import numpy as np
from scipy.integrate import solve_ivp
import matplotlib.pyplot as plt

class SelfAwareNetwork:
    def __init__(self, num_neurons, learning_rate):
        self.num_neurons = num_neurons
        self.learning_rate = learning_rate
        self.weights = np.random.rand(num_neurons)
        self.state = np.zeros(num_neurons)
    
    def activation_function(self, x, t, n):
        hbar = 1.0545718e-34  # Reduced Planck constant in J·s
        omega = 1/np.sqrt(137)  # Angular frequency related to fine-structure constant
        term1 = hbar * omega * (n + 0.5)
        term2 = np.sin(omega * x + np.pi/4) * np.exp(-t)
        return term1 + term2
    
    def neuron_dynamics(self, t, y):
        n = np.arange(self.num_neurons)
        dydt = -y + self.activation_function(y, t, n)
        return dydt
    
    def update_weights(self, state):
        self.weights += self.learning_rate * state
    
    def solve_dynamics(self, t_span, y0):
        sol = solve_ivp(self.neuron_dynamics, t_span, y0, method='RK45', vectorized=False)
        return sol.t, sol.y
    
    def evaluate_performance(self, target_state):
        error = np.linalg.norm(self.state - target_state)
        return error
    
    def adjust_learning_rate(self, performance_metric):
        if performance_metric > 0.1:
            self.learning_rate *= 0.9
        else:
            self.learning_rate *= 1.1
    
    def self_optimize(self, target_state, t_span, y0):
        t, y = self.solve_dynamics(t_span, y0)
        self.state = y[:, -1]
        performance = self.evaluate_performance(target_state)
        self.adjust_learning_rate(performance)
        self.update_weights(self.state)
    
    def plot_state_evolution(self, t, y):
        plt.plot(t, y.T)
        plt.xlabel('Time')
        plt.ylabel('Neuron States')
        plt.title('State Evolution of Neurons')
        plt.show()

# Example usage
network = SelfAwareNetwork(num_neurons=3, learning_rate=0.01)  # Reduced the number of neurons
t_span = (0, 5)  # Shortened the time span
y0 = np.random.rand(3)  # Adjusted for the reduced number of neurons
target_state = np.ones(3)

network.self_optimize(target_state, t_span, y0)
t, y = network.solve_dynamics(t_span, y0)
network.plot_state_evolution(t, y)