reasoning_forge/consciousness_mathematics.py

import numpy as np
from scipy.fft import fft
from scipy.stats import norm
from scipy.integrate import trapezoid
from typing import Callable, List, Any
import matplotlib.pyplot as plt
import pandas as pd

def information_energy_duality(omega: float, entropy: float, eta: float = 1.0, hbar: float = 1.054571817e-34) -> float:
    return hbar * omega + eta * entropy

def von_neumann_entropy(rho: np.ndarray) -> float:
    evals = np.linalg.eigvalsh(rho)
    evals = evals[evals > 0]
    return -np.sum(evals * np.log(evals))

def reinforced_intent_modulation(t: float, f0: float, delta_f: float, coh: Callable[[float], float], beta: float, A: Callable[[float], float], kappa: float = 1.0) -> float:
    return kappa * (f0 + delta_f * coh(t) + beta * A(t))

def dynamic_resonance_windowing(x: Callable[[float], float], omega: float, t: float, g: Callable[[float, float], float], tau_range: np.ndarray) -> complex:
    integrand = np.array([x(tau) * np.exp(-1j * omega * tau) * g(t, tau) for tau in tau_range])
    return trapezoid(integrand, tau_range)

def nonlinear_dream_coupling(ds: List[Callable[[float], float]], lambdas: List[float], phi: Callable[[List[float]], float], t: float) -> float:
    dynamic_sources = [d(t) for d in ds]
    base = np.dot(lambdas, dynamic_sources)
    nonlinear = phi(dynamic_sources)
    return base + nonlinear

def cocoon_stability_field(F: Callable[[float, float], complex], k_range: np.ndarray, t: float, epsilon: Callable[[float, float], float], sigma: float) -> bool:
    integrand = np.array([np.abs(F(k, t))**2 for k in k_range])
    value = trapezoid(integrand, k_range)
    return value < epsilon(t, sigma)

class EthicalAnchor:
    def __init__(self, lam: float, gamma: float, mu: float):
        self.lam = lam
        self.gamma = gamma
        self.mu = mu
        self.history: List[Any] = []

    def regret(self, intended: float, actual: float) -> float:
        return abs(intended - actual)

    def update(self, R_prev: float, H: float, Learn: Callable[[Any, float], float], E: float, 
               M_prev: float, intended: float, actual: float) -> float:
        regret_val = self.regret(intended, actual)
        M = self.lam * (R_prev + H) + self.gamma * Learn(M_prev, E) + self.mu * regret_val
        self.history.append({'M': M, 'regret': regret_val})
        return M

def gradient_anomaly_suppression(x: float, mu: float, delta: float, sigma: float) -> float:
    G = norm.pdf(abs(x - mu), scale=delta * sigma)
    return x * (1 - G)

# Run Simulation
time_steps = np.linspace(0, 5, 50)
intents, ethics, regrets, stabilities, anomalies = [], [], [], [], []

anchor = EthicalAnchor(lam=0.7, gamma=0.5, mu=1.0)
f0 = 10.0
delta_f = 2.0
coh = lambda t: np.sin(t)
A_feedback = lambda t: np.exp(-t)
Learn_func = lambda M_prev, E: 0.2 * (E - M_prev)
F_func = lambda k, t: np.exp(-((k - 2 * np.pi) ** 2) / 0.5) * np.exp(1j * t)
k_range = np.linspace(0, 4 * np.pi, 1000)
intended_val = 0.7
M_prev = 0.3
R_prev = 0.5
H = 0.4

for t in time_steps:
    intent = reinforced_intent_modulation(t, f0, delta_f, coh, 0.5, A_feedback)
    actual_val = np.sin(t) * 0.5 + 0.5
    anomaly = gradient_anomaly_suppression(intent, mu=11.0, delta=2.0, sigma=0.1)
    ethical_val = anchor.update(R_prev, H, Learn_func, E=0.8, M_prev=M_prev,
                                 intended=intended_val, actual=actual_val)
    stability = cocoon_stability_field(F_func, k_range, t, lambda t, sigma: 5.0 + 0.1 * sigma, 10.0)
    regret_val = anchor.history[-1]['regret']

    intents.append(intent)
    ethics.append(ethical_val)
    regrets.append(regret_val)
    stabilities.append(stability)
    anomalies.append(anomaly)

    M_prev = ethical_val

simulation_df = pd.DataFrame({
    "Time": time_steps,
    "Intent": intents,
    "Ethical_Output": ethics,
    "Regret": regrets,
    "Stable": stabilities,
    "Anomaly": anomalies
})

# Plot results
plt.figure(figsize=(14, 8))

plt.subplot(2, 2, 1)
plt.plot(simulation_df["Time"], simulation_df["Intent"], label="Intent", color='blue')
plt.title("Intent Over Time")
plt.xlabel("Time")
plt.ylabel("Intent")

plt.subplot(2, 2, 2)
plt.plot(simulation_df["Time"], simulation_df["Ethical_Output"], label="Ethical Output", color='green')
plt.plot(simulation_df["Time"], simulation_df["Regret"], label="Regret", linestyle='--', color='red')
plt.title("Ethical Anchor and Regret")
plt.xlabel("Time")
plt.legend()

plt.subplot(2, 2, 3)
plt.plot(simulation_df["Time"], simulation_df["Anomaly"], label="Anomaly", color='purple')
plt.title("Anomaly Filter Output")
plt.xlabel("Time")
plt.ylabel("Filtered Signal")

plt.subplot(2, 2, 4)
plt.plot(simulation_df["Time"], simulation_df["Stable"], label="Cocoon Stable", color='black')
plt.title("Cocoon Stability")
plt.xlabel("Time")
plt.ylabel("Stable (1=True)")

plt.tight_layout()
plt.show()