Create EVE emitonal code

EVE emitonal code

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+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.")