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No commits in common. "3b64ef62e15877b3dff517f876b0698e9d62c867" and "bada025b33d4079ec32b062bf2d55ed3fb6a9ab1" have entirely different histories.
3b64ef62e1
...
bada025b33
@ -8,7 +8,6 @@ dependencies = [
|
||||
"pre-commit>=4.2.0",
|
||||
"pydantic>=2.11.5",
|
||||
"pygame>=2.6.1",
|
||||
"pygame-gui>=0.6.14",
|
||||
"pytest>=8.3.5",
|
||||
]
|
||||
|
||||
|
||||
@ -1,53 +0,0 @@
|
||||
# This file was autogenerated by uv via the following command:
|
||||
# uv pip compile -o requirements.txt pyproject.toml
|
||||
annotated-types==0.7.0
|
||||
# via pydantic
|
||||
cfgv==3.4.0
|
||||
# via pre-commit
|
||||
distlib==0.3.9
|
||||
# via virtualenv
|
||||
filelock==3.18.0
|
||||
# via virtualenv
|
||||
identify==2.6.12
|
||||
# via pre-commit
|
||||
iniconfig==2.1.0
|
||||
# via pytest
|
||||
nodeenv==1.9.1
|
||||
# via pre-commit
|
||||
numpy==2.3.0
|
||||
# via dynamicsystemabstraction (pyproject.toml)
|
||||
packaging==25.0
|
||||
# via pytest
|
||||
platformdirs==4.3.8
|
||||
# via virtualenv
|
||||
pluggy==1.6.0
|
||||
# via pytest
|
||||
pre-commit==4.2.0
|
||||
# via dynamicsystemabstraction (pyproject.toml)
|
||||
pydantic==2.11.7
|
||||
# via dynamicsystemabstraction (pyproject.toml)
|
||||
pydantic-core==2.33.2
|
||||
# via pydantic
|
||||
pygame==2.6.1
|
||||
# via dynamicsystemabstraction (pyproject.toml)
|
||||
pygame-ce==2.5.5
|
||||
# via pygame-gui
|
||||
pygame-gui==0.6.14
|
||||
# via dynamicsystemabstraction (pyproject.toml)
|
||||
pygments==2.19.1
|
||||
# via pytest
|
||||
pytest==8.4.1
|
||||
# via dynamicsystemabstraction (pyproject.toml)
|
||||
python-i18n==0.3.9
|
||||
# via pygame-gui
|
||||
pyyaml==6.0.2
|
||||
# via pre-commit
|
||||
typing-extensions==4.14.0
|
||||
# via
|
||||
# pydantic
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||||
# pydantic-core
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||||
# typing-inspection
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||||
typing-inspection==0.4.1
|
||||
# via pydantic
|
||||
virtualenv==20.31.2
|
||||
# via pre-commit
|
||||
52
uv.lock
generated
52
uv.lock
generated
@ -47,7 +47,6 @@ dependencies = [
|
||||
{ name = "pre-commit" },
|
||||
{ name = "pydantic" },
|
||||
{ name = "pygame" },
|
||||
{ name = "pygame-gui" },
|
||||
{ name = "pytest" },
|
||||
]
|
||||
|
||||
@ -62,7 +61,6 @@ requires-dist = [
|
||||
{ name = "pre-commit", specifier = ">=4.2.0" },
|
||||
{ name = "pydantic", specifier = ">=2.11.5" },
|
||||
{ name = "pygame", specifier = ">=2.6.1" },
|
||||
{ name = "pygame-gui", specifier = ">=0.6.14" },
|
||||
{ name = "pytest", specifier = ">=8.3.5" },
|
||||
]
|
||||
|
||||
@ -315,47 +313,6 @@ wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/7e/11/17f7f319ca91824b86557e9303e3b7a71991ef17fd45286bf47d7f0a38e6/pygame-2.6.1-cp313-cp313-win_amd64.whl", hash = "sha256:813af4fba5d0b2cb8e58f5d95f7910295c34067dcc290d34f1be59c48bd1ea6a", size = 10620084, upload-time = "2024-09-29T11:48:51.587Z" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "pygame-ce"
|
||||
version = "2.5.5"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/c9/be/af69521e694442dbde5db29069953f25367ddacaa50d9ae644745853d37c/pygame_ce-2.5.5.tar.gz", hash = "sha256:a7f297c223c6e35f16d65d47a19757005763ea7e90795ccc37c0bc562364ae6b", size = 5821935, upload-time = "2025-06-07T07:33:03.501Z" }
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||||
wheels = [
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[[package]]
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name = "pygame-gui"
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version = "0.6.14"
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source = { registry = "https://pypi.org/simple" }
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dependencies = [
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{ name = "pygame-ce" },
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{ name = "python-i18n" },
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]
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wheels = [
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[[package]]
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name = "pytest"
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version = "8.3.5"
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@ -371,15 +328,6 @@ wheels = [
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[[package]]
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[[package]]
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name = "pyyaml"
|
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version = "6.0.2"
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@ -1,8 +1,475 @@
|
||||
from world.base.neural import FlexibleNeuralNetwork
|
||||
import numpy as np
|
||||
import random
|
||||
from copy import deepcopy
|
||||
|
||||
from config.constants import MAX_VELOCITY, MAX_ROTATIONAL_VELOCITY
|
||||
from world.behavioral import BehavioralModel
|
||||
|
||||
|
||||
class FlexibleNeuralNetwork:
|
||||
"""
|
||||
A flexible neural network that can mutate its structure and weights.
|
||||
Supports variable topology with cross-layer connections.
|
||||
"""
|
||||
|
||||
def __init__(self, input_size=2, output_size=2, empty_start=True):
|
||||
self.input_size = input_size
|
||||
self.output_size = output_size
|
||||
|
||||
# Network structure: list of layers, each layer is a list of neurons
|
||||
# Each neuron is represented by its connections and bias
|
||||
self.layers = []
|
||||
|
||||
# Initialize network based on empty_start parameter
|
||||
if empty_start:
|
||||
self._initialize_empty_network()
|
||||
else:
|
||||
self._initialize_basic_network()
|
||||
|
||||
self.network_cost = self.calculate_network_cost()
|
||||
|
||||
def _initialize_basic_network(self):
|
||||
"""Initialize a basic network with input->output connections only."""
