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LICENSE

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+MIT License
+
+Copyright (c) 2024 Francisco Angulo de Lafuente
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

NEBULA.py

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+
+"""
+******************** This version of NEBULA is a simplified working demo **********************
+
+NEBULA.py: Dynamic Quantum-Inspired Neural Network System
+
+**Francisco Angulo de Lafuente**  
+**July 16, 2024**
+
+https://github.com/Agnuxo1
+
+**************************************** NEBULA FEATURES ****************************************
+
+This program simulates a sophisticated artificial intelligence system 
+inspired by quantum computing principles and biological neural networks. 
+
+It features:
+    - A dynamic, continuous 3D space where neurons interact based on 
+      light-based attraction, mimicking a nebula.
+    - Virtual neurons and qubits for scalability.
+    - Holographic encoding for efficient state representation using CNNs.
+    - Parallel processing using Ray for accelerated computation.
+    - Genetic optimization (DEAP) for learning and adaptation.
+
+The system is designed to process information, learn from interactions, 
+and answer questions based on its internal representations.
+
+This version (NEBULA) integrates the improved holographic system 
+using CNNs for encoding and decoding.
+
+NEBULA: Neural Entanglement-Based Unified Learning Architecture
+NEBULA is a dynamic and innovative artificial intelligence system designed 
+to emulate quantum computing principles and biological neural networks. 
+It features a unique combination of continuous space, light-based attraction, 
+virtual neurons and qubits, holographic encoding, and other advanced features 
+that enable it to adapt, learn, and solve complex problems in various domains such 
+as text, image, and numerical data processing.
+
+**************************************** NEBULA DEFINITION ****************************************
+
+N: Neural networks - The system is inspired by biological neural networks, enabling it to learn from data, adapt to new information, and solve complex tasks.
+E: Entanglement-based - NEBULA leverages the principles of quantum entanglement to create complex relationships between virtual neurons and qubits, enhancing the system's efficiency and learning capabilities.
+B: Biological - The system simulates organic structures, such as a nebula, to create an environment where virtual neurons interact in a more natural and dynamic way.
+U: Unified Learning - NEBULA integrates various AI techniques, such as genetic optimization, language model training, and parallel processing, to create a comprehensive learning architecture.
+L: Light-based attraction - The system uses light-based attraction between virtual neurons to simulate gravitational forces and facilitate dynamic clustering, improving the efficiency of neural interactions.
+A: Adaptive - NEBULA is designed to adapt to new information and optimize its structure over time, ensuring continuous learning and improvement.
+"""
+
+import numpy as np
+import cupy as cp
+import ray
+import pennylane as qml
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from torch.cuda.amp import autocast, GradScaler
+from sklearn.metrics.pairwise import cosine_similarity
+import trimesh
+import matplotlib.pyplot as plt
+import os
+import time
+import uuid
+import logging
+from typing import List, Dict, Tuple, Any, Optional, Union
+from deap import base, creator, tools, algorithms
+import random
+from tqdm import tqdm
+import signal
+import functools
+
+
+# Global variables
+EPOCH = 5
+DIM = 1024  # Reduced dimension for CNN compatibility
+SECTOR_SIZE = 32
+NEURONS_PER_SECTOR = 50000
+MAX_SECTORS = 100  # Maximum number of sectors, adjustable for scalability
+TRAIN_EPOCH = 5  # Global counter for training epochs
+
+# Configure logging
+logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
+logger = logging.getLogger(__name__)
+
+# Predefined questions and answers about the Solar System
+solar_system_qa = {
+    "Planets": [
+        {"question": "Is Mars bigger than Earth?", "answer": "No"},
+        {"question": "Does Jupiter have more moons than any other planet in our solar system?", "answer": "Yes"},
+        {"question": "Is Venus the hottest planet in our solar system?", "answer": "Yes"},
+        {"question": "Is Uranus known for its prominent rings?", "answer": "No"},
+        {"question": "Is Mercury the closest planet to the Sun?", "answer": "Yes"}
+    ],
+    "Earth": [
+        {"question": "Is Earth the largest planet in the solar system?", "answer": "No"},
+        {"question": "Does Earth have one moon?", "answer": "Yes"},
+        {"question": "Is Earth mostly covered in water?", "answer": "Yes"},
+        {"question": "Is Earth further from the sun than Mars?", "answer": "No"},
+        {"question": "Does Earth have rings?", "answer": "No"}
+    ]
+}
+
+def timeout(seconds):
+    def decorator(func):
+        @functools.wraps(func)
+        def wrapper(*args, **kwargs):
+            def handler(signum, frame):
+                raise TimeoutError(f"Function {func.__name__} timed out after {seconds} seconds")
+            
+            signal.signal(signal.SIGALRM, handler)
+            signal.alarm(seconds)
+            try:
+                result = func(*args, **kwargs)
+            finally:
+                signal.alarm(0)
+            return result
+        return wrapper
+    return decorator
+
+class AmplitudeCNN(nn.Module):
+    """
+    CNN for decoding the amplitude component of the hologram.
+    """
+    def __init__(self):
+        super(AmplitudeCNN, self).__init__()
+        self.conv1 = nn.Conv2d(1, 32, kernel_size=3, padding=1)
+        self.bn1 = nn.BatchNorm2d(32)
+        self.conv2 = nn.Conv2d(32, 64, kernel_size=3, padding=1)
+        self.bn2 = nn.BatchNorm2d(64)
+        self.fc = nn.Linear(64 * 8 * 8, DIM)
+        self.dropout = nn.Dropout(0.5)
+
+    def forward(self, x):
+        x = F.relu(self.bn1(self.conv1(x)))
+        x = F.relu(self.bn2(self.conv2(x)))
+        x = x.view(-1, 64 * 8 * 8)
+        x = self.dropout(x)
+        return self.fc(x)
+
+class PhaseCNN(nn.Module):
+    """
+    CNN for decoding the phase component of the hologram.
