def get_invalid4(cfg: Config): """ Genome with connection from start to recurrent node, and from another recurrent node to the output. Configuration: 0 1 | 2> 3> | -1 -2 -3 """ # Create a dummy genome genome = Genome( key=4, num_outputs=cfg.genome.num_outputs, bot_config=cfg.bot, ) # Reset the nodes genome.nodes[0] = OutputNodeGene(key=0, cfg=cfg.genome) # OutputNode 0 genome.nodes[0].bias = 0 genome.nodes[1] = OutputNodeGene(key=1, cfg=cfg.genome) # OutputNode 1 genome.nodes[1].bias = 0 genome.nodes[2] = SimpleNodeGene(key=2, cfg=cfg.genome) # Hidden node genome.nodes[2].bias = 0 genome.nodes[3] = SimpleNodeGene(key=3, cfg=cfg.genome) # Hidden node genome.nodes[3].bias = 0 # Reset the connections genome.connections = dict() for key in [(-1, 2), (2, 2), (3, 3), (3, 1)]: genome.connections[key] = ConnectionGene(key=key, cfg=cfg.genome) genome.connections[key].weight = 1 genome.connections[key].enabled = True return genome
def get_invalid5(cfg: Config): """ Genome with connections between the hidden nodes and to one output node. Configuration: 0 1 | 2--3 -1 -2 -3 """ # Create a dummy genome genome = Genome( key=4, num_outputs=cfg.genome.num_outputs, bot_config=cfg.bot, ) # Reset the nodes genome.nodes[0] = OutputNodeGene(key=0, cfg=cfg.genome) # OutputNode 0 genome.nodes[0].bias = 0 genome.nodes[1] = OutputNodeGene(key=1, cfg=cfg.genome) # OutputNode 1 genome.nodes[1].bias = 0 genome.nodes[2] = SimpleNodeGene(key=2, cfg=cfg.genome) # Hidden node genome.nodes[2].bias = 0 genome.nodes[3] = SimpleNodeGene(key=3, cfg=cfg.genome) # Hidden node genome.nodes[3].bias = 0 # Reset the connections genome.connections = dict() for key in [(2, 3), (3, 2), (3, 1)]: genome.connections[key] = ConnectionGene(key=key, cfg=cfg.genome) genome.connections[key].weight = 1 genome.connections[key].enabled = True return genome
def get_valid2(cfg: Config): """ Network with a recurrent connection (at node 2). Configuration: 0 1 / 2> / \ -1 -2 -3 """ # Create a dummy genome genome = Genome( key=2, num_outputs=cfg.genome.num_outputs, bot_config=cfg.bot, ) # Reset the nodes genome.nodes[0] = OutputNodeGene(key=0, cfg=cfg.genome) # OutputNode 0 genome.nodes[0].bias = 0 genome.nodes[1] = OutputNodeGene(key=1, cfg=cfg.genome) # OutputNode 1 genome.nodes[1].bias = 0 genome.nodes[2] = SimpleNodeGene(key=2, cfg=cfg.genome) # Hidden node genome.nodes[2].bias = 0 # Reset the connections genome.connections = dict() for key in [(-1, 2), (-2, 2), (2, 0), (2, 2)]: genome.connections[key] = ConnectionGene(key=key, cfg=cfg.genome) genome.connections[key].weight = 1 genome.connections[key].enabled = True return genome
def get_genome2(cfg: Config): """ Genome with all biases set to 0, only simple hidden nodes used, all connections enabled with weight 1. Configuration: 0 1 / | 2 \ / | -1 -2 -3 """ # Create a dummy genome genome = Genome( key=2, num_outputs=cfg.genome.num_outputs, bot_config=cfg.bot, ) # Reset the nodes genome.nodes[0] = OutputNodeGene(key=0, cfg=cfg.genome) # OutputNode 0 genome.nodes[0].bias = 0 genome.nodes[1] = OutputNodeGene(key=1, cfg=cfg.genome) # OutputNode 1 genome.nodes[1].bias = 0 genome.nodes[2] = SimpleNodeGene(key=2, cfg=cfg.genome) # Hidden node genome.nodes[2].bias = 0 # Reset the connections genome.connections = dict() for key in [(-1, 2), (2, 0), (-3, 1)]: genome.connections[key] = ConnectionGene(key=key, cfg=cfg.genome) genome.connections[key].weight = 1 genome.connections[key].enabled = True return genome