|
||||
# Input layer (no actual neurons, just placeholders)
|
||||
input_layer = [{'type': 'input', 'id': i} for i in range(self.input_size)]
|
||||
|
||||
# Output layer with connections to all inputs
|
||||
output_layer = []
|
||||
for i in range(self.output_size):
|
||||
neuron = {
|
||||
'type': 'output',
|
||||
'id': f'out_{i}',
|
||||
'bias': random.uniform(-1, 1),
|
||||
'connections': [] # List of (source_layer, source_neuron, weight)
|
||||
}
|
||||
|
||||
# Connect to all input neurons
|
||||
for j in range(self.input_size):
|
||||
neuron['connections'].append((0, j, random.uniform(-2, 2)))
|
||||
|
||||
output_layer.append(neuron)
|
||||
|
||||
self.layers = [input_layer, output_layer]
|
||||
|
||||
def _initialize_empty_network(self):
|
||||
"""Initialize an empty network with no connections or biases."""
|
||||
# Input layer (no actual neurons, just placeholders)
|
||||
input_layer = [{'type': 'input', 'id': i} for i in range(self.input_size)]
|
||||
|
||||
# Output layer with no connections and zero bias
|
||||
output_layer = []
|
||||
for i in range(self.output_size):
|
||||
neuron = {
|
||||
'type': 'output',
|
||||
'id': f'out_{i}',
|
||||
'bias': 0.0,
|
||||
'connections': [] # Empty connections list
|
||||
}
|
||||
output_layer.append(neuron)
|
||||
|
||||
self.layers = [input_layer, output_layer]
|
||||
|
||||
def _remove_duplicate_connections(self):
|
||||
"""Remove duplicate connections and keep only the last weight for each unique connection."""
|
||||
for layer in self.layers[1:]: # Skip input layer
|
||||
for neuron in layer:
|
||||
if 'connections' not in neuron:
|
||||
continue
|
||||
|
||||
# Use a dictionary to track unique connections by (source_layer, source_neuron)
|
||||
unique_connections = {}
|
||||
|
||||
for source_layer, source_neuron, weight in neuron['connections']:
|
||||
connection_key = (source_layer, source_neuron)
|
||||
# Keep the last weight encountered for this connection
|
||||
unique_connections[connection_key] = weight
|
||||
|
||||
# Rebuild connections list without duplicates
|
||||
neuron['connections'] = [
|
||||
(source_layer, source_neuron, weight)
|
||||
for (source_layer, source_neuron), weight in unique_connections.items()
|
||||
]
|
||||
|
||||
def _connection_exists(self, target_neuron, source_layer_idx, source_neuron_idx):
|
||||
"""Check if a connection already exists between two neurons."""
|
||||
if 'connections' not in target_neuron:
|
||||
return False
|
||||
|
||||
for source_layer, source_neuron, weight in target_neuron['connections']:
|
||||
if source_layer == source_layer_idx and source_neuron == source_neuron_idx:
|
||||
return True
|
||||
return False
|
||||
|
||||
def forward(self, inputs):
|
||||
"""
|
||||
Forward pass through the network.
|
||||
|
||||
:param inputs: List or array of input values
|
||||
:return: List of output values
|
||||
"""
|
||||
if len(inputs) != self.input_size:
|
||||
raise ValueError(f"Expected {self.input_size} inputs, got {len(inputs)}")
|
||||
|
||||
# Store activations for each layer
|
||||
activations = [inputs] # Input layer activations
|
||||
|
||||
# Process each subsequent layer
|
||||
for layer_idx in range(1, len(self.layers)):
|
||||
layer_activations = []
|
||||
|
||||
for neuron in self.layers[layer_idx]:
|
||||
if neuron['type'] == 'input':
|
||||
continue # Skip input neurons in hidden layers
|
||||
|
||||
# Calculate weighted sum of inputs
|
||||
weighted_sum = 0.0 # Start with 0 instead of bias
|
||||
|
||||
# Only add bias if neuron has connections
|
||||
if 'connections' in neuron and len(neuron['connections']) > 0:
|
||||
weighted_sum = neuron['bias']
|
||||
|
||||
for source_layer, source_neuron, weight in neuron['connections']:
|
||||
if source_layer < len(activations):
|
||||
if source_neuron < len(activations[source_layer]):
|
||||
weighted_sum += activations[source_layer][source_neuron] * weight
|
||||
|
||||
# Apply activation function (tanh for bounded output)
|
||||
# If no connections and no bias applied, this will be tanh(0) = 0
|
||||
activation = np.tanh(weighted_sum)
|
||||
layer_activations.append(activation)
|
||||
|
||||
activations.append(layer_activations)
|
||||
|
||||
return activations[-1] # Return output layer activations
|
||||
|
||||
def mutate(self, mutation_rate=0.1):
|
||||
"""
|
||||
Create a mutated copy of this network.
|
||||
|
||||
:param mutation_rate: Base probability multiplied by specific mutation weights
|
||||
:return: New mutated FlexibleNeuralNetwork instance
|
||||
"""
|
||||
mutated = deepcopy(self)
|
||||
|
||||
# Weighted mutations (probability = mutation_rate * weight)
|
||||
# Higher weights = more likely to occur
|
||||
mutations = [
|
||||
(mutated._mutate_weights, 5.0), # Most common - fine-tune existing
|
||||
(mutated._mutate_biases, 3.0), # Common - adjust neuron thresholds
|
||||
(mutated._add_connection, 1.5), # Moderate - grow connectivity
|
||||
(mutated._remove_connection, 0.8), # Less common - reduce connectivity
|
||||
(mutated._add_neuron, 0.3), # Rare - structural growth
|
||||
(mutated._remove_neuron, 0.1), # Very rare - structural reduction
|
||||
(mutated._add_layer, 0.05), # New: create a new layer (very rare)
|
||||
]
|
||||
|
||||
# Apply weighted random mutations
|
||||
for mutation_func, weight in mutations:
|
||||
if random.random() < (mutation_rate * weight):
|
||||
mutation_func()
|
||||
|
||||
# Clean up any duplicate connections that might have been created
|
||||
mutated._remove_duplicate_connections()
|
||||
|
||||
# Ensure the network maintains basic connectivity
|
||||
mutated._ensure_network_connectivity()
|
||||
|
||||
mutated.network_cost = mutated.calculate_network_cost()
|
||||
|
||||
return mutated
|
||||
|
||||
def _mutate_weights(self):
|
||||
"""Slightly modify existing connection weights."""