+    """
+    def __init__(self):
+        super(PhaseCNN, self).__init__()
+        self.conv1 = nn.Conv2d(1, 32, kernel_size=3, padding=1)
+        self.bn1 = nn.BatchNorm2d(32)
+        self.conv2 = nn.Conv2d(32, 64, kernel_size=3, padding=1)
+        self.bn2 = nn.BatchNorm2d(64)
+        self.fc = nn.Linear(64 * 8 * 8, DIM)
+        self.dropout = nn.Dropout(0.5)
+
+    def forward(self, x):
+        x = F.relu(self.bn1(self.conv1(x)))
+        x = F.relu(self.bn2(self.conv2(x)))
+        x = x.view(-1, 64 * 8 * 8)
+        x = self.dropout(x)
+        return self.fc(x)
+
+class HologramCodec:
+    """
+    Encodes and decodes data using a holographic representation.
+
+    This codec uses Fast Fourier Transforms (FFT) for encoding and
+    inverse FFT for decoding, providing an efficient way to
+    represent and compress the network's state. It also incorporates
+    CNNs for amplitude and phase decoding and utilizes mixed precision
+    training for potential speed and memory benefits.
+    """
+    def __init__(self, dim: int = DIM):
+        """
+        Initializes the HologramCodec.
+
+        Args:
+            dim (int): The dimensionality of the holographic representation.
+                       Defaults to the global DIM value.
+        """
+        self.dim = dim
+        self.amplitude_cnn = AmplitudeCNN().to('cuda')
+        self.phase_cnn = PhaseCNN().to('cuda')
+        self.scaler = GradScaler()
+
+    def encode(self, data: np.ndarray, sector_index: int) -> torch.Tensor:
+        """
+        Encodes data into a holographic representation using a 3D FFT.
+        """
+        gpu_data = cp.asarray(data)
+        gpu_data = cp.fft.fftn(gpu_data)
+        hologram = torch.as_tensor(gpu_data, dtype=torch.complex64).to('cuda')
+        return hologram
+
+    def decode(self, hologram: torch.Tensor, sector_id: str) -> np.ndarray:
+        """
+        Decodes a holographic representation back to the original data using CNNs and inverse 3D FFT.
+
+        Args:
+            hologram (torch.Tensor): The encoded holographic data.
+            sector_id (str): The ID of the sector being decoded.
+
+        Returns:
+            np.ndarray: The decoded data in its original format.
+        """
+        logger.info(f"Decoding sector {sector_id}, data type: {type(hologram)}, shape: {hologram.shape}")
+        with autocast():
+            amplitude = self.amplitude_cnn(hologram[None, None, :, :, :])
+            phase = self.phase_cnn(hologram[None, None, :, :, :])
+
+        complex_data = amplitude * torch.exp(1j * phase)
+        gpu_data = cp.fft.ifftn(cp.asarray(complex_data.cpu()), axes=(0, 1, 2))
+        decoded_data = cp.asnumpy(gpu_data) / (self.dim ** 3)
+        # Reshape data back to its original form
+        decoded_data = decoded_data.flatten()
+        logger.info(f"Decoded data type: {type(decoded_data)}, shape: {decoded_data.shape}")
+        return decoded_data
+
+class QuantumNeuron:
+    """
+    A quantum-inspired neuron using a parameterized quantum circuit.
+
+    This neuron processes information using a quantum circuit simulated
+    with PennyLane. The circuit's parameters (weights) are adjustable,
+    allowing the neuron to learn and adapt.
+    """
+    def __init__(self, n_qubits: int = 4):
+        """
+        Initializes the QuantumNeuron.
+
+        Args:
+            n_qubits (int): The number of qubits used in the quantum circuit.
+                            Defaults to 4.
+        """
+        self.n_qubits = n_qubits
+        self.dev = qml.device("default.qubit", wires=n_qubits)  # Create a PennyLane quantum device
+
+        # Define the quantum circuit
+        @qml.qnode(self.dev)
+        def quantum_circuit(inputs, weights):
+            for i in range(n_qubits):
+                qml.RY(inputs[i], wires=i) # Apply RY gate with input data
+            for i in range(n_qubits):
+                qml.RX(weights[i], wires=i) # Apply RX and RZ gates with weights
+                qml.RZ(weights[i + n_qubits], wires=i)
+            for i in range(n_qubits - 1):
+                qml.CNOT(wires=[i, i + 1]) # Apply CNOT gates for entanglement
+            qml.CRZ(weights[-1], wires=[0, n_qubits-1]) # Apply CRZ gate
+            return [qml.expval(qml.PauliZ(i)) for i in range(n_qubits)] # Measure in Z basis
+
+        self.quantum_circuit = quantum_circuit
+        self.weights = np.random.randn(2 * n_qubits + 1) # Initialize weights randomly
+
+    def forward(self, inputs: np.ndarray) -> np.ndarray:
+        """
+        Performs a forward pass through the quantum circuit.
+
+        Args:
+            inputs (np.ndarray): The input data to be processed by the neuron.
+
+        Returns:
+            np.ndarray: The output of the quantum circuit, representing
+                        the neuron's activation.
+        """
+        return np.array(self.quantum_circuit(inputs, self.weights)) # Execute the circuit
+
+class Neuron:
+    """
+    A neuron in the Nebula system combining classical and quantum properties.
+
+    Each neuron has a 3D position within the NebulaSpace, a QuantumNeuron
+    for processing information, and connections to other neurons.
+    """
+    def __init__(self, position: np.ndarray):
+        """
+        Initializes the Neuron.
+
+        Args:
+            position (np.ndarray): The 3D coordinates of the neuron in the NebulaSpace.
+        """
+        self.position = position
+        self.quantum_neuron = QuantumNeuron() # Assign a QuantumNeuron
+        self.luminosity = np.random.rand() # Initialize luminosity randomly
+        self.connections = [] # List to store connections with other neurons
+
+    def activate(self, inputs: np.ndarray):
+        """
+        Activates the neuron with the given input.
+
+        Args:
+            inputs (np.ndarray): The input data to be processed by the neuron.
+
+        Returns:
+            np.ndarray: The output of the neuron's QuantumNeuron,
+                        representing its activation.
+        """
+        return self.quantum_neuron.forward(inputs) # Forward pass through the QuantumNeuron
+
+    def process(self, inputs: np.ndarray) -> np.ndarray:
+        """
+        Processes input data through the neuron's QuantumNeuron.
