import os
import numpy as np
import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import Dataset, DataLoader
from tensorflow import keras
from transformers import BertTokenizer, BertModel
import matplotlib.pyplot as plt
from matplotlib.animation import FuncAnimation
import networkx as nx
import psutil
from tqdm import tqdm
import time
import json
import logging
# Configure logging
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(name)s - %(levelname)s - %(message)s')
logger = logging.getLogger(__name__)
class WordDatabase:
"""
Manages the word database and provides methods for accessing and updating words.
"""
def __init__(self, database_path=None):
self.words = {}
self.relationships = {}
self.domain_mappings = {}
self.database_path = database_path
if database_path and os.path.exists(database_path):
self.load_database(database_path)
def load_database(self, path):
"""Load existing word database from disk"""
logger.info(f"Loading word database from {path}")
try:
with open(path, 'r', encoding='utf-8') as f:
data = json.load(f)
self.words = data.get('words', {})
self.relationships = data.get('relationships', {})
self.domain_mappings = data.get('domain_mappings', {})
logger.info(f"Successfully loaded {len(self.words)} words")
except Exception as e:
logger.error(f"Error loading database: {e}")
# Initialize with empty database
self.words = {}
self.relationships = {}
self.domain_mappings = {}
def save_database(self, path=None):
"""Save current database to disk"""
save_path = path or self.database_path
if not save_path:
logger.warning("No database path specified for saving")
return False
logger.info(f"Saving database with {len(self.words)} words to {save_path}")
try:
data = {
'words': self.words,
'relationships': self.relationships,
'domain_mappings': self.domain_mappings
}
with open(save_path, 'w', encoding='utf-8') as f:
json.dump(data, f, ensure_ascii=False, indent=2)
return True
except Exception as e:
logger.error(f"Error saving database: {e}")
return False
def add_word(self, word, definition=None, domain=None, metadata=None):
"""Add a new word to the database or update an existing one"""
if word not in self.words:
self.words[word] = {
'definition': definition or '',
'domains': [domain] if domain else [],
'usage_count': 1,
'relationships': [],
'metadata': metadata or {},
'created_at': time.time()
}
else:
self.words[word]['usage_count'] += 1
if domain and domain not in self.words[word]['domains']:
self.words[word]['domains'].append(domain)
if definition and not self.words[word]['definition']:
self.words[word]['definition'] = definition
if metadata:
self.words[word]['metadata'].update(metadata)
# Update domain mappings
if domain:
if domain not in self.domain_mappings:
self.domain_mappings[domain] = []
if word not in self.domain_mappings[domain]:
self.domain_mappings[domain].append(word)
def add_relationship(self, word1, word2, relationship_type, strength=0.5, metadata=None):
"""Create a relationship between two words"""
# Ensure both words exist
if word1 not in self.words:
self.add_word(word1)
if word2 not in self.words:
self.add_word(word2)
# Create relationship key
rel_key = f"{word1}:{word2}"
# Add or update relationship
if rel_key not in self.relationships:
self.relationships[rel_key] = {
'type': relationship_type,
'strength': strength,
'metadata': metadata or {},
'created_at': time.time()
}
else:
# Update existing relationship
current = self.relationships[rel_key]
current['strength'] = (current['strength'] + strength) / 2 # Average strength
if metadata:
current['metadata'].update(metadata)
# Update word relationship lists
if word2 not in self.words[word1]['relationships']:
self.words[word1]['relationships'].append(word2)
if word1 not in self.words[word2]['relationships']:
self.words[word2]['relationships'].append(word1)
def get_word(self, word):
"""Retrieve a word and its data from the database"""
return self.words.get(word, None)
def get_relationships(self, word, max_depth=1):
"""Get all relationships for a word up to a certain depth"""
if word not in self.words:
return []
if max_depth <= 0:
return []
relationships = []
direct_relationships = self.words[word]['relationships']
for related_word in direct_relationships:
rel_key = f"{word}:{related_word}"
reverse_key = f"{related_word}:{word}"
# Get the relationship data
rel_data = self.relationships.get(rel_key, self.relationships.get(reverse_key))
if rel_data:
relationships.append({
'word': related_word,
'relationship_type': rel_data['type'],
'strength': rel_data['strength'],
'metadata': rel_data['metadata']
})
# Recursively get deeper relationships if needed
if max_depth > 1:
deeper = self.get_relationships(related_word, max_depth - 1)
relationships.extend(deeper)
return relationships
def get_domain_words(self, domain, limit=100):
"""Get words associated with a particular domain"""
if domain not in self.domain_mappings:
return []
words = self.domain_mappings[domain]
if limit:
return words[:limit]
return words
def get_total_chunks(self, chunk_size=100):
"""Get the number of chunks when processing database in chunks"""
return (len(self.words) + chunk_size - 1) // chunk_size
def get_chunk(self, chunk_index, chunk_size=100):
"""Get a specific chunk of words"""
all_words = list(self.words.keys())
start_idx = chunk_index * chunk_size
end_idx = min(start_idx + chunk_size, len(all_words))
chunk_words = all_words[start_idx:end_idx]
chunk = {
'words': chunk_words,
'data': {word: self.words[word] for word in chunk_words}
}
return chunk
class PatternRecognitionNetwork(nn.Module):
"""
Neural network for pattern recognition across multiple levels.