|
||||
for layer in self.layers[1:]: # Skip input layer
|
||||
for neuron in layer:
|
||||
if 'connections' in neuron:
|
||||
for i in range(len(neuron['connections'])):
|
||||
if random.random() < 0.3: # 30% chance to mutate each weight
|
||||
source_layer, source_neuron, weight = neuron['connections'][i]
|
||||
# Add small random change
|
||||
new_weight = weight + random.uniform(-0.5, 0.5)
|
||||
neuron['connections'][i] = (source_layer, source_neuron, new_weight)
|
||||
|
||||
def _mutate_biases(self):
|
||||
"""Slightly modify neuron biases."""
|
||||
for layer in self.layers[1:]: # Skip input layer
|
||||
for neuron in layer:
|
||||
if 'bias' in neuron and random.random() < 0.3:
|
||||
neuron['bias'] += random.uniform(-0.5, 0.5)
|
||||
|
||||
def _add_connection(self):
|
||||
"""Add a new random connection."""
|
||||
if len(self.layers) < 2:
|
||||
return
|
||||
|
||||
# Find layers with neurons
|
||||
valid_target_layers = []
|
||||
for i in range(1, len(self.layers)):
|
||||
if len(self.layers[i]) > 0:
|
||||
valid_target_layers.append(i)
|
||||
|
||||
if not valid_target_layers:
|
||||
return
|
||||
|
||||
# Pick a random target neuron (not in input layer)
|
||||
target_layer_idx = random.choice(valid_target_layers)
|
||||
target_neuron_idx = random.randint(0, len(self.layers[target_layer_idx]) - 1)
|
||||
target_neuron = self.layers[target_layer_idx][target_neuron_idx]
|
||||
|
||||
if 'connections' not in target_neuron:
|
||||
return
|
||||
|
||||
# Find valid source layers (must have neurons and be before target)
|
||||
valid_source_layers = []
|
||||
for i in range(target_layer_idx):
|
||||
if len(self.layers[i]) > 0:
|
||||
valid_source_layers.append(i)
|
||||
|
||||
if not valid_source_layers:
|
||||
return
|
||||
|
||||
# Pick a random source (from any previous layer with neurons)
|
||||
source_layer_idx = random.choice(valid_source_layers)
|
||||
source_neuron_idx = random.randint(0, len(self.layers[source_layer_idx]) - 1)
|
||||
|
||||
# Check if connection already exists using the helper method
|
||||
if self._connection_exists(target_neuron, source_layer_idx, source_neuron_idx):
|
||||
return # Connection already exists, don't add duplicate
|
||||
|
||||
# Add new connection
|
||||
new_weight = random.uniform(-2, 2)
|
||||
target_neuron['connections'].append((source_layer_idx, source_neuron_idx, new_weight))
|
||||
|
||||
def _remove_connection(self):
|
||||
"""Remove a random connection."""
|
||||
for layer in self.layers[1:]:
|
||||
for neuron in layer:
|
||||
if 'connections' in neuron and len(neuron['connections']) > 1:
|
||||
if random.random() < 0.1: # 10% chance to remove a connection
|
||||
neuron['connections'].pop(random.randint(0, len(neuron['connections']) - 1))
|
||||
|
||||
def _add_neuron(self):
|
||||
"""Add a new neuron to a random hidden layer or create a new hidden layer."""
|
||||
if len(self.layers) == 2: # Only input and output layers
|
||||
# Create a new hidden layer
|
||||
hidden_neuron = {
|
||||
'type': 'hidden',
|
||||
'id': f'hidden_{random.randint(1000, 9999)}',
|
||||
'bias': random.uniform(-1, 1),
|
||||
'connections': []
|
||||
}
|
||||
|
||||
# Connect to some input neurons (avoid duplicates)
|
||||
for i in range(self.input_size):
|
||||
if random.random() < 0.7: # 70% chance to connect to each input
|
||||
if not self._connection_exists(hidden_neuron, 0, i):
|
||||
hidden_neuron['connections'].append((0, i, random.uniform(-2, 2)))
|
||||
|
||||
# Insert hidden layer
|
||||
self.layers.insert(1, [hidden_neuron])
|
||||
|
||||
# Update output layer connections to potentially use new hidden neuron
|
||||
for neuron in self.layers[-1]: # Output layer (now at index 2)
|
||||
if random.random() < 0.5: # 50% chance to connect to new hidden neuron
|
||||
if not self._connection_exists(neuron, 1, 0):
|
||||
neuron['connections'].append((1, 0, random.uniform(-2, 2)))
|
||||
|
||||
else:
|
||||
# Add neuron to existing hidden layer
|
||||
# Find hidden layers that exist
|
||||
hidden_layer_indices = []
|
||||
for i in range(1, len(self.layers) - 1):
|
||||
if i < len(self.layers): # Safety check
|
||||
hidden_layer_indices.append(i)
|
||||
|
||||
if not hidden_layer_indices:
|
||||
return
|
||||
|
||||
hidden_layer_idx = random.choice(hidden_layer_indices)
|
||||
new_neuron = {
|
||||
'type': 'hidden',
|
||||
'id': f'hidden_{random.randint(1000, 9999)}',
|
||||
'bias': random.uniform(-1, 1),
|
||||
'connections': []
|
||||
}
|
||||
|
||||
# Connect to some neurons from previous layers (avoid duplicates)
|
||||
for layer_idx in range(hidden_layer_idx):
|
||||
if len(self.layers[layer_idx]) > 0: # Only if layer has neurons
|
||||
for neuron_idx in range(len(self.layers[layer_idx])):
|
||||
if random.random() < 0.3: # 30% chance to connect
|
||||
if not self._connection_exists(new_neuron, layer_idx, neuron_idx):
|
||||
new_neuron['connections'].append((layer_idx, neuron_idx, random.uniform(-2, 2)))
|
||||
|
||||
self.layers[hidden_layer_idx].append(new_neuron)
|
||||
|
||||
# Update connections from later layers to potentially connect to this new neuron
|
||||
new_neuron_idx = len(self.layers[hidden_layer_idx]) - 1
|
||||
for later_layer_idx in range(hidden_layer_idx + 1, len(self.layers)):
|
||||
if len(self.layers[later_layer_idx]) > 0: # Only if layer has neurons
|
||||
for neuron in self.layers[later_layer_idx]:
|
||||
if random.random() < 0.2: # 20% chance to connect to new neuron
|
||||
if not self._connection_exists(neuron, hidden_layer_idx, new_neuron_idx):
|
||||
neuron['connections'].append((hidden_layer_idx, new_neuron_idx, random.uniform(-2, 2)))
|
||||
|
||||
def _remove_neuron(self):
|
||||
"""Remove a random neuron from hidden layers."""