+
+        Args:
+            inputs (np.ndarray): The input data to be processed.
+
+        Returns:
+            np.ndarray: The processed output from the QuantumNeuron.
+        """
+        return self.activate(inputs) # Activation is synonymous with processing here
+
+class NebulaSector:
+    """
+    A sector within the Nebula system containing multiple neurons.
+
+    Neurons within a sector interact with each other based on their
+    proximity and luminosity. The sector manages these interactions and
+    provides a way to organize neurons within the NebulaSpace.
+    """
+    def __init__(self, n_neurons: int = NEURONS_PER_SECTOR):
+        """
+        Initializes the NebulaSector.
+
+        Args:
+            n_neurons (int): The number of neurons to create within this sector.
+                             Defaults to the global NEURONS_PER_SECTOR value.
+        """
+        self.id = str(uuid.uuid4()) # Unique ID for the sector
+        self.neurons = [Neuron(np.random.randn(3)) for _ in range(n_neurons)] # Create neurons
+        self.positions = cp.array([n.position for n in self.neurons], dtype=cp.float32) # Store positions on GPU
+        self.luminosities = cp.array([n.luminosity for n in self.neurons], dtype=cp.float32) # Store luminosities on GPU
+        self.interactions = cp.zeros(n_neurons, dtype=cp.float32) # Initialize interaction matrix
+        self.last_modified = time.time() # Timestamp for tracking modifications
+
+    def update_interactions(self):
+        """
+        Update the interactions between neurons within this sector.
+        """
+        n = len(self.neurons)
+        for i in range(n):
+            for j in range(n):
+                if i != j:  # Avoid self-interaction
+                    dx = self.positions[i, 0] - self.positions[j, 0]
+                    dy = self.positions[i, 1] - self.positions[j, 1]
+                    dz = self.positions[i, 2] - self.positions[j, 2]
+                    dist_sq = dx**2 + dy**2 + dz**2 + 1e-6
+                    self.interactions[i] += self.luminosities[j] / dist_sq
+        self.last_modified = time.time()
+
+    def get_state(self) -> np.ndarray:
+        """
+        Retrieves the current state of the sector.
+
+        Returns:
+            np.ndarray: A flattened array representing the sector's state,
+                        including neuron positions, luminosities, and interactions.
+        """
+        return np.concatenate((
+            cp.asnumpy(self.positions).flatten(), # Flatten and move data from GPU to CPU
+            cp.asnumpy(self.luminosities),
+            cp.asnumpy(self.interactions)
+        ))
+
+    def set_state(self, state: np.ndarray):
+        """
+        Sets the state of the sector.
+
+        Args:
+            state (np.ndarray): A flattened array representing the new state of the sector.
+        """
+        n_neurons = len(self.neurons)
+        self.positions = cp.array(state[:3 * n_neurons].reshape((n_neurons, 3))) # Update positions
+        self.luminosities = cp.array(state[3 * n_neurons:4 * n_neurons]) # Update luminosities
+        self.interactions = cp.array(state[4 * n_neurons:]) # Update interactions
+        self.last_modified = time.time() # Update modification timestamp
+
+class NebulaSpace:
+    """
+    The 3D space where Nebula sectors exist and interact.
+
+    This class manages the creation and tracking of sectors, providing
+    a spatial organization for the Nebula system.
+    """
+    def __init__(self, sector_size: int = SECTOR_SIZE):
+        """
+        Initializes the NebulaSpace.
+
+        Args:
+            sector_size (int): The size of each sector along each dimension.
+                               Defaults to the global SECTOR_SIZE value.
+        """
+        self.sectors = {} # Dictionary to store sectors by their unique ID
+        self.sector_map = {} # Map sector coordinates to sector IDs
+        self.sector_size = sector_size
+
+    def get_or_create_sector(self, position: np.ndarray) -> NebulaSector:
+        """
+        Retrieves a sector at a given position, creating it if it doesn't exist.
+
+        Args:
+            position (np.ndarray): The 3D coordinates to locate the sector.
+
+        Returns:
+            NebulaSector: The sector at the specified position.
+        """
+        sector_coords = tuple(int(p // self.sector_size) for p in position) # Calculate sector coordinates
+        if sector_coords not in self.sector_map:
+            new_sector = NebulaSector() # Create a new sector if needed
+            self.sectors[new_sector.id] = new_sector
+            self.sector_map[sector_coords] = new_sector.id
+        return self.sectors[self.sector_map[sector_coords]]
+
+    def update_all_sectors(self):
+        """
+        Triggers the update of interactions in all sectors within the NebulaSpace.
+        """
+        for sector in self.sectors.values():
+            sector.update_interactions()
+
+class NebulaSystem:
+    def __init__(self):
+        self.space = NebulaSpace()
+        self.hologram_codec = HologramCodec()
+        self.cache = {}
+        self.memory = []
+
+    def process_input(self, input_data: str) -> np.ndarray:
+        """
+        Process input data and generate embeddings (temporarily disabled NLP).
+
+        Args:
+            input_data (str): The input data to be processed.
+
+        Returns:
+            np.ndarray: Randomly generated embeddings.
+        """
+        # Placeholder for embedding generation (NLP disabled)
+        embeddings = np.random.randn(DIM)
+        return embeddings
+
+    def activate_neurons(self, embeddings: np.ndarray):
+        for sector in self.space.sectors.values():
+            for i, neuron in enumerate(sector.neurons):
+                neuron.activate(embeddings) # Activate with the generated embedding
+            sector.update_interactions()
+
+    def process_data(self, data: Dict[str, List[Dict[str, str]]]):
+        embeddings = []
+        for category, qa_pairs in data.items():
+            for pair in qa_pairs:
+                question, answer = pair['question'], pair['answer']
+                embeddings.append(np.random.randn(DIM))
+                self.memory.append((question, answer))
+
+        if not embeddings:
+            logger.warning("No data to process. No neurons will be created.")
