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Refactor hopper training and add brain state analysis with resonance space #67

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4 changes: 1 addition & 3 deletions requirements.txt
Original file line number Diff line number Diff line change
@@ -29,7 +29,6 @@ pygraphviz
torch
torchvision
torchaudio
torch-geometric

# Web scraping
aiohttp
@@ -42,5 +41,4 @@ langchain-ollama

# Gymnasium
gymnasium
pygame
"gymnasium[other]"
pygame
167 changes: 167 additions & 0 deletions sound-interaction/resonance_space.py
Original file line number Diff line number Diff line change
@@ -0,0 +1,167 @@
import numpy as np
import pyaudio
import scipy.signal as signal
from dataclasses import dataclass
from typing import List, Optional
import random


@dataclass
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suggestion: Make ResonancePattern immutable using frozen=True

Since ResonancePattern represents a pattern at a specific point in time, it should be immutable. Add frozen=True to the dataclass decorator.

Suggested change
@dataclass
@dataclass(frozen=True)

class ResonancePattern:
frequency: float
amplitude: float
phase: float
duration: float


class ResonanceSpace:
def __init__(
self, sample_rate: int = 44100, buffer_size: int = 1024, memory_depth: int = 10
):
self.sample_rate = sample_rate
self.buffer_size = buffer_size
self.memory_depth = memory_depth
self.memory: List[np.ndarray] = []
self.resonance_patterns: List[ResonancePattern] = []

def process_input(self, audio_buffer: np.ndarray) -> np.ndarray:
# Zorg dat we werken met dubbelprecisie voor FFT stabiliteit
audio_buffer = audio_buffer.astype(np.float64)

# Update geheugen
self.memory.append(audio_buffer)
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suggestion (performance): Use collections.deque instead of list for memory buffer

Using list.pop(0) is O(n) complexity. Replace the memory list with collections.deque which provides O(1) operations for both ends.

from collections import deque

# Earlier in __init__:
self.memory = deque(maxlen=self.memory_depth)

# Replace the append/pop logic with:
self.memory.append(audio_buffer)

if len(self.memory) > self.memory_depth:
self.memory.pop(0)

# Windowing om spectral leakage te verminderen
window = np.hanning(len(audio_buffer))
windowed_signal = audio_buffer * window

# Analyseer frequenties
freqs = np.fft.fftfreq(len(windowed_signal), 1 / self.sample_rate)
spectrum = np.fft.fft(windowed_signal)

# Creëer nieuwe resonantie patronen
dominant_freqs = self._find_dominant_frequencies(spectrum, freqs)
self._generate_resonance_patterns(dominant_freqs)

# Genereer response
response = self._create_response()
return response.astype(np.float32)

def _find_dominant_frequencies(
self, spectrum: np.ndarray, freqs: np.ndarray, num_peaks: int = 3
) -> List[float]:
# Alleen positieve frequenties beschouwen
magnitude = np.abs(spectrum)
half = len(magnitude) // 2
pos_magnitude = magnitude[:half]
pos_freqs = freqs[:half]

peaks = signal.find_peaks(pos_magnitude)[0]

if len(peaks) == 0:
return [random.uniform(200, 2000) for _ in range(num_peaks)]

# Sorteer peaks op magnitude en neem de top
sorted_peaks = sorted(peaks, key=lambda p: pos_magnitude[p], reverse=True)
top_peaks = sorted_peaks[:num_peaks]

# Voeg subtiele random variatie toe, niet alleen door random selectie
result_freqs = []
for peak in top_peaks:
base_freq = pos_freqs[peak]
result_freqs.append(base_freq * (1 + random.uniform(-0.05, 0.05)))

return result_freqs
Comment on lines +70 to +76
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suggestion (code-quality): We've found these issues:

Suggested change
# Voeg subtiele random variatie toe, niet alleen door random selectie
result_freqs = []
for peak in top_peaks:
base_freq = pos_freqs[peak]
result_freqs.append(base_freq * (1 + random.uniform(-0.05, 0.05)))
return result_freqs
return [
pos_freqs[peak] * (1 + random.uniform(-0.05, 0.05))
for peak in top_peaks
]


def _generate_resonance_patterns(self, frequencies: List[float]):
new_patterns = []
for freq in frequencies:
freq_variation = freq * (1 + random.uniform(-0.1, 0.1))
amplitude = random.uniform(0.3, 0.8)
phase = random.uniform(0, 2 * np.pi)
duration = random.uniform(0.5, 2.0)

new_patterns.append(
ResonancePattern(
frequency=freq_variation,
amplitude=amplitude,
phase=phase,
duration=duration,
)
)

self.resonance_patterns.extend(new_patterns)

