neat implementation up to mutate
This commit is contained in:
parent
4a05baa103
commit
cf4d773c10
8 changed files with 468 additions and 144 deletions
labirinth_ai/Models
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@ -44,8 +44,8 @@ class BaseModel(nn.Module):
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class BaseDataSet(Dataset):
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def __init__(self, states, targets):
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assert len(states) == len(targets), "Needs to have as many states as targets!"
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self.states = torch.tensor(states, dtype=torch.float32)
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self.targets = torch.tensor(targets, dtype=torch.float32)
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self.states = torch.tensor(np.array(states), dtype=torch.float32)
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self.targets = torch.tensor(np.array(targets), dtype=torch.float32)
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def __len__(self):
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return len(self.states)
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@ -69,7 +69,7 @@ def create_loss_function(action):
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def from_numpy(x):
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return torch.tensor(x, dtype=torch.float32)
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return torch.tensor(np.array(x), dtype=torch.float32)
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def train(states, targets, model, optimizer):
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@ -3,40 +3,16 @@ from torch import nn
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import numpy as np
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import tqdm
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from torch.utils.data import Dataset, DataLoader
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from labirinth_ai.Models.BaseModel import device
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class NodeGene:
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valid_types = ['sensor', 'hidden', 'output']
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def __init__(self, node_id, node_type, bias=None):
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assert node_type in self.valid_types, 'Unknown node type!'
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self.node_id = node_id
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self.node_type = node_type
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if node_type == 'hidden':
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assert bias is not None, 'Expected a bias for hidden node types!'
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self.bias = bias
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else:
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self.bias = None
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class ConnectionGene:
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def __init__(self, start, end, enabled, innovation_num, weight=None, recurrent=False):
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self.start = start
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self.end = end
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self.enabled = enabled
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self.innvovation_num = innovation_num
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self.recurrent = recurrent
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if weight is None:
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self.weight = np.random.random(1)[0] * 2 - 1.0
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else:
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self.weight = weight
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from labirinth_ai.Models.BaseModel import device, BaseDataSet, create_loss_function, create_optimizer
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from labirinth_ai.Models.Genotype import Genotype
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class EvolutionModel(nn.Module):
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evolutionary = True
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def __init__(self, view_dimension, action_num, channels, genes=None):
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def __init__(self, view_dimension, action_num, channels, genes: Genotype = None, genotype_class=None):
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if genotype_class is None:
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genotype_class = Genotype
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super(EvolutionModel, self).__init__()
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self.flatten = nn.Flatten()
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@ -46,25 +22,29 @@ class EvolutionModel(nn.Module):
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if genes is None:
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self.num_input_nodes = channels * (2 * self.viewD + 1) * (2 * self.viewD + 1) + 2
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self.genes = genotype_class(action_num, self.num_input_nodes)
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else:
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self.num_input_nodes = len(list(filter(lambda element: element[1].node_type == 'sensor', genes.nodes.items())))
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assert self.num_input_nodes > 0, 'Network needs to have sensor nodes!'
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is_input_over = False
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is_output_over = False
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for key, node in genes.nodes.items():
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if node.node_type == 'sensor':
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if is_input_over:
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raise ValueError('Node genes need to follow the order sensor, output, hidden!')
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self.genes = {'nodes': {}, 'connections': []}
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node_id = 0
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for _ in range(self.num_input_nodes):
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self.genes['nodes'][node_id] = NodeGene(node_id, 'sensor')
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node_id += 1
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first_action = node_id
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for _ in range(action_num * 2):
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self.genes['nodes'][node_id] = NodeGene(node_id, 'output')
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node_id += 1
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if node.node_type == 'output':
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is_input_over = True
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if is_output_over:
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raise ValueError('Node genes need to follow the order sensor, output, hidden!')