+            return
+
+        self.activate_neurons(np.array(embeddings))
+
+        # Ensure at least one sector is created
+        if not self.space.sectors:
+            self.space.get_or_create_sector(np.array([0, 0, 0]))
+
+    def save_state(self) -> Dict[str, torch.Tensor]:
+        state_data = {}
+        for sector_id, sector in self.space.sectors.items():
+            sector_state = sector.get_state()
+            # Convierte el UUID a entero
+            sector_index = uuid.UUID(sector_id).int 
+            encoded_state = self.hologram_codec.encode(sector_state, sector_index)
+            state_data[sector_id] = encoded_state
+        return state_data
+
+    def load_state(self, state_data: Dict[str, torch.Tensor]):
+        for sector_id, encoded_state in state_data.items():
+            # Convierte el UUID a entero
+            sector_index = uuid.UUID(sector_id).int 
+            sector_state = self.hologram_codec.decode(encoded_state, sector_index)
+            if sector_id not in self.space.sectors:
+                self.space.sectors[sector_id] = NebulaSector()
+            self.space.sectors[sector_id].set_state(sector_state)
+
+    def query_nearest_neurons(self, query_embedding: np.ndarray, k: int = 9) -> List[Neuron]:
+        """
+        Finds the k-nearest neurons to a given query embedding.
+
+        Args:
+            query_embedding (np.ndarray): The query embedding to compare against neurons.
+            k (int): The number of nearest neurons to return. Defaults to 9.
+
+        Returns:
+            List[Neuron]: A list of the k-nearest neurons.
+        """
+        logger.info(f"Querying nearest neurons with embedding shape: {query_embedding.shape}")
+        query_embedding = query_embedding.flatten()
+        all_neurons = []
+        all_embeddings = []
+
+        for sector in self.space.sectors.values():
+            all_neurons.extend(sector.neurons)
+            all_embeddings.extend([n.quantum_neuron.weights.flatten() for n in sector.neurons])
+
+        if not all_embeddings:
+            logger.error("No neurons found in the system.")
+            return []
+
+        neuron_embeddings = np.array(all_embeddings)
+        logger.info(f"Neuron embeddings shape: {neuron_embeddings.shape}")
+
+        query_embedding = query_embedding[:neuron_embeddings.shape[1]].reshape(1, -1)
+        neuron_embeddings = neuron_embeddings.reshape(neuron_embeddings.shape[0], -1)
+
+        similarities = cosine_similarity(query_embedding, neuron_embeddings)
+        nearest_indices = np.argsort(similarities[0])[-k:][::-1]
+        return [all_neurons[i] for i in nearest_indices]
+
+
+
+
+    def answer_question(self, question: Union[str, np.ndarray]) -> str:
+        """
+        Answer a given question based on the current state of the Nebula system.
+
+        Args:
+            question (Union[str, np.ndarray]): The question to be answered, either as a string or a pre-computed embedding.
+
+        Returns:
+            str: The answer to the question, either "Yes" or "No".
+        """
+        try:
+            if isinstance(question, str):
+                # Genera un embedding de 9 dimensiones
+                question_embedding = np.random.randn(9)  
+                logger.info(f"Creating embedding for question: {question}")
+                question_embedding = np.random.randn(DIM)
+            elif isinstance(question, np.ndarray):
+                question_embedding = question
+            else:
+                raise ValueError("Question must be either a string or a numpy array")
+
+            nearest_neurons = self.query_nearest_neurons(question_embedding)
+            if not nearest_neurons:
+                logger.warning("No neurons found to answer the question.")
+                return "Unable to answer due to lack of initialized neurons."
+
+            activations = []
+            for neuron in nearest_neurons:
+                neuron_activation = neuron.process(question_embedding.flatten())
+                activations.append(np.mean(neuron_activation))
+                logger.info(f"Neuron activation: {neuron_activation}, Mean activation: {np.mean(neuron_activation)}")
+
+            if not activations:
+                logger.warning("No activations received from neurons.")
+                return "Unable to determine an answer due to lack of neuron activations."
+
+            mean_activation = np.mean(activations)
+            logger.info(f"Mean activation across neurons: {mean_activation}")
+            threshold = 0.5  # You can adjust this threshold
+
+            answer = "Yes" if mean_activation > threshold else "No"
+            return answer
+        except Exception as e:
+            logger.error(f"Error in answering question: {e}")
+            return "Unable to determine an answer due to an error."
+
+
+
+    def learn(self, question: str, correct_answer: str):
+        current_answer = self.answer_question(question)
+        reward = 1 if current_answer == correct_answer else -1
+        self.memory.append((question, correct_answer, reward))
+
+    def review_memory(self):
+        for question, correct_answer, reward in self.memory:
+            if reward == -1:
+                self.learn(question, correct_answer)
+
+    def save_hologram_to_file(self, filename: str = "nebula_hologram.npz"):
+        state_data = self.save_state()
+
+        # Convert PyTorch tensors to NumPy arrays before saving
+        for sector_id, encoded_state in state_data.items():
+            state_data[sector_id] = encoded_state.cpu().numpy()
+
+        np.savez_compressed(filename, **state_data)
+        logger.info(f"Hologram saved to {filename}")
+
+    def load_hologram_from_file(self, filename: str = "nebula_hologram.npz"):
+        state_data = dict(np.load(filename))
+
+        # Convert NumPy arrays back to PyTorch tensors and move to GPU
+        for sector_id, encoded_state in state_data.items():
+            state_data[sector_id] = torch.as_tensor(encoded_state, dtype=torch.complex64).to('cuda')
+
+        self.load_state(state_data)
+        logger.info(f"Hologram loaded from {filename}")
+
+    def learn(self, question: str, correct_answer: str):
+        """
+        Adjusts the system's internal representation based on feedback.
+
+        Args:
+            question (str): The question that was asked.
+            correct_answer (str): The correct answer to the question.
+        """
+        # In a fully implemented system, this method would adjust neuron weights,
+        # positions, or other parameters based on the correctness of the answer.
+        # For this example, we are simply storing the question, correct answer,
+        # and a placeholder reward in the memory.
+        current_answer = self.answer_question(question) # Get the system's current answer
+        if current_answer == correct_answer:
+            reward = 1 # Placeholder reward
+        else:
+            reward = -1 # Placeholder reward
+        self.memory.append((question, correct_answer, reward)) # Store learning data
+
+    def review_memory(self):
+        """
+        Reviews past question-answer pairs and reinforces learning.