"""
def __init__(self, input_dim, hidden_dim, output_dim):
super(PatternRecognitionNetwork, self).__init__()
# Token-level pattern recognition
self.token_encoder = nn.Sequential(
nn.Linear(input_dim, hidden_dim),
nn.ReLU(),
nn.Dropout(0.3),
nn.Linear(hidden_dim, hidden_dim)
)
# Syntactic pattern recognition
self.syntactic_encoder = nn.Sequential(
nn.Linear(hidden_dim, hidden_dim),
nn.ReLU(),
nn.Dropout(0.3),
nn.Linear(hidden_dim, hidden_dim)
)
# Semantic pattern recognition
self.semantic_encoder = nn.Sequential(
nn.Linear(hidden_dim, hidden_dim),
nn.ReLU(),
nn.Dropout(0.3),
nn.Linear(hidden_dim, hidden_dim)
)
# Final output layer
self.output_layer = nn.Linear(hidden_dim * 3, output_dim)
# Attention mechanism for context
self.context_attention = nn.MultiheadAttention(hidden_dim, num_heads=4)
def forward(self, x, context=None):
# Token-level patterns
token_patterns = self.token_encoder(x)
# Syntactic patterns
syntactic_patterns = self.syntactic_encoder(token_patterns)
# Apply context attention if available
if context is not None:
syntactic_patterns, _ = self.context_attention(
syntactic_patterns.unsqueeze(0),
context.unsqueeze(0),
context.unsqueeze(0)
)
syntactic_patterns = syntactic_patterns.squeeze(0)
# Semantic patterns
semantic_patterns = self.semantic_encoder(syntactic_patterns)
# Concatenate all pattern levels
combined = torch.cat([token_patterns, syntactic_patterns, semantic_patterns], dim=1)
# Final output
output = self.output_layer(combined)
return output
class LanguageModel(nn.Module):
"""
Language model for processing and generating text.
"""
def __init__(self, vocab_size, embed_dim, hidden_dim):
super(LanguageModel, self).__init__()
self.embedding = nn.Embedding(vocab_size, embed_dim)
self.lstm = nn.LSTM(embed_dim, hidden_dim, batch_first=True, num_layers=2, bidirectional=True)
self.output = nn.Linear(hidden_dim * 2, vocab_size)
def forward(self, x):
embedded = self.embedding(x)
output, (hidden, cell) = self.lstm(embedded)
logits = self.output(output)
return logits
class CausalReasoningSystem:
"""
System for detecting and validating causal relationships.
"""
def __init__(self):
self.causal_graph = {}
self.confidence_scores = {}
def add_causal_relation(self, cause, effect, confidence=0.5, evidence=None):
"""Add a causal relationship with evidence"""
relation_id = f"{cause}→{effect}"
self.causal_graph[relation_id] = {
'cause': cause,
'effect': effect,
'confidence': confidence,
'evidence': evidence or [],
'created_at': time.time()
}
def verify_relation(self, cause, effect):
"""Verify if a causal relation is valid and return confidence"""
relation_id = f"{cause}→{effect}"
if relation_id in self.causal_graph:
return self.causal_graph[relation_id]['confidence']
# Check for temporal inconsistency (effect before cause)
reverse_id = f"{effect}→{cause}"
if reverse_id in self.causal_graph:
# Potential logical contradiction
return -1 * self.causal_graph[reverse_id]['confidence']
return 0.0 # No known relationship
def detect_inconsistencies(self):
"""Find logical inconsistencies in the causal graph"""
inconsistencies = []
# Check for cycles in the causal graph
visited = set()
for relation_id in self.causal_graph:
cause, effect = relation_id.split('→')
# Simple cycle detection
path = [cause]
if self._has_cycle(effect, path, set(path)):
inconsistencies.append({
'type': 'cycle',
'path': path + [effect],
'relations': [f"{path[i]}→{path[i+1]}" for i in range(len(path) - 1)] + [f"{path[-1]}→{effect}"]
})
# Check for direct contradictions (A→B and B→A with high confidence)
for relation_id in self.causal_graph:
cause, effect = relation_id.split('→')
reverse_id = f"{effect}→{cause}"
if reverse_id in self.causal_graph:
confidence = self.causal_graph[relation_id]['confidence']
reverse_confidence = self.causal_graph[reverse_id]['confidence']
if confidence > 0.7 and reverse_confidence > 0.7:
inconsistencies.append({
'type': 'contradiction',
'relations': [relation_id, reverse_id],
'confidences': [confidence, reverse_confidence]
})
return inconsistencies
def _has_cycle(self, node, path, visited):
"""Helper method for cycle detection"""
if node in visited:
return True
visited.add(node)
path.append(node)
for relation_id in self.causal_graph:
cause, effect = relation_id.split('→')
if cause == node and effect not in visited:
if self._has_cycle(effect, path, visited):
return True
path.pop()
return False
def resolve_inconsistencies(self, inconsistencies):
"""Attempt to resolve detected inconsistencies"""
resolved = []
for inconsistency in inconsistencies:
if inconsistency['type'] == 'contradiction':
# For direct contradictions, keep the relation with higher confidence
rel1, rel2 = inconsistency['relations']
conf1, conf2 = inconsistency['confidences']
if conf1 > conf2:
if rel2 in self.causal_graph:
del self.causal_graph[rel2]
resolved.append({
'type': 'contradiction',
'resolved': True,
'kept': rel1,
'removed': rel2
})
else:
if rel1 in self.causal_graph:
del self.causal_graph[rel1]
resolved.append({
'type': 'contradiction',
'resolved': True,
'kept': rel2,
'removed': rel1
})
elif inconsistency['type'] == 'cycle':
# For cycles, remove the weakest link in the cycle
weakest_link = None
min_confidence = 1.0
for relation in inconsistency['relations']:
if relation in self.causal_graph:
confidence = self.causal_graph[relation]['confidence']
if confidence < min_confidence:
min_confidence = confidence
weakest_link = relation
if weakest_link and weakest_link in self.causal_graph:
del self.causal_graph[weakest_link]
resolved.append({
'type': 'cycle',
'resolved': True,
'removed': weakest_link
})
return resolved
class MemorySystem:
"""
Multi-tiered memory system for the autobot.