def get_valid1(cfg: Config): """ Simple network with only one input and one output used. Configuration: 0 1 | | | -1 -2 -3 """ # Create a dummy genome genome = Genome( key=1, num_outputs=cfg.genome.num_outputs, bot_config=cfg.bot, ) # Reset the nodes genome.nodes[0] = OutputNodeGene(key=0, cfg=cfg.genome) # OutputNode 0 genome.nodes[0].bias = 0 genome.nodes[1] = OutputNodeGene(key=1, cfg=cfg.genome) # OutputNode 1 genome.nodes[1].bias = 0 genome.nodes[2] = SimpleNodeGene(key=2, cfg=cfg.genome) # Hidden node genome.nodes[2].bias = 0 # Reset the connections genome.connections = dict() for key in [(-3, 1)]: genome.connections[key] = ConnectionGene(key=key, cfg=cfg.genome) genome.connections[key].weight = 1 genome.connections[key].enabled = True return genome
def get_circular2(cfg: Config): """ Genome with circular connections, not connected to the output genome. Configuration: 0 1 2---3 | | -1 -2 -3 """ # Create a dummy genome genome = Genome( key=2, num_outputs=cfg.genome.num_outputs, bot_config=cfg.bot, ) # Reset the nodes genome.nodes[0] = OutputNodeGene(key=0, cfg=cfg.genome) # OutputNode 0 genome.nodes[0].bias = 0 genome.nodes[1] = OutputNodeGene(key=1, cfg=cfg.genome) # OutputNode 1 genome.nodes[1].bias = 0 genome.nodes[2] = SimpleNodeGene(key=2, cfg=cfg.genome) # Hidden node genome.nodes[2].bias = 0 genome.nodes[3] = SimpleNodeGene(key=3, cfg=cfg.genome) # Hidden node genome.nodes[3].bias = 0 # Reset the connections genome.connections = dict() for key in [(-1, 2), (2, 3), (3, 2), (-2, 3)]: genome.connections[key] = ConnectionGene(key=key, cfg=cfg.genome) genome.connections[key].weight = 1 genome.connections[key].enabled = True return genome
def get_pruned2(cfg: Config): """ Genome with partially valid connections and nodes (dangling node on other hidden node). Configuration: 0 1 / 2---3> | -1 -2 -3 """ # Create a dummy genome genome = Genome( key=2, num_outputs=cfg.genome.num_outputs, bot_config=cfg.bot, ) # Reset the nodes genome.nodes[0] = OutputNodeGene(key=0, cfg=cfg.genome) # OutputNode 0 genome.nodes[0].bias = 0 genome.nodes[1] = OutputNodeGene(key=1, cfg=cfg.genome) # OutputNode 1 genome.nodes[1].bias = 0 genome.nodes[2] = SimpleNodeGene(key=2, cfg=cfg.genome) # Hidden node genome.nodes[2].bias = 0 genome.nodes[3] = SimpleNodeGene(key=3, cfg=cfg.genome) # Hidden node genome.nodes[3].bias = 0 # Reset the connections genome.connections = dict() for key in [(-1, 2), (2, 0), (2, 3), (3, 3)]: genome.connections[key] = ConnectionGene(key=key, cfg=cfg.genome) genome.connections[key].weight = 1 genome.connections[key].enabled = True return genome
def get_invalid3(cfg: Config): """ Genome without connections to the input nodes. Configuration: 0 1 | 2> -1 -2 -3 """ # Create a dummy genome genome = Genome( key=3, num_outputs=cfg.genome.num_outputs, bot_config=cfg.bot, ) # Reset the nodes genome.nodes[0] = OutputNodeGene(key=0, cfg=cfg.genome) # OutputNode 0 genome.nodes[0].bias = 0 genome.nodes[1] = OutputNodeGene(key=1, cfg=cfg.genome) # OutputNode 1 genome.nodes[1].bias = 0 genome.nodes[2] = SimpleNodeGene(key=2, cfg=cfg.genome) # Hidden node genome.nodes[2].bias = 0 # Reset the connections genome.connections = dict() for key in [(2, 2), (2, 1)]: genome.connections[key] = ConnectionGene(key=key, cfg=cfg.genome) genome.connections[key].weight = 1 genome.connections[key].enabled = True return genome
