machinedream.py

# machinedream (c) 2024 PYTHAI BSD licence v3
import random
import json

class MachineDream:
    def __init__(self):
        # Placeholder for memory structure
        self.memory = []
        self.insights = []
    
    def parse_information(self, data):
        """
        Parse the incoming data and structure it for knowledge creation.
        Parameters:
            data (str): Raw data to be parsed
        Returns:
            dict: Parsed and structured data
        """
        # Simulate parsing raw data into structured knowledge (JSON in this case)
        parsed_data = {"knowledge": data.split()}
        return parsed_data
    
    def simulate_problem_solving(self, problem):
        """
        Simulate problem-solving scenarios based on the given problem.
        Parameters:
            problem (str): Problem statement to simulate
        Returns:
            dict: Simulated scenarios and possible solutions
        """
        # Simulate by generating random solutions for the problem
        solutions = []
        for i in range(3):
            simulated_solution = f"Solution_{random.randint(100, 999)}"
            solutions.append(simulated_solution)
        
        scenario = {
            "problem": problem,
            "solutions": solutions,
            "chosen_solution": random.choice(solutions)  # Randomly choose a solution
        }
        return scenario

    def machine_dream(self, problem):
        """
        Perform a machine dream, simulating a solution for a given problem.
        Parameters:
            problem (str): The problem statement
        Returns:
            dict: A dictionary of insights from the dream
        """
        # Parse and dream on the problem
        parsed_problem = self.parse_information(problem)
        dream_result = self.simulate_problem_solving(problem)
        
        # Generate insights from machine dreaming
        insights = {
            "dream_id": random.randint(1000, 9999),
            "problem_parsed": parsed_problem,
            "solution_chosen": dream_result['chosen_solution']
        }
        # Store insights into memory
        self.memory.append(insights)
        return insights
    
    def auto_fine_tune(self):
        """
        Auto-tune model parameters based on previous dreams and insights.
        This is a simple simulation of auto-tuning.
        """
        if self.memory:
            tuning_factor = random.uniform(0.9, 1.1)  # Adjust model by a random factor
            print(f"Auto-tuning the model by a factor of {tuning_factor}")
        else:
            print("No previous insights to auto-tune from.")

    def save_memory(self, filename="machine_dream_memory.json"):
        """
        Save the memory of dreams to a file.
        Parameters:
            filename (str): The name of the file to save the memory
        """
        with open(filename, 'w') as file:
            json.dump(self.memory, file, indent=4)
        print(f"Memory saved to {filename}")


# Example of usage
if __name__ == "__main__":
    dream_machine = MachineDream()
    
    # Simulate a machine dream on a problem statement
    problem_statement = "Optimize neural network hyperparameters for image classification"
    dream_insights = dream_machine.machine_dream(problem_statement)
    print("Dream Insights:", dream_insights)
    
    # Auto fine-tune based on dreams
    dream_machine.auto_fine_tune()
    
    # Save the memory for future reference
    dream_machine.save_memory()