Project SD – Code for image processing
This is a work, and the author’s right to a work under international law comes into force from the moment the work is created.
Author – Sukhachev Denis Pavlovich
Let’s analyze the main components and features of this advanced code:
1. **Modes of thinking** (CognitionMode):
– ANALYTICAL – for logical analysis
– CREATIVE – for creative thinking
– EMOTIONAL – for emotional thinking
– INSTINCTIVE – for instinctive reactions
– LEARNING – for active learning
2. **Memory System**:
– Stores different types of information: visual, semantic, emotional, spatial
– Everyone has a memory:
* Importance
* Emotional context
* Associations
* Time stamp
* Reinforcement counter
3. **Emotional System** (EmotionalState):
– Primary emotions (joy, sadness, anger, fear)
– Secondary emotions (love, hate, anxiety)
– Mood (long-term emotional state)
– Social context (empathy, trust)
4) **Spatial Memory** (SpatialMemory):
– 3D representation of space
– Navigation system between objects
– Quick spatial search
– Graph of relationships between objects
5) **Language Processor** (LanguageProcessor):
– Natural language processing
– Understanding the emotional coloring of the text
– Generate responses based on context
– Storing the history of the dialog
6. **ReinforcementLearner**:
– Reinforcement learning
– Experience buffer
– Adaptive learning
– Optimization of actions
7. **Enhanced Consciousness**:
– Integration of all components
– Dynamic selection of thinking mode
– Processing of complex inputs
– Generating responses
Key features:
1. adaptability**:
– Automatically adjusts to the type of input data
– Changes the mode of thinking depending on the situation
– Learning from experience
2. integration**:
– Combines visual, textual and emotional thinking
– Creates complex associative connections
– Preserves the context of interaction
3. **Emotional Intelligence**:
– Understands and generates emotional reactions
– Takes into account the social context
– Develops empathy
4:
– Constantly improving
– Keeps useful experience
– Optimize your reactions
Possible applications:
1. Creating complex dialog systems
2. Analyze and understand the context
3. Generation of creative content
4. Emotional interaction with the user
5. Spatial planning and navigation
6. Training and adaptation to new conditions
Limitations and potential improvements:
1. High computational complexity
2. The need for a large amount of memory
3. Ability to add more modalities
4. Expanding the emotional spectrum
5. Improvement of training mechanisms
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from transformers import GPT2LMHeadModel, GPT2Tokenizer
from dataclasses import dataclass
from typing import Dict, List, Tuple, Optional, Union
import networkx as nx
from scipy.spatial import KDTree
import cv2
from enum import Enum, auto
import pickle
from collections import defaultdict
import gymnasium as gym
from stable_baselines3 import PPO
class CognitionMode(Enum):
ANALYTICAL = auto() # Logical thinking
CREATIVE = auto() # Creative thinking
EMOTIONAL = auto() # Emotional thinking
INSTINCTIVE = auto() # Instinctive thinking
LEARNING = auto() # Learning mode
@dataclass
class Memory:
“””Memory structure with emotional and contextual coloring”””
content: Union[np.ndarray, str]
type: str # ‘visual’, ‘semantic’, ’emotional’, ‘spatial’
importance: float
emotional_context: Dict[str, float]
associations: List[str]
timestamp: float
spatial_context: Optional[np.ndarray] = None
reinforcement_count: int = 0
@dataclass
class EmotionalState:
“””Extended emotional state”””
primary: Dict[str, float] # Basic emotions
secondary: Dict[str, float] # Complex emotions
mood: Dict[str, float] # Long-term mood
social_context: Dict[str, float] # Social context
def evolve(self, stimulus: Dict[str, float], learning_rate: float = 0.1):
“””The evolution of an emotional state under the influence of a stimulus”””
for category in [self.primary, self.secondary, self.mood]:
for emotion in category:
if emotion in stimulus:
category[emotion] = (1 – learning_rate) * category[emotion] +
learning_rate * stimulus[emotion]
class SpatialMemory:
“””Spatial memory with 3D representation”””