"""
def __init__(self, working_memory_limit=100, semantic_memory_limit=10000):
# Working memory (currently active processing)
self.working_memory = {
'capacity': working_memory_limit,
'items': {},
'priority_queue': []
}
# Semantic memory (long-term knowledge)
self.semantic_memory = {
'capacity': semantic_memory_limit,
'items': {},
'access_frequency': {}
}
# Episodic memory (past interactions)
self.episodic_memory = {
'interactions': [],
'last_accessed': {}
}
def add_to_working_memory(self, key, data, priority=1.0):
"""Add an item to working memory with priority"""
# Check if we need to make space
if len(self.working_memory['items']) >= self.working_memory['capacity']:
# Remove lowest priority item
self.working_memory['priority_queue'].sort(key=lambda x: x[1])
remove_key, _ = self.working_memory['priority_queue'].pop(0)
del self.working_memory['items'][remove_key]
# Add new item
self.working_memory['items'][key] = {
'data': data,
'created_at': time.time(),
'priority': priority
}
# Update priority queue
self.working_memory['priority_queue'].append((key, priority))
def get_from_working_memory(self, key):
"""Retrieve an item from working memory"""
if key in self.working_memory['items']:
return self.working_memory['items'][key]['data']
return None
def clear_working_memory(self):
"""Clear all items from working memory"""
self.working_memory['items'] = {}
self.working_memory['priority_queue'] = []
def add_to_semantic_memory(self, key, data):
"""Add knowledge to semantic memory"""
if key in self.semantic_memory['items']:
# Update existing entry
self.semantic_memory['items'][key]['data'] = data
self.semantic_memory['items'][key]['updated_at'] = time.time()
else:
# Add new entry
self.semantic_memory['items'][key] = {
'data': data,
'created_at': time.time(),
'updated_at': time.time()
}
# Update access frequency
if key not in self.semantic_memory['access_frequency']:
self.semantic_memory['access_frequency'][key] = 0
self.semantic_memory['access_frequency'][key] += 1
def get_from_semantic_memory(self, key):
"""Retrieve knowledge from semantic memory"""
if key in self.semantic_memory['items']:
# Update access frequency
self.semantic_memory['access_frequency'][key] += 1
return self.semantic_memory['items'][key]['data']
return None
def record_interaction(self, question, response, confidence):
"""Record an interaction in episodic memory"""
interaction = {
'timestamp': time.time(),
'question': question,
'response': response,
'confidence': confidence
}
self.episodic_memory['interactions'].append(interaction)
def find_similar_interactions(self, query, limit=5):
"""Find similar past interactions by simple keyword matching"""
# In a real implementation, this would use semantic similarity
matches = []
query_words = set(query.lower().split())
for interaction in self.episodic_memory['interactions']:
question = interaction['question'].lower()
question_words = set(question.split())
# Calculate simple overlap
overlap = len(query_words.intersection(question_words))
if overlap > 0:
matches.append({
'interaction': interaction,
'overlap': overlap
})
# Sort by overlap score
matches.sort(key=lambda x: x['overlap'], reverse=True)
return [m['interaction'] for m in matches[:limit]]
def get_working_memory_usage(self):
"""Get current usage of working memory"""
return {
'used': len(self.working_memory['items']),
'total': self.working_memory['capacity'],
'percentage': (len(self.working_memory['items']) / self.working_memory['capacity']) * 100
}
def get_semantic_memory_usage(self):
"""Get current usage of semantic memory"""
return {
'used': len(self.semantic_memory['items']),
'total': self.semantic_memory['capacity'],
'percentage': (len(self.semantic_memory['items']) / self.semantic_memory['capacity']) * 100
}
def get_episodic_memory_size(self):
"""Get size of episodic memory"""
return len(self.episodic_memory['interactions'])
class PerformanceMonitor:
"""
Tracks performance metrics and system resource usage.
"""
def __init__(self):
self.metrics = {
'processing_rates': [],
'memory_usage': [],
'accuracy': [],
'confidence': [],
'response_times': [],
'operations': {}
}
self.current_task = {
'name': None,
'start_time': None,
'progress': 0,
'status': 'idle'
}
self.convergence_history = []
self.convergence_threshold = 0.001
self.convergence_window = 5
def start_task(self, task_name):
"""Start tracking a new task"""
self.current_task = {
'name': task_name,
'start_time': time.time(),
'progress': 0,
'status': 'running'
}
def update_task_progress(self, progress):
"""Update progress of current task (0-100)"""
self.current_task['progress'] = progress
def end_task(self, task_name):
"""End tracking for a task"""
if self.current_task['name'] == task_name:
duration = time.time() - self.current_task['start_time']
if task_name not in self.metrics['operations']:
self.metrics['operations'][task_name] = []
self.metrics['operations'][task_name].append({
'duration': duration,
'timestamp': time.time()
})
self.current_task = {
'name': None,
'start_time': None,
'progress': 0,
'status': 'idle'
}
def track_memory_usage(self):
"""Track current memory usage"""
usage = {
'timestamp': time.time(),
'system': {
'percent': psutil.virtual_memory().percent,
'used': psutil.virtual_memory().used,
'total': psutil.virtual_memory().total
},
'process': {
'used': psutil.Process().memory_info().rss
}
}
self.metrics['memory_usage'].append(usage)
return usage
def track_processing_rate(self, items_processed, time_taken):
"""Track processing rate"""
rate = items_processed / time_taken if time_taken > 0 else 0
self.metrics['processing_rates'].append({
'timestamp': time.time(),
'rate': rate,
'items': items_processed,
'duration': time_taken
})
return rate
def record_accuracy(self, accuracy):
"""Record accuracy metric"""
self.metrics['accuracy'].append({
'timestamp': time.time(),
'value': accuracy
})
# Update convergence history
self.convergence_history.append(accuracy)
if len(self.convergence_history) > self.convergence_window:
self.convergence_history.pop(0)
def has_converged(self):
"""Check if performance has converged based on recent history"""
if len(self.convergence_history) < self.convergence_window:
return False
# Calculate improvement rate over window
improvements = [abs(self.convergence_history[i] - self.convergence_history[i-1])
for i in range(1, len(self.convergence_history))]
avg_improvement = sum(improvements) / len(improvements)
return avg_improvement < self.convergence_threshold
def get_average_processing_rate(self, window=10):
"""Get average processing rate over recent window"""
if not self.metrics['processing_rates']:
return 0
recent = self.metrics['processing_rates'][-window:]
if not recent:
return 0
return sum(r['rate'] for r in recent) / len(recent)
def get_current_task_status(self):
"""Get current task status"""
if self.current_task['name']:
elapsed = time.time() - self.current_task['start_time']
# Estimate time remaining based on progress
estimated_total = 0
if self.current_task['progress'] > 0:
estimated_total = elapsed / (self.current_task['progress'] / 100)
remaining = max(0, estimated_total - elapsed)
return {
'task': self.current_task['name'],
'progress': self.current_task['progress'],
'elapsed': elapsed,
'estimated_remaining': remaining,
'status': self.current_task['status']
}
return {
'task': None,
'progress': 0,
'elapsed': 0,
'estimated_remaining': 0,
'status': 'idle'
}
class WordProcessingTracker:
"""
Tracks which words are being processed and visualizes the process.