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for index in range(self.num_input_nodes):
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for action in range(action_num * 2):
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self.genes['connections'].append(
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ConnectionGene(index, first_action + action, True, index*(action_num * 2) + action)
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)
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if node.node_type == 'hidden':
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is_output_over = True
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self.genes = genes
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self.incoming_connections = {}
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for connection in self.genes['connections']:
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for connection in self.genes.connections:
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if connection.end not in self.incoming_connections.keys():
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self.incoming_connections[connection.end] = []
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self.incoming_connections[connection.end].append(connection)
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@ -73,16 +53,17 @@ class EvolutionModel(nn.Module):
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self.indices = {}
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self.has_recurrent = False
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non_recurrent_indices = {}
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self.non_recurrent_indices = {}
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self.recurrent_indices = {}
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with torch.no_grad():
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for key, value in self.incoming_connections.items():
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value.sort(key=lambda element: element.start)
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lin = nn.Linear(len(value), 1, bias=self.genes['nodes'][key].bias is not None)
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lin = nn.Linear(len(value), 1, bias=self.genes.nodes[key].bias is not None)
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for index, connection in enumerate(value):
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lin.weight[0, index] = value[index].weight
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if self.genes['nodes'][key].bias is not None:
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lin.bias[0] = self.genes['nodes'][key].bias
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if self.genes.nodes[key].bias is not None:
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lin.bias[0] = self.genes.nodes[key].bias
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non_lin = nn.ELU()
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sequence = nn.Sequential(
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@ -93,15 +74,17 @@ class EvolutionModel(nn.Module):
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self.layers[key] = sequence
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self.indices[key] = list(map(lambda element: element.start, value))
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non_recurrent_indices[key] = list(filter(lambda element: not element.recurrent, value))
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if not self.has_recurrent and len(non_recurrent_indices[key]) != len(self.indices[key]):
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self.non_recurrent_indices[key] = list(filter(lambda element: not element.recurrent, value))
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self.recurrent_indices[key] = list(filter(lambda element: element.recurrent, value))
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if not self.has_recurrent and len(self.non_recurrent_indices[key]) != len(self.indices[key]):
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self.has_recurrent = True
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non_recurrent_indices[key] = list(map(lambda element: element.start, non_recurrent_indices[key]))
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self.non_recurrent_indices[key] = list(map(lambda element: element.start, self.non_recurrent_indices[key]))
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self.recurrent_indices[key] = list(map(lambda element: element.start, self.recurrent_indices[key]))
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rank_of_node = {}
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for i in range(self.num_input_nodes):
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rank_of_node[i] = 0
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layers_to_add = list(non_recurrent_indices.items())
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layers_to_add = list(self.non_recurrent_indices.items())
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while len(layers_to_add) > 0:
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for index, (key, incoming_nodes) in enumerate(list(layers_to_add)):
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max_rank = -1
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@ -120,44 +103,123 @@ class EvolutionModel(nn.Module):
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ranked_layers = list(rank_of_node.items())
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ranked_layers.sort(key=lambda element: element[1])
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ranked_layers = list(filter(lambda element: element[1] > 0, ranked_layers))
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self.layer_order = list(map(lambda element: element[0], ranked_layers))
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self.memory = torch.Tensor((max(map(lambda element: element[1].node_id, self.genes['nodes'].items())) + 1))
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def forward(self, x, memory=None):
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ranked_layers = list(map(lambda element: (element, 0),
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filter(lambda recurrent_element:
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recurrent_element not in list(
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map(lambda ranked_layer: ranked_layer[0], ranked_layers)
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),
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list(filter(lambda recurrent_keys:
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len(self.recurrent_indices[recurrent_keys]) > 0,
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self.recurrent_indices.keys()))))) + ranked_layers
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self.layer_order = list(map(lambda element: element[0], ranked_layers))
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self.memory_size = (max(map(lambda element: element[1].node_id, self.genes.nodes.items())) + 1)
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self.memory = torch.Tensor(self.memory_size)
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self.output_range = range(self.num_input_nodes, self.num_input_nodes + self.action_num * 2)
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def forward(self, x, last_memory=None):
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x_flat = self.flatten(x)
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if memory is None:
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memory = torch.Tensor(self.memory)
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outs = []
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for batch_element in x_flat:
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memory[0:self.num_input_nodes] = batch_element
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for layer_index in self.layer_order:
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memory[layer_index] = self.layers[layer_index](memory[self.indices[layer_index]])
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outs.append(memory[self.num_input_nodes: self.num_input_nodes + self.action_num * 2])
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outs = torch.stack(outs)
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self.memory = torch.Tensor(memory)
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return torch.reshape(outs, (x.shape[0], 4, 2))
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else:
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memory[:, 0:self.num_input_nodes] = x
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if last_memory is not None:
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last_memory_flat = self.flatten(last_memory)
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elif self.has_recurrent:
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raise ValueError('Recurrent networks need to be passed their previous memory!')
