensemble weights: {self.weights}")
        logging.info(f"Meta-weights norm: {np.linalg.norm(self.meta_weights):.4f}")

    def _check_runaway(self) -> None:
        norm = np.linalg.norm(self.meta_weights)
        if norm > self.runaway_threshold:
            logging.warning(
                f"Runaway detected! Meta-weights norm {norm:.4f} exceeds threshold {self.runaway_threshold}"
            )

    def predict(self, input_data: np.ndarray) -> np.ndarray:
        """Generate predictions by combining base model outputs."""
        # Collect predictions
        base_predictions = np.stack([m.predict(input_data) for m in self.models])
        # Adjust ensemble weights using non-linear meta-weights
        adjusted = self.weights * (1 + np.tanh(self.meta_weights))
        adjusted /= adjusted.sum()  # Normalize
        # Combine predictions
        return np.tensordot(adjusted, base_predictions, axes=(0, 0))

if __name__ == '__main__':
    # Example usage
    training_data = {
        'supervised': np.random.randn(100, 10),
        'unsupervised': np.random.randn(100, 10),
        'reinforcement': np.random.randn(100, 10),
        'validation': np.random.randn(20, 10),
    }
    runaway_ai = RunawayMetaLearner()
    runaway_ai.train(training_data)

    # Simulate predictions on new data
    new_data = np.random.randn(5, 10)
    prediction = runaway_ai.predict(new_data)
    print("Predictions:", prediction)