testing_Activate.cpp

testing_Activate.cpp

void printGenes(std::vector<NeuronGene>& genes)
  {
    for (size_t i = 0; i < genes.size(); ++i)
    {
      std::cout << "Id: " << genes[i].Id() << std::endl;
      std::cout << "Type: " << genes[i].Type() << std::endl;
      std::cout << "Input: " << genes[i].Input() << std::endl;
      std::cout << "Activation: " << genes[i].Activation() << std::endl;
      std::cout << "Depth: " << genes[i].Depth() << std::endl;
      std::cout << "..................................\n";
    }
  }

  void printLinks(std::vector<LinkGene>& links)
  {
    for (size_t i = 0; i < links.size(); ++i)
    {
      std::cout << "Id: " << links[i].FromNeuronId() << std::endl;
      std::cout << "FromNeuronId: " << links[i].FromNeuronId() << std::endl;
      std::cout << "ToNeuronId: " << links[i].ToNeuronId() << std::endl;
      std::cout << "Weight: " << links[i].Weight() << std::endl;
      std::cout << "Enabled: " << links[i].Enabled() << std::endl;
      std::cout << "..................................\n";
    }
  }

  // Activate genome. The last dimension of input is always 1 for bias. 0 means no bias.
  void Activate(std::vector<double>& input) {
    assert(input.size() == aNumInput);

    SortLinkGenes();

    // Set all neurons' input to be 0.
    for (ssize_t i=0; i<NumNeuron(); ++i) {
      aNeuronGenes[i].Input(0);
    }

    std::cout << "===============================================\n";
    printGenes(aNeuronGenes);
    std::cout << "-----------------------------------------------\n";
    printLinks(aLinkGenes);
    std::cout << "===============================================\n";


    // Set input neurons.
    for (ssize_t i=0; i<aNumInput; ++i) {
      aNeuronGenes[i].Activation(input[i]);  // assume INPUT, BIAS, OUTPUT, HIDDEN sequence
    }

    std::cout << "===============================================\n";
    printGenes(aNeuronGenes);
    std::cout << "-----------------------------------------------\n";
    printLinks(aLinkGenes);
    std::cout << "===============================================\n";

    // Activate hidden and output neurons.
    for (ssize_t i = 0; i < NumLink(); ++i) {
      if (aLinkGenes[i].Enabled()) {
        ssize_t toNeuronIdx = GetNeuronIndex(aLinkGenes[i].ToNeuronId());
        ssize_t fromNeuronIdx = GetNeuronIndex(aLinkGenes[i].FromNeuronId());
        double input = aNeuronGenes[toNeuronIdx].Input() + 
                       aNeuronGenes[fromNeuronIdx].Activation() * aLinkGenes[i].Weight();



        std::cout << "!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!\n";
        std::cout << "from: " << aNeuronGenes[fromNeuronIdx].Id() << std::endl;
        std::cout << "to: " << aNeuronGenes[toNeuronIdx].Id() << std::endl;
        std::cout << "aNeuronGenes[toNeuronIdx].Input(): " << aNeuronGenes[toNeuronIdx].Input() << std::endl;
        std::cout << "aNeuronGenes[fromNeuronIdx].Activation(): " << aNeuronGenes[fromNeuronIdx].Activation() << std::endl;
        std::cout << "aLinkGenes[i].Weight(): " << aLinkGenes[i].Weight() << std::endl;
        std::cout << "input: " << input << std::endl;
        std::cout << "!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!\n";
        aNeuronGenes[toNeuronIdx].Input(input);
        
        if (i == NumLink() - 1) {
          std::cout << "output id: " << aNeuronGenes[toNeuronIdx].Id() << std::endl;
          aNeuronGenes[toNeuronIdx].CalcActivation();

          std::cout << "activation: " << aNeuronGenes[toNeuronIdx].Activation() << std::endl;
        } else if (GetNeuronIndex(aLinkGenes[i + 1].ToNeuronId()) != toNeuronIdx) {
          aNeuronGenes[toNeuronIdx].CalcActivation();
          std::cout << "activation: " << aNeuronGenes[toNeuronIdx].Activation() << std::endl;
        }
      }
    }

    std::cout << "===============================================\n";
    printGenes(aNeuronGenes);
    std::cout << "-----------------------------------------------\n";
    printLinks(aLinkGenes);
    std::cout << "===============================================\n";


    exit(0);
  }