def get_topology22222(gid: int, cfg: Config): """ Create a uniformly and randomly sampled genome of fixed topology: Sigmoid with bias 1.5 --> Actuation default of 95,3% (key=0, bias=1.5) (key=1, bias=?) ____ / / / / SRU-X / | _____/ | / (key=-1) """ # Create an initial dummy genome with fixed configuration genome = Genome( key=gid, num_outputs=cfg.genome.num_outputs, bot_config=cfg.bot, ) # Setup the parameter-ranges conn_range = cfg.genome.weight_max_value - cfg.genome.weight_min_value bias_range = cfg.genome.bias_max_value - cfg.genome.bias_min_value rnn_range = cfg.genome.rnn_max_value - cfg.genome.rnn_min_value # Create the nodes genome.nodes[0] = OutputNodeGene(key=0, cfg=cfg.genome) # OutputNode 0 genome.nodes[0].bias = 1.5 # Drive with 0.953 actuation by default genome.nodes[1] = OutputNodeGene(key=1, cfg=cfg.genome) # OutputNode 1 genome.nodes[1].bias = random( ) * bias_range + cfg.genome.bias_min_value # Uniformly sampled bias genome.nodes[2] = ConvexSruNodeGene(key=2, cfg=cfg.genome, input_keys=[-1]) # Hidden node genome.nodes[2].bias = 0 # Bias is irrelevant for GRU-node # Create the connections genome.connections = dict() # input2gru key = (-1, 2) genome.connections[key] = ConnectionGene(key=key, cfg=cfg.genome) genome.connections[key].weight = 1 # Simply forward distance genome.connections[key].enabled = True # gru2output - Uniformly sampled key = (2, 1) genome.connections[key] = ConnectionGene(key=key, cfg=cfg.genome) genome.connections[key].weight = 3 # Enforce capabilities of full spectrum genome.connections[key].enabled = True # input2output - Uniformly sampled key = (-1, 1) genome.connections[key] = ConnectionGene(key=key, cfg=cfg.genome) genome.connections[ key].weight = random() * conn_range + cfg.genome.weight_min_value genome.connections[key].enabled = True genome.update_rnn_nodes(config=cfg.genome) return genome
def get_topology2(cfg, random_init: bool = False): """ Simple genome with only two connections: 0 1 / | 2 / \ / -1 """ # Create an initial dummy genome with fixed configuration genome = Genome( key=0, num_outputs=cfg.genome.num_outputs, bot_config=cfg.bot, ) # Create the nodes genome.nodes[0] = OutputNodeGene(key=0, cfg=cfg.genome) # OutputNode 0 genome.nodes[0].bias = 0 # Drive with 0.5 actuation by default genome.nodes[1] = OutputNodeGene(key=1, cfg=cfg.genome) # OutputNode 1 genome.nodes[1].bias = 0 # Drive with 0.5 actuation by default genome.nodes[2] = GruNodeGene(key=2, cfg=cfg.genome, input_keys=[-1], input_keys_full=[-1]) # Hidden node genome.nodes[2].bias = 0 # Bias is irrelevant for GRU-node if not random_init: genome.nodes[2].bias_h = np.zeros((3, )) genome.nodes[2].weight_xh_full = np.zeros((3, 1)) genome.nodes[2].weight_hh = np.zeros((3, 1)) # Create the connections genome.connections = dict() # Input-GRU key = (-1, 2) genome.connections[key] = ConnectionGene(key=key, cfg=cfg.genome) genome.connections[key].weight = 1 # Simply forward distance genome.connections[key].enabled = True # GRU-Output key = (2, 0) genome.connections[key] = ConnectionGene(key=key, cfg=cfg.genome) genome.connections[ key].weight = 3 # Increase magnitude to be a value between -3..3 (possible to steer left wheel) genome.connections[key].enabled = True # GRU-Output key = (-1, 1) genome.connections[key] = ConnectionGene(key=key, cfg=cfg.genome) genome.connections[key].weight = -1 genome.connections[key].enabled = True genome.update_rnn_nodes(config=cfg.genome) return genome
def test_distance_failure(self): """> Test if the distance-parameter is updated correctly after unsuccessful read.""" # Folder must be root to load in make_net properly if os.getcwd().split('\\')[-1] == 'tests': os.chdir('..') # Setup the nodes to compare cfg = Config() output_node = OutputNodeGene(key=0, cfg=cfg.genome) simple_node = SimpleNodeGene(key=1, cfg=cfg.genome) # Create an empty NodeComparingCache cache = NodeComparingCache() # Fetch the result of the distance-measure result = cache( node0=output_node, node1=simple_node, conn0={}, conn1={}, cfg=cfg.genome, ) # Check the distance-parameter self.assertEqual(result, (False, None)) self.assertEqual(cache.comparable[(0, 1)], False) self.assertFalse((1, 0) in cache.comparable)