|
||||
if len(self.layers) <= 2: # No hidden layers
|
||||
return
|
||||
|
||||
# Find hidden layers that have neurons
|
||||
valid_hidden_layers = []
|
||||
for layer_idx in range(1, len(self.layers) - 1): # Only hidden layers
|
||||
if len(self.layers[layer_idx]) > 0:
|
||||
valid_hidden_layers.append(layer_idx)
|
||||
|
||||
if not valid_hidden_layers:
|
||||
return
|
||||
|
||||
# Pick a random hidden layer with neurons
|
||||
layer_idx = random.choice(valid_hidden_layers)
|
||||
neuron_idx = random.randint(0, len(self.layers[layer_idx]) - 1)
|
||||
|
||||
# Remove the neuron
|
||||
self.layers[layer_idx].pop(neuron_idx)
|
||||
|
||||
# Remove connections to this neuron from later layers
|
||||
for later_layer_idx in range(layer_idx + 1, len(self.layers)):
|
||||
for neuron in self.layers[later_layer_idx]:
|
||||
if 'connections' in neuron:
|
||||
neuron['connections'] = [
|
||||
(src_layer, src_neuron, weight)
|
||||
for src_layer, src_neuron, weight in neuron['connections']
|
||||
if not (src_layer == layer_idx and src_neuron == neuron_idx)
|
||||
]
|
||||
|
||||
# Adjust neuron indices for remaining neurons in the same layer
|
||||
for later_layer_idx in range(layer_idx + 1, len(self.layers)):
|
||||
for neuron in self.layers[later_layer_idx]:
|
||||
if 'connections' in neuron:
|
||||
adjusted_connections = []
|
||||
for src_layer, src_neuron, weight in neuron['connections']:
|
||||
if src_layer == layer_idx and src_neuron > neuron_idx:
|
||||
# Adjust index down by 1 since we removed a neuron
|
||||
adjusted_connections.append((src_layer, src_neuron - 1, weight))
|
||||
else:
|
||||
adjusted_connections.append((src_layer, src_neuron, weight))
|
||||
neuron['connections'] = adjusted_connections
|
||||
|
||||
# Remove empty hidden layers to keep network clean
|
||||
if len(self.layers[layer_idx]) == 0:
|
||||
self.layers.pop(layer_idx)
|
||||
|
||||
# Adjust all layer indices in connections that reference layers after the removed one
|
||||
for layer in self.layers:
|
||||
for neuron in layer:
|
||||
if 'connections' in neuron:
|
||||
adjusted_connections = []
|
||||
for src_layer, src_neuron, weight in neuron['connections']:
|
||||
if src_layer > layer_idx:
|
||||
adjusted_connections.append((src_layer - 1, src_neuron, weight))
|
||||
else:
|
||||
adjusted_connections.append((src_layer, src_neuron, weight))
|
||||
neuron['connections'] = adjusted_connections
|
||||
|
||||
def _add_layer(self):
|
||||
"""Add a new hidden layer at a random position with at least one neuron."""
|
||||
if len(self.layers) < 2:
|
||||
return # Need at least input and output layers
|
||||
|
||||
# Choose a position between input and output layers
|
||||
insert_idx = random.randint(1, len(self.layers) - 1)
|
||||
# Create a new hidden neuron
|
||||
new_neuron = {
|
||||
'type': 'hidden',
|
||||
'id': f'hidden_{random.randint(1000, 9999)}',
|
||||
'bias': random.uniform(-1, 1),
|
||||
'connections': []
|
||||
}
|
||||
# Connect to all neurons in the previous layer
|
||||
for prev_idx in range(len(self.layers[insert_idx - 1])):
|
||||
if random.random() < 0.5:
|
||||
new_neuron['connections'].append((insert_idx - 1, prev_idx, random.uniform(-2, 2)))
|
||||
# Insert the new layer
|
||||
self.layers.insert(insert_idx, [new_neuron])
|
||||
# Connect neurons in the next layer to the new neuron
|
||||
if insert_idx + 1 < len(self.layers):
|
||||
for neuron in self.layers[insert_idx + 1]:
|
||||
if 'connections' in neuron and random.random() < 0.5:
|
||||
neuron['connections'].append((insert_idx, 0, random.uniform(-2, 2)))
|
||||
|
||||
def _ensure_network_connectivity(self):
|
||||
"""Ensure the network maintains basic connectivity from inputs to outputs."""