# Beperk aantal patronen
if len(self.resonance_patterns) > 5:
self.resonance_patterns = self.resonance_patterns[-5:]

def _create_response(self) -> np.ndarray:
t = np.linspace(
0, self.buffer_size / self.sample_rate, self.buffer_size, endpoint=False
)
response = np.zeros_like(t)

for pattern in self.resonance_patterns:
wave = pattern.amplitude * np.sin(
2 * np.pi * pattern.frequency * t + pattern.phase
)
# Envelope
wave *= np.exp(-t / pattern.duration)
# Harmonischen toevoegen
wave += 0.1 * np.sin(3 * 2 * np.pi * pattern.frequency * t)

response += wave

max_val = np.max(np.abs(response))
if max_val > 1e-9:
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issue: Add more robust handling of near-zero maximum values

The current threshold of 1e-9 could still lead to numerical instability. Consider using np.finfo(response.dtype).eps as a more robust threshold and handle the case where max_val is below this threshold.

response /= max_val

return response


class AudioInterface:
def __init__(self, resonance_space: ResonanceSpace):
self.resonance_space = resonance_space
self.p = pyaudio.PyAudio()
self.stream = None

def start_stream(self):
def callback(in_data, frame_count, time_info, status):
audio_buffer = np.frombuffer(in_data, dtype=np.float32)
response = self.resonance_space.process_input(audio_buffer)
out_data = response.tobytes()
return (out_data, pyaudio.paContinue)

self.stream = self.p.open(
format=pyaudio.paFloat32,
channels=1,
rate=self.resonance_space.sample_rate,
input=True,
output=True,
frames_per_buffer=self.resonance_space.buffer_size,
stream_callback=callback,
)

def stop_stream(self):
if self.stream:
self.stream.stop_stream()
self.stream.close()
self.p.terminate()


if __name__ == "__main__":
space = ResonanceSpace()
interface = AudioInterface(space)
print("Starting resonance space...")
interface.start_stream()

try:
input("Press Enter to stop...")
except KeyboardInterrupt:
pass
finally:
print("Closing resonance space...")
interface.stop_stream()
688 changes: 688 additions & 0 deletions three_layered/layered_brain.py
Original file line number Diff line number Diff line change
@@ -0,0 +1,688 @@
import torch
import torch.nn as nn
from transformers import GPT2Tokenizer
import seaborn as sns
import pandas as pd
from pathlib import Path
import numpy as np
from scipy.signal import find_peaks

import torch
import torch.nn as nn
from transformers import GPT2Tokenizer # For real text processing
import seaborn as sns
from collections import deque
import numpy as np


import torch
import torch.nn as nn
import matplotlib.pyplot as plt
from torch.utils.data import DataLoader, TensorDataset
import numpy as np

import torch
import torch.nn as nn


class BrainInABoxV4(nn.Module):
def __init__(
self,
vocab_size,
embed_dim,
hidden_dim,
dropout=0.1,
memory_size=100,
num_layers=2,
num_heads=8,
activation="relu",
):
super().__init__()

# Enhanced configuration
self.config = {
"vocab_size": vocab_size,
"embed_dim": embed_dim,
"hidden_dim": hidden_dim,
"dropout": dropout,
"memory_size": memory_size,
"num_layers": num_layers,
"num_heads": num_heads,
"activation": activation,
}

# Core architecture with configurable activation
self.embedding = nn.Embedding(vocab_size, embed_dim)
encoder_layer = nn.TransformerEncoderLayer(
d_model=embed_dim,
nhead=num_heads,
dropout=dropout,
batch_first=True,
activation=activation,
)
self.reasoning = nn.TransformerEncoder(encoder_layer, num_layers=num_layers)

# Enhanced state representation
self.state_repr = nn.Sequential(
nn.Linear(embed_dim, hidden_dim),
nn.LayerNorm(hidden_dim),
self._get_activation(activation),
nn.Dropout(dropout),
nn.Linear(hidden_dim, hidden_dim), # Additional layer for deeper processing
nn.LayerNorm(hidden_dim),
self._get_activation(activation),
nn.Dropout(dropout),
)

self.output_projection = nn.Linear(embed_dim, vocab_size)

