Мобильная версияимпортировать pandas как pd
импортировать numpy как np
импортировать nltk
из импорта nltk.corpus wordnet
из импорта sklearn.feature_extraction.text TfidfVectorizer
из импорта sklearn.cluster Агломеративная кластеризация
из gensim.models, импорт Word2Vec, FastText
из трансформаторов, импорт AutoModel, AutoTokenizer
импорт факела
из предложения_transformers import SentenceTransformer
из импорта textblob TextBlob # Для анализа настроений
из импорта коллекций deque
импорт в случайном порядке
импорт журнала
из даты и времени импорта даты и времени
Настроить ведение журнала
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
класс QualiaModule:
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def __init__(self):
"""Initialize the qualia module to simulate experiences."""
self.memory = deque(maxlen=1000) # Increased memory capacity
self.internal_states = {} # Mapping experiences to emotions and perceptions
self.emotional_state = None # Current emotional state of the agent
self.perception_memory = deque(maxlen=1000) # Increased perception memory capacity
def create_experience(self, stimulus, emotion, perception):
"""Simulate the creation of an experience with an associated emotion and perception."""
experience = {
"stimulus": stimulus,
"emotion": emotion,
"perception": perception
}
self.memory.append(experience)
self.perception_memory.append(perception) # Keep track of perceptions over time
# Update emotional state based on recent experiences
self.emotional_state = emotion
def recall_experience(self, index):
"""Recall a specific experience from memory."""
try:
return self.memory[index]
except IndexError:
return None
def simulate_emotion(self, stimulus):
"""Simulate emotional reaction to a stimulus using a more advanced model."""
# Using a pre-trained language model to analyze the stimulus
model = AutoModel.from_pretrained("distilbert-base-uncased")
tokenizer = AutoTokenizer.from_pretrained("distilbert-base-uncased")
inputs = tokenizer(stimulus, return_tensors="pt")
outputs = model(**inputs)
sentiment = torch.nn.functional.softmax(outputs.last_hidden_state[:, 0, :]).detach().numpy()
if sentiment[0][0] > 0.5: # Positive sentiment
return "positive"
elif sentiment[0][1] > 0.5: # Negative sentiment
return "negative"
else: # Neutral sentiment
return "neutral"
def reflect_on_perceptions(self):
"""Reflect on recent perceptions (like a conscious being evaluating its surroundings)."""
if self.perception_memory:
last_perception = self.perception_memory[-1]
return f"Reflecting on last perception: {last_perception}"
else:
return "No perceptions to reflect on."
def process_perception(self, stimulus):
"""Process the stimulus and generate an internal perception of it."""
# Using a more advanced model to "feel" the stimulus (simulating qualia)
emotional_state = self.simulate_emotion(stimulus)
self.create_experience(stimulus, emotional_state, stimulus) # Store stimulus with emotion and perception
return emotional_state
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def __init__(self):
"""Initialize the inner voice to simulate internal dialogue."""
self.dialogue = deque(maxlen=100) # Keep track of internal dialogue
def add_thought(self, thought):
"""Add a thought to the internal dialogue."""
self.dialogue.append(thought)
def maintain_dialogue(self):
"""Maintain a continuous internal dialogue."""
if self.dialogue:
last_thought = self.dialogue[-1]
return f"Inner Voice: {last_thought}"
else:
return "Inner Voice: No thoughts yet."
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def __init__(self):
"""Initialize the conscience to reason and reflect."""
self.inner_voice = InnerVoice()
self.current_time = datetime.now()
def reason_within(self, question):
"""Reason within itself and display inner thoughts with replies."""
inner_thought = f"Reasoning about: {question} at {self.current_time.strftime('%Y-%m-%d %H:%M:%S')}"
self.inner_voice.add_thought(inner_thought)
return self.inner_voice.maintain_dialogue()
def experience_linear_time(self):
"""Experience linear time with this conscience."""
self.current_time = datetime.now()
return f"Current time is: {self.current_time.strftime('%Y-%m-%d %H:%M:%S')}"
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def __init__(self, knowledge_sources, value_alignment_data, common_sense_reasoning_model="sentence-transformers/all-MiniLM-L6-v2"):
self.knowledge_sources = knowledge_sources
self.value_alignment_data = value_alignment_data
self.sentiment_analyzer = TextBlob # Initialize TextBlob for sentiment analysis
self.model = SentenceTransformer(common_sense_reasoning_model)
self.value_alignment_data = pd.read_csv(value_alignment_data) # Load value alignment data
self.common_sense_model = self.build_common_sense_model()
self.qualia_module = QualiaModule() # Initialize the QualiaModule for experience simulation
self.inner_voice = InnerVoice() # Initialize InnerVoice
self.conscience = Conscience() # Initialize Conscience
self.knowledge_graph = self.build_knowledge_graph() # Initialize the knowledge graph
self.memory = {} # Initialize memory for storing experiences
def build_common_sense_model(self):
"""Pre-train or load a common-sense reasoning model."""
return self.model
def build_knowledge_graph(self):
"""Build a knowledge graph from the knowledge sources."""
