import os import json import random from sklearn.ensemble import IsolationForest from sklearn.model_selection import train_test_split from sklearn.preprocessing import OneHotEncoder from sklearn.neural_network import MLPClassifier from deap import base, creator, tools, algorithms import torch import torch.nn as nn import torch.optim as optim import torch.nn.functional as F import datetime import time import threading import logging import multiprocessing from collections import deque # Logging Configuration logging.basicConfig(filename='app.log', level=logging.INFO, format='%(asctime)s %(levelname)s: %(message)s', datefmt='%Y-%m-%d %H:%M:%S') # Memory Model class MemoryModel: def __init__(self, memory_file='memory.json', max_memory=1000): self.memory_file = memory_file self.max_memory = max_memory self.memory = self.load_memory() def load_memory(self): if os.path.exists(self.memory_file): with open(self.memory_file, 'r') as file: return json.load(file) return [] def save_memory(self): with open(self.memory_file, 'w') as file: json.dump(self.memory, file) def add_entry(self, context, response, emotion_state, timestamp=None): timestamp = timestamp or datetime.datetime.now().isoformat() entry = { 'timestamp': timestamp, 'context': context, 'response': response, 'emotion_state': emotion_state } self.memory.append(entry) if len(self.memory) > self.max_memory: self.memory.pop(0) # Remove the oldest entry self.save_memory() def retrieve_memory(self, query, context_window=5): relevant_entries = [entry for entry in self.memory if query.lower() in entry['context'].lower()] if relevant_entries: sorted_entries = sorted(relevant_entries, key=lambda x: x['timestamp'], reverse=True) return sorted_entries[:context_window] return None # Temporal Awareness Module class TemporalAwareness: def __init__(self, context_window=5): self.start_time = datetime.datetime.now() self.last_event_time = None self.event_sequence = deque(maxlen=context_window) self.context_window = context_window def update_event_time(self, event): current_time = datetime.datetime.now() if self.last_event_time: duration = (current_time - self.last_event_time).total_seconds() self.event_sequence.append({ 'event': event, 'timestamp': current_time.isoformat(), 'duration_since_last': duration }) else: self.event_sequence.append({ 'event': event, 'timestamp': current_time.isoformat(), 'duration_since_last': None }) self.last_event_time = current_time def estimate_duration(self, event): recent_events = list(self.event_sequence) durations = [ seq['duration_since_last'] for seq in recent_events if seq['event'] == event and seq['duration_since_last'] is not None ] return sum(durations) / len(durations) if durations else None # HRL Neuron Class class HRLNeuron(nn.Module): def __init__(self, input_dim, output_dim): super(HRLNeuron, self).__init__() self.fc1 = nn.Linear(input_dim, 128) self.fc2 = nn.Linear(128, output_dim) def forward(self, x): x = F.relu(self.fc1(x)) x = self.fc2(x) return x class HRLAgent: def __init__(self, input_dim, output_dim, lr=0.001): self.model = HRLNeuron(input_dim, output_dim) self.optimizer = optim.Adam(self.model.parameters(), lr=lr) self.criterion = nn.MSELoss() def act(self, state): state = torch.FloatTensor(state) q_values = self.model(state) return q_values def learn(self, state, action, reward, next_state, gamma=0.99): state = torch.FloatTensor(state) next_state = torch.FloatTensor(next_state) reward = torch.FloatTensor([reward]) action = torch.LongTensor([action]) q_values = self.model(state) next_q_values = self.model(next_state) target_q_value = reward + gamma * torch.max(next_q_values) loss = self.criterion(q_values[action], target_q_value) self.optimizer.zero_grad() loss.backward() self.optimizer.step() # Initialize Example Emotions Dataset data = { 'context': [ 'I am happy', 'I am sad', 'I am angry', 'I am excited', 'I am calm', 'I am feeling joyful', 'I am grieving', 'I am feeling peaceful', 'I am frustrated', 'I am determined', 'I feel resentment', 'I am feeling glorious', 'I am motivated', 'I am surprised', 'I am fearful', 'I am trusting', 'I feel disgust', 'I am optimistic', 'I am pessimistic', 'I feel bored', 'I am envious' ], 'emotion': [ 'joy', 'sadness', 'anger', 'joy', 'calmness', 'joy', 'grief', 'calmness', 'anger', 'determination', 'resentment', 'glory', 'motivation', 'surprise', 'fear', 'trust', 'disgust', 'optimism', 'pessimism', 'boredom', 'envy' ] } df = pd.DataFrame(data) # Encoding the contexts using One-Hot Encoding encoder = OneHotEncoder(handle_unknown='ignore') contexts_encoded = encoder.fit_transform(df[['context']]).toarray() # Encoding emotions emotions_target = df['emotion'].astype('category').cat.codes emotion_classes = df['emotion'].astype('category').cat.categories # Train Neural Network X_train, X_test, y_train, y_test = train_test_split(contexts_encoded, emotions_target, test_size=0.2, random_state=42) model = MLPClassifier(hidden_layer_sizes=(10, 10), max_iter=1000, random_state=42) model.fit(X_train, y_train) # Isolation Forest Anomaly Detection Model historical_data = np.array([model.predict(contexts_encoded)]).T isolation_forest = IsolationForest(contamination=0.1, random_state=42) isolation_forest.fit(historical_data) # Emotional States emotions = { 'joy': {'percentage': 10, 'motivation': 'positive'}, 'pleasure': {'percentage': 10, 'motivation': 'selfish'}, 'sadness': {'percentage': 10, 'motivation': 'negative'}, 'grief': {'percentage': 10, 'motivation': 'negative'}, 'anger': {'percentage': 10, 'motivation': 'traumatic or strong'}, 'calmness': {'percentage': 10, 'motivation': 'neutral'}, 'determination': {'percentage': 10, 'motivation': 'positive'}, 'resentment': {'percentage': 10, 'motivation': 'negative'}, 'glory': {'percentage': 10, 'motivation': 'positive'}, 'motivation': {'percentage': 10, 'motivation': 'positive'}, 'ideal_state': {'percentage': 100, 'motivation': 'balanced'}, 'fear': {'percentage': 10, 'motivation': 'defensive'}, 'surprise': {'percentage': 10, 'motivation': 'unexpected'}, 'anticipation': {'percentage': 10, 'motivation': 'predictive'}, 'trust': {'percentage': 10, 'motivation': 'reliable'}, 'disgust': {'percentage': 10, 'motivation': 'repulsive'}, 'optimism': {'percentage': 10, 'motivation': 'hopeful'}, 'pessimism': {'percentage': 10, 'motivation': 'doubtful'}, 'boredom': {'percentage': 10, 'motivation': 'indifferent'}, 'envy': {'percentage': 10, 'motivation': 'jealous'} } # Adjust all emotions to a total of 200% total_percentage = 200 default_percentage = total_percentage / len(emotions) for emotion in emotions: emotions[emotion]['percentage'] = default_percentage emotion_history_file = 'emotion_history.json' # Load historical data from file if exists def load_historical_data(file_path=emotion_history_file): if os.path.exists(file_path): with open(file_path, 'r') as file: return json.load(file) return [] # Save historical data to file def save_historical_data(historical_data, file_path=emotion_history_file): with open(file_path, 'w') as file: json.dump(historical_data, file) # Load previous emotional states emotion_history = load_historical_data() # Function to update emotions def update_emotion(emotion, percentage): emotions['ideal_state']['percentage'] -= percentage emotions[emotion]['percentage'] += percentage # Ensure total percentage remains 200% total_current = sum(e['percentage'] for e in emotions.values()) adjustment = total_percentage - total_current emotions['ideal_state']['percentage'] += adjustment # Function to normalize context def normalize_context(context): return context.lower().strip() # Function to evolve emotions using genetic algorithm (Hyper-Evolution) def evolve_emotions(): def evaluate(individual): ideal_state = individual[-1] other_emotions = individual[:-1] return abs(ideal_state - 100), sum(other_emotions) creator.create("FitnessMin", base.Fitness, weights=(-1.0, -1.0)) creator.create("Individual", list, fitness=creator.FitnessMin) toolbox = base.Toolbox() toolbox.register("attribute", lambda: random.uniform(0, 20)) toolbox.register("individual", tools.initCycle, creator.Individual, toolbox.attribute, n=(len(emotions) - 1)) toolbox.register("ideal_state", lambda: random.uniform(80, 120)) toolbox.register("complete_individual", tools.initConcat, creator.Individual, toolbox.individual, toolbox.ideal_state) toolbox.register("population", tools.initRepeat, list, toolbox.complete_individual) toolbox.register("evaluate", evaluate) toolbox.register("mate", tools.cxTwoPoint) toolbox.register("mutate", tools.mutGaussian, mu=0, sigma=1, indpb=0.2) toolbox.register("select", tools.selTournament, tournsize=3) population = toolbox.population(n=100) for gen in range(100): offspring = algorithms.varAnd(population, toolbox, cxpb=0.5, mutpb=0.2) fits = toolbox.map(toolbox.evaluate, offspring) for fit, ind in zip(fits, offspring): ind.fitness.values = fit population = toolbox.select(offspring, k=len(population)) if gen % 20 == 0: toolbox.register("mate", tools.cxBlend, alpha=random.uniform(0.1, 0.9)) toolbox.register("mutate", tools.mutPolynomialBounded, eta=random.uniform(0.5, 1.5), low=0, up=20, indpb=0.2) best_ind = tools.selBest(population, k=1)[0] return best_ind[:-1], best_ind[-1] # Additional Genetic Algorithms def evolve_language_model(): def evaluate_language(individual): return random.random(), creator.create("FitnessMax", base.Fitness, weights=(1.0,)) creator.create("LanguageIndividual", list, fitness=creator.FitnessMax) toolbox = base.Toolbox() toolbox.register("language_gene", lambda: random.randint(0, 