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memory = torch.Tensor(self.memory_size)
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outs = []
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for batch_index, batch_element in enumerate(x_flat):
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memory[0:self.num_input_nodes] = batch_element
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for layer_index in self.layer_order:
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memory[:, layer_index] = self.layers[layer_index](memory[:, self.indices[layer_index]])
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return torch.reshape(
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memory[:, self.num_input_nodes: self.num_input_nodes + self.action_num * 2],
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(x.shape[0], 4, 2))
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non_recurrent_in = memory[self.non_recurrent_indices[layer_index]]
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non_recurrent_in = torch.stack([non_recurrent_in])
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if self.has_recurrent and len(self.recurrent_indices[layer_index]) > 0:
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recurrent_in = last_memory_flat[batch_index, self.recurrent_indices[layer_index]]
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recurrent_in = torch.stack([recurrent_in])
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combined_in = torch.concat([non_recurrent_in, recurrent_in], dim=1)
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else:
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combined_in = non_recurrent_in
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memory[layer_index] = self.layers[layer_index](combined_in)
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outs.append(memory[self.num_input_nodes: self.num_input_nodes + self.action_num * 2])
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outs = torch.stack(outs)
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self.memory = torch.Tensor(memory)
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return torch.reshape(outs, (x.shape[0], outs.shape[1]//2, 2))
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def update_genes_with_weights(self):
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for key, value in self.incoming_connections.items():
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value.sort(key=lambda element: element.start)
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sequence = self.layers[key]
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lin = sequence[0]
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for index, connection in enumerate(value):
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value[index].weight = float(lin.weight[0, index])
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if self.genes.nodes[key].bias is not None:
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self.genes.nodes[key].bias = float(lin.bias[0])
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class RecurrentDataSet(BaseDataSet):
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def __init__(self, states, targets, memory):
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super().__init__(states, targets)
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assert len(states) == len(memory), "Needs to have as many states as memories!"
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self.memory = torch.tensor(np.array(memory), dtype=torch.float32)
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def __getitem__(self, idx):
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return self.states[idx], self.memory[idx], self.targets[idx]
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def train_recurrent(states, memory, targets, model, optimizer):
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for action in range(model.action_num):
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data_set = RecurrentDataSet(states[action], targets[action], memory[action])
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dataloader = DataLoader(data_set, batch_size=64, shuffle=True)
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loss_fn = create_loss_function(action)
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size = len(dataloader)
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model.train()
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for batch, (X, M, y) in enumerate(dataloader):
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X, y, M = X.to(device), y.to(device), M.to(device)
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# Compute prediction error
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pred = model(X, M)
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loss = loss_fn(pred, y)
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# Backpropagation
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optimizer.zero_grad()
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loss.backward(retain_graph=True)
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optimizer.step()
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if batch % 100 == 0:
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loss, current = loss.item(), batch * len(X)
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print(f"loss: {loss:>7f} [{current:>5d}/{size:>5d}]")
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model.eval()
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del data_set
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del dataloader
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if __name__ == '__main__':
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sample = np.random.random((1, 486))
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sample = np.random.random((1, 1))
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last_memory = np.zeros((1, 3))
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model = EvolutionModel(5, 4, 4).to(device)
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print(model)
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from labirinth_ai.Models.Genotype import NodeGene, ConnectionGene, Genotype
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genes = Genotype(nodes={0: NodeGene(0, 'sensor'), 1: NodeGene(1, 'output'), 2: NodeGene(2, 'hidden', 1)},
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connections=[ConnectionGene(0, 2, True, 0, recurrent=True), ConnectionGene(2, 1, True, 1, 1)])
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model = EvolutionModel(1, 1, 1, genes)
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model = model.to(device)
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# print(model)
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print(model.has_recurrent)
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test = model(torch.tensor(sample, dtype=torch.float32))
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test = model(torch.tensor(sample, dtype=torch.float32), torch.tensor(last_memory, dtype=torch.float32))
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# test = test.cpu().detach().numpy()
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print(test)
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# print(test)
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state = np.random.random((1, 486))
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target = np.random.random((4, 2))
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state = np.random.random((1, 1))
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memory = np.random.random((1, 1))
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target = np.random.random((2, 1))
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states = [
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[state],
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[state],
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[target],
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[target],
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]
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memories = [
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[memory],
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[memory],
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[memory],
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[memory],
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]
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optimizer = torch.optim.RMSprop(model.parameters(), lr=1e-3)
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from labirinth_ai.Models.BaseModel import train
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train(states, targets, model, optimizer)
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train_recurrent(states, memories, targets, model, optimizer)
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139
labirinth_ai/Models/Genotype.py
Normal file
139
labirinth_ai/Models/Genotype.py
Normal file
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@ -0,0 +1,139 @@
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from abc import abstractmethod
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from typing import List, Dict
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import numpy as np
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class NodeGene:
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valid_types = ['sensor', 'hidden', 'output']
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def __init__(self, node_id, node_type, bias=None):
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assert node_type in self.valid_types, 'Unknown node type!'
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self.node_id = node_id
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self.node_type = node_type
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if node_type == 'hidden':
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assert bias is not None, 'Expected a bias for hidden node types!'