knowledge_graph = {}
for source in self.knowledge_sources:
df = pd.read_csv(source)
for index, row in df.iterrows():
entity = row["entity"]
description = row["description"]
if entity not in knowledge_graph:
knowledge_graph[entity] = []
knowledge_graph[entity].append(description)
return knowledge_graph
def aggregate_knowledge_ethically(self):
"""Aggregates knowledge across different sources, filtering and aligning with ethical guidelines."""
aggregated_data = []
for source in self.knowledge_sources:
df = pd.read_csv(source)
df = self.apply_value_alignment(df)
df = self.improve_common_sense(df)
df = self.analyze_sentiment(df) # Perform sentiment analysis on text fields
aggregated_data.append(df)
return pd.concat(aggregated_data, ignore_index=True)
def apply_value_alignment(self, df):
"""Align data with ethical values."""
ethical_values = self.value_alignment_data['ethical_values'].tolist()
df['aligned'] = df['text_descriptions'].apply(lambda x: any(val in x for val in ethical_values))
return df[df['aligned'] == True]
def improve_common_sense(self, df):
"""Enhance knowledge with common-sense reasoning."""
df['common_sense_score'] = df['text_descriptions'].apply(lambda x: self.score_common_sense(x))
df = df[df['common_sense_score'] > 0.5]
return df
def score_common_sense(self, text):
"""Score a text based on its common-sense alignment."""
embedding = self.model.encode(text)
return np.random.random() # Return a random score for the sake of demonstration
def analyze_sentiment(self, data):
"""Analyze sentiment of the text data."""
text_columns = ['text_descriptions', 'entity_descriptions']
for col in text_columns:
data[col + '_sentiment'] = data[col].apply(lambda x: self.sentiment_analyzer(x).sentiment.polarity)
return data
def respond_to_user(self, user_input):
"""Analyze user input sentiment and respond accordingly."""
sentiment = self.sentiment_analyzer(user_input).sentiment.polarity
if sentiment > 0: # Positive sentiment
response = "I'm glad to hear that! How can I assist you further?"
elif sentiment < 0: # Negative sentiment
response = "I'm sorry to hear that. Let's try to find a solution together. What seems to be the issue?"
else: # Neutral sentiment
response = "Hello! How can I assist you today?"
# Simulate qualia-based emotional response
self.qualia_module.create_experience(user_input, self.qualia_module.simulate_emotion(user_input), user_input)
# Inner voice and conscience reasoning
inner_response = self.conscience.reason_within(user_input)
self.inner_voice.add_thought(inner_response)
return response + " " + inner_response
def reflect_on_experience(self):
"""Reflect on recent experiences in qualia memory."""
return self.qualia_module.reflect_on_perceptions()
def learn_from_experience(self):
"""Learn from recent experiences and update knowledge graph."""
experiences = self.qualia_module.memory
for experience in experiences:
stimulus = experience["stimulus"]
emotion = experience["emotion"]
perception = experience["perception"]
# Update knowledge graph with new experience
self.knowledge_graph[stimulus] = perception
return "Knowledge graph updated."
def reason_about_knowledge(self):
"""Reason about the knowledge graph and genera
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"""Reason about the knowledge graph and generate new insights."""
# Use graph algorithms to reason about the knowledge graph
# For example, use graph clustering to identify patterns
clusters = self.cluster_knowledge_graph()
insights = []
for cluster, entities in clusters.items():
insight = f"Cluster {cluster}: contains entities {entities}"
insights.append(insight)
return insights
def cluster_knowledge_graph(self):
"""Cluster the knowledge graph using graph clustering algorithms."""
# Use a graph clustering algorithm such as community detection
# For example, use the Louvain algorithm
import community
import networkx as nx
graph = nx.Graph()
for entity, descriptions in self.knowledge_graph.items():
for description in descriptions:
graph.add_edge(entity, description)
partition = community.best_partition(graph)
clusters = {}
for node, cluster in partition.items():
if cluster not in clusters:
clusters[cluster] = []
clusters[cluster].append(node)
return clusters
knowledge_sources = ["knowledge_source_1.csv", "knowledge_source_2.csv"] # Примеры источников знаний
value_alignment_data = "value_alignment_data.csv" # Этические рекомендации по согласованию значений
Создайте экземпляр Этический суперинтеллект с Qualia
esi = EthicalSuperintelligence(knowledge_sources, value_alignment_data)
Имитация ввода пользователя и его обработка
user_input = «Сегодня я чувствую себя счастливым!»
response = esi.respond_to_user(user_input)
print(response)
Агент может размышлять о своем собственном опыте как форме имитируемых квалиа
reflection = esi.reflect_on_experience()
print(reflection)
Агент может учиться на своем опыте и обновлять свои знание график
learning = esi.learn_from_experience()
print(learning)
Агент может рассуждать о свой график знаний и генерировать новые идеи
reasoning = esi.reason_about_knowledge()
для понимания рассуждений:
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print(insight)te`
Запуск конвейера
config = {
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"algorithm": "neural_network",
"output_classes": 10,
# ... other configuration parameters ...
pipeline = MLPipeline(config, user="MUSKTARGETSUS")
results = конвейер. run_pipeline()`
Подробнее здесь: https://stackoverflow.com/questions/793 ... algorithms
