// GET: Training/Delete/5
public ActionResult Delete(int id)
{
training tr = trs.getOneTraining(id);
trs.DeleteTraining(id);
return(RedirectToAction("Index"));
}
public ActionResult Delete(int id, FormCollection collection)
{
training t = trainingService.GetById(id);
trainingService.Delete(t);
trainingService.Commit();
return(RedirectToAction("Index"));
}
// Start is called before the first frame update
public void trainingStart()
{
// Crée des gènes randoms
int size = batches * carsPerBatch;
genes = new training[size];
for (int i = 0; i < size; i++)
{
genes[i] = new training(layers);
}
newBatch();
}
public ActionResult Create(training t)
{
if (ModelState.IsValid)
{
trs.addTraining(t);
return(RedirectToAction("Index"));
}
else
{
return(View());
}
}
public ActionResult Edit(int id, training train)
{
training tr = trs.getOneTraining(id);
tr.lieu = train.lieu;
tr.dateDebut = train.dateDebut;
tr.dateFin = train.dateFin;
tr.nombreInscrit = train.nombreInscrit;
tr.theme = train.