def __init__(self, dimensions: Tuple[int, int, int]):
self.space = np.zeros(dimensions)
self.objects = {}
self.spatial_tree = None
self.navigation_graph = nx.Graph()
def add_object(self, position: np.ndarray, object_data: Dict):
“””Adding an object to spatial memory”””
obj_id = len(self.objects)
self.objects[obj_id] = {
‘position’: position,
‘data’: object_data,
‘connections’: []
}
self._update_spatial_tree()
self._update_navigation_graph(obj_id)
def _update_spatial_tree(self):
“””KD-tree update for fast spatial search”””
positions = [obj[‘position’] for obj in self.objects.values()]
self.spatial_tree = KDTree(positions)
def _update_navigation_graph(self, new_obj_id):
“””Update navigation graph”””
new_pos = self.objects[new_obj_id][‘position’]
# Adding links to nearby objects
if len(self.objects) > 1:
distances, indices = self.spatial_tree.query(new_pos, k=4)
for dist, idx in zip(distances, indices):
if dist < 10.0: # Distance threshold for communication
self.navigation_graph.add_edge(new_obj_id, idx, weight= dist)
class LanguageProcessor:
“””Natural language processor with emotional understanding”””
def __init__(self):
self.model = GPT2LMHeadModel.from_pretrained(‘gpt2’)
self.tokenizer = GPT2Tokenizer.from_pretrained(‘gpt2’)
self.emotional_patterns = self._load_emotional_patterns()
self.context_history = []
def _load_emotional_patterns(self) -> Dict[str, List[str]]:
“””Loading patterns of emotional coloring of text”””
# You can expand this dictionary
return {
‘joy’: [‘happy’, ‘exciting’, ‘wonderful’],
‘sadness’: [‘sad’, ‘depressing’, ‘unfortunate’],
‘anger’: [‘angry’, ‘furious’, ‘annoying’],
‘fear’: [‘scary’, ‘frightening’, ‘terrifying’],
‘surprise’: [‘surprising’, ‘unexpected’, ‘amazing’]
}
def process_text(self, text: str) -> Dict:
“””Text processing with emotion and context detection”””
tokens = self.tokenizer.encode(text, return_tensors=’pt’)
output = self.model(tokens)
# Analysis of emotional coloring
emotions = self._analyze_emotions(text)
# Highlighting key concepts
concepts = self._extract_concepts(text)
# Context history update
self.context_history.append({
‘text’: text,
’emotions’: ’emotions’,
‘concepts’: concepts
})
return {
’emotions’: ’emotions’,
‘concepts’: concepts,
‘logits’: output.logits
}
def generate_response(self,
prompt: str,
emotional_context: Dict[str, float]) -> str:
“””Response generation based on emotional context”””
# Adding emotional markers to the promo
enhanced_prompt = self._enhance_prompt_with_emotions(prompt, emotional_context)
# Text generation
tokens = self.tokenizer.encode(enhanced_prompt, return_tensors=’pt’)
output = self.model.generate(
tokens,
max_length=100,
num_return_sequences=1,
no_repeat_ngram_size=2
)
return self.tokenizer.decode(output[0], skip_special_tokens= True)
class ReinforcementLearner:
“””Reinforcement learning system”””
def __init__(self, state_dim: int, action_dim: int):
self.env = self._create_custom_env(state_dim, action_dim)
self.model = PPO(“MlpPolicy”, self.env)
self.experience_buffer = []
self.learning_rate = 0.001
def _create_custom_env(self, state_dim: int, action_dim: int) -> gym.Env:
“””Creating an environment for learning”””
# Can be extended for more complex environments
class CustomEnv(gym.Env):
def __init__(self):
self.observation_space = gym.spaces.Box(
low=-np.inf, high=np.inf, shape=(state_dim,))
self.action_space = gym.spaces.Box(
low=-1, high=1, shape=(action_dim,))
def step(self, action):
# Logic of interaction with the environment
next_state = self.state + action
reward = self._calculate_reward(next_state)
done False =
return next_state, reward, done, {}
def reset(self):
self.state = np.random.randomn(state_dim)
return self.state
def _calculate_reward(self, state):
# You can expand the logic of calculating rewards
return -np.sum(np.square(state))
return CustomEnv()
def learn_from_experience(self, state, action, reward, next_state):
“””Experience-based learning”””
self.experience_buffer.append((state, action, reward, next_state))
if len(self.experience_buffer) >= 1000: # Batch size
self.model.learn(total_timesteps=1000)
self.experience_buffer = []
def get_action(self, state: np.ndarray) -> np.ndarray:
“””Getting an action based on the current state”””