# Enhanced memory tracking
self.hidden_dim = hidden_dim
self.memory_states = deque(maxlen=memory_size)
self.attention_patterns = deque(maxlen=memory_size)
self.attention_maps = []

# Enhanced metrics tracking
self.metrics = {
"memory_evolution": [],
"attention_dynamics": [],
"state_transitions": [],
"gradient_norms": [],
"layer_activations": [],
"confidence_scores": [],
}

# New: Adaptive learning components
self.adaptive_threshold = nn.Parameter(torch.tensor([0.5]))
self.confidence_weights = nn.Parameter(torch.ones(num_heads))

# New: State analysis tools
self.state_analyzer = StateAnalyzer(hidden_dim)

def _get_activation(self, activation_name):
"""Dynamic activation function selection"""
activations = {
"relu": nn.ReLU(),
"gelu": nn.GELU(),
"selu": nn.SELU(),
"leaky_relu": nn.LeakyReLU(),
}
return activations.get(activation_name.lower(), nn.ReLU())

def forward(self, x, state=None, return_attention=False):
# Enhanced state initialization
if state is None:
state = self.initialize_state(x)

# Enhanced attention capture
attention_weights = []

def hook_fn(module, input, output):
# Handle different output formats safely
if isinstance(output, tuple):
# Some implementations return (output, attention_weights)
if len(output) > 1 and output[1] is not None:
attention_weights.append(output[1].detach())
else:
# If output is just the tensor, we'll use a different approach
attention_weights.append(output.detach())

# Register hooks for attention capture
handles = []
for layer in self.reasoning.layers:
handles.append(layer.self_attn.register_forward_hook(hook_fn))

try:
# Enhanced forward pass with confidence scoring
emb = self.embedding(x)
reasoned = self.reasoning(emb)
new_state = self.state_repr(reasoned[:, -1, :])

# Adaptive state update
confidence_score = torch.sigmoid(
torch.matmul(new_state, state.transpose(-2, -1)).mean()
)
new_state = confidence_score * new_state + (1 - confidence_score) * state

output = self.output_projection(reasoned)

# Enhanced monitoring - only if we captured attention weights
if attention_weights:
self._update_monitoring_data(
attention_weights, new_state, confidence_score
)

if return_attention:
return output, new_state, attention_weights
return output, new_state

finally:
# Always clean up hooks
for handle in handles:
handle.remove()

def _update_monitoring_data(self, attention_weights, new_state, confidence_score):
"""Enhanced monitoring data updates"""
# Existing monitoring
self.memory_states.append(new_state.mean(dim=0).detach().cpu())

# Enhanced attention processing
if attention_weights:
processed_attention = self._process_attention_weights(attention_weights)
self.attention_patterns.append(processed_attention)
self.attention_maps.append(processed_attention)

# Enhanced metrics
self._update_metrics(new_state, confidence_score)

def _process_attention_weights(self, attention_weights):
"""Process and analyze attention patterns"""
# Combine attention from all layers
combined_attention = torch.stack(attention_weights)
# Weight by learned confidence
weighted_attention = combined_attention * self.confidence_weights.view(-1, 1, 1)
return weighted_attention.mean(dim=0).cpu()

def _update_metrics(self, new_state, confidence_score):
"""Update enhanced metrics"""
if len(self.memory_states) > 1:
self.metrics["memory_evolution"].append(
torch.norm(self.memory_states[-1] - self.memory_states[-2]).item()
)
self.metrics["confidence_scores"].append(confidence_score.item())

# Add gradient tracking if training
if self.training and new_state.grad is not None:
self.metrics["gradient_norms"].append(torch.norm(new_state.grad).item())

def initialize_state(self, x):
"""Enhanced state initialization"""
batch_size = x.size(0)
device = x.device