|
||||
# Check if output neurons have any connections
|
||||
output_layer = self.layers[-1]
|
||||
|
||||
for i, output_neuron in enumerate(output_layer):
|
||||
if 'connections' not in output_neuron or len(output_neuron['connections']) == 0:
|
||||
# Output neuron has no connections - reconnect to input layer
|
||||
for j in range(self.input_size):
|
||||
if not self._connection_exists(output_neuron, 0, j):
|
||||
output_neuron['connections'].append((0, j, random.uniform(-2, 2)))
|
||||
break # Add at least one connection
|
||||
|
||||
# Ensure at least one path exists from input to output
|
||||
if len(self.layers) > 2: # Has hidden layers
|
||||
# Check if any hidden neurons are connected to inputs
|
||||
has_input_connection = False
|
||||
for layer_idx in range(1, len(self.layers) - 1): # Hidden layers
|
||||
for neuron in self.layers[layer_idx]:
|
||||
if 'connections' in neuron:
|
||||
for src_layer, src_neuron, weight in neuron['connections']:
|
||||
if src_layer == 0: # Connected to input
|
||||
has_input_connection = True
|
||||
break
|
||||
if has_input_connection:
|
||||
break
|
||||
if has_input_connection:
|
||||
break
|
||||
|
||||
# If no hidden neuron connects to input, create one
|
||||
if not has_input_connection and len(self.layers) > 2:
|
||||
first_hidden_layer = self.layers[1]
|
||||
if len(first_hidden_layer) > 0:
|
||||
first_neuron = first_hidden_layer[0]
|
||||
if 'connections' in first_neuron:
|
||||
# Add connection to first input
|
||||
if not self._connection_exists(first_neuron, 0, 0):
|
||||
first_neuron['connections'].append((0, 0, random.uniform(-2, 2)))
|
||||
|
||||
def get_structure_info(self):
|
||||
"""Return information about the network structure."""
|
||||
info = {
|
||||
'total_layers': len(self.layers),
|
||||
'layer_sizes': [len(layer) for layer in self.layers],
|
||||
'total_connections': 0,
|
||||
'total_neurons': sum(len(layer) for layer in self.layers),
|
||||
'network_cost': self.network_cost
|
||||
}
|
||||
|
||||
for layer in self.layers[1:]:
|
||||
for neuron in layer:
|
||||
if 'connections' in neuron:
|
||||
info['total_connections'] += len(neuron['connections'])
|
||||
|
||||
return info
|
||||
|
||||
def calculate_network_cost(self):
|
||||
"""
|
||||
Estimate the computational cost of the network.
|
||||
Cost is defined as the total number of connections plus the number of neurons
|
||||
(i.e., total multiply-accumulate operations and activations per forward pass).
|
||||
"""
|
||||
total_connections = 0
|
||||
total_neurons = 0
|
||||
for layer in self.layers[1:]: # Skip input layer (no computation)
|
||||
for neuron in layer:
|
||||
total_neurons += 1
|
||||
if 'connections' in neuron:
|
||||
total_connections += len(neuron['connections'])
|
||||
return total_connections + total_neurons
|
||||
|
||||
|
||||
class CellBrain(BehavioralModel):
|
||||
"""
|
||||
Enhanced CellBrain using a flexible neural network with input normalization.
|
||||
|
||||
@ -1,467 +0,0 @@
|
||||
import numpy as np
|
||||
import random
|
||||
from copy import deepcopy
|
||||
|
||||
|
||||
class FlexibleNeuralNetwork:
|
||||
"""
|
||||
A flexible neural network that can mutate its structure and weights.
|
||||
Supports variable topology with cross-layer connections.
|
||||
"""
|
||||
|
||||
def __init__(self, input_size=2, output_size=2, empty_start=True):
|
||||
self.input_size = input_size
|
||||
self.output_size = output_size
|
||||
|
||||
# Network structure: list of layers, each layer is a list of neurons
|
||||
# Each neuron is represented by its connections and bias
|
||||
self.layers = []
|
||||
|
||||
# Initialize network based on empty_start parameter
|
||||
if empty_start:
|
||||
self._initialize_empty_network()
|
||||
else:
|
||||
self._initialize_basic_network()
|
||||
|
||||
self.network_cost = self.calculate_network_cost()
|
||||
|
||||
def _initialize_basic_network(self):
|
||||
"""Initialize a basic network with input->output connections only."""
|
||||
# Input layer (no actual neurons, just placeholders)
|
||||
input_layer = [{'type': 'input', 'id': i} for i in range(self.input_size)]
|
||||
|
||||
# Output layer with connections to all inputs
|
||||
output_layer = []
|
||||
for i in range(self.output_size):
|
||||
neuron = {
|
||||
'type': 'output',
|
||||
'id': f'out_{i}',
|
||||
'bias': random.uniform(-1, 1),
|
||||
'connections': [] # List of (source_layer, source_neuron, weight)
|
||||
}
|
||||
|
||||
# Connect to all input neurons
|
||||
for j in range(self.input_size):
|
||||
neuron['connections'].append((0, j, random.uniform(-2, 2)))
|
||||
|
||||
output_layer.append(neuron)
|
||||
|
||||
self.layers = [input_layer, output_layer]
|
||||
|
||||
def _initialize_empty_network(self):
|
||||
"""Initialize an empty network with no connections or biases."""
|
||||
# Input layer (no actual neurons, just placeholders)
|
||||
input_layer = [{'type': 'input', 'id': i} for i in range(self.input_size)]
|
||||
|
||||
# Output layer with no connections and zero bias
|
||||
output_layer = []
|
||||
for i in range(self.output_size):
|
||||
neuron = {
|
||||
'type': 'output',
|
||||
'id': f'out_{i}',
|
||||
'bias': 0.0,
|
||||
'connections': [] # Empty connections list
|
||||
}
|
||||
output_layer.append(neuron)
|
||||
|
||||
self.layers = [input_layer, output_layer]
|
||||
|
||||
def _remove_duplicate_connections(self):
|
||||
"""Remove duplicate connections and keep only the last weight for each unique connection."""
|
||||
for layer in self.layers[1:]: # Skip input layer
|
||||
for neuron in layer:
|
||||
if 'connections' not in neuron:
|
||||
continue
|
||||
|
||||
# Use a dictionary to track unique connections by (source_layer, source_neuron)
|
||||
unique_connections = {}
|
||||
|
||||
for source_layer, source_neuron, weight in neuron['connections']:
|
||||
connection_key = (source_layer, source_neuron)
|
||||
# Keep the last weight encountered for this connection
|
||||
unique_connections[connection_key] = weight
|
||||
|
||||
# Rebuild connections list without duplicates
|
||||
neuron['connections'] = [
|
||||
(source_layer, source_neuron, weight)
|
||||
for (source_layer, source_neuron), weight in unique_connections.items()
|
||||
]
|
||||
|
||||
def _connection_exists(self, target_neuron, source_layer_idx, source_neuron_idx):
|
||||
"""Check if a connection already exists between two neurons."""