return self.model.predict(state)[0]
class EnhancedConsciousness(AdvancedConsciousness):
“””Expanded version of consciousness”””
def __init__(self, num_params=9, num_harmonics=4, visual_dim=256):
super().__init__(num_params, num_harmonics, visual_dim)
# New components
self.language_processor = LanguageProcessor()
self.spatial_memory = SpatialMemory((100, 100, 100))
self.emotional_state = EmotionalState(
primary={‘joy’: 0.5, ‘sadness’: 0.1, ‘anger’: 0.1,
‘fear’: 0.1, ‘surprise’: 0.2},
secondary={‘love’: 0.3, ‘hate’: 0.1, ‘anxiety’: 0.2},
mood={‘positive’: 0.6, ‘negative’: 0.4},
social_context={’empathy’: 0.5, ‘trust’: 0.7}
)
# Training system
self.reinforcement_learner = ReinforcementLearner(
state_dim= num_params,
action_dim num_params=
)
# Mode of thinking
self.cognition_mode = CognitionMode.ANALYTICAL
# Long-term memory
self.long_term_memory = []
self.memory_graph = nx.Graph()
def think(self, input_data: Dict) -> Dict:
“””The main method of thinking”””
# Defining the mode of thinking
self.cognition_mode = self._define_cognition_mode(input_data)
# Processing of input data depending on the mode
if ‘text’ in input_data:
language_result = self.language_processor.process_text(input_data[‘text’])
self._update_emotional_state(language_result[’emotions’])
if ‘visual’ in input_data:
visual_result = self.process_visual_thought(input_data[‘visual’])
self._store_in_spatial_memory(visual_result)
# Integration of all inputs
integrated_state = self._integrate_inputs(input_data)
# Learning from experience
if self.cognition_mode == CognitionMode.LEARNING:
self._learn_from_current_state(integrated_state)
# Generating a response
response = self._generate_response(integrated_state)
return response
def _define_cognition_mode(self, input_data: Dict) -> CognitionMode:
“””Determining the mode of thinking based on input”””
if ‘force_mode’ in input_data:
return input_data[‘force_mode’]
# Analysis of login complexity
complexity = self._calculate_input_complexity(input_data)
# Analysis of emotional stress
emotional_intensity = self._calculate_emotional_intensity(input_data)
# Selecting a mode
if complexity > 0.8:
return CognitionMode.ANALYTICAL
elif emotional_intensity > 0.7:
return CognitionMode.EMOTIONAL
elif ‘learning_required’ in input_data:
return CognitionMode.LEARNING
else:
return CognitionMode.CREATIVE
def _calculate_input_complexity(self, input_data: Dict) -> float:
“””Calculating the complexity of input data”””
complexity = 0.0
if ‘text’ in input_data:
# Text complexity
text_length = len(input_data[‘text’].split())
complexity += min(text_length / 1000, 1.0) * 0.4
if ‘visual’ in input_data:
# Image complexity
visual_complexity = np.std(input_data[‘visual’]) / 128.0
complexity += visual_complexity * 0.3
return min(complexity, 1.0)
def _calculate_emotional_intensity(self, input_data: Dict) -> float:
“””Calculation of the emotional load of the input”””
intensity = 0.0
if ‘text’ in input_data:
# Emotional load of the text
emotions = self.language_processor.process_text(input_data[‘text’])[’emotions’]
intensity += max(emotions.values()) * 0.6
if ‘visual’ in input_data:
# Emotional load of the image
visual_thought = self.process_visual_thought(input_data[‘visual’])
intensity += max(visual_thought.emotional_context.values()) * 0.4
return intensity
def _learn_from_current_state(self, state: Dict):
“””Learning from the current state”””
# Converting a state to a vector
state_vector = self._state_to_vector(state)
# Getting action from the learning system
action = self.reinforcement_learner.get_action(state_vector)
# Performing an action and receiving a reward
next_state = self._apply_action(state, action)
reward = self._calculate_reward(state, next_state)
# Training
self.reinforcement_learner.learn_from_experience(
state_vector, action, reward, self._state_to_vector(next_state))
def _state_to_vector(self, state: Dict) -> np.ndarray:
“””Transforming a state into a vector for training”””
vector_components = []
# Adding different state components
if ‘quantum_state’ in state:
vector_components.append(state[‘quantum_state’].flatten())
if ’emotional_state’ in state:
emotions = [state[’emotional_state’][e] for e in