"""
def __init__(self):
self.current_word = None
self.processing_stage = 'idle'
self.processing_history = []
self.start_time = None
self.words_processed = 0
def set_current_word(self, word, stage='processing'):
"""Set the current word being processed"""
if self.current_word:
# Record previous word processing time
end_time = time.time()
duration = end_time - (self.start_time or end_time)
self.processing_history.append({
'word': self.current_word,
'stage': self.processing_stage,
'start_time': self.start_time,
'end_time': end_time,
'duration': duration
})
self.words_processed += 1
self.current_word = word
self.processing_stage = stage
self.start_time = time.time()
def get_processing_rate(self, window=60):
"""Calculate words processed per second over the last time window"""
if not self.processing_history:
return 0
# Get history entries from the last window seconds
cutoff_time = time.time() - window
recent = [entry for entry in self.processing_history if entry['end_time'] > cutoff_time]
if not recent:
return 0
# Count words and time span
word_count = len(recent)
time_span = recent[-1]['end_time'] - recent[0]['start_time']
if time_span <= 0:
return 0
return word_count / time_span
def get_current_word_info(self):
"""Get information about the current word being processed"""
if not self.current_word:
return {
'word': None,
'stage': 'idle',
'duration': 0
}
return {
'word': self.current_word,
'stage': self.processing_stage,
'duration': time.time() - (self.start_time or time.time())
}
class StrongAutobot:
"""
Main class for the Autobot system, implemented with PyTorch and Keras.
"""
def __init__(self, database_path=None, embedding_dim=300, hidden_dim=512):
# Initialize word database
self.word_database = WordDatabase(database_path)
# Initialize tokenizer
self.tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
self.bert_model = BertModel.from_pretrained('bert-base-uncased')
# Initialize PyTorch models
self.pattern_recognition = PatternRecognitionNetwork(
input_dim=embedding_dim,
hidden_dim=hidden_dim,
output_dim=embedding_dim
)
# Initialize memory systems
self.memory_system = MemorySystem()
# Initialize reasoning system
self.reasoning_system = CausalReasoningSystem()
# Initialize monitoring systems
self.performance_monitor = PerformanceMonitor()
self.word_tracker = WordProcessingTracker()
# Configure visualization system
self.enable_visualization = True
self.visualization = {
'memory_fig': None,
'word_fig': None,
'performance_fig': None
}
# Initialize metrics
self.metrics = {
'accuracy': 0,
'coverage': 0,
'consistency': 0
}
self.running = True
logger.info("StrongAutobot initialized")
def initialize_system(self):
"""Initialize the system components and start monitoring"""
logger.info("Starting system initialization")
self.performance_monitor.start_task("system_initialization")
# Setup visualization if enabled
if self.enable_visualization:
self.setup_visualization()
total_words = len(self.word_database.words)
logger.info(f"Loading initial embeddings for {total_words} words")
# Process in chunks to avoid memory issues
chunk_size = 100
total_chunks = self.word_database.get_total_chunks(chunk_size)
for i in range(total_chunks):
self.performance_monitor.update_task_progress((i / total_chunks) * 100)
chunk = self.word_database.get_chunk(i, chunk_size)
# Process each word in chunk
for word in chunk['words']:
self.word_tracker.set_current_word(word, 'initializing')
# Update visualization
if self.enable_visualization:
self.update_visualization()
# Get word data
word_data = chunk['data'][word]
# Add to memory system
self.memory_system.add_to_semantic_memory(word, word_data)
# Track memory usage
self.performance_monitor.track_memory_usage()
# Calculate processing rate
processing_rate = self.word_tracker.get_processing_rate()
logger.info(f"Chunk {i+1}/{total_chunks} processed. Rate: {processing_rate:.2f} words/sec")
self.performance_monitor.end_task("system_initialization")
logger.info("System initialization complete")
import os
import numpy as np
import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import Dataset, DataLoader
from tensorflow import keras
import tensorflow as tf
from sklearn.metrics.pairwise import cosine_similarity
import matplotlib.pyplot as plt
from matplotlib.animation import FuncAnimation
import networkx as nx
import psutil
from tqdm import tqdm
import time
import json
import logging
import threading
import queue
# Continue from previous artifact
class StrongAutobot:
def setup_visualization(self):
"""Setup matplotlib visualization figures"""
plt.ion() # Enable interactive mode
# Create figure for memory visualization
self.visualization['memory_fig'], memory_ax = plt.subplots(figsize=(8, 4))
memory_ax.set_title('Memory Usage')
memory_ax.set_xlabel('Time')
memory_ax.set_ylabel('Memory Usage %')
memory_ax.grid(True)
# Memory usage lines
self.visualization['memory_lines'] = {
'working': memory_ax.plot([], [], 'b-', label='Working Memory')[0],
'semantic': memory_ax.plot([], [], 'g-', label='Semantic Memory')[0],
'system': memory_ax.plot([], [], 'r-', label='System Memory')[0]
}
memory_ax.legend()
# Create figure for word tracking
self.visualization['word_fig'], word_ax = plt.subplots(figsize=(10, 6))
word_ax.set_title('Word Relationship Network')
self.visualization['word_network'] = nx.Graph()
# Create figure for performance tracking
self.visualization['performance_fig'], (perf_ax1, perf_ax2) = plt.subplots(2, 1, figsize=(8, 6))
perf_ax1.set_title('Processing Rate')
