Changed the name of BuildPhenotype to BuildCPPN.

CMakeLists.txt

@@ -49,12 +49,19 @@ if(NOT SKBUILD)
 
     add_library(MultiNEAT SHARED ${SOURCE_FILES})
 
+    # target_include_directories(
+    #     MultiNEAT
+    #     PRIVATE
+    #     PUBLIC $<BUILD_INTERFACE:${CMAKE_CURRENT_SOURCE_DIR}/src>
+    #            $<INSTALL_INTERFACE:${CMAKE_INSTALL_INCLUDEDIR}/multineat>
+    #            cereal/include)
+
     target_include_directories(
-        MultiNEAT
-        PRIVATE
-        PUBLIC $<BUILD_INTERFACE:${CMAKE_CURRENT_SOURCE_DIR}/src>
-               $<INSTALL_INTERFACE:${CMAKE_INSTALL_INCLUDEDIR}/multineat>
-               cereal/include)
+    MultiNEAT
+    PRIVATE
+    PUBLIC $<BUILD_INTERFACE:${CMAKE_CURRENT_SOURCE_DIR}/src>
+            $<INSTALL_INTERFACE:${CMAKE_INSTALL_INCLUDEDIR}/multineat>
+            $<BUILD_INTERFACE:${CMAKE_CURRENT_SOURCE_DIR}/cereal/include>)
 
     # Not doing this anymore because not all package managers have it.
     # target_link_libraries(MultiNEAT PUBLIC cereal::cereal)

src/Genome.cpp

@@ -291,7 +291,7 @@ namespace NEAT
 
         m_ID = a_ID;
         int t_innovnum = 1, t_nnum = 1;
-        
+
         // override seed_type if 0 hidden units are specified
         if ((a_SeedType == 1) && (a_NumHidden == 0))
         {
@@ -487,7 +487,7 @@ namespace NEAT
                 // Start very minimally - connect a random input to each output
                 // Also connect the bias to every output
                 for (unsigned int i = 0; i < a_NumOutputs; i++)
-                {
+                {          
                     int t_inp_id = t_RNG.RandInt(1, a_NumInputs - 1);
                     int t_bias_id = a_NumInputs;
                     int t_outp_id = a_NumInputs + 1 + i;
@@ -807,7 +807,7 @@ namespace NEAT
     bool Genome::HasLoops()
     {
         NeuralNetwork net;
-        BuildPhenotype(net);
+        BuildCPPN(net);
 
         // convert the net to a Boost::Graph object
         Graph graph(NumNeurons());
@@ -837,7 +837,7 @@ namespace NEAT
 
 
     // This builds a fastnetwork structure out from the genome
-    void Genome::BuildPhenotype(NeuralNetwork &a_Net) const
+    void Genome::BuildCPPN(NeuralNetwork &a_Net) const
     {
         // first clear out the network
         a_Net.Clear();
@@ -989,7 +989,7 @@ namespace NEAT
         // Begin querying the CPPN
         // Create the neural network that will represent the CPPN
         NeuralNetwork t_temp_phenotype(true, rng);
-        BuildPhenotype(t_temp_phenotype);
+        BuildCPPN(t_temp_phenotype);
         t_temp_phenotype.Flush();
 
         // To ensure network relaxation
@@ -3669,7 +3669,7 @@ namespace NEAT
 
 
         NeuralNetwork t_temp_phenotype(true, rng);
-        BuildPhenotype(t_temp_phenotype);
+        BuildCPPN(t_temp_phenotype);
 
         // Find Inputs to Hidden connections.
         for (unsigned int i = 0; i < input_count; i++)

src/Genome.h

@@ -294,7 +294,7 @@ namespace NEAT
         void SetOffspringAmount(double a_oa);
 
         // This builds a fastnetwork structure out from the genome
-        void BuildPhenotype(NeuralNetwork &net) const;
+        void BuildCPPN(NeuralNetwork &net) const;
 
         // Projects the phenotype's weights back to the genome
         void DerivePhenotypicChanges(NeuralNetwork &a_Net);

src/NeuralNetwork.cpp

@@ -263,10 +263,11 @@ NeuralNetwork::NeuralNetwork()
     // clean up other neuron data as well
     for (unsigned int i = 0; i < m_neurons.size(); i++)
     {
-        m_neurons[i].m_a = 1;
+        m_neurons[i].m_a = 0.5; // default 1
         m_neurons[i].m_b = 0;
-        m_neurons[i].m_timeconst = m_neurons[i].m_bias =
-                m_neurons[i].m_membrane_potential = 0;
+        m_neurons[i].m_timeconst = 0;
+        m_neurons[i].m_bias = 0;
+        m_neurons[i].m_membrane_potential = 0;
     }
     Clear();
 }
@@ -339,15 +340,15 @@ void NeuralNetwork::Activate()
     // this will happen.
     for (unsigned int i = 0; i < m_connections.size(); i++)
     {
-        m_neurons[m_connections[i].m_target_neuron_idx].m_activesum +=
-                m_connections[i].m_signal;
+        m_neurons[m_connections[i].m_target_neuron_idx].m_activesum += m_connections[i].m_signal;
     }
     // Now loop nodes_activesums, pass the signals through the activation function
     // and store the result back to nodes_activations
     // also skip inputs since they do not get an activation
-    for (unsigned int i = m_num_inputs; i < m_neurons.size(); i++)
+    for (unsigned int i = m_num_inputs; i < m_neurons.size(); i++) 
     {
         double x = m_neurons[i].m_activesum;
+        // std::cout << "active sum, i.e x = " << x << std::endl;
         m_neurons[i].m_activesum = 0;
         // Apply the activation function
         double y = 0.0;

src/python/Genome.cpp

@@ -43,7 +43,7 @@ void export_Genome(pybind11::module_& mod) {
 
         .def("PrintAllTraits", &Genome::PrintAllTraits)
 
-        .def("BuildPhenotype", &Genome::BuildPhenotype)
+        .def("BuildCPPN", &Genome::BuildCPPN)
         .def("BuildHyperNEATPhenotype", &Genome::BuildHyperNEATPhenotype)
         .def("BuildESHyperNEATPhenotype", &Genome::BuildESHyperNEATPhenotype)
 

test_multineat.py

@@ -0,0 +1,5 @@
+import multineat
+
+genotype = multineat.Genome()
+
+print(genotype)

tests/python/test_raw_genome.py

@@ -65,7 +65,7 @@ def parameters():
         return params
 
     def activate_network(self, genome, _input=None):
-        genome.BuildPhenotype(self._net)
+        genome.BuildCPPN(self._net)
         _input = np.array([1, 2, 3], dtype=float) if _input is None else _input
         self._net.Input(_input)
         self._net.ActivateAllLayers()

tests/python/test_xor.py

@@ -14,7 +14,7 @@ class XorTest(unittest.TestCase):
     def test_xor(self):
         def evaluate(genome):
             net = NEAT.NeuralNetwork()
-            genome.BuildPhenotype(net)
+            genome.BuildCPPN(net)
 
             error = 0
 

tests/xor/main.cpp

@@ -29,7 +29,7 @@ double xortest(Genome& g)
     double f = 0;
 
     NeuralNetwork net;
-    g.BuildPhenotype(net);
+    g.BuildCPPN(net);
 
     static const std::vector< std::vector< double > > inputs {
             {0.0,0.0,1.0},