import numpy as np
import matplotlib.pyplot as plt
from sklearn.preprocessing import StandardScaler
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Conv1D, MaxPooling1D, Flatten, Dense, Dropout, BatchNormalization
from tensorflow.keras.callbacks import EarlyStopping, LearningRateScheduler
from tensorflow.keras.utils import to_categorical
from qiskit import QuantumCircuit, Aer, execute
import hashlib
from scipy.signal import fftconvolve
from prometheus_client import start_http_server, Summary, Gauge
import random
from dataclasses import dataclass
from typing import Tuple, List, Dict
Constants
SPEED_OF_LIGHT = 299792458 # m/s
BASE_FREQUENCIES = [5760, 6480, 7200, 8604] # Cosmic harmonics (1000000118)
SANKOFA_MATRIX = np.array([[0, 1, 0], [1, 1, 1], [0, 1, 0]]) # Adinkra encoding (1000000119)
Prometheus Metrics
REQUEST_TIME = Summary(‘request_processing_seconds’, ‘Time spent processing request’)
MODEL_ACCURACY = Gauge(‘model_accuracy’, ‘Accuracy of the GW model’)
SYSTEM_ENTROPY = Gauge(‘system_entropy’, ‘Measure of system disorder’)
@dataclass
class SimulationParameters:
“”“Configuration parameters for the Aethyris-Quantum Synthesis.”“”
time_steps: int = 100
num_classes: int = 2 # Signal vs. noise
gw_frequency: float = 100.0 # Hz, typical for LIGO
simulation_duration: int = 200
learn_rate: float = 0.001
n_iter: int = 100
num_qubits: int = 4
circuit_depth: int = 3
hadamard_probability: float = 0.5
plasma_temp: float = 10000 # K
plasma_density: float = 1e18 # m^-3
plasma_field: float = 1 # T
class Feedback:
“”“Represents feedback for iterative AI refinement.”“”
def init(self, satisfactory: bool, accuracy: float = 0.0):
self.satisfactory = satisfactory
self.accuracy = accuracy
def is_satisfactory(self) -> bool:
return self.satisfactory
class QuantumChaosEngine:
“”“Simulates quantum chaotic circuits for entropy and encoding.”“”
def init(self, params: SimulationParameters):
self.num_qubits = params.num_qubits
self.hadamard_probability = params.hadamard_probability
self.circuit_depth = params.circuit_depth
def create_chaotic_circuit(self, seed_str: str) -> QuantumCircuit:
"""Generate a chaotic quantum circuit."""
seed_hash = int(hashlib.sha256(seed_str.encode('utf-8')).hexdigest(), 16) % (10 ** 8)
np.random.seed(seed_hash)
qc = QuantumCircuit(self.num_qubits)
for _ in range(self.circuit_depth):
for qubit in range(self.num_qubits):
if np.random.random() < self.hadamard_probability:
qc.h(qubit)
theta, phi = np.random.uniform(0, np.pi), np.random.uniform(0, 2 * np.pi)
qc.rx(theta, qubit)
qc.ry(phi, qubit)
for qubit in range(self.num_qubits - 1):
qc.cx(qubit, qubit + 1)
return qc
def compute_entropy(self, statevector):
"""Calculate quantum entropy."""
probabilities = np.abs(statevector.data) ** 2
entropy = -np.sum(probabilities * np.log2(probabilities + 1e-10))
SYSTEM_ENTROPY.set(entropy)
return entropy
class AethyrisQuantumAI:
“”“Main AI controller integrating GW analysis, quantum simulations, and plasma control.”“”
def init(self, params: SimulationParameters):
self.params = params
self.model = self.create_model()
self.quantum_engine = QuantumChaosEngine(params)
self.plasma_state = self.initialize_plasma()
self.setup_monitoring()
def setup_monitoring(self):
"""Initialize Prometheus monitoring."""
start_http_server(8000)
def create_model(self) -> Sequential:
"""Create CNN for GW analysis."""