perf_ax1.set_xlabel('Time')
perf_ax1.set_ylabel('Words/sec')
perf_ax1.grid(True)
perf_ax2.set_title('Model Performance')
perf_ax2.set_xlabel('Iteration')
perf_ax2.set_ylabel('Metrics')
perf_ax2.grid(True)
# Performance lines
self.visualization['performance_lines'] = {
'rate': perf_ax1.plot([], [], 'b-', label='Processing Rate')[0],
'accuracy': perf_ax2.plot([], [], 'g-', label='Accuracy')[0],
'consistency': perf_ax2.plot([], [], 'r-', label='Consistency')[0],
'coverage': perf_ax2.plot([], [], 'y-', label='Coverage')[0]
}
perf_ax1.legend()
perf_ax2.legend()
# Show all figures
for fig_key in self.visualization:
if fig_key.endswith('_fig'):
self.visualization[fig_key].tight_layout()
self.visualization[fig_key].show()
def update_visualization(self):
"""Update all visualization components"""
if not self.enable_visualization:
return
try:
# Update memory usage visualization
self._update_memory_visualization()
# Update word processing visualization
self._update_word_visualization()
# Update performance visualization
self._update_performance_visualization()
# Redraw all figures
for fig_key in self.visualization:
if fig_key.endswith('_fig'):
self.visualization[fig_key].canvas.draw_idle()
self.visualization[fig_key].canvas.flush_events()
except Exception as e:
logger.error(f"Error updating visualization: {e}")
def _update_memory_visualization(self):
"""Update memory usage visualization"""
# Get memory data
working_memory = self.memory_system.get_working_memory_usage()['percentage']
semantic_memory = self.memory_system.get_semantic_memory_usage()['percentage']
system_memory = psutil.virtual_memory().percent
# Update data for memory lines
x_time = time.time() - self.start_time
# Extend line data
x_data = self.visualization['memory_lines']['working'].get_xdata()
x_data = np.append(x_data, x_time)
# Working memory
y_data = self.visualization['memory_lines']['working'].get_ydata()
y_data = np.append(y_data, working_memory)
self.visualization['memory_lines']['working'].set_data(x_data, y_data)
# Semantic memory
y_data = self.visualization['memory_lines']['semantic'].get_ydata()
y_data = np.append(y_data, semantic_memory)
self.visualization['memory_lines']['semantic'].set_data(x_data, y_data)
# System memory
y_data = self.visualization['memory_lines']['system'].get_ydata()
y_data = np.append(y_data, system_memory)
self.visualization['memory_lines']['system'].set_data(x_data, y_data)
# Update axes limits
mem_ax = self.visualization['memory_fig'].axes[0]
mem_ax.set_xlim(max(0, x_time - 60), x_time + 5) # Show last 60 seconds
mem_ax.set_ylim(0, 110) # 0-110% range
# Update title with current word
word_info = self.word_tracker.get_current_word_info()
if word_info['word']:
mem_ax.set_title(f"Memory Usage (Current Word: {word_info['word']})")
else:
mem_ax.set_title("Memory Usage")
def _update_word_visualization(self):
"""Update word relationship visualization"""
word_info = self.word_tracker.get_current_word_info()
current_word = word_info['word']
if not current_word:
return
# Clear previous graph
word_ax = self.visualization['word_fig'].axes[0]
word_ax.clear()
# Reset graph
G = nx.Graph()
# Add current word as central node
G.add_node(current_word)
# Get relationships for current word
relationships = self.word_database.get_relationships(current_word, max_depth=2)
# Add related words and edges
for rel in relationships:
related_word = rel['word']
strength = rel['strength']
rel_type = rel['relationship_type']
# Add node if not exists
if not G.has_node(related_word):
G.add_node(related_word)
# Add edge with relationship data
G.add_edge(current_word, related_word, weight=strength, type=rel_type)
# Define colors for relationship types
color_map = {
'semantic': 'blue',
'syntactic': 'green',
'etymological': 'purple',
'default': 'gray'
}
# Define node positions using spring layout
pos = nx.spring_layout(G)
# Draw nodes
nx.draw_networkx_nodes(G, pos,
node_color=['red' if node == current_word else 'skyblue' for node in G.nodes()],
node_size=[700 if node == current_word else 300 for node in G.nodes()],
alpha=0.8,
ax=word_ax)
# Draw edges with colors based on relationship type
for u, v, data in G.edges(data=True):
rel_type = data.get('type', 'default')
color = color_map.get(rel_type, color_map['default'])
width = data.get('weight', 0.5) * 3 # Scale width by strength
nx.draw_networkx_edges(G, pos,
edgelist=[(u, v)],
width=width,
alpha=0.7,
edge_color=color,
ax=word_ax)
# Draw labels
nx.draw_networkx_labels(G, pos, font_size=10, font_weight='bold', ax=word_ax)
# Set title
word_ax.set_title(f"Word Relationships: {current_word} (Stage: {word_info['stage']})")
word_ax.axis('off')
def _update_performance_visualization(self):
"""Update performance visualization"""
# Update processing rate
rate_ax = self.visualization['performance_fig'].axes[0]
processing_rate = self.word_tracker.get_processing_rate()
x_time = time.time() - self.start_time
# Extend line data for processing rate
x_data = self.visualization['performance_lines']['rate'].get_xdata()
x_data = np.append(x_data, x_time)
y_data = self.visualization['performance_lines']['rate'].get_ydata()
y_data = np.append(y_data, processing_rate)
self.visualization['performance_lines']['rate'].set_data(x_data, y_data)
# Update rate axis limits
rate_ax.set_xlim(max(0, x_time - 60), x_time + 5) # Show last 60 seconds
rate_ax.set_ylim(0, max(y_data) * 1.2 if len(y_data) > 0 and max(y_data) > 0 else 10)
# Update metrics if available
performance_data = self.performance_monitor.metrics