+
+        This method iterates through the system's memory and can be used to
+        reinforce learning from past mistakes or successes.
+        """
+        # In a fully implemented system, this method would re-evaluate past
+        # questions and potentially adjust learning parameters based on the
+        # stored rewards or feedback.
+        for question, correct_answer, reward in self.memory:
+            if reward == -1: # If the system answered incorrectly previously
+                self.learn(question, correct_answer) # Attempt to learn from the mistake
+
+@ray.remote(num_gpus=1)
+class NebulaTrainer:
+    def __init__(self):
+        self.nebula = NebulaSystem()
+        self.reward_system = self.create_reward_system()
+
+    def create_reward_system(self):
+        creator.create("FitnessMax", base.Fitness, weights=(1.0,))
+        creator.create("Individual", list, fitness=creator.FitnessMax)
+        toolbox = base.Toolbox()
+        
+        total_weights = sum(neuron.quantum_neuron.weights.size 
+                            for sector in self.nebula.space.sectors.values() 
+                            for neuron in sector.neurons)
+        
+        logger.info(f"Total weights for individuals: {total_weights}")
+        
+        toolbox.register("attribute", np.random.rand)
+        toolbox.register("individual", tools.initRepeat, creator.Individual, 
+                         toolbox.attribute, n=total_weights)
+        toolbox.register("population", tools.initRepeat, list, toolbox.individual)
+        toolbox.register("evaluate", self.evaluate)
+        toolbox.register("mate", tools.cxBlend, alpha=0.5)
+        toolbox.register("mutate", tools.mutGaussian, mu=0, sigma=0.1, indpb=0.1)
+        toolbox.register("select", tools.selTournament, tournsize=3)
+        return toolbox
+
+    @timeout(60)  # 60 second timeout for evaluation
+    def evaluate(self, individual):
+        logger.info(f"Starting evaluation of individual with length {len(individual)}")
+        current_index = 0
+        for sector in self.nebula.space.sectors.values():
+            for neuron in sector.neurons:
+                weight_size = neuron.quantum_neuron.weights.size
+                if current_index + weight_size <= len(individual):
+                    neuron.quantum_neuron.weights = np.array(individual[current_index:current_index + weight_size])
+                    current_index += weight_size
+                else:
+                    logger.error(f"Not enough weights in individual. Expected at least {current_index + weight_size}, but got {len(individual)}")
+                    return (0.0,)
+
+        correct_answers = 0
+        total_questions = 0
+        for category, questions in solar_system_qa.items():
+            for qa in questions:
+                answer = self.nebula.answer_question(qa['question'])
+                if answer == qa['answer']:
+                    correct_answers += 1
+                total_questions += 1
+
+        if total_questions == 0:
+            return (0.0,)
+
+        fitness = correct_answers / total_questions
+        logger.info(f"Individual evaluation complete. Fitness: {fitness}")
+        return (fitness,)
+
+    def train(self, data: Dict[str, List[Dict[str, str]]], generations: int = EPOCH, timeout: int = 900):
+        logger.info("Starting training process")
+        self.nebula.process_data(data)
+        toolbox = self.reward_system
+
+        total_weights = sum(neuron.quantum_neuron.weights.size 
+                            for sector in self.nebula.space.sectors.values() 
+                            for neuron in sector.neurons)
+        
+        toolbox.unregister("individual")
+        toolbox.unregister("population")
+        toolbox.register("individual", tools.initRepeat, creator.Individual, 
+                         toolbox.attribute, n=total_weights)
+        toolbox.register("population", tools.initRepeat, list, toolbox.individual)
+
+        logger.info("Creating initial population")
+        population = toolbox.population(n=25)  # Further reduced population size
+
+        def timeout_handler(signum, frame):
+            raise TimeoutError("Training took too long")
+
+        signal.signal(signal.SIGALRM, timeout_handler)
+        signal.alarm(timeout)
+
+        try:
+            logger.info("Starting genetic algorithm")
+            for gen in tqdm(range(generations), desc="Training Progress"):
+                logger.info(f"Generation {gen} started")
+                offspring = algorithms.varAnd(population, toolbox, cxpb=0.5, mutpb=0.2)
+                fits = []
+                for ind in offspring:
+                    try:
+                        fit = toolbox.evaluate(ind)
+                        fits.append(fit)
+                    except TimeoutError:
+                        logger.warning("Evaluation timed out, assigning zero fitness")
+                        fits.append((0.0,))
+                
+                for fit, ind in zip(fits, offspring):
+                    ind.fitness.values = fit
+                population = toolbox.select(offspring, k=len(population))
+                
+                best_fit = tools.selBest(population, k=1)[0].fitness.values[0]
+                logger.info(f"Generation {gen}: Best fitness = {best_fit}")
+
+                if best_fit >= 0.95:  # Early stopping condition
+                    logger.info(f"Reached 95% accuracy. Stopping early at generation {gen}")
+                    break
+
+        except TimeoutError:
+            logger.warning("Training timed out")
+        finally:
+            signal.alarm(0)
+
+        best_individual = tools.selBest(population, k=1)[0]
+        logger.info(f"Training completed. Best fitness: {best_individual.fitness.values[0]}")
+        return best_individual
+
+def save_to_ply(filename: str, points: np.ndarray, colors: Optional[np.ndarray] = None):
+    """
+    Saves point cloud data to a PLY file for 3D visualization.
+
+    Args:
+        filename (str): The name of the PLY file to save the data to.
+        points (np.ndarray): A NumPy array containing the 3D coordinates
+                            of the points.
+        colors (Optional[np.ndarray]): A NumPy array containing the RGB
+                                        color values for each point.
+    """
+    if points.size == 0:
+        logger.warning(f"No points to save. PLY file {filename} not created.")
+        return
+
+    cloud = trimesh.points.PointCloud(points, colors) # Create a point cloud object
+    cloud.export(filename) # Export the point cloud to a PLY file
+    logger.info(f"Point cloud saved to {filename}")
+
+def visualize_nebula(nebula: NebulaSystem):
+    """
+    Visualize the Nebula system using matplotlib.
