def test_clean_start(self):
"""> Test if the restart indeed removes all the parameters."""
# Folder must be root to load in make_net properly
if os.getcwd().split('\\')[-1] == 'tests': os.chdir('..')
# Get the nodes
cfg = Config()
cfg.genome.compatibility_weight = 1
node0 = SimpleNodeGene(key=0, cfg=cfg.genome)
node1 = SimpleNodeGene(key=1, cfg=cfg.genome)
node3 = SimpleNodeGene(key=2, cfg=cfg.genome)
# Create an empty NodeComparingCache
cache = NodeComparingCache()
# Feed the nodes to the NodeComparingCache (successful matches)
cache(node0, node1, {}, {}, cfg.genome)
cache(node0, node3, {}, {}, cfg.genome)
cache(node1, node3, {}, {}, cfg.genome)
# Reset the cache
cache.reset()
self.assertEqual(cache.comparable, dict())
self.assertEqual(next(cache.indexer), 0)
self.assertEqual(cache.index_map, dict())
def test_distance_success(self):
"""> Test if the distance-parameter is updated correctly after successful 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()
cfg.genome.compatibility_weight = 1
node0 = SimpleNodeGene(key=0, cfg=cfg.genome)
node0.bias = 0
node1 = SimpleNodeGene(key=1, cfg=cfg.genome)
node1.bias = 1
conn_set1 = get_connections(receiving_key=0, sending_keys={-1, -2}, cfg=cfg.genome)
conn_set2 = get_connections(receiving_key=1, sending_keys={-1, -2}, cfg=cfg.genome)
# Create an empty NodeComparingCache
cache = NodeComparingCache()
# Cache the result of the distance-measure
result = cache(
node0=node0,
node1=node1,
conn0=conn_set1,
conn1=conn_set2,
cfg=cfg.genome,
)
# Check the distance-parameter
self.assertEqual(result, (True, 1.0))
self.assertEqual(cache.comparable[(0, 1)], True)
self.assertFalse((1, 0) in cache.comparable)
def test_increasing_nodes(self):
"""> Test if the index-parameter keeps increasing with each newly fed node."""
# Folder must be root to load in make_net properly
if os.getcwd().split('\\')[-1] == 'tests': os.chdir('..')
# Get the nodes
cfg = Config()
cfg.genome.compatibility_weight = 1
node0 = SimpleNodeGene(key=0, cfg=cfg.genome)
node1 = SimpleNodeGene(key=1, cfg=cfg.genome)
node3 = SimpleNodeGene(key=2, cfg=cfg.genome)
# Create an empty NodeComparingCache
cache = NodeComparingCache()
# Feed the nodes to the NodeComparingCache (successful matches)
r1 = cache(node0, node1, {}, {}, cfg.genome)
r2 = cache(node0, node3, {}, {}, cfg.genome)
r3 = cache(node1, node3, {}, {}, cfg.genome)
self.assertTrue(r1[0])
self.assertTrue(r2[0])
self.assertTrue(r3[0])
# Get the current indexer-value
self.assertEqual(next(cache.indexer), 3)
# Check the index_map, keys should be merged
self.assertEqual(cache.index_map[node0.key], cache.index_map[node1.key])
self.assertEqual(cache.index_map[node1.key], cache.index_map[node3.key])
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_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_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 test_successful_distance(self):
"""> Test when a distance-measure should be performed."""
# 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()
cfg.genome.compatibility_weight = 1
node0 = SimpleNodeGene(key=0, cfg=cfg.genome)
node0.bias = 0
node1 = SimpleNodeGene(key=1, cfg=cfg.genome)
node1.bias = 1
conn_set1 = get_connections(receiving_key=0, sending_keys={-1, -2}, cfg=cfg.genome)
conn_set2 = get_connections(receiving_key=1, sending_keys={-1, -2}, cfg=cfg.genome)
# Create an empty NodeComparingCache
cache = NodeComparingCache()
# Fetch the result of the distance-measure
result = cache(
node0=node0,
node1=node1,
conn0=conn_set1,
conn1=conn_set2,
cfg=cfg.genome,
)
self.assertEqual(result, (True, 1.0)) # Only distance-difference is in the bias
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_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_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_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_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 test_different_connections(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()
node0 = SimpleNodeGene(key=0, cfg=cfg.genome)
node1 = SimpleNodeGene(key=1, cfg=cfg.genome)
conn_set1 = get_connections(receiving_key=0, sending_keys={-1, -2}, cfg=cfg.genome)
conn_set2 = get_connections(receiving_key=1, sending_keys={-2, -3}, cfg=cfg.genome)
# Create an empty NodeComparingCache
cache = NodeComparingCache()
# Fetch the result of the distance-measure
result = cache(
node0=node0,
node1=node1,
conn0=conn_set1,
conn1=conn_set2,
cfg=cfg.genome,
)
self.assertEqual(result, (False, None))
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 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 create_node(config: GenomeConfig, node_id: int):
node = SimpleNodeGene(node_id, cfg=config)
return node
def get_simple_node_gene(key, config):
return SimpleNodeGene(key, config)