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self.bias = bias
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else:
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self.bias = None
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class ConnectionGene:
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def __init__(self, start, end, enabled, innovation_num, weight=None, recurrent=False):
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self.start = start
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self.end = end
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self.enabled = enabled
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self.innvovation_num = innovation_num
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self.recurrent = recurrent
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if weight is None:
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self.weight = np.random.random(1)[0] * 2 - 1.0
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else:
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self.weight = weight
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class Genotype:
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def __init__(self, action_num: int = None, num_input_nodes: int = None,
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nodes: Dict[int, NodeGene] = None, connections: List[ConnectionGene] = None):
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self.nodes = {}
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self.connections = []
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if action_num is not None and num_input_nodes is not None:
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node_id = 0
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for _ in range(num_input_nodes):
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self.nodes[node_id] = NodeGene(node_id, 'sensor')
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node_id += 1
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first_action = node_id
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for _ in range(action_num * 2):
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self.nodes[node_id] = NodeGene(node_id, 'output')
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node_id += 1
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for index in range(num_input_nodes):
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for action in range(action_num * 2):
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self.connections.append(
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ConnectionGene(index, first_action + action, True, index * (action_num * 2) + action)
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)
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if nodes is not None and connections is not None:
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self.nodes = nodes
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self.connections = connections
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def calculate_rank_of_nodes(self):
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rank_of_node = {}
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nodes_to_rank = list(self.nodes.items())
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while len(nodes_to_rank) > 0:
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for list_index, (id, node) in enumerate(nodes_to_rank):
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incoming_connections = list(filter(lambda connection: connection.end == id and
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not connection.recurrent, self.connections))
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if len(incoming_connections) == 0:
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rank_of_node[id] = 0
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nodes_to_rank.pop(list_index)
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break
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incoming_connections_starts = list(map(lambda connection: connection.start, incoming_connections))
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start_ranks = list(map(lambda element: rank_of_node[element[0]],
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filter(lambda start_node: start_node[0] in incoming_connections_starts and
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start_node[0] in rank_of_node.keys(),
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self.nodes.items())))
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if len(start_ranks) == len(incoming_connections):
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rank_of_node[id] = max(start_ranks) + 1
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nodes_to_rank.pop(list_index)
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break
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return rank_of_node
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@abstractmethod
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def mutate(self, innovation_num) -> int:
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"""
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Decides whether or not to mutate this network. Then returns the new innovation number.
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:param innovation_num: Current innovation number
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:return: Updated innovation number
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"""
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# return innovation_num
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raise NotImplementedError()
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@abstractmethod
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def cross(self, other):
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raise NotImplementedError()
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# return self
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class NeatLike(Genotype):
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connection_add_thr = 0.3
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node_add_thr = 0.3
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||||
|
||||
def mutate(self, innovation_num, allow_recurrent=False) -> int:
|
||||
"""
|
||||
Decides whether or not to mutate this network. Then returns the new innovation number.
|
||||
:param allow_recurrent: Optional parameter allowing or disallowing recurrent connections to form
|
||||
:param innovation_num: Current innovation number
|
||||
:return: Updated innovation number
|
||||
"""
|
||||
# add connection
|
||||
if np.random.random(1)[0] < self.connection_add_thr or True:
|
||||
nodes = list(self.nodes.keys())
|
||||
rank_of_node = self.calculate_rank_of_nodes()
|
||||
end_nodes = list(filter(lambda node: rank_of_node[node] > 0, nodes))
|
||||
|
||||
connection_tuple = list(map(lambda connection: (connection.start, connection.end), self.connections))
|
||||
|
||||
start = np.random.randint(0, len(nodes))
|
||||
end = np.random.randint(0, len(end_nodes))
|
||||
|
||||
tries = 50
|
||||
while (rank_of_node[end_nodes[end]] == 0 or
|
||||
((not allow_recurrent) and rank_of_node[nodes[start]] > rank_of_node[end_nodes[end]])
|
||||
or nodes[start] == end_nodes[end] or (nodes[start], end_nodes[end]) in connection_tuple) and\
|
||||
tries > 0:
|
||||
end = np.random.randint(0, len(end_nodes))
|
||||
if (not allow_recurrent) and rank_of_node[nodes[start]] > rank_of_node[end_nodes[end]]:
|
||||
start = np.random.randint(0, len(nodes))
|
||||
tries -= 1
|
||||
if tries > 0:
|
||||
innovation_num += 1
|
||||
self.connections.append(
|
||||
ConnectionGene(nodes[start], end_nodes[end], True, innovation_num,
|
||||
recurrent=rank_of_node[nodes[start]] > rank_of_node[end_nodes[end]]))
|
||||
#todo add node
|
||||
|
||||
return innovation_num
|
||||
|
||||
def cross(self, other):
|
||||
return self
|
Loading…
Add table
Add a link
Reference in a new issue