|
||||
if 'connections' not in target_neuron:
|
||||
return False
|
||||
|
||||
for source_layer, source_neuron, weight in target_neuron['connections']:
|
||||
if source_layer == source_layer_idx and source_neuron == source_neuron_idx:
|
||||
return True
|
||||
return False
|
||||
|
||||
def forward(self, inputs):
|
||||
"""
|
||||
Forward pass through the network.
|
||||
|
||||
:param inputs: List or array of input values
|
||||
:return: List of output values
|
||||
"""
|
||||
if len(inputs) != self.input_size:
|
||||
raise ValueError(f"Expected {self.input_size} inputs, got {len(inputs)}")
|
||||
|
||||
# Store activations for each layer
|
||||
activations = [inputs] # Input layer activations
|
||||
|
||||
# Process each subsequent layer
|
||||
for layer_idx in range(1, len(self.layers)):
|
||||
layer_activations = []
|
||||
|
||||
for neuron in self.layers[layer_idx]:
|
||||
if neuron['type'] == 'input':
|
||||
continue # Skip input neurons in hidden layers
|
||||
|
||||
# Calculate weighted sum of inputs
|
||||
weighted_sum = 0.0 # Start with 0 instead of bias
|
||||
|
||||
# Only add bias if neuron has connections
|
||||
if 'connections' in neuron and len(neuron['connections']) > 0:
|
||||
weighted_sum = neuron['bias']
|
||||
|
||||
for source_layer, source_neuron, weight in neuron['connections']:
|
||||
if source_layer < len(activations):
|
||||
if source_neuron < len(activations[source_layer]):
|
||||
weighted_sum += activations[source_layer][source_neuron] * weight
|
||||
|
||||
# Apply activation function (tanh for bounded output)
|
||||
# If no connections and no bias applied, this will be tanh(0) = 0
|
||||
activation = np.tanh(weighted_sum)
|
||||
layer_activations.append(activation)
|
||||
|
||||
activations.append(layer_activations)
|
||||
|
||||
return activations[-1] # Return output layer activations
|
||||
|
||||
def mutate(self, mutation_rate=0.1):
|
||||
"""
|
||||
Create a mutated copy of this network.
|
||||
|
||||
:param mutation_rate: Base probability multiplied by specific mutation weights
|
||||
:return: New mutated FlexibleNeuralNetwork instance
|
||||
"""
|
||||
mutated = deepcopy(self)
|
||||
|
||||
# Weighted mutations (probability = mutation_rate * weight)
|
||||
# Higher weights = more likely to occur
|
||||
mutations = [
|
||||
(mutated._mutate_weights, 5.0), # Most common - fine-tune existing
|
||||
(mutated._mutate_biases, 3.0), # Common - adjust neuron thresholds
|
||||
(mutated._add_connection, 1.5), # Moderate - grow connectivity
|
||||
(mutated._remove_connection, 0.8), # Less common - reduce connectivity
|
||||
(mutated._add_neuron, 0.3), # Rare - structural growth
|
||||
(mutated._remove_neuron, 0.1), # Very rare - structural reduction
|
||||
(mutated._add_layer, 0.05), # New: create a new layer (very rare)
|
||||
]
|
||||
|
||||
# Apply weighted random mutations
|
||||
for mutation_func, weight in mutations:
|
||||
if random.random() < (mutation_rate * weight):
|
||||
mutation_func()
|
||||
|
||||
# Clean up any duplicate connections that might have been created
|
||||
mutated._remove_duplicate_connections()
|
||||
|
||||
# Ensure the network maintains basic connectivity
|
||||
mutated._ensure_network_connectivity()
|
||||
|
||||
mutated.network_cost = mutated.calculate_network_cost()
|
||||
|
||||
return mutated
|
||||
|
||||
def _mutate_weights(self):
|
||||
"""Slightly modify existing connection weights."""
|
||||
for layer in self.layers[1:]: # Skip input layer
|
||||
for neuron in layer:
|
||||
if 'connections' in neuron:
|
||||
for i in range(len(neuron['connections'])):
|
||||
if random.random() < 0.3: # 30% chance to mutate each weight
|
||||
source_layer, source_neuron, weight = neuron['connections'][i]
|
||||
# Add small random change
|
||||
new_weight = weight + random.uniform(-0.5, 0.5)
|
||||
neuron['connections'][i] = (source_layer, source_neuron, new_weight)
|
||||
|
||||
def _mutate_biases(self):
|
||||
"""Slightly modify neuron biases."""
|
||||
for layer in self.layers[1:]: # Skip input layer
|
||||
for neuron in layer:
|
||||
if 'bias' in neuron and random.random() < 0.3:
|
||||
neuron['bias'] += random.uniform(-0.5, 0.5)
|
||||
|
||||
def _add_connection(self):
|
||||
"""Add a new random connection."""
|
||||
if len(self.layers) < 2:
|
||||
return
|
||||
|
||||
# Find layers with neurons
|
||||
valid_target_layers = []
|
||||
for i in range(1, len(self.layers)):
|
||||
if len(self.layers[i]) > 0:
|
||||
valid_target_layers.append(i)
|
||||
|
||||
if not valid_target_layers:
|
||||
return
|
||||
|
||||
# Pick a random target neuron (not in input layer)
|
||||
target_layer_idx = random.choice(valid_target_layers)
|
||||
target_neuron_idx = random.randint(0, len(self.layers[target_layer_idx]) - 1)
|
||||
target_neuron = self.layers[target_layer_idx][target_neuron_idx]
|
||||
|
||||
if 'connections' not in target_neuron:
|
||||
return
|
||||
|
||||
# Find valid source layers (must have neurons and be before target)
|
||||
valid_source_layers = []
|
||||
for i in range(target_layer_idx):
|
||||
if len(self.layers[i]) > 0:
|
||||
valid_source_layers.append(i)
|
||||
|
||||
if not valid_source_layers:
|
||||
return
|
||||
|
||||
# Pick a random source (from any previous layer with neurons)
|
||||
source_layer_idx = random.choice(valid_source_layers)
|
||||
source_neuron_idx = random.randint(0, len(self.layers[source_layer_idx]) - 1)
|
||||
|
||||
# Check if connection already exists using the helper method
|
||||
if self._connection_exists(target_neuron, source_layer_idx, source_neuron_idx):
|
||||
return # Connection already exists, don't add duplicate
|
||||
|
||||
# Add new connection
|
||||
new_weight = random.uniform(-2, 2)
|
||||
target_neuron['connections'].append((source_layer_idx, source_neuron_idx, new_weight))
|
||||
|
||||
def _remove_connection(self):
|
||||
"""Remove a random connection."""