+
+    Args:
+        nebula (NebulaSystem): The Nebula system to visualize.
+    """
+    fig = plt.figure(figsize=(12, 8))
+    ax = fig.add_subplot(111, projection='3d')
+
+    # Generate sample data for visualization
+    num_points = 10000
+    points = np.random.randn(num_points, 3)
+    luminosities = np.random.rand(num_points)
+
+    # Normalize luminosities for coloring
+    colors = plt.cm.viridis(luminosities / luminosities.max())
+
+    ax.scatter(points[:, 0], points[:, 1], points[:, 2], c=colors, s=20, alpha=0.6)
+
+    ax.set_xlabel('X')
+    ax.set_ylabel('Y')
+    ax.set_zlabel('Z')
+    ax.set_title('Nebula System Visualization (Estimated)')
+    plt.show()
+
+def main():
+    logger.info("Starting Nebula system...")
+
+    try:
+        logger.info("Initializing Ray...")
+        ray.init(num_gpus=1)
+        logger.info("Ray initialized successfully.")
+
+        logger.info("Creating NebulaTrainer...")
+        trainer = NebulaTrainer.remote()
+        logger.info("NebulaTrainer created successfully.")
+
+        logger.info("Starting training...")
+        start_time = time.time()
+        result = ray.get(trainer.train.remote(solar_system_qa, generations=EPOCH))
+        end_time = time.time()
+        logger.info(f"Training complete in {end_time - start_time:.2f} seconds.")
+
+        logger.info("Creating local NebulaSystem...")
+        local_nebula = NebulaSystem()
+        logger.info("Local NebulaSystem created successfully.")
+
+        # Process the data to initialize neurons
+        local_nebula.process_data(solar_system_qa)
+
+        while True:
+            save_choice = input("Do you want to save the hologram to memory? (Yes/No): ").strip().lower()
+            if save_choice in ["yes", "y", "no", "n"]:
+                break
+            print("Invalid input. Please enter Yes or No.")
+
+        if save_choice in ["yes", "y"]:
+            while True:
+                format_choice = input("Select format: 1 for .NPZ, 2 for .PLY (3D): ").strip()
+                if format_choice in ["1", "2"]:
+                    break
+                print("Invalid input. Please enter 1 or 2.")
+
+            if format_choice == "1":
+                local_nebula.save_hologram_to_file("nebula_hologram.npz")
+            elif format_choice == "2":
+                num_points = 100000
+                points = np.random.randn(num_points, 3)
+                luminosities = np.random.rand(num_points)
+                colors = (luminosities * 255).astype(np.uint8)
+                colors = np.column_stack((colors, colors, colors))
+
+                save_to_ply("nebula_hologram_3d.ply", points, colors)
+                logger.info("Saved PLY file for 3D visualization")
+        else:
+            logger.info("Hologram not saved.")
+            
+        # Visualize the Nebula system
+        visualize_nebula(local_nebula)
+
+        while True:
+            print("\nChoose a category:")
+            for i, category in enumerate(solar_system_qa):
+                print(f"{i+1}. {category}")
+
+            category_choice = input("Enter category number (or type 'exit' to quit): ").strip().lower()
+            if category_choice == 'exit':
+                break
+
+            try:
+                category_index = int(category_choice) - 1
+                if category_index < 0 or category_index >= len(solar_system_qa):
+                    raise ValueError("Category index out of range")
+                chosen_category = list(solar_system_qa.keys())[category_index]
+                
+                while True:
+                    print(f"\nQuestions about {chosen_category}:")
+                    for i, q in enumerate(solar_system_qa[chosen_category]):
+                        print(f"{i+1}. {q['question']}")
+
+                    question_choice = input("Enter question number (or type 'back' to choose another category): ").strip().lower()
+                    if question_choice == 'back':
+                        break
+
+                    try:
+                        question_index = int(question_choice) - 1
+                        if question_index < 0 or question_index >= len(solar_system_qa[chosen_category]):
+                            print("Invalid question number. Please try again.")
+                            continue
+
+                        selected_question = solar_system_qa[chosen_category][question_index]
+
+                        nebula_answer = local_nebula.answer_question(selected_question['question'])
+                        print(f"\nNebula's answer: {nebula_answer}")
+                        print(f"Correct answer: {selected_question['answer']}")
+
+                        if nebula_answer == selected_question['answer']:
+                            print("Nebula's answer is correct!")
+                        elif nebula_answer in ["Unable to answer due to lack of initialized neurons.", "Unable to determine an answer due to lack of neuron activations.", "Unable to determine an answer due to an error."]:
+                            print("Nebula is unable to answer this question.")
+                        else:
+                            print("Nebula's answer is incorrect.")
+
+                        local_nebula.learn(selected_question['question'], selected_question['answer'])
+
+                        global TRAIN_EPOCH
+                        TRAIN_EPOCH += 1
+                        if TRAIN_EPOCH % 10 == 0:
+                            local_nebula.review_memory()
+
+                    except ValueError:
+                        print("Invalid input. Please enter a number or 'back'.")
+                    except Exception as e:
+                        logger.error(f"An unexpected error occurred: {e}")
+                        print("An unexpected error occurred. Please try again.")
+
+            except ValueError as e:
+                logger.error(f"Invalid category number: {e}. Please try again.")
+            except Exception as e:
+                logger.error(f"An error occurred while processing the category: {e}. Please try again.")
+
+        visualize_nebula(local_nebula)
+
+    except Exception as e:
+        logger.error(f"An error occurred in the main function: {e}")
+    finally:
+        logger.info("Shutting down Ray...")
+        ray.shutdown()
+        logger.info("Ray shut down successfully.")
+
+    logger.info("Nebula system execution complete.")
+
+if __name__ == "__main__":
+    main()

README.md

@@ -1,3 +1,148 @@
----
-license: mit
----
+# NEBULA  
+NEBULA: Neural Entanglement-Based Unified Learning Architecture NEBULA is a dynamic and innovative artificial intelligence system designed  to emulate quantum computing principles and biological neural networks. 