model = Sequential([
Conv1D(32, 3, activation='relu', input_shape=(self.params.time_steps, 1)),
BatchNormalization(),
MaxPooling1D(2),
Conv1D(64, 3, activation='relu'),
BatchNormalization(),
MaxPooling1D(2),
Flatten(),
Dense(50, activation='relu'),
Dropout(0.5),
Dense(self.params.num_classes, activation='softmax')
])
model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
return model
def generate_synthetic_gw_data(self, num_samples: int = 1000) -> Tuple[np.ndarray, np.ndarray]:
"""Generate synthetic GW data mimicking LIGO signals."""
t = np.linspace(0, 1, self.params.time_steps)
data = []
labels = []
for _ in range(num_samples):
if random.random() < 0.5: # Signal
strain = 1e-21 * np.sin(2 * np.pi * self.params.gw_frequency * t + np.random.uniform(0, 2 * np.pi))
labels.append(1)
else: # Noise
strain = np.random.normal(0, 1e-22, t.shape)
labels.append(0)
data.append(strain)
return np.array(data), np.array(labels)
def encode_adinkra(self, data: np.ndarray) -> np.ndarray:
"""Encode GW data using Sankofa matrix."""
return fftconvolve(data, SANKOFA_MATRIX, mode='same')
def initialize_plasma(self) -> Dict:
"""Initialize plasma parameters."""
return {
'temp': self.params.plasma_temp,
'density': self.params.plasma_density,
'field': self.params.plasma_field
}
def monitor_plasma(self) -> Dict:
"""Monitor plasma state with Hermetic dynamics."""
polarity_factor = np.sin(2 * np.pi * BASE_FREQUENCIES[0] / SPEED_OF_LIGHT)
current_state = {
'temp': self.plasma_state['temp'] * (1 + 0.01 * polarity_factor),
'density': self.plasma_state['density'] * (1 + 0.005 * polarity_factor),
'field': self.plasma_state['field'] * (1 + 0.002 * polarity_factor)
}
return current_state
def calculate_error(self, desired_state: Dict, current_state: Dict) -> float:
"""Calculate error for plasma control."""
error = np.sqrt(
(desired_state['temp'] - current_state['temp'])**2 +
(desired_state['density'] - current_state['density'])**2 +
(desired_state['field'] - current_state['field'])**2
)
return error
def adjust_control(self, error: float):
"""Adjust plasma parameters based on error."""
threshold = 0.1
k_B, k_E = 0.01, 0.01
if abs(error) > threshold:
delta_B = k_B * error
delta_E = k_E * error
self.plasma_state['field'] += delta_B
self.plasma_state['temp'] += delta_E # Proxy for power input
def ethical_adaptive_control(self, desired_state: Dict, learn_rate: float, n_iter: int):
"""Control plasma system with ethical considerations."""
beta1, beta2, epsilon = 0.9, 0.999, 1e-8
m, v = 0, 0
for t in range(1, n_iter + 1):
current_state = self.monitor_plasma()
error = self.calculate_error(desired_state, current_state)
scores = self.evaluate_decision_matrix(current_state)
grad = lambda x: error # Simplified gradient
m = beta1 * m + (1 - beta1) * grad(error)
v = beta2 * v + (1 - beta2) * (grad(error) ** 2)
m_hat = m / (1 - beta1 ** t)
v_hat = v / (1 - beta2 ** t)
self.adjust_control(learn_rate * m_hat / (v_hat ** 0.5 + epsilon))
MODEL_ACCURACY.set(scores['A']) # Example: Use autonomy score
def evaluate_decision_matrix(self, current_state: Dict) -> Dict:
"""Evaluate plasma state for ethical considerations."""
scores = {
'A': 0.8 if abs(current_state['temp'] - self.params.plasma_temp) < 100 else 0.6, # Autonomy
'B': 0.9 if current_state['density'] > 0.9 * self.params.plasma_density else 0.7, # Beneficence
'N': 0.7 if current_state['field'] > 0.9 * self.params.plasma_field else 0.5 # Non-maleficence
}
return scores
def train_gw_model(self, data: np.ndarray, labels: np.ndarray):
"""Train the CNN on GW data."""