|
||||
for layer in self.layers[1:]:
|
||||
for neuron in layer:
|
||||
if 'connections' in neuron and len(neuron['connections']) > 1:
|
||||
if random.random() < 0.1: # 10% chance to remove a connection
|
||||
neuron['connections'].pop(random.randint(0, len(neuron['connections']) - 1))
|
||||
|
||||
def _add_neuron(self):
|
||||
"""Add a new neuron to a random hidden layer or create a new hidden layer."""
|
||||
if len(self.layers) == 2: # Only input and output layers
|
||||
# Create a new hidden layer
|
||||
hidden_neuron = {
|
||||
'type': 'hidden',
|
||||
'id': f'hidden_{random.randint(1000, 9999)}',
|
||||
'bias': random.uniform(-1, 1),
|
||||
'connections': []
|
||||
}
|
||||
|
||||
# Connect to some input neurons (avoid duplicates)
|
||||
for i in range(self.input_size):
|
||||
if random.random() < 0.7: # 70% chance to connect to each input
|
||||
if not self._connection_exists(hidden_neuron, 0, i):
|
||||
hidden_neuron['connections'].append((0, i, random.uniform(-2, 2)))
|
||||
|
||||
# Insert hidden layer
|
||||
self.layers.insert(1, [hidden_neuron])
|
||||
|
||||
# Update output layer connections to potentially use new hidden neuron
|
||||
for neuron in self.layers[-1]: # Output layer (now at index 2)
|
||||
if random.random() < 0.5: # 50% chance to connect to new hidden neuron
|
||||
if not self._connection_exists(neuron, 1, 0):
|
||||
neuron['connections'].append((1, 0, random.uniform(-2, 2)))
|
||||
|
||||
else:
|
||||
# Add neuron to existing hidden layer
|
||||
# Find hidden layers that exist
|
||||
hidden_layer_indices = []
|
||||
for i in range(1, len(self.layers) - 1):
|
||||
if i < len(self.layers): # Safety check
|
||||
hidden_layer_indices.append(i)
|
||||
|
||||
if not hidden_layer_indices:
|
||||
return
|
||||
|
||||
hidden_layer_idx = random.choice(hidden_layer_indices)
|
||||
new_neuron = {
|
||||
'type': 'hidden',
|
||||
'id': f'hidden_{random.randint(1000, 9999)}',
|
||||
'bias': random.uniform(-1, 1),
|
||||
'connections': []
|
||||
}
|
||||
|
||||
# Connect to some neurons from previous layers (avoid duplicates)
|
||||
for layer_idx in range(hidden_layer_idx):
|
||||
if len(self.layers[layer_idx]) > 0: # Only if layer has neurons
|
||||
for neuron_idx in range(len(self.layers[layer_idx])):
|
||||
if random.random() < 0.3: # 30% chance to connect
|
||||
if not self._connection_exists(new_neuron, layer_idx, neuron_idx):
|
||||
new_neuron['connections'].append((layer_idx, neuron_idx, random.uniform(-2, 2)))
|
||||
|
||||
self.layers[hidden_layer_idx].append(new_neuron)
|
||||
|
||||
# Update connections from later layers to potentially connect to this new neuron
|
||||
new_neuron_idx = len(self.layers[hidden_layer_idx]) - 1
|
||||
for later_layer_idx in range(hidden_layer_idx + 1, len(self.layers)):
|
||||
if len(self.layers[later_layer_idx]) > 0: # Only if layer has neurons
|
||||
for neuron in self.layers[later_layer_idx]:
|
||||
if random.random() < 0.2: # 20% chance to connect to new neuron
|
||||
if not self._connection_exists(neuron, hidden_layer_idx, new_neuron_idx):
|
||||
neuron['connections'].append((hidden_layer_idx, new_neuron_idx, random.uniform(-2, 2)))
|
||||
|
||||
def _remove_neuron(self):
|
||||
"""Remove a random neuron from hidden layers."""