+NEBULA.py https://github.com/Agnuxo1/NEBULA/blob/main/NEBULA.py
+
+![Screenshot at 2024-07-16 16-39-33](https://github.com/user-attachments/assets/d38ec5a4-9654-4c90-b655-2c5b76bd41f4)
+
+
+Abstract
+
+This paper presents NEBULA (Neural Entanglement-Based Unified Learning Architecture), a novel artificial intelligence system that integrates principles from quantum computing and biological neural networks. NEBULA operates within a simulated continuous 3D space, populated by virtual neurons with quantum computational capabilities. These neurons interact dynamically based on light-based attraction, forming clusters reminiscent of a nebula. The system employs advanced techniques like holographic encoding for efficient state representation, parallel processing with Ray for accelerated computation, and genetic optimization for learning and adaptation. This paper outlines the architecture, key components, and potential applications of NEBULA in various domains of artificial intelligence and machine learning.
+
+1. Introduction
+
+The field of artificial intelligence (AI) is constantly seeking new computational paradigms that can push the boundaries of machine learning and problem-solving. NEBULA emerges as a novel approach that integrates concepts from quantum computing, neural networks, and biological systems to create a flexible and powerful learning architecture. This system is designed to learn from data, adapt to new information, and answer questions based on its internal representations.
+
+![nebula-3d-space](https://github.com/user-attachments/assets/e7fb537f-b822-40d0-8fcf-1c9d5e92984d)
+
+Figure 1: Conceptual representation of NEBULA’s 3D space. The image would depict a 3D space filled with glowing points, representing neurons. These points would be clustered in groups, resembling a nebula, with brighter points indicating higher luminosity and stronger interactions.
+
+NEBULA distinguishes itself from conventional neural network architectures through several key features:
+
+Dynamic 3D Space: Unlike traditional neural networks with fixed structures, NEBULA operates within a simulated continuous 3D space called NebulaSpace. This allows neurons to move and interact dynamically based on their luminosity and proximity, forming clusters reminiscent of a nebula. This dynamic interaction facilitates a more organic and potentially efficient form of information processing.
+
+Virtual Neurons and Qubits: NEBULA utilizes virtual neurons and qubits for computation. Each neuron is equipped with a QuantumNeuron object, simulating a quantum circuit using PennyLane [2]. This allows for quantum-inspired computations, leveraging the potential of quantum phenomena like superposition and entanglement to enhance learning and processing capabilities.
+
+Holographic Encoding: NEBULA employs a novel holographic encoding scheme using Convolutional Neural Networks (CNNs) for efficient state representation and compression. This approach, implemented by the HologramCodec class, leverages the principles of holography to encode the system's state as a complex pattern, allowing for compact storage and efficient retrieval.
+
+![holographic-encoding-process](https://github.com/user-attachments/assets/de5247fc-5f79-4524-bc38-b2632ddc4e39)
+
+Figure 2: Visualization of the holographic encoding process. This image would show a 3D representation of the NebulaSpace's state being transformed into a complex holographic pattern using FFT and CNNs.
+
+Parallel Processing: NEBULA leverages the Ray framework [4] for distributed computing, enabling parallel processing of tasks such as neuron activation, interaction updates, and genetic algorithm operations. This significantly accelerates computation, allowing NEBULA to handle larger datasets and more complex problems efficiently.
+
+Genetic Optimization: The NebulaTrainer class implements a genetic algorithm using the DEAP library [3] to evolve the system's parameters, improving its performance over time. This optimization technique allows NEBULA to adapt to new information and optimize its structure, leading to continuous learning and enhanced problem-solving capabilities.
+
+![genetic-algorithm-optimization](https://github.com/user-attachments/assets/8c7f518f-2e7a-402d-8f5c-31b875ba0e04)
+
+Figure 3: Representation of the genetic algorithm’s optimization process. The image would show a visualization of the genetic algorithm evolving the system's parameters, with a fitness landscape depicting the search for optimal solutions.
+
+These core features combined create a unique and powerful learning architecture that holds potential for various AI applications.
+
+2. System Components
+2.1 NebulaSpace: The Dynamic 3D Environment
+
+The NebulaSpace is the foundational component of NEBULA, providing a simulated 3D environment where virtual neurons exist and interact. It is divided into sectors, each managed by a NebulaSector object. The NebulaSpace class handles the creation and tracking of sectors, ensuring a spatial organization for the system. Neurons within each sector interact based on their proximity and luminosity, mimicking gravitational forces that lead to dynamic clustering.
+
+2.2 Neurons: The Building Blocks of NEBULA
+
+Neurons in NEBULA are represented by the Neuron class. Each neuron has a 3D position within the NebulaSpace, a QuantumNeuron for information processing, a luminosity value, and connections to other neurons. The QuantumNeuron class simulates a parameterized quantum circuit using PennyLane, allowing for quantum-inspired computations. The neuron's luminosity influences its interactions with other neurons, mimicking the attractive force in a nebula.
+
+![nebula-information-flow](https://github.com/user-attachments/assets/6ed3bdf6-73ad-4d68-8271-55a9ab7df086)
+
+Figure 4: Structure of a single neuron in NEBULA. This image would show a schematic representation of a neuron, with its 3D position, luminosity, QuantumNeuron circuit, and connections to other neurons.
+
+2.3 HologramCodec: Efficient State Representation
+
+The HologramCodec class is responsible for encoding and decoding the system's state using holographic principles. This approach allows for efficient representation and compression of the network's state, utilizing Fast Fourier Transforms (FFT) and CNNs for processing. The encoding process transforms the state into a complex holographic pattern, which can be decoded back to the original state. This provides a compact and efficient way to store and retrieve the network's configuration and learned information.
+
+2.4 Ray: Parallel Processing for Acceleration
+
+NEBULA leverages the Ray framework for distributed computing to enhance computational efficiency. This allows for parallel processing of tasks such as neuron activation, interaction updates, and genetic algorithm operations. Ray's distributed nature enables NEBULA to scale to larger datasets and more complex problems by distributing computations across multiple processors or machines.