def get_deep_genome3(cfg: Config): """ Genome with all biases set to 0, only simple hidden nodes used, all connections enabled with weight 1. Configuration: 0 1 | | 16 | | \ | 15 | | | | | 14 | | | | | -1 -2 -3 """ # Create a dummy genome genome = Genome( key=3, num_outputs=cfg.genome.num_outputs, bot_config=cfg.bot, ) # Reset the nodes genome.nodes[0] = OutputNodeGene(key=0, cfg=cfg.genome) # OutputNode 0 genome.nodes[0].bias = 0 genome.nodes[1] = OutputNodeGene(key=1, cfg=cfg.genome) # OutputNode 1 genome.nodes[1].bias = 0 for k in [14, 15, 16]: genome.nodes[k] = SimpleNodeGene(key=k, cfg=cfg.genome) # Hidden node genome.nodes[k].bias = 0 # Reset the connections genome.connections = dict() for key in [(-1, 14), (14, 15), (15, 16), (16, 0), (-2, 16), (-3, 1)]: genome.connections[key] = ConnectionGene(key=key, cfg=cfg.genome) genome.connections[key].weight = 1 genome.connections[key].enabled = True return genome
def test_different_type(self): """> Test if False is returned when comparing nodes of different type.""" # Folder must be root to load in make_net properly if os.getcwd().split('\\')[-1] == 'tests': os.chdir('..') # Setup the nodes to compare cfg = Config() output_node = OutputNodeGene(key=0, cfg=cfg.genome) simple_node = SimpleNodeGene(key=1, cfg=cfg.genome) # Create an empty NodeComparingCache cache = NodeComparingCache() # Fetch the result of the distance-measure result = cache( node0=output_node, node1=simple_node, conn0={}, conn1={}, cfg=cfg.genome, ) self.assertEqual(result, (False, None))
def create_output_node(config: GenomeConfig, node_id: int): node = OutputNodeGene(node_id, cfg=config) return node
def get_topology1(gid: int, cfg: Config): """ Create a uniformly and randomly sampled genome of fixed topology: (key=0, bias=1.5) (key=1, bias=0) ____ / / / / GRU / | _____/ | / (key=-1) """ # Create an initial dummy genome with fixed configuration genome = Genome( key=gid, num_outputs=cfg.genome.num_outputs, bot_config=cfg.bot, ) # Create the nodes genome.nodes[0] = OutputNodeGene(key=0, cfg=cfg.genome) # OutputNode 0 genome.nodes[0].bias = 1.5 # Drive with full actuation by default genome.nodes[1] = OutputNodeGene(key=1, cfg=cfg.genome) # OutputNode 1 genome.nodes[1].bias = 0 # Drive with 0.5 actuation by default genome.nodes[2] = GruNodeGene(key=2, cfg=cfg.genome, input_keys=[-1], input_keys_full=[-1]) # Hidden node genome.nodes[2].bias = 0 # Bias is irrelevant for GRU-node # Setup the parameter-ranges conn_range = cfg.genome.weight_max_value - cfg.genome.weight_min_value bias_range = cfg.genome.bias_max_value - cfg.genome.bias_min_value rnn_range = cfg.genome.rnn_max_value - cfg.genome.rnn_min_value # Uniformly sample the genome's GRU-component genome.nodes[2].bias_h = rand_arr( (3, )) * bias_range + cfg.genome.bias_min_value genome.nodes[2].weight_xh_full = rand_arr( (3, 1)) * rnn_range + cfg.genome.weight_min_value genome.nodes[2].weight_hh = rand_arr( (3, 1)) * rnn_range + cfg.genome.weight_min_value # Create the connections genome.connections = dict() # input2gru key = (-1, 2) genome.connections[key] = ConnectionGene(key=key, cfg=cfg.genome) genome.connections[key].weight = 1 # Simply forward distance genome.connections[key].enabled = True # gru2output - Uniformly sampled key = (2, 1) genome.connections[key] = ConnectionGene(key=key, cfg=cfg.genome) genome.connections[ key].weight = random() * conn_range + cfg.genome.weight_min_value genome.connections[key].enabled = True # input2output - Uniformly sampled key = (-1, 1) genome.connections[key] = ConnectionGene(key=key, cfg=cfg.genome) genome.connections[ key].weight = random() * conn_range + cfg.genome.weight_min_value genome.connections[key].enabled = True genome.update_rnn_nodes(config=cfg.genome) return genome