scaler = StandardScaler()
data_scaled = scaler.fit_transform(data)
data_reshaped = data_scaled.reshape((data_scaled.shape[0], data_scaled.shape[1], 1))
labels_categorical = to_categorical(labels, num_classes=self.params.num_classes)
early_stopping = EarlyStopping(monitor='val_loss', patience=3, restore_best_weights=True)
lr_scheduler = LearningRateScheduler(lambda epoch, lr: lr * 0.95 if epoch > 5 else lr)
history = self.model.fit(
data_reshaped, labels_categorical, epochs=20, batch_size=32, validation_split=0.2,
callbacks=[early_stopping, lr_scheduler], verbose=1
)
loss, accuracy = self.model.evaluate(data_reshaped, labels_categorical, verbose=0)
MODEL_ACCURACY.set(accuracy)
return history, loss, accuracy
def toroid_of_mind_and_code(self, ideas: str) -> str:
"""Iterative AI refinement loop."""
code = self.implement(ideas)
while True:
feedback = self.execute_and_get_feedback(code)
if feedback.is_satisfactory():
break
ideas = self.refine_ideas(feedback)
code = self.implement(ideas)
return code
def implement(self, ideas: str) -> str:
"""Translate ideas into code (placeholder)."""
# Example: Adjust model architecture based on ideas
if "increase_filters" in ideas:
self.model = Sequential([
Conv1D(64, 3, activation='relu', input_shape=(self.params.time_steps, 1)),
BatchNormalization(),
MaxPooling1D(2),
Conv1D(128, 3, activation='relu'),
BatchNormalization(),
MaxPooling1D(2),
Flatten(),
Dense(100, activation='relu'),
Dropout(0.5),
Dense(self.params.num_classes, activation='softmax')
])
self.model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
return "Updated GW model with Adinkra encoding and plasma control"
def execute_and_get_feedback(self, code: str) -> Feedback:
"""Execute code and evaluate performance."""
data, labels = self.generate_synthetic_gw_data()
_, loss, accuracy = self.train_gw_model(data, labels)
satisfactory = accuracy > 0.85 # Threshold for satisfactory performance
return Feedback(satisfactory=satisfactory, accuracy=accuracy)
def refine_ideas(self, feedback: Feedback) -> str:
"""Refine ideas based on feedback."""
if feedback.accuracy < 0.85:
return "increase_filters, add_quantum_entropy"
return "maintain_current_architecture"
def run_simulation(self):
"""Run the integrated simulation."""
# Generate and train on synthetic GW data
data, labels = self.generate_synthetic_gw_data()
encoded_data = self.encode_adinkra(data)
history, loss, accuracy = self.train_gw_model(encoded_data, labels)
print(f'Model Loss: {loss:.4f}, Model Accuracy: {accuracy:.4f}')
# Quantum simulation
qc = self.quantum_engine.create_chaotic_circuit("simulation")
job = execute(qc, Aer.get_backend('statevector_simulator'))
statevector = job.result().get_statevector()
entropy = self.quantum_engine.compute_entropy(statevector)
# Plasma control
desired_state = {'temp': self.params.plasma_temp, 'density': self.params.plasma_density, 'field': self.params.plasma_field}
self.ethical_adaptive_control(desired_state, self.params.learn_rate, self.params.n_iter)
# Visualize results
plt.figure(figsize=(12, 4))
plt.subplot(1, 2, 1)
plt.plot(history.history['accuracy'], label='Training Accuracy')
plt.plot(history.history['val_accuracy'], label='Validation Accuracy')
plt.title(f'GW Model Training (Entropy: {entropy:.2f})')
plt.xlabel('Epoch')
plt.ylabel('Accuracy')
plt.legend()
plt.subplot(1, 2, 2)
plt.imshow(encoded_data[0].reshape(-1, 1), cmap='viridis', aspect='auto')
plt.title('Adinkra-Encoded GW Signal')
plt.colorbar()
plt.show()
Run Simulation
if name == ‘main’:
params = SimulationParameters()
aqs = AethyrisQuantumAI(params)
initial_ideas = “initial_gw_model_with_adinkra”
final_code = aqs.toroid_of_mind_and_code(initial_ideas)
aqs.run_simulation()