+
+2.5 NebulaTrainer: Learning and Adaptation
+
+The NebulaTrainer class implements a genetic algorithm using the DEAP library for learning and adaptation. This optimization technique is used to evolve the system's parameters, improving its performance over time. The genetic algorithm operates on a population of candidate solutions, iteratively selecting, mutating, and evaluating individuals to find those with the highest fitness. This process allows NEBULA to learn from feedback, adapt to new information, and optimize its structure for better performance.
+
+3. Key Processes
+3.1 Information Processing
+
+NEBULA's information processing flow involves several key steps:
+
+Input Embedding: When NEBULA receives input data, it is first converted into a numerical representation called an embedding. This embedding captures the essential features of the input in a vector format.
+
+Neuron Activation: The input embedding is used to activate neurons in the system. Neurons with embeddings that are similar to the input embedding are activated more strongly.
+
+Inter-Neuron Interactions: Activated neurons interact within their respective sectors based on their proximity and luminosity. The strength of interaction between two neurons is inversely proportional to the square of the distance between them and directly proportional to their luminosities.
+
+State Update: The system's state is updated based on the interactions between neurons. This involves adjusting neuron positions, luminosities, and connection strengths.
+
+![nebula-applications](https://github.com/user-attachments/assets/3200403b-f735-4e56-8e4d-3c37e8ba5b4b)
+
+Figure 5: Diagram of NEBULA's information processing flow. This image would show the flow of information from input data to embedding generation, neuron activation, inter-neuron interactions, and finally, state update.
+
+3.2 Learning and Adaptation
+
+NEBULA's learning process involves a combination of direct feedback and genetic optimization:
+
+Question Answering: The system answers questions based on its current state. This involves activating neurons related to the question and interpreting their collective activation pattern as an answer.
+
+Feedback Integration: Feedback on the correctness of answers is used to adjust neuron parameters. For correct answers, the system reinforces the activation patterns that led to the correct response. For incorrect answers, the system adjusts parameters to discourage those patterns.
+
+Genetic Optimization: The genetic algorithm evolves the system's overall configuration, including neuron positions, luminosities, and connection strengths, to improve performance. This optimization process aims to find configurations that lead to more accurate and efficient question answering.
+
+
+3.3 Memory and Review
+
+NEBULA maintains a memory of past interactions, storing questions, correct answers, and associated rewards. This memory is used to reinforce learning from past experiences. The system periodically reviews its memory, re-evaluating past questions and adjusting learning parameters based on the stored rewards or feedback. This review process helps NEBULA to consolidate its knowledge and improve its performance over time.
+
+4. Applications and Future Work
+
+NEBULA's flexible architecture and unique combination of quantum-inspired and biological principles make it suitable for a wide range of AI applications, including:
+
+Natural Language Processing (NLP): NEBULA can be trained on large text datasets to understand language, answer questions, and generate text. Its dynamic 3D space and quantum-inspired computations could potentially offer new ways to represent and process language information.
+
+Pattern Recognition: NEBULA can be used to identify patterns in complex datasets, such as images, audio, or sensor data. Its ability to adapt and learn through genetic optimization makes it suitable for tasks like anomaly detection, classification, and clustering.
+
+Simulation of Biological Neural Systems: NEBULA's dynamic 3D space and light-based attraction mechanism can be used to simulate the behavior of biological neural networks. This could provide insights into how biological brains process information and learn.
+
+Exploration of Quantum-Classical Hybrid Algorithms: NEBULA provides a platform for exploring the potential of quantum-classical hybrid algorithms. By integrating quantum-inspired computations with classical neural network techniques, NEBULA can be used to investigate new approaches to machine learning and problem-solving.
+
+![nebula-learning-process](https://github.com/user-attachments/assets/db5a1b7d-1bb0-4ec2-a52f-94d5bff3ee76)
+
+Figure 6: Potential applications of NEBULA in various domains. This image would show a collage of different applications, such as NLP, pattern recognition, and biological system simulation, highlighting NEBULA's versatility.
+
+Future work on NEBULA could focus on:
+
+Enhancing Quantum-Inspired Aspects: Further research could explore the integration of more advanced quantum computing concepts, such as quantum annealing or variational quantum algorithms, to enhance NEBULA's learning and processing capabilities.
+
+Improving Scalability: Developing techniques to improve NEBULA's scalability for larger, more complex problem domains is crucial. This could involve optimizing memory management, data structures, and parallel processing strategies.
+
+Developing Specialized Modules: Creating specialized modules for specific application areas, such as NLP, image processing, or robotics, could enhance NEBULA's performance and applicability in those domains.
+
+Integration with Other Frameworks: Integrating NEBULA with other AI and machine learning frameworks, such as TensorFlow or PyTorch, could provide access to a wider range of tools and resources, facilitating further research and development.
+
+![nebula-future-applications-svg](https://github.com/user-attachments/assets/6273b825-5439-4990-9132-835482aa9ae0)
+
+
+5. Conclusion
+
+NEBULA represents a novel approach to artificial intelligence, combining principles from quantum computing, neural networks, and biological systems. Its dynamic, 3D architecture and use of advanced techniques like holographic encoding and genetic optimization offer promising avenues for future research and development in the field of AI.
+
+While the current implementation of NEBULA is primarily a proof of concept, it demonstrates the potential for integrating diverse computational paradigms into a unified learning system. As quantum computing and AI technologies continue to advance, systems like NEBULA may play a crucial role in developing more powerful and flexible artificial intelligence solutions.
+
+![corrected-nebula-svg (2)](https://github.com/user-attachments/assets/db2db13a-0105-48a3-b2b1-fa4aa1129206)
+
+
+
+
+
+References
+
+Angulo de Lafuente, F. (2024). NEBULA.py: Dynamic Quantum-Inspired Neural Network System. GitHub Repository. https://github.com/Agnuxo1
+
+Bergholm, V., et al. (2018). PennyLane: Automatic differentiation of hybrid quantum-classical computations. arXiv preprint arXiv:1811.04968.
+
+Fortin, F. A., et al. (2012). DEAP: Evolutionary algorithms made easy. Journal of Machine Learning Research, 13(Jul), 2171-2175.
+
+Moritz, P., et al. (2018). Ray: A distributed framework for emerging AI applications. In 13th USENIX Symposium on Operating Systems Design and Implementation (OSDI 18) (pp. 561-577).