if 'accuracy' in performance_data and performance_data['accuracy']:
acc_data = performance_data['accuracy']
x_iterations = list(range(len(acc_data)))
y_accuracy = [d['value'] for d in acc_data]
self.visualization['performance_lines']['accuracy'].set_data(x_iterations, y_accuracy)
# Update other metrics if available
if hasattr(self, 'metrics'):
x_iterations_ext = list(range(len(x_iterations) + 1))
if 'consistency' in self.metrics:
consistency_data = y_accuracy + [self.metrics['consistency']]
self.visualization['performance_lines']['consistency'].set_data(x_iterations_ext, consistency_data)
if 'coverage' in self.metrics:
coverage_data = y_accuracy + [self.metrics['coverage']]
self.visualization['performance_lines']['coverage'].set_data(x_iterations_ext, coverage_data)
# Update metrics axis limits
metrics_ax = self.visualization['performance_fig'].axes[1]
metrics_ax.set_xlim(0, max(len(x_iterations) + 2, 10))
metrics_ax.set_ylim(0, 1.1)
# Update task status in title
task_status = self.performance_monitor.get_current_task_status()
if task_status['task']:
progress = task_status['progress']
rate_ax.set_title(f"Processing Rate - {task_status['task']} ({progress:.1f}%)")
else:
rate_ax.set_title("Processing Rate")
def train_on_word_database(self, epochs=3, batch_size=64):
"""Train the system on the word database"""
logger.info(f"Starting training for {epochs} epochs with batch size {batch_size}")
self.performance_monitor.start_task("training")
# Create optimizer for pattern recognition network
optimizer = optim.Adam(self.pattern_recognition.parameters(), lr=0.001)
# Set start time for visualization
self.start_time = time.time()
# Process in epochs
for epoch in range(epochs):
logger.info(f"Starting epoch {epoch+1}/{epochs}")
# Process in chunks
chunk_size = 100
total_chunks = self.word_database.get_total_chunks(chunk_size)
# Track epoch metrics
epoch_loss = 0.0
words_processed = 0
for i in range(total_chunks):
# Update progress
progress = ((epoch * total_chunks) + i) / (epochs * total_chunks) * 100
self.performance_monitor.update_task_progress(progress)
# Get chunk
chunk = self.word_database.get_chunk(i, chunk_size)
# Skip empty chunks
if not chunk['words']:
continue
# Process each word in chunk
for word in chunk['words']:
# Update word tracker
self.word_tracker.set_current_word(word, 'training')
# Get word data and relationships
word_data = chunk['data'][word]
relationships = word_data.get('relationships', [])
# Skip words with no relationships
if not relationships:
continue
# Get embeddings for word and related words
word_embedding = self._get_word_embedding(word)
# Get context from related words
context_embeddings = []
for related in relationships[:5]: # Limit to 5 related words for efficiency
rel_embedding = self._get_word_embedding(related)
if rel_embedding is not None:
context_embeddings.append(rel_embedding)
# Skip if no context
if not context_embeddings:
continue
# Average context embeddings
context = torch.stack(context_embeddings).mean(dim=0)
# Train pattern recognition network
self.pattern_recognition.train()
optimizer.zero_grad()
# Forward pass with word embedding and context
output = self.pattern_recognition(word_embedding, context)
# Simple loss function: try to predict the context from the word
loss = nn.functional.mse_loss(output, context)
# Backward pass and optimization
loss.backward()
optimizer.step()
# Update metrics
epoch_loss += loss.item()
words_processed += 1
# Update memory
self.memory_system.add_to_semantic_memory(word, {
'embedding': word_embedding.detach().numpy(),
'data': word_data
})
# Update visualization
if self.enable_visualization and i % 10 == 0: # Update every 10 words to avoid slowdown
self.update_visualization()
# Log progress
if i % 10 == 0 or i == total_chunks - 1:
avg_loss = epoch_loss / max(1, words_processed)
processing_rate = self.word_tracker.get_processing_rate()
logger.info(f"Epoch {epoch+1}/{epochs}, Chunk {i+1}/{total_chunks}, "
f"Loss: {avg_loss:.4f}, Rate: {processing_rate:.2f} words/sec")
# Calculate epoch metrics
avg_epoch_loss = epoch_loss / max(1, words_processed)
# Evaluate model
accuracy = self._evaluate_model()
# Record metrics
self.performance_monitor.record_accuracy(accuracy)
# Update metrics dict
self.metrics = {
'accuracy': accuracy,
'consistency': self._evaluate_consistency(),
'coverage': self._evaluate_coverage()
}
logger.info(f"Epoch {epoch+1}/{epochs} completed. "
f"Loss: {avg_epoch_loss:.4f}, Accuracy: {accuracy:.4f}, "
f"Consistency: {self.metrics['consistency']:.4f}, "
f"Coverage: {self.metrics['coverage']:.4f}")
# Check for convergence
if self.performance_monitor.has_converged():
logger.info(f"Training converged after {epoch+1} epochs")
break
self.performance_monitor.end_task("training")
logger.info("Training completed")
def _get_word_embedding(self, word):
"""Get embedding for a word using BERT model"""
# Check if already in memory
cached = self.memory_system.get_from_semantic_memory(word)
if cached and 'embedding' in cached:
return torch.tensor(cached['embedding'])
# Get embedding from BERT
try:
inputs = self.tokenizer(word, return_tensors="pt")
with torch.no_grad():
outputs = self.bert_model(**inputs)
# Use mean of last hidden state as embedding
word_embedding = outputs.last_hidden_state.mean(dim=1).squeeze()
return word_embedding
except Exception as e:
logger.error(f"Error getting embedding for word '{word}': {e}")
return None
def _evaluate_model(self, test_size=100):
"""Evaluate model performance on a sample of words"""