# Initialize with learned parameters
init_state = torch.randn(batch_size, self.hidden_dim, device=device)
init_state = init_state * self.adaptive_threshold
return init_state

def visualize_brain_activity(self, show_memory=True, show_attention=True):
"""Comprehensive visualization of brain activity"""
if not (self.memory_states or self.attention_patterns):
print("No monitoring data available yet")
return

plt.figure(figsize=(15, 10))

if show_memory and self.memory_states:
plt.subplot(2, 1, 1)
states = torch.stack(list(self.memory_states))
sns.heatmap(states.numpy(), cmap="RdYlBu_r")
plt.title("Memory Evolution")
plt.ylabel("Time Step")
plt.xlabel("Memory Dimension")

if show_attention and self.attention_patterns:
plt.subplot(2, 1, 2)
attention_data = torch.stack(list(self.attention_patterns))
sns.heatmap(attention_data.mean(0).numpy(), cmap="viridis")
plt.title("Average Attention Pattern")
plt.xlabel("Token Position")
plt.ylabel("Token Position")

plt.tight_layout()
plt.show()


class StateAnalyzer:
"""New component for analyzing brain states"""

def __init__(self, hidden_dim):
self.hidden_dim = hidden_dim
self.state_history = []

def analyze_state(self, state):
"""Analyze state characteristics"""
self.state_history.append(state.detach())

analysis = {
"complexity": self._compute_complexity(state),
"stability": self._compute_stability(),
"patterns": self._detect_patterns(),
}
return analysis

def _compute_complexity(self, state):
return torch.norm(state, p="fro").item()

def _compute_stability(self):
if len(self.state_history) < 2:
return None
return torch.norm(self.state_history[-1] - self.state_history[-2]).item()

def _detect_patterns(self):
if len(self.state_history) < 3:
return None
# Implement pattern detection logic
return None


# Let's take it for a spin!🚗
def test_drive_brain():
# Create a small model for testing
model = BrainInABoxV4(
vocab_size=1000, # Smaller for testing
embed_dim=64, # Compact but functional
hidden_dim=128, # Decent memory size
)

# Create some fake input data
batch_size = 3
seq_length = 5
input_ids = torch.randint(0, 1000, (batch_size, seq_length))

# Run it!
reasoned_output, new_state = model(input_ids)

print(f"Input shape: {input_ids.shape}")
print(f"Reasoning output shape: {reasoned_output.shape}")
print(f"New state shape: {new_state.shape}")

return model


if __name__ == "__main__":
model = test_drive_brain()


# Create a simple training loop with visualization
def train_and_visualize(model, epochs=5, batch_size=32):
# Generate some dummy sequential data
seq_length = 10
vocab_size = 1000
dataset_size = 1000

# Create synthetic data
X = torch.randint(0, vocab_size, (dataset_size, seq_length))
# Create targets as next token prediction
y = torch.randint(0, vocab_size, (dataset_size,))

dataset = TensorDataset(X, y)
dataloader = DataLoader(dataset, batch_size=batch_size, shuffle=True)

optimizer = torch.optim.AdamW(model.parameters(), lr=0.001)
criterion = nn.CrossEntropyLoss()

losses = []

print("Starting training...")
for epoch in range(epochs):
epoch_loss = 0
for batch_x, batch_y in dataloader:
optimizer.zero_grad()

# Forward pass
output, state = model(batch_x)

# Get predictions for the last token
logits = output[:, -1, :] # [batch_size, vocab_size]

loss = criterion(logits, batch_y)
loss.backward()
optimizer.step()

epoch_loss += loss.item()

avg_loss = epoch_loss / len(dataloader)
losses.append(avg_loss)
print(f"Epoch {epoch+1}: Loss = {avg_loss:.4f}")

# Visualize attention patterns periodically
if epoch % 2 == 0 and hasattr(model, "attention_maps") and model.attention_maps:
visualize_attention(model.attention_maps[-1], epoch)

return losses


def visualize_attention(attention_map, epoch):
plt.figure(figsize=(8, 6))

try:
# Convert to numpy and ensure 2D
attention_display = attention_map.cpu().numpy()

# If 1D, reshape to 2D square
if len(attention_display.shape) == 1:
size = int(np.sqrt(len(attention_display)))
attention_display = attention_display.reshape(size, size)

# Create heatmap
sns.heatmap(attention_display, cmap="viridis", center=0)
plt.title(f"Attention Pattern - Epoch {epoch+1}")
plt.xlabel("Token Position")
plt.ylabel("Token Position")
plt.show()
except Exception as e:
print(f"Warning: Could not visualize attention map: {e}")
plt.close()


# Let's run it!
vocab_size = 1000
model = BrainInABoxV4(
vocab_size=vocab_size, # Use same vocab_size as in training data
embed_dim=64,
hidden_dim=128,
)
losses = train_and_visualize(model)

