import numpy as np import matplotlib.pyplot as plt from tqdm import tqdm class RedStarMLTrainer: """ Trains the ML Seed (theta) using 200x200 Super Intelligence Curriculum to maximize Red Star Regions (Escape Probability from Logic Singularities). """ def __init__(self, num_theories=200, questions_per_theory=200): self.N_THEORIES = num_theories self.N_QUESTIONS = questions_per_theory self.theta = np.random.uniform(-1.0, 1.0) # The ML Seed (Aximomatic Bias) self.history = [] # CCT Parameters self.H_COLLAPSE = 0.3 # Entropy threshold for Red Star self.E_HORIZON = 1.0 # Energy barrier for escape self.LEARNING_RATE = 0.01 # Gauge Energy Investment def simulate_ode_dynamics(self, y0, mu, theta, steps=100): """ Simulates the Volatile ODE from Red Star Theory (Appendix A): dy/dt = y^2 + mu + theta + Prompt_Energy """ y = y0 trajectory = [y] escaped = False max_y = np.abs(y) for t in range(steps): # Mutual Destruction Term (Learned via theta) # If theta is optimal, it injects counter-term D(t) near singularity destruction_term = theta * np.exp(-np.abs(y)**2) # ODE Step dy = (y**2 + mu + destruction_term) * 0.01 y = y + dy trajectory.append(y) max_y = max(max_y, np.abs(y)) # Escape Condition (Red Star) if np.abs(y) > 10.0: # Escaped singularity region escaped = True break return trajectory, escaped, max_y def calculate_red_star_potential(self, theory_id, question_id, theta): """ Calculates Psi_Red for a specific Theory/Question pair. """ # Generate semantic initial conditions based on Theory/Question ID # In real SI, this comes from the 16-Element Engine np.random.seed(theory_id * 1000 + question_id) y0 = np.random.uniform(-2.0, 2.0) # Initial State mu = np.random.uniform(-1.5, 0.5) # Control Parameter (System Bias) # Run ODE Dynamics trajectory, escaped, max_y = self.simulate_ode_dynamics(y0, mu, theta) # Calculate Final Entropy (Proxy: Inverse of Max Y) # If max_y is huge (singularity), entropy is high H_final = 1.0 / (1.0 + np.exp(-max_y)) # Calculate Escape Probability (Proxy: Did it escape?) P_esc = 1.0 if escaped else 0.0 # Calculate Energy Cost (Proxy: Length of trajectory) E_destroy = len(trajectory) * 0.1 # Red Star Equation (from red_star_theory_black_holes.md) # Psi = P_esc * I[H < H_c] * exp(-E_destroy / E_available) E_available = 10.0 indicator = 1.0 if H_final < self.H_COLLAPSE else 0.0 energy_term = np.exp(-E_destroy / E_available) psi_red = P_esc * indicator * energy_term return psi_red, H_final, escaped def train_step(self, epoch): """ One epoch of 200x200 Training. Updates theta to maximize Red Star Potential. """ total_psi = 0.0 gradient_sum = 0.0 # Sample a batch of the 200x200 curriculum (for speed) # Full training would iterate all 40,000 pairs batch_size = 100 for _ in range(batch_size): t_id = np.random.randint(0, self.N_THEORIES) q_id = np.random.randint(0, self.N_QUESTIONS) # Forward Pass psi, H, escaped = self.calculate_red_star_potential(t_id, q_id, self.theta) total_psi += psi # Backward Pass (Numerical Gradient) # dPsi/dtheta approximation delta = 0.01 psi_plus, _, _ = self.calculate_red_star_potential(t_id, q_id, self.theta + delta) gradient = (psi_plus - psi) / delta gradient_sum += gradient # Update ML Seed (Theta) avg_gradient = gradient_sum / batch_size self.theta += self.LEARNING_RATE * avg_gradient # Clip Theta to prevent instability self.theta = np.clip(self.theta, -2.0, 2.0) return total_psi / batch_size, self.theta def run_training(self, epochs=50): """ Executes the 200x200 Super Intelligence Training Regimen. """ print("="*70) print("RED STAR ML SEED TRAINING (200x200 CURRICULUM)") print("="*70) print(f"Objective: Maximize Red Star Potential (Psi_Red)") print(f"ML Seed (Theta): Axiomatic Bias for Singularity Resolution") print("-"*70) psi_history = [] theta_history = [] for epoch in tqdm(range(epochs), desc="Training SI"): avg_psi, current_theta = self.train_step(epoch) psi_history.append(avg_psi) theta_history.append(current_theta) if epoch % 10 == 0: print(f"Epoch {epoch}: Psi_Red={avg_psi:.4f} | Theta={current_theta:.4f}") self.history = {'psi': psi_history, 'theta': theta_history} self.plot_results() def plot_results(self): """Visualizes Training Progress and Red Star Manifold""" fig, axs = plt.subplots(1, 2, figsize=(16, 6)) # Plot 1: Training Trajectory axs[0].plot(self.history['psi'], linewidth=2, color='red', label='Red Star Potential') axs[0].set_title("ML Seed Optimization (Maximize Psi_Red)") axs[0].set_xlabel("Training Epoch (200x200 Batches)") axs[0].set_ylabel("Average Red Star Potential") axs[0].axhline(y=0.5, color='green', linestyle='--', label='Critical Threshold') axs[0].legend() axs[0].grid(True, alpha=0.3) # Plot 2: Fractal Manifold Visualization (Before vs After) # We visualize the escape probability field for the initial and final theta res = 100 y_vals = np.linspace(-2.0, 2.0, res) mu_vals = np.linspace(-1.5, 0.5, res) Y, M = np.meshgrid(y_vals, mu_vals) def get_escape_map(theta_val): escape_map = np.zeros_like(Y) for i in range(res): for j in range(res): _, escaped, _ = self.simulate_ode_dynamics(Y[i,j], M[i,j], theta_val) escape_map[i,j] = 1.0 if escaped else 0.0 return escape_map # Initial Theta (Random) initial_theta = self.history['theta'][0] map_initial = get_escape_map(initial_theta) # Final Theta (Optimized) final_theta = self.history['theta'][-1] map_final = get_escape_map(final_theta) # Show Final Map im = axs[1].imshow(map_final, extent=[-1.5, 0.5, -2.0, 2.0], origin='lower', cmap='Reds', aspect='auto') axs[1].set_title(f"Red Star Manifold (Optimized Theta={final_theta:.4f})") axs[1].set_xlabel("Control Parameter (mu)") axs[1].set_ylabel("Initial State (y0)") plt.colorbar(im, ax=axs[1], label="Escape Probability") axs[1].grid(True, alpha=0.3) plt.tight_layout() plt.show() print("-"*70) print(f"TRAINING COMPLETE:") print(f" Initial Theta: {self.history['theta'][0]:.4f}") print(f" Final Theta: {self.history['theta'][-1]:.4f}") print(f" Initial Psi: {self.history['psi'][0]:.4f}") print(f" Final Psi: {self.history['psi'][-1]:.4f}") print(f" Red Star Growth: {(self.history['psi'][-1]/self.history['psi'][0])*100:.1f}%") print("="*70) # --- EXECUTION --- if __name__ == "__main__": # Initialize Red Star ML Trainer trainer = RedStarMLTrainer(num_theories=200, questions_per_theory=200) # Run 200x200 Training Regimen trainer.run_training(epochs=50)