Fixed old FFGenome tests to use DefaultGenome.

Added convenience add node/connection methods to DefaultGenome.
Moved gene validation into validate() methods.

Author: CodeReclaimers

neat/genes.py

@@ -7,21 +7,22 @@ class NodeGene(object):
     """ Encodes parameters for a single artificial neuron. """
 
     def __init__(self, key, bias, response, aggregation, activation):
-        # TODO: Move these asserts into an external validation mechanism that can be omitted at runtime if desired.
-        #       Maybe this class should implement a validate(config) method that can optionally be called
-        #       by the NEAT framework?
-        # TODO: Validate aggregation and activation against current configuration.
-        assert type(bias) is float
-        assert type(response) is float
-        assert type(aggregation) is str
-        assert type(activation) is str
-
         self.key = key
         self.bias = bias
         self.response = response
         self.aggregation = aggregation
         self.activation = activation
 
+        # TODO: Implement an external validation mechanism that can be omitted at runtime if desired.
+        self.validate()
+
+    def validate(self):
+        # TODO: Validate aggregation and activation against current configuration.
+        assert type(self.bias) is float
+        assert type(self.response) is float
+        assert type(self.aggregation) is str
+        assert type(self.activation) is str
+
     def __str__(self):
         return 'NodeGene(key= {0}, bias={1}, response={2}, aggregation={3}, activation={4})'.format(
             self.key, self.bias, self.response, self.aggregation, self.activation)
@@ -57,12 +58,6 @@ def mutate(self, config):
 
 class ConnectionGene(object):
     def __init__(self, input_id, output_id, weight, enabled):
-        # TODO: Move these asserts into an external validation mechanism that can be omitted at runtime if desired.
-        assert type(input_id) is int
-        assert type(output_id) is int
-        assert type(weight) is float
-        assert type(enabled) is bool
-
         self.key = (input_id, output_id)
         self.input = input_id
         self.output = output_id
@@ -72,6 +67,15 @@ def __init__(self, input_id, output_id, weight, enabled):
         # provide a similar effect depending on the weight range and mutation rate.
         self.enabled = enabled
 
+        # TODO: Implement an external validation mechanism that can be omitted at runtime if desired.
+        self.validate()
+
+    def validate(self):
+        assert type(self.key) is tuple
+        assert len(self.key) == 2
+        assert type(self.weight) is float
+        assert type(self.enabled) is bool
+
     # TODO: Factor out mutation into a separate class.
     def mutate(self, config):
         self.weight = config.mutate_weight(self.weight)

neat/genome.py

@@ -42,6 +42,14 @@ def __init__(self, key, config):
         self.fitness = None
         self.cross_fitness = None
 
+    def add_node(self, key, bias, response, aggregation, activation):
+        # TODO: Add validation of this node addition.
+        self.nodes[key] = NodeGene(key, bias, response, aggregation, activation)
+
+    def add_connection(self, input_key, output_key, weight, enabled):
+        # TODO: Add validation of this connection addition.
+        self.connections[input_key, output_key] = ConnectionGene(input_key, output_key, weight, enabled)
+
     def mutate(self, config):
         """ Mutates this genome. """
 

tests/test_feedforward.py

@@ -1,7 +1,8 @@
 import random
 
 from neat import nn
-from neat.genome import FFGenome
+from neat.config import Config
+from neat.genome import DefaultGenome
 from neat.genes import NodeGene, ConnectionGene
 
 
@@ -157,11 +158,14 @@ def test_fuzz_feed_forward_layers():
 
 
 def test_simple_nohidden():
-    g = FFGenome(0, [0, 1], [2])
-    g.nodes[2] = NodeGene(2, 0.0, 1.0, 'sum', 'tanh')
-    g.connections[(0, 2)] = ConnectionGene(0, 2, 1.0, True)
-    g.connections[(1, 2)] = ConnectionGene(1, 2, -1.0, True)
-    net = nn.create_feed_forward_phenotype(g)
+    config = Config()
+    config.set_input_output_sizes(2, 1)
+    g = DefaultGenome(0, config)
+    g.add_node(0, 0.0, 1.0, 'sum', 'tanh')
+    g.add_connection(-1, 0, 1.0, True)
+    g.add_connection(-2, 0, -1.0, True)
+
+    net = nn.create_feed_forward_phenotype(g, config)
 
     v00 = net.serial_activate([0.0, 0.0])
     assert_almost_equal(v00[0], 0.0, 1e-3)
@@ -177,15 +181,18 @@ def test_simple_nohidden():
 
 
 def test_simple_hidden():
-    g = FFGenome(0, [0, 1], [2])
-    g.nodes[2] = NodeGene(2, 0.0, 1.0, 'sum', 'identity')
-    g.nodes[3] = NodeGene(3, -0.5, 5.0, 'sum', 'sigmoid')
-    g.nodes[4] = NodeGene(3, -1.5, 5.0, 'sum', 'sigmoid')
-    g.connections[(0, 3)] = ConnectionGene(0, 3, 1.0, True)
-    g.connections[(1, 4)] = ConnectionGene(1, 4, 1.0, True)
-    g.connections[(3, 2)] = ConnectionGene(3, 2, 1.0, True)
-    g.connections[(4, 2)] = ConnectionGene(4, 2, -1.0, True)
-    net = nn.create_feed_forward_phenotype(g)
+    config = Config()
+    config.set_input_output_sizes(2, 1)
+    g = DefaultGenome(0, config)
+
+    g.add_node(0, 0.0, 1.0, 'sum', 'identity')
+    g.add_node(1, -0.5, 5.0, 'sum', 'sigmoid')
+    g.add_node(2, -1.5, 5.0, 'sum', 'sigmoid')
+    g.add_connection(-1, 1, 1.0, True)
+    g.add_connection(-2, 2, 1.0, True)
+    g.add_connection(1, 0, 1.0, True)
+    g.add_connection(2, 0, -1.0, True)
+    net = nn.create_feed_forward_phenotype(g, config)
 
     v00 = net.serial_activate([0.0, 0.0])
     assert_almost_equal(v00[0], 0.195115, 1e-3)