# Plot training progress
plt.figure(figsize=(10, 5))
plt.plot(losses, "b-", label="Training Loss")
plt.title("Training Progress")
plt.xlabel("Epoch")
plt.ylabel("Loss")
plt.legend()
plt.grid(True)
plt.show()


class TextBrainAnalyzer:
def __init__(self, model_config=None):
self.tokenizer = GPT2Tokenizer.from_pretrained("gpt2")
self.model = (
BrainInABoxV4(vocab_size=50257, embed_dim=256, hidden_dim=512)
if model_config is None
else model_config
)

# Initialize pattern storage with empty lists
self.pattern_store = {
"narrative": {"memory": [], "attention": []},
"analytical": {"memory": [], "attention": []},
}

# Generate more varied sample texts
self.default_texts = {
"narrative": [
"One morning, when Gregor Samsa woke from troubled dreams, he found himself transformed in his bed into a horrible vermin.",
"The old man and the sea was a tale of courage and perseverance, as the fisherman battled both nature and his own limitations.",
"In the quiet village, beneath the ancient oak trees, stories were passed down through generations.",
"The detective examined the crime scene carefully, noting every detail that might lead to solving the mystery.",
"Through the mist, she could barely make out the outline of the ancient castle on the hill.",
],
"analytical": [
"The analysis of artificial intelligence systems reveals complex patterns of information processing.",
"Quantum mechanics demonstrates the probabilistic nature of subatomic particles and their interactions.",
"Economic systems exhibit emergent properties through the collective behavior of individual agents.",
"The fundamental principles of thermodynamics govern energy transfer in closed systems.",
"Statistical analysis of large datasets requires careful consideration of sampling methodologies.",
],
}

self.text_categories = {
"narrative": {
"fiction": "stories, novels, creative writing",
"personal": "blogs, diaries, memoirs",
"journalistic": "news articles, features",
},
"analytical": {
"scientific": "research papers, technical docs",
"philosophical": "theoretical discussions",
"business": "reports, analysis documents",
},
"conversational": {
"dialogue": "transcripts, chat logs",
"social": "social media posts",
"informal": "casual communications",
},
}

# Add data quality metrics
self.data_metrics = {
"samples_per_category": {},
"avg_length": {},
"vocabulary_diversity": {},
"complexity_scores": {},
}

def process_text(self, text_path=None, text_type="narrative"):
"""Process multiple text samples while tracking memory evolution"""
try:
if text_path:
try:
texts = [Path(text_path).read_text()]
except FileNotFoundError:
print(
f"File {text_path} not found, using default {text_type} texts..."
)
texts = self.default_texts[text_type]
else:
print(f"Using default {text_type} texts...")
texts = self.default_texts[text_type]

print(f"Processing {len(texts)} {text_type} texts...")

# Process each text sample
for idx, text in enumerate(texts, 1):
tokens = self.tokenizer.encode(text)
input_tensor = torch.tensor([tokens])

output, state = self.model(input_tensor)

if output is not None and state is not None:
# Store memory patterns
self.pattern_store[text_type]["memory"].append(
state.mean(dim=0).detach().cpu()
)

# Store attention patterns if available
if (
hasattr(self.model, "attention_patterns")
and self.model.attention_patterns
):
self.pattern_store[text_type]["attention"].extend(
[
p.mean().detach().cpu()
for p in self.model.attention_patterns
]
)

print(f"Processed sample {idx}/{len(texts)}")

print(
f"Stored patterns for {text_type}: Memory={len(self.pattern_store[text_type]['memory'])}, Attention={len(self.pattern_store[text_type]['attention'])}"
)
return True

except Exception as e:
print(f"Error processing text: {e}")
return False

def compare_text_types(self):
"""Compare patterns between narrative and analytical texts"""
if not all(
len(self.pattern_store[t]["memory"]) > 0
for t in ["narrative", "analytical"]
):
print("Error: Not enough data collected for comparison")
return

plt.figure(figsize=(15, 10))

# Plot memory patterns
plt.subplot(2, 1, 1)
for text_type in ["narrative", "analytical"]:
if self.pattern_store[text_type]["memory"]:
patterns = torch.stack(self.pattern_store[text_type]["memory"])
mean_pattern = patterns.mean(dim=0).numpy()
std_pattern = patterns.std(dim=0).numpy()
x = np.arange(len(mean_pattern))

plt.plot(x, mean_pattern, label=f"{text_type.capitalize()} (mean)")
plt.fill_between(
x, mean_pattern - std_pattern, mean_pattern + std_pattern, alpha=0.2
)

plt.title("Memory Pattern Comparison")
plt.xlabel("Memory Dimension")
plt.ylabel("Activation")
plt.legend()

