""" telepathy_enhancer.py ML function that iteratively enhances human telepathic energy (inter-brain coherence) using reinforcement learning / Bayesian optimization. Based on Q-CCT Telepathy Framework: shared collapse → maximized coherence. """ import numpy as np from typing import Callable, Tuple, Optional from scipy.stats import pearsonr from sklearn.gaussian_process import GaussianProcessRegressor from sklearn.gaussian_process.kernels import RBF, Matern, WhiteKernel from scipy.special import erf class TelepathicEnergyOptimizer: """ Machine Learning optimizer that learns the best sensory feedback to maximize inter-brain coherence (a proxy for telepathic energy). The human (or pair of humans) receives feedback parameters x (e.g., frequency, intensity, delay, visual pattern). The environment returns a telepathy score y. The optimizer iterates to find x* that maximizes y. Use case: real-time EEG from two subjects, compute coherence in alpha/gamma bands. """ def __init__(self, param_bounds: np.ndarray, n_initial_random: int = 8, acquisition_func: str = 'ei', noise_level: float = 0.05): """ Args: param_bounds: shape (n_params, 2) – lower and upper bound for each parameter. n_initial_random: number of random exploration trials before GP model. acquisition_func: 'ei' (expected improvement) or 'ucb' (upper confidence bound). noise_level: assumed observation noise. """ self.bounds = param_bounds self.n_params = param_bounds.shape[0] self.n_init = n_initial_random self.acq = acquisition_func self.noise = noise_level # History of tried parameters and observed telepathy scores self.X = [] # list of parameter vectors self.y = [] # list of telepathy scores (higher = better) # Gaussian Process surrogate model kernel = Matern(nu=2.5) * RBF(length_scale=np.ones(self.n_params)) + WhiteKernel(noise_level=noise_level) self.gp = GaussianProcessRegressor(kernel=kernel, alpha=1e-6, normalize_y=True, n_restarts_optimizer=5) def _suggest_parameter(self) -> np.ndarray: """Use acquisition function to propose next best parameter set.""" if len(self.X) < self.n_init: # Random exploration return np.random.uniform(self.bounds[:, 0], self.bounds[:, 1]) # Update GP with all data X_arr = np.array(self.X) y_arr = np.array(self.y).reshape(-1, 1) self.gp.fit(X_arr, y_arr.ravel()) # Grid search over parameter space n_grid = 30 # per dimension (simple grid, can be replaced with LHS) grids = [np.linspace(self.bounds[i,0], self.bounds[i,1], n_grid) for i in range(self.n_params)] if self.n_params == 1: candidates = grids[0].reshape(-1,1) else: # meshgrid meshes = np.meshgrid(*grids, indexing='ij') candidates = np.stack([m.ravel() for m in meshes], axis=1) # Acquisition values mu, sigma = self.gp.predict(candidates, return_std=True) if self.acq == 'ei': # Expected Improvement best_y = np.max(self.y) imp = mu - best_y Z = imp / (sigma + 1e-9) ei = imp * self._cdf(Z) + sigma * self._pdf(Z) acq_val = ei else: # UCB beta = 2.0 acq_val = mu + beta * sigma best_idx = np.argmax(acq_val) return candidates[best_idx] @staticmethod def _cdf(x): return 0.5 * (1 + erf(x / np.sqrt(2))) @staticmethod def _pdf(x): return np.exp(-0.5 * x**2) / np.sqrt(2 * np.pi) def iterate(self, telepathy_measurement_fn: Callable[[np.ndarray], float], max_iterations: int = 30, verbose: bool = True) -> Tuple[np.ndarray, list]: """ Main iterative ML loop. Args: telepathy_measurement_fn: function that takes a parameter vector x (e.g., [frequency, intensity, visual_delay]) and returns a telepathy score. This function must run one trial (e.g., 60 sec of EEG recording) and compute inter-brain coherence averaged over the trial. max_iterations: number of optimization steps. verbose: print progress. Returns: best_parameters (np.ndarray), history of scores (list). """ for i in range(max_iterations): # Suggest next parameter set x_next = self._suggest_parameter() # Perform one trial and measure telepathy energy y_next = telepathy_measurement_fn(x_next) # Append to history self.X.append(x_next.tolist()) self.y.append(y_next) if verbose: print(f"Iter {i+1}/{max_iterations} | x = {np.round(x_next,3)} | telepathy score = {y_next:.4f} | best so far = {max(self.y):.4f}") best_idx = np.argmax(self.y) best_params = np.array(self.X[best_idx]) return best_params, self.y # ---------------------------------------------------------------------- # Example of a real telepathy measurement function (mock / substitute) # In a real setup, replace with EEG coherence between two subjects. # ---------------------------------------------------------------------- def mock_telepathy_measurement(params: np.ndarray) -> float: """ Mock function: simulates that certain audio‑visual frequencies enhance coherence. True optimum: params = [7.83 Hz (Schumann), 0.7 intensity, 0.05 sec delay]. """ freq, intensity, delay = params # Gaussian‑like peak at optimum ideal_freq = 7.83 ideal_intensity = 0.7 ideal_delay = 0.05 score = 0.5 * np.exp(-((freq - ideal_freq)**2) / 8.0) \ + 0.3 * np.exp(-((intensity - ideal_intensity)**2) / 0.1) \ + 0.2 * np.exp(-((delay - ideal_delay)**2) / 0.002) # add noise score += np.random.normal(0, 0.03) return np.clip(score, 0.0, 1.0) # ---------------------------------------------------------------------- # Real EEG‑based telepathy function (pseudo‑code – integrate with hardware) # ---------------------------------------------------------------------- def real_telepathy_measurement(params: np.ndarray) -> float: """ Use two EEG headsets (e.g., Muse 2, OpenBCI). Present auditory/visual stimulus defined by params. Compute inter‑brain coherence in alpha (8‑12 Hz) and gamma (30‑45 Hz). Return a combined score: coherence + phase locking value. """ # 1. Apply stimulus (sound frequency = params[0], intensity = params[1], delay = params[2]) # 2. Record 60 seconds of EEG from two subjects # 3. Compute coherence between homologous channels (e.g., Fz, Pz) using Welch's method # 4. Telepathy score = mean(coherence_alpha) + 0.5*mean(coherence_gamma) # Placeholder – replace with actual hardware integration. raise NotImplementedError("Integrate with real EEG device and stimulus generator.") # ---------------------------------------------------------------------- # Run iterative optimization # ---------------------------------------------------------------------- if __name__ == "__main__": # Parameter space: [frequency (Hz), intensity (0..1), delay (sec)] bounds = np.array([[4.0, 12.0], # Theta/Alpha range [0.2, 1.0], # stimulus intensity [0.01, 0.2]]) # delay between left/right ear, or visual flicker offset optimizer = TelepathicEnergyOptimizer(param_bounds=bounds, n_initial_random=6, acquisition_func='ei', noise_level=0.02) # Use mock for demonstration (replace with real_telepathy_measurement in lab) best_params, history = optimizer.iterate(telepathy_measurement_fn=mock_telepathy_measurement, max_iterations=25, verbose=True) print("\n" + "="*50) print(f"OPTIMAL TELEPATHIC PARAMETERS found:") print(f" Frequency : {best_params[0]:.2f} Hz") print(f" Intensity : {best_params[1]:.2f}") print(f" Delay : {best_params[2]:.3f} sec") print(f" Max telepathy score : {max(history):.4f}") print("="*50)