|
||||
if len(self.layers) <= 2: # No hidden layers
|
||||
return
|
||||
|
||||
# Find hidden layers that have neurons
|
||||
valid_hidden_layers = []
|
||||
for layer_idx in range(1, len(self.layers) - 1): # Only hidden layers
|
||||
if len(self.layers[layer_idx]) > 0:
|
||||
valid_hidden_layers.append(layer_idx)
|
||||
|
||||
if not valid_hidden_layers:
|
||||
return
|
||||
|
||||
# Pick a random hidden layer with neurons
|
||||
layer_idx = random.choice(valid_hidden_layers)
|
||||
neuron_idx = random.randint(0, len(self.layers[layer_idx]) - 1)
|
||||
|
||||
# Remove the neuron
|
||||
self.layers[layer_idx].pop(neuron_idx)
|
||||
|
||||
# Remove connections to this neuron from later layers
|
||||
for later_layer_idx in range(layer_idx + 1, len(self.layers)):
|
||||
for neuron in self.layers[later_layer_idx]:
|
||||
if 'connections' in neuron:
|
||||
neuron['connections'] = [
|
||||
(src_layer, src_neuron, weight)
|
||||
for src_layer, src_neuron, weight in neuron['connections']
|
||||
if not (src_layer == layer_idx and src_neuron == neuron_idx)
|
||||
]
|
||||
|
||||
# Adjust neuron indices for remaining neurons in the same layer
|
||||
for later_layer_idx in range(layer_idx + 1, len(self.layers)):
|
||||
for neuron in self.layers[later_layer_idx]:
|
||||
if 'connections' in neuron:
|
||||
adjusted_connections = []
|
||||
for src_layer, src_neuron, weight in neuron['connections']:
|
||||
if src_layer == layer_idx and src_neuron > neuron_idx:
|
||||
# Adjust index down by 1 since we removed a neuron
|
||||
adjusted_connections.append((src_layer, src_neuron - 1, weight))
|
||||
else:
|
||||
adjusted_connections.append((src_layer, src_neuron, weight))
|
||||
neuron['connections'] = adjusted_connections
|
||||
|
||||
# Remove empty hidden layers to keep network clean
|
||||
if len(self.layers[layer_idx]) == 0:
|
||||
self.layers.pop(layer_idx)
|
||||
|
||||
# Adjust all layer indices in connections that reference layers after the removed one
|
||||
for layer in self.layers:
|
||||
for neuron in layer:
|
||||
if 'connections' in neuron:
|
||||
adjusted_connections = []
|
||||
for src_layer, src_neuron, weight in neuron['connections']:
|
||||
if src_layer > layer_idx:
|
||||
adjusted_connections.append((src_layer - 1, src_neuron, weight))
|
||||
else:
|
||||
adjusted_connections.append((src_layer, src_neuron, weight))
|
||||
neuron['connections'] = adjusted_connections
|
||||
|
||||
def _add_layer(self):
|
||||
"""Add a new hidden layer at a random position with at least one neuron."""
|
||||
if len(self.layers) < 2:
|
||||
return # Need at least input and output layers
|
||||
|
||||
# Choose a position between input and output layers
|
||||
insert_idx = random.randint(1, len(self.layers) - 1)
|
||||
# Create a new hidden neuron
|
||||
new_neuron = {
|
||||
'type': 'hidden',
|
||||
'id': f'hidden_{random.randint(1000, 9999)}',
|
||||
'bias': random.uniform(-1, 1),
|
||||
'connections': []
|
||||
}
|
||||
# Connect to all neurons in the previous layer
|
||||
for prev_idx in range(len(self.layers[insert_idx - 1])):
|
||||
if random.random() < 0.5:
|
||||
new_neuron['connections'].append((insert_idx - 1, prev_idx, random.uniform(-2, 2)))
|
||||
# Insert the new layer
|
||||
self.layers.insert(insert_idx, [new_neuron])
|
||||
# Connect neurons in the next layer to the new neuron
|
||||
if insert_idx + 1 < len(self.layers):
|
||||
for neuron in self.layers[insert_idx + 1]:
|
||||
if 'connections' in neuron and random.random() < 0.5:
|
||||
neuron['connections'].append((insert_idx, 0, random.uniform(-2, 2)))
|
||||
|
||||
def _ensure_network_connectivity(self):
|
||||
"""Ensure the network maintains basic connectivity from inputs to outputs."""
|
||||
# Check if output neurons have any connections
|
||||
output_layer = self.layers[-1]
|
||||
|
||||
for i, output_neuron in enumerate(output_layer):
|
||||
if 'connections' not in output_neuron or len(output_neuron['connections']) == 0:
|
||||
# Output neuron has no connections - reconnect to input layer
|
||||
for j in range(self.input_size):
|
||||
if not self._connection_exists(output_neuron, 0, j):
|
||||
output_neuron['connections'].append((0, j, random.uniform(-2, 2)))
|
||||
break # Add at least one connection
|
||||
|
||||
# Ensure at least one path exists from input to output
|
||||
if len(self.layers) > 2: # Has hidden layers
|
||||
# Check if any hidden neurons are connected to inputs
|
||||
has_input_connection = False
|
||||
for layer_idx in range(1, len(self.layers) - 1): # Hidden layers
|
||||
for neuron in self.layers[layer_idx]:
|
||||
if 'connections' in neuron:
|
||||
for src_layer, src_neuron, weight in neuron['connections']:
|
||||
if src_layer == 0: # Connected to input
|
||||
has_input_connection = True
|
||||
break
|
||||
if has_input_connection:
|
||||
break
|
||||
if has_input_connection:
|
||||
break
|
||||
|
||||
# If no hidden neuron connects to input, create one
|
||||
if not has_input_connection and len(self.layers) > 2:
|
||||
first_hidden_layer = self.layers[1]
|
||||
if len(first_hidden_layer) > 0:
|
||||
first_neuron = first_hidden_layer[0]
|
||||
if 'connections' in first_neuron:
|
||||
# Add connection to first input
|
||||
if not self._connection_exists(first_neuron, 0, 0):
|
||||
first_neuron['connections'].append((0, 0, random.uniform(-2, 2)))
|
||||
|
||||
def get_structure_info(self):
|
||||
"""Return information about the network structure."""
|
||||
info = {
|
||||
'total_layers': len(self.layers),
|
||||
'layer_sizes': [len(layer) for layer in self.layers],
|
||||
'total_connections': 0,
|
||||
'total_neurons': sum(len(layer) for layer in self.layers),
|
||||
'network_cost': self.network_cost
|
||||
}
|
||||
|
||||
for layer in self.layers[1:]:
|
||||
for neuron in layer:
|
||||
if 'connections' in neuron:
|
||||
info['total_connections'] += len(neuron['connections'])
|
||||
|
||||
return info
|
||||
|
||||
def calculate_network_cost(self):
|
||||
"""
|
||||
Estimate the computational cost of the network.
|
||||
Cost is defined as the total number of connections plus the number of neurons
|
||||
(i.e., total multiply-accumulate operations and activations per forward pass).
|
||||
"""
|
||||
total_connections = 0
|
||||
total_neurons = 0
|
||||
for layer in self.layers[1:]: # Skip input layer (no computation)
|
||||
for neuron in layer:
|
||||
total_neurons += 1
|
||||
if 'connections' in neuron:
|
||||
total_connections += len(neuron['connections'])
|
||||
return total_connections + total_neurons
|
||||
Loading…
x
Reference in New Issue
Block a user