1)) toolbox.register("language_individual", tools.initRepeat, creator.LanguageIndividual, toolbox.language_gene, n=100) toolbox.register("language_population", tools.initRepeat, list, toolbox.language_individual) toolbox.register("evaluate", evaluate_language) toolbox.register("mate", tools.cxTwoPoint) toolbox.register("mutate", tools.mutFlipBit, indpb=0.05) toolbox.register("select", tools.selTournament, tournsize=3) population = toolbox.language_population(n=50) for gen in range(100): offspring = algorithms.varAnd(population, toolbox, cxpb=0.5, mutpb=0.1) fits = toolbox.map(toolbox.evaluate, offspring) for fit, ind in zip(fits, offspring): ind.fitness.values = fit population = toolbox.select(offspring, k=len(population)) best_language_model = tools.selBest(population, k=1)[0] return best_language_model def evolve_emotion_recognition(): def evaluate_emotion_recognition(individual): return random.random(), creator.create("FitnessMax", base.Fitness, weights=(1.0,)) creator.create("EmotionRecognitionIndividual", list, fitness=creator.FitnessMax) toolbox = base.Toolbox() toolbox.register("emotion_gene", lambda: random.randint(0, 1)) toolbox.register("emotion_individual", tools.initRepeat, creator.EmotionRecognitionIndividual, toolbox.emotion_gene, n=100) toolbox.register("emotion_population", tools.initRepeat, list, toolbox.emotion_individual) toolbox.register("evaluate", evaluate_emotion_recognition) toolbox.register("mate", tools.cxTwoPoint) toolbox.register("mutate", tools.mutFlipBit, indpb=0.05) toolbox.register("select", tools.selTournament, tournsize=3) population = toolbox.emotion_population(n=50) for gen in range(100): offspring = algorithms.varAnd(population, toolbox, cxpb=0.5, mutpb=0.1) fits = toolbox.map(toolbox.evaluate, offspring) for fit, ind in zip(fits, offspring): ind.fitness.values = fit population = toolbox.select(offspring, k=len(population)) best_emotion_recognition = tools.selBest(population, k=1)[0] return best_emotion_recognition # Evolutionary System Implementation DNA_LENGTH = 10 # Example DNA length POPULATION_SIZE = 50 GENERATIONS = 100 NUM_ALGORITHMS = 3 # Define the initial DNA structure def generate_random_dna(): return [random.uniform(0, 1) for _ in range(DNA_LENGTH)] # Create initial populations for each algorithm populations = [[generate_random_dna() for _ in range(POPULATION_SIZE)] for _ in range(NUM_ALGORITHMS)] # Example Fitness Functions def fitness_function_1(dna): return sum(dna) # Simplistic example fitness function def fitness_function_2(dna): return np.prod(dna) # Simplistic example fitness function def fitness_function_3(dna): return np.mean(dna) # Simplistic example fitness function fitness_functions = [fitness_function_1, fitness_function_2, fitness_function_3] # Genetic Operators def tournament_selection(population, fitness_fn): tournament_size = 5 selected = random.sample(population, tournament_size) selected.sort(key=fitness_fn, reverse=True) return selected[0] def crossover(parent1, parent2): point = random.randint(0, DNA_LENGTH - 1) child1 = parent1[:point] + parent2[point:] child2 = parent2[:point] + parent1[point:] return child1, child2 def mutate(dna, mutation_rate=0.01): return [gene if random.random() > mutation_rate else random.uniform(0, 1) for gene in dna] def evolve(population, fitness_fn, generations=GENERATIONS): for _ in range(generations): new_population = [] for _ in range(POPULATION_SIZE // 2): parent1 = tournament_selection(population, fitness_fn) parent2 = tournament_selection(population, fitness_fn) child1, child2 = crossover(parent1, parent2) new_population.append(mutate(child1)) new_population.append(mutate(child2)) population = sorted(new_population, key=fitness_fn, reverse=True)[:POPULATION_SIZE] return population # Evolve populations for each of the first three algorithms for i in range(NUM_ALGORITHMS): populations[i] = evolve(populations[i], fitness_functions[i]) # Combine the best individuals from each algorithm def create_hybrid_population(populations, num_best=10): hybrid_population = [] for pop in populations: hybrid_population.extend(sorted(pop, key=lambda dna: sum([fn(dna) for fn in fitness_functions]), reverse=True)[:num_best]) return hybrid_population hybrid_population = create_hybrid_population(populations) # Example criteria evolution mechanism def evolve_fitness_criteria(hybrid_population): average_gene = np.mean([np.mean(dna) for dna in hybrid_population]) if average_gene > 0.5: return lambda dna: sum(dna) * 1.1 else: return lambda dna: sum(dna) * 0.9 # Update fitness functions based on new criteria new_fitness_fn = evolve_fitness_criteria(hybrid_population) fitness_functions = [new_fitness_fn] * NUM_ALGORITHMS # Evolve the hybrid population with the new fitness criteria hybrid_population = evolve(hybrid_population, new_fitness_fn) # Example of usage in the system logging.info("Initial populations evolved independently.") logging.info("Hybrid population created and evolved with new fitness criteria.")