import numpy as np
# Dummy implementations for the base models.
class SupervisedModel:
def __init__(self):
pass
def train(self, data):
# Simulated training procedure
pass
def evaluate(self, data):
# Simulated evaluation returns a random performance metric
return np.random.random()
def predict(self, data):
# Simulated predictions as random numbers matching input shape
return np.random.random(data.shape)
class UnsupervisedModel:
def __init__(self):
pass
def train(self, data):
pass
def evaluate(self, data):
return np.random.random()
def predict(self, data):
return np.random.random(data.shape)
class ReinforcementModel:
def __init__(self):
pass
def train(self, data):
pass
def evaluate(self, data):
return np.random.random()
def predict(self, data):
return np.random.random(data.shape)
# The meta-learning ensemble designed to "run away"
class RunawayMetaLearner:
def __init__(self):
self.supervised_model = SupervisedModel()
self.unsupervised_model = UnsupervisedModel()
self.reinforcement_model = ReinforcementModel()
# Start with equal ensemble weights for the base models.
self.weights = np.array([1/3, 1/3, 1/3])
# A meta-model that will adjust the ensemble weights over time.
# Here we use a simple weight vector that will be updated based on model performance.
self.meta_model_weights = np.random.randn(3)
def train(self, data):
# Train each model with its corresponding data slice.
self.supervised_model.train(data['supervised'])
self.unsupervised_model.train(data['unsupervised'])
self.reinforcement_model.train(data['reinforcement'])
# Update the ensemble and meta weights based on validation performance.
self.update_weights(data['validation'])
def update_weights(self, validation_data):
# Evaluate performance of each base model.
supervised_perf = self.supervised_model.evaluate(validation_data)
unsupervised_perf = self.unsupervised_model.evaluate(validation_data)
reinforcement_perf = self.reinforcement_model.evaluate(validation_data)
# Create an array of performance metrics.
perf_array = np.array([supervised_perf, unsupervised_perf, reinforcement_perf])
total = np.sum(perf_array) + 1e-8 # Avoid division by zero.
# Dynamically update ensemble weights proportional to performance.
self.weights = perf_array / total
# Simulate a meta-learning update (a dummy gradient step):
# The meta_model_weights learn from the discrepancy between current weights and performance.
learning_rate = 0.01
gradient = (perf_array - self.weights) # A placeholder gradient.
self.meta_model_weights += learning_rate * gradient
def predict(self, input_data):
# Get predictions from each base model.
supervised_pred = self.supervised_model.predict(input_data)
unsupervised_pred = self.unsupervised_model.predict(input_data)
reinforcement_pred = self.reinforcement_model.predict(input_data)
# Combine predictions into one array.
base_preds = np.array([supervised_pred, unsupervised_pred, reinforcement_pred])
# Use the meta-model weights to dynamically adjust ensemble weights.
# The tanh activation simulates a non-linear adjustment.
adjusted_weights = self.weights * (1 + np.tanh(self.meta_model_weights))
# Normalize to ensure the weights sum to 1.
adjusted_weights /= np.sum(adjusted_weights)
# Combine predictions according to the adjusted weights.
combined_pred = np.tensordot(adjusted_weights, base_preds, axes=1)
return combined_pred
# Example usage:
if __name__ == '__main__':
# Simulated training data structured for different learning modes.
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)