self.pattern_recognition.eval()
# Sample random words for testing
all_words = list(self.word_database.words.keys())
if len(all_words) <= test_size:
test_words = all_words
else:
test_words = np.random.choice(all_words, size=test_size, replace=False)
correct = 0
total = 0
for word in test_words:
# Get word relationships
relationships = self.word_database.get_relationships(word)
if not relationships:
continue
# Get word embedding
word_embedding = self._get_word_embedding(word)
if word_embedding is None:
continue
# Predict relationships using pattern recognition
with torch.no_grad():
predicted_context = self.pattern_recognition(word_embedding)
# Get actual related word embeddings
actual_embeddings = []
for rel in relationships[:5]: # Top 5 relationships
rel_word = rel['word']
rel_embedding = self._get_word_embedding(rel_word)
if rel_embedding is not None:
actual_embeddings.append(rel_embedding)
if not actual_embeddings:
continue
# Calculate similarity between prediction and actual
actual_context = torch.stack(actual_embeddings).mean(dim=0)
similarity = torch.cosine_similarity(predicted_context.unsqueeze(0),
actual_context.unsqueeze(0)).item()
# Consider prediction correct if similarity is high
if similarity > 0.7:
correct += 1
total += 1
# Calculate accuracy
accuracy = correct / max(1, total)
return accuracy
def _evaluate_consistency(self):
"""Evaluate logical consistency of the relationships"""
# Check for inconsistencies in causal relationships
inconsistencies = self.reasoning_system.detect_inconsistencies()
# Calculate consistency score (1.0 = no inconsistencies)
total_relations = len(self.reasoning_system.causal_graph)
if total_relations == 0:
return 1.0
consistency = 1.0 - (len(inconsistencies) / total_relations)
return max(0.0, consistency)
def _evaluate_coverage(self):
"""Evaluate knowledge coverage of the database"""
# Count words with relationships
words_with_relations = sum(1 for word in self.word_database.words
if self.word_database.words[word]['relationships'])
# Calculate coverage score
total_words = len(self.word_database.words)
if total_words == 0:
return 0.0
coverage = words_with_relations / total_words
return coverage
def process_query(self, question):
"""Process a user query and generate a response based on the word database"""
logger.info(f"Processing query: {question}")
self.performance_monitor.start_task("query_processing")
# Clear and update working memory
self.memory_system.clear_working_memory()
self.memory_system.add_to_working_memory('query', question, priority=1.0)
# Set query as current "word" for visualization
self.word_tracker.set_current_word(question[:30] + "..." if len(question) > 30 else question,
'query_analysis')
# Track start time for performance measurement
query_start_time = time.time()
# Update visualization
if self.enable_visualization:
self.update_visualization()
# Extract key terms from the question
key_terms = self._extract_key_terms(question)
# Retrieve relevant information for each key term
relevant_info = []
for term in key_terms:
# Set current word for visualization
self.word_tracker.set_current_word(term, 'retrieving')
if self.enable_visualization:
self.update_visualization()
# Get information about this term
term_info = self.word_database.get_word(term)
if term_info:
# Get related words
relationships = self.word_database.get_relationships(term)
relevant_info.append({
'term': term,
'info': term_info,
'relationships': relationships
})
# Add to working memory
self.memory_system.add_to_working_memory(term, term_info, priority=0.8)
# Find similar past interactions
similar_interactions = self.memory_system.find_similar_interactions(question)
# Apply causal reasoning if appropriate
self.word_tracker.set_current_word(question[:30] + "..." if len(question) > 30 else question,
'reasoning')
if self.enable_visualization:
self.update_visualization()
# Formulate response based on context and retrieved information
self.word_tracker.set_current_word(question[:30] + "..." if len(question) > 30 else question,
'formulating_response')
if self.enable_visualization:
self.update_visualization()
response, confidence = self._formulate_response(question, relevant_info, similar_interactions)
# Calculate processing time
processing_time = time.time() - query_start_time
# Record interaction in episodic memory
self.memory_system.record_interaction(question, response, confidence)
# Learn from this interaction
self._learn_from_interaction(question, key_terms, response, confidence)
# End task tracking
self.performance_monitor.end_task("query_processing")
logger.info(f"Query processed in {processing_time:.2f} seconds with confidence {confidence:.2f}")
# Return response with metadata
return {
'response': response,
'confidence': confidence,
'processing_time': processing_time,
'key_terms': key_terms,
'relevant_info_count': len(relevant_info)
}
def _extract_key_terms(self, text):
"""Extract key terms from input text"""
# Tokenize text
tokens = [token.lower() for token in text.split()]
# Remove stopwords (simplified approach)
stopwords = set(['the', 'a', 'an', 'and', 'or', 'but', 'in', 'on', 'at', 'to', 'for', 'with',
'is', 'are', 'was', 'were', 'be', 'been', 'being', 'have', 'has', 'had',
'do', 'does', 'did', 'can', 'could', 'will', 'would', 'should', 'shall',
'may', 'might', 'must', 'of', 'by', 'as', 'that', 'this', 'these', 'those'])
filtered_tokens = [token for token in tokens if token.lower() not in stopwords and len(token) > 2]
