Genetic Algorithm now only saves the best MLP

neural_network/include/GeneticAlgorithm.hpp

@@ -43,7 +43,9 @@ class GeneticAlgorithm : public NeuralNetwork
     /// @param nbOutputs The size of the mlp output layer..
     Individual(dynet::ParameterCollection & model, int nbInputs, const std::string & topology, int nbOutputs);
     /// @brief Create a blank individual, so that it will be loaded from a saved model.
-    Individual();
+    ///
+    /// @param bestId The id of the individual that was saved in the file.
+    Individual(unsigned int bestId);
     void becomeChildOf(Individual * other);
     void becomeChildOf(Individual * mom, Individual * dad);
     void mutate(float probability);

neural_network/src/GeneticAlgorithm.cpp

@@ -39,19 +39,7 @@ void GeneticAlgorithm::init(int nbInputs, const std::string & topology, int nbOu
 
 std::vector<float> GeneticAlgorithm::predict(FeatureModel::FeatureDescription & fd)
 {
-  std::vector<float> prediction = generation[0]->mlp.predict(fd);
-
-  for (unsigned int i = 1; i < generation.size(); i++)
-  {
-    auto otherPred = generation[i]->mlp.predict(fd);
-    for (unsigned int j = 0; j < prediction.size(); j++)
-      prediction[j] += otherPred[j];
-  }
-
-    for (unsigned int j = 0; j < prediction.size(); j++)
-      prediction[j] /= generation.size();
-
-  return prediction;
+  return generation[0]->mlp.predict(fd);
 }
 
 float GeneticAlgorithm::update(FeatureModel::FeatureDescription & fd, int gold)
@@ -128,16 +116,10 @@ float GeneticAlgorithm::update(FeatureModel::FeatureDescription & fd, int gold)
 void GeneticAlgorithm::save(const std::string & filename)
 {
   File * file = new File(filename, "w");
-  fprintf(file->getDescriptor(), "%d %d %s\n", nbInputs, nbOutputs, topology.c_str());
+  fprintf(file->getDescriptor(), "%u\n", generation[0]->id);
   delete file;
-
-  unsigned int quarter = generation.size() / 4;
-
-  for (unsigned int i = 0; i < quarter; i++)
-  {
-    generation[i]->mlp.saveStruct(filename);
-    generation[i]->mlp.saveParameters(filename);
-  }
+  generation[0]->mlp.saveStruct(filename);
+  generation[0]->mlp.saveParameters(filename);
 }
 
 void GeneticAlgorithm::printTopology(FILE * output)
@@ -156,37 +138,17 @@ void GeneticAlgorithm::printTopology(FILE * output)
 void GeneticAlgorithm::load(const std::string & filename)
 {
   File * file = new File(filename, "r");
-  int i, o;
-  char buffer[1024];
-  if (fscanf(file->getDescriptor(), "%d %d %[^\n]\n", &i, &o, buffer) != 3)
+  unsigned int bestId;
+  if (fscanf(file->getDescriptor(), "%u\n", &bestId) != 1)
   {
-    fprintf(stderr, "ERROR (%s) : file \'%s\' bad format. Aborting.\n", ERRINFO, filename.c_str());
+    fprintf(stderr, "ERROR (%s) : expected best id when reading file \'%s\'. Aborting.\n", ERRINFO, filename.c_str());
     exit(1);
   }
-
   delete file;
+  generation.emplace_back(new Individual(bestId));
 
-  this->nbInputs = i;
-  this->nbOutputs = o;
-  this->topology = buffer;
-
-  auto splited = split(topology, ' ');
-  if (splited.size() != 2 || !isNum(splited[0]))
-  {
-    fprintf(stderr, "ERROR (%s) : wrong topology \'%s\'. Aborting.\n", ERRINFO, topology.c_str());
-    exit(1);
-  }
-
-  int nbElems = std::stoi(splited[0]);
-
-  for (int i = 0; i < nbElems; i++)
-    generation.emplace_back(new Individual());
-
-  for (int i = 0; i < nbElems; i++)
-  {
-    generation[i]->mlp.loadStruct(model, filename, 0);
-    generation[i]->mlp.loadParameters(model, filename);
-  }
+  generation[0]->mlp.loadStruct(model, filename, 0);
+  generation[0]->mlp.loadParameters(model, filename);
 }
 
 GeneticAlgorithm::Individual::Individual(dynet::ParameterCollection & model, int nbInputs, const std::string & topology, int nbOutputs) : mlp("MLP_" + std::to_string(idCount))
@@ -199,10 +161,10 @@ GeneticAlgorithm::Individual::Individual(dynet::ParameterCollection & model, int
   mutate(1.0);
 }
 
-GeneticAlgorithm::Individual::Individual()
-  : mlp("MLP_" + std::to_string(idCount))
+GeneticAlgorithm::Individual::Individual(unsigned int bestId)
+  : mlp("MLP_" + std::to_string(bestId))
 {
-  this->id = idCount++;
+  this->id = bestId;
   this->loss = 0.0;
   this->value = 0.0;
 }