КОД:
import numpy as np
from typing import List, Dict, Tuple, Optional
import networkx as nx
from dataclasses import dataclass
import math
@dataclass
class Vector3D:
x: float
y: float
z: float
def magnitude(self) -> float:
return math.sqrt(self.x**2 + self.y**2 + self.z**2)
def normalize(self) -> 'Vector3D':
mag = self.magnitude()
if mag == 0:
return Vector3D(0, 0, 0)
return Vector3D(self.x/mag, self.y/mag, self.z/mag)
@dataclass
class Weight3D:
"""3D weight structure containing spatial and functional components"""
position: Vector3D # Spatial position in 3D space
magnitude: float # Classical weight value
frequency: float # Oscillation frequency for dynamic behavior
phase: float # Phase of oscillation
def get_effective_weight(self, time: float) -> float:
"""Calculate effective weight considering temporal dynamics"""
return self.magnitude * math.sin(self.frequency * time + self.phase)
class Locus:
"""Represents an information locus in the network"""
def __init__(self, name: str, position: Vector3D):
self.name = name
self.position = position
self.connections: Dict[str, Weight3D] = {}
self.activation_history: List[float] = []
self.semantic_features: Dict[str, float] = {}
def add_connection(self, target_name: str, weight: Weight3D):
self.connections[target_name] = weight
def update_semantic_features(self, features: Dict[str, float]):
"""Upd ate semantic features based on new information"""
for key, value in features.items():
if key in self.semantic_features:
self.semantic_features[key] = (self.semantic_features[key] + value) / 2
else:
self.semantic_features[key] = value
class DynamicLayer:
"""Layer with dynamic structure that can adapt to input patterns"""
def __init__(self, initial_size: int):
self.loci: Dict[str, Locus] = {}
self.size = initial_size
def add_locus(self, name: str, position: Vector3D) -> Locus:
locus = Locus(name, position)
self.loci[name] = locus
return locus
def find_similar_patterns(self, features: Dict[str, float], threshold: float = 0.8) -> List[str]:
"""Find existing loci with similar semantic features"""
similar_loci = []
for name, locus in self.loci.items():
similarity = self._calculate_semantic_similarity(locus.semantic_features, features)
if similarity > threshold:
similar_loci.append(name)
return similar_loci
def _calculate_semantic_similarity(self, features1: Dict[str, float],
features2: Dict[str, float]) -> float:
"""Calculate semantic similarity between two feature sets"""
common_keys = se t(features1.keys()) & set(features2.keys())
if not common_keys:
return 0.0
similarity = sum(1 - abs(features1[k] - features2[k]) for k in common_keys)
return similarity / len(common_keys)
class AdaptivePerceptron3D:
"""3D perceptron with dynamic structure and locus formation"""
def __init__(self, input_size: int, hidden_size: int, output_size: int):
self.input_layer = DynamicLayer(input_size)
self.hidden_layer = DynamicLayer(hidden_size)
self.output_layer = DynamicLayer(output_size)
self.time = 0.0
self.learning_rate = 0.1
# Initialize control center for managing locus formation
self.control_center = self._create_control_center()
def _create_control_center(self) -> nx.Graph:
"""Create control center graph for managing network topology"""
G = nx.Graph()
G.add_node('controller', type='main')
return G
def process_input(self, input_data: np.ndarray,
context: Optional[Dict[str, float]] = None) -> np.ndarray:
"""Process input data with context awareness"""
# Extract features and context
features = self._extract_features(input_data)
# Check for similar existing patterns
similar_patterns = self.hidden_layer.find_similar_patterns(features)
# Create new locus if no similar patterns found
if not similar_patterns:
new_locus_name = f"locus_{len(self.hidden_layer.loci)}"
position = self._calculate_optimal_position(features)
new_locus = self.hidden_layer.add_locus(new_locus_name, position)
new_locus.update_semantic_features(features)
# Update network structure
self._update_network_structure()
# Process through layers
hidden_activation = self._process_layer(input_data, self.hidden_layer)
output = self._process_layer(hidden_activation, self.output_layer)
self.time += 0.1 # Update internal time
return output
def _extract_features(self, input_data: np.ndarray) -> Dict[str, float]:
"""Extract semantic features from input data"""
features = {}
# Simple feature extraction - can be made more sophisticated
for i, value in enumerate(input_data):
features[f"feature_{i}"] = float(value)
return features
def _calculate_optimal_position(self, features: Dict[str, float]) -> Vector3D:
"""Calculate optimal position for new locus based on features"""
# Simple positioning strategy - can be made more sophisticated
x = sum(v * math.cos(k.count('_')) for k, v in features.items())
y = sum(v * math.sin(k.count('_')) for k, v in features.items())
z = sum(v for v in features.values()) / len(features)
return Vector3D(x, y, z)
def _update_network_structure(self):
"""Update network structure based on recent activations and patterns"""
# Implement logic for structural adaptation
# This could include creating new connections, removing unused ones,
# and optimizing the network topology
pass
def _process_layer(self, input_data: np.ndarray,
layer: DynamicLayer) -> np.ndarray:
"""Process data through a layer with 3D weights and dynamic structure"""
output = np.zeros(layer.size)
for i, locus in enumerate(layer.loci.values()):
weighted_sum = 0
for j, weight in enumerate(locus.connections.values()):
effective_weight = weight.get_effective_weight(self.time)
weighted_sum += input_data[j] * effective_weight
output[i] = self._activation_function(weighted_sum)
return output
def _activation_function(self, x: float) -> float:
"""Non-linear activation function"""
return 1 / (1 + math.exp(-x))
def train(self, input_data: np.ndarray, target: np.ndarray):
"""Train the network using backpropagation with 3D weight updates"""
# Forward pass
output = self.process_input(input_data)
# Calculate error
error = target - output
# Backward pass (simplified)
self._update_weights(error)
def _update_weights(self, error: np.ndarray):
"""Update 3D weights based on error signal"""
# Implement weight update logic considering 3D structure
# This should update position, magnitude, frequency, and phase
# of weights based on the error signal
pass
# Example usage
if __name__ == "__main__":
# Create network
network = AdaptivePerceptron3D(input_size=10, hidden_size=20, output_size=5)
# Generate sample data
input_data = np.random.random(10)
target = np.random.random(5)
# Process input
output = network.process_input(input_data)
print(f"Output: {output}")
# Train network
network.train(input_data, target)