# Filter to tokens in our database
known_terms = [token for token in filtered_tokens if token in self.word_database.words]
# If no known terms, try partial matches
if not known_terms:
for token in filtered_tokens:
for word in self.word_database.words:
if token in word or word in token:
known_terms.append(word)
# Limit to top terms (by usage count if available)
if len(known_terms) > 5:
term_counts = [(term, self.word_database.words.get(term, {}).get('usage_count', 0))
for term in known_terms]
term_counts.sort(key=lambda x: x[1], reverse=True)
known_terms = [term for term, _ in term_counts[:5]]
return known_terms
def _formulate_response(self, question, relevant_info, similar_interactions):
"""Generate a response based on retrieved information"""
# If no relevant information found
if not relevant_info:
if similar_interactions:
# Use similar past interaction
prev_interaction = similar_interactions[0]
confidence = 0.5 # Medium confidence since it's based on similarity
return f"Based on similar questions I've seen before, {prev_interaction['response']}", confidence
else:
return "I don't have enough information to answer that question accurately.", 0.1
# Combine information from all relevant terms
combined_info = {}
for info in relevant_info:
term = info['term']
term_data = info['info']
# Store term definition
if 'definition' in term_data and term_data['definition']:
combined_info[term] = term_data['definition']
# Add domain information if available
if 'domains' in term_data and term_data['domains']:
if term not in combined_info:
combined_info[term] = ""
combined_info[term] += f" (Domains: {', '.join(term_data['domains'])})"
# Add relationship information
if info['relationships']:
top_relations = info['relationships'][:3] # Top 3 relationships
related_terms = [rel['word'] for rel in top_relations]
if term not in combined_info:
combined_info[term] = ""
combined_info[term] += f" Related: {', '.join(related_terms)}."
# Determine question type (simplified)
if question.lower().startswith('what is') or question.lower().startswith('what are'):
question_type = 'definition'
elif question.lower().startswith('how'):
question_type = 'process'
elif question.lower().startswith('why'):
question_type = 'explanation'
elif question.lower().startswith('who'):
question_type = 'person'
elif question.lower().startswith('where'):
question_type = 'location'
elif question.lower().startswith('when'):
question_type = 'time'
else:
question_type = 'general'
# Format response based on question type
response = ""
if question_type == 'definition':
term = relevant_info[0]['term']
if term in combined_info:
response = f"{term.capitalize()} is {combined_info[term]}"
else:
response = f"I have information about {term}, but no specific definition."
elif question_type == 'explanation':
# Try to use causal relationships for explanations
for info in relevant_info:
term = info['term']
for rel in info['relationships']:
related = rel['word']
causal_conf = self.reasoning_system.verify_relation(term, related)
if causal_conf > 0.5:
response = f"This happens because {term} causes {related}. "
break
causal_conf = self.reasoning_system.verify_relation(related, term)
if causal_conf > 0.5:
response = f"This happens because {related} causes {term}. "
break
# If no causal relationships found, provide general information
if not response:
response = "Based on the information I have, "
for term, info in combined_info.items():
response += f"{term} is {info} "
else:
# General format for other question types
response = "Based on my knowledge, "
for term, info in combined_info.items():
response += f"{term} is {info} "
# Calculate confidence based on relevance and coverage
term_coverage = len(relevant_info) / max(1, len(self._extract_key_terms(question)))
info_detail = sum(len(str(info)) for info in combined_info.values()) / 100 # Normalize
confidence = min(0.9, term_coverage * 0.7 + min(info_detail, 0.3))
return response.strip(), confidence
def _learn_from_interaction(self, question, key_terms, response, confidence):
"""Learn from the interaction to improve future responses"""
# Only learn from high-confidence responses
if confidence < 0.6:
return
# Associate question terms with each other
for i, term1 in enumerate(key_terms):
for term2 in key_terms[i+1:]:
# Add relationship between terms
self.word_database.add_relationship(
term1, term2,
relationship_type='co-occurrence',
strength=0.7,
metadata={'source': 'user_query'}
)
# Increment usage count for terms
for term in key_terms:
if term in self.word_database.words:
self.word_database.words[term]['usage_count'] += 1
# Look for potential causal relationships in question-answer
for term1 in key_terms:
for term2 in key_terms:
if term1 != term2:
# Define causal phrases to detect potential causal relationships
causal_phrases = [
f"{term1} causes {term2}",
f"{term1} results in {term2}",
f"{term1} leads to {term2}",
f"because of {term1}, {term2}",
f"{term2} is caused by {term1}"
]
# Check if any causal phrase is in the response
for phrase in causal_phrases:
if phrase.lower() in response.lower():
# Calculate confidence based on phrase detection and response quality
phrase_confidence = confidence * 0.8 # Slightly reduced confidence
# Add causal relationship to reasoning system
self.reasoning_system.add_causal_relation(
term1,
term2,
confidence=phrase_confidence,
evidence=[{
'source': 'interaction',
'question': question,
'response': response,
'detected_phrase': phrase
}]
)
# Break after first match to avoid duplicate entries
break