def get_output_node_gene(key, config): return OutputNodeGene(key, config)
def get_topology(pop_name, gid: int, cfg: Config): """ Create a uniformly and randomly sampled genome of fixed topology: Sigmoid with bias 1.5 --> Actuation default of 95,3% (key=0, bias=1.5) (key=1, bias=?) ____ / / / / GRU / | _____/ | / (key=-1) """ # Create an initial dummy genome with fixed configuration genome = Genome( key=gid, num_outputs=cfg.genome.num_outputs, bot_config=cfg.bot, ) # Setup the parameter-ranges conn_range = cfg.genome.weight_max_value - cfg.genome.weight_min_value bias_range = cfg.genome.bias_max_value - cfg.genome.bias_min_value rnn_range = cfg.genome.rnn_max_value - cfg.genome.rnn_min_value # Create the output nodes genome.nodes[0] = OutputNodeGene(key=0, cfg=cfg.genome) # OutputNode 0 genome.nodes[0].bias = 1.5 # Drive with 0.953 actuation by default genome.nodes[1] = OutputNodeGene(key=1, cfg=cfg.genome) # OutputNode 1 if pop_name in [P_BIASED]: genome.nodes[1].bias = normal( 1.5, .1) # Initially normally distributed around bias of other output else: genome.nodes[1].bias = random( ) * bias_range + cfg.genome.bias_min_value # Uniformly sampled bias # Setup the recurrent unit if pop_name in [P_GRU_NR]: genome.nodes[2] = GruNoResetNodeGene(key=2, cfg=cfg.genome, input_keys=[-1], input_keys_full=[-1]) # Hidden genome.nodes[2].bias_h = rand_arr( (2, )) * bias_range + cfg.genome.bias_min_value genome.nodes[2].weight_xh_full = rand_arr( (2, 1)) * rnn_range + cfg.genome.weight_min_value genome.nodes[2].weight_hh = rand_arr( (2, 1)) * rnn_range + cfg.genome.weight_min_value else: genome.nodes[2] = GruNodeGene(key=2, cfg=cfg.genome, input_keys=[-1], input_keys_full=[-1]) # Hidden node genome.nodes[2].bias_h = rand_arr( (3, )) * bias_range + cfg.genome.bias_min_value genome.nodes[2].weight_xh_full = rand_arr( (3, 1)) * rnn_range + cfg.genome.weight_min_value genome.nodes[2].weight_hh = rand_arr( (3, 1)) * rnn_range + cfg.genome.weight_min_value genome.nodes[2].bias = 0 # Bias is irrelevant for GRU-node # Create the connections genome.connections = dict() # input2gru - Uniformly sampled on the positive spectrum key = (-1, 2) genome.connections[key] = ConnectionGene(key=key, cfg=cfg.genome) if pop_name in [P_BIASED]: genome.connections[ key].weight = 6 # Maximize connection, GRU can always lower values flowing through else: genome.connections[ key].weight = random() * conn_range + cfg.genome.weight_min_value genome.connections[key].enabled = True # gru2output - Uniformly sampled on the positive spectrum key = (2, 1) genome.connections[key] = ConnectionGene(key=key, cfg=cfg.genome) genome.connections[ key].weight = random() * conn_range + cfg.genome.weight_min_value if pop_name in [P_BIASED]: genome.connections[key].weight = abs( genome.connections[key].weight) # Always positive! genome.connections[key].enabled = True # input2output - Uniformly sampled key = (-1, 1) genome.connections[key] = ConnectionGene(key=key, cfg=cfg.genome) genome.connections[ key].weight = random() * conn_range + cfg.genome.weight_min_value if pop_name in [P_BIASED]: genome.connections[key].weight = -abs( genome.connections[key].weight) # Always negative! genome.connections[key].enabled = True # Enforce the topology constraints enforce_topology(pop_name=pop_name, genome=genome) genome.update_rnn_nodes(config=cfg.genome) return genome