# Plot attention patterns
plt.subplot(2, 1, 2)
for text_type in ["narrative", "analytical"]:
if self.pattern_store[text_type]["attention"]:
attention_patterns = torch.stack(
[
torch.tensor(p)
for p in self.pattern_store[text_type]["attention"]
]
)
mean_attention = attention_patterns.mean(dim=0).numpy()
std_attention = attention_patterns.std(dim=0).numpy()
x = np.arange(len(mean_attention))

plt.plot(x, mean_attention, label=f"{text_type.capitalize()} (mean)")
plt.fill_between(
x,
mean_attention - std_attention,
mean_attention + std_attention,
alpha=0.2,
)

plt.title("Attention Pattern Comparison")
plt.xlabel("Token Position")
plt.ylabel("Attention Strength")
plt.legend()

plt.tight_layout()
plt.show()

def analyze_complexity(self):
"""Analyze text complexity through pattern variability"""
if not all(
len(self.pattern_store[t]["memory"]) > 0
for t in ["narrative", "analytical"]
):
print("Error: Not enough data collected for complexity analysis")
return

plt.figure(figsize=(10, 6))

for text_type in ["narrative", "analytical"]:
if self.pattern_store[text_type]["memory"]:
patterns = torch.stack(self.pattern_store[text_type]["memory"])
variability = patterns.std(dim=0).numpy()
plt.plot(variability, label=f"{text_type.capitalize()}")

plt.title("Pattern Complexity Analysis")
plt.xlabel("Memory Dimension")
plt.ylabel("Pattern Variability")
plt.legend()
plt.show()

def analyze_patterns(self):
"""Advanced pattern analysis"""
results = {
"temporal_patterns": self._analyze_temporal_patterns(),
"cross_category_correlations": self._analyze_correlations(),
"complexity_metrics": self._calculate_complexity_metrics(),
"attention_dynamics": self._analyze_attention_flow(),
}

# Visualization enhancements
self._plot_advanced_metrics(results)
return results

def _analyze_temporal_patterns(self):
"""Analyze how patterns evolve over time"""
temporal_features = {
"memory_evolution": [],
"attention_shifts": [],
"state_transitions": [],
}
# Implementation here
return temporal_features


# Example usage
analyzer = TextBrainAnalyzer()

# Process both text types with multiple samples
print("\nProcessing narrative texts...")
analyzer.process_text(text_type="narrative")

print("\nProcessing analytical texts...")
analyzer.process_text(text_type="analytical")

# Compare patterns
print("\nComparing text patterns...")
analyzer.compare_text_types()

# Analyze complexity
print("\nAnalyzing pattern complexity...")
analyzer.analyze_complexity()


class ModelValidator:
def __init__(self):
self.validation_metrics = {
"cross_validation_scores": [],
"robustness_tests": [],
"bias_metrics": [],
}

def validate_patterns(self, pattern_data):
"""Validate pattern recognition accuracy"""
# Implementation here
pass

def test_generalization(self, test_data):
"""Test model generalization capabilities"""
# Implementation here
pass


class PatternVisualizer:
def __init__(self):
self.plot_config = {
"style": "seaborn-darkgrid",
"dimensions": (15, 10),
"interactive": True,
}

def create_interactive_visualization(self, pattern_data):
"""Create interactive visualizations using plotly"""
# Implementation here
pass

def generate_pattern_comparison(self, categories):
"""Generate comparative visualizations across categories"""
# Implementation here
pass


class DataIntegrator:
def __init__(self):
self.data_sources = {
"academic": ["arxiv", "pubmed", "google_scholar"],
"social": ["twitter", "reddit", "blog_feeds"],
"professional": ["technical_docs", "business_reports"],
}

def fetch_and_process_data(self, source_type, parameters):
"""Fetch and process data from external sources"""
# Implementation here
pass


# Initialize model
model = BrainInABoxV4(
vocab_size=50257, # GPT-2 vocab size
embed_dim=256,
hidden_dim=512,
memory_size=100, # Number of states to track
)

# After training/inference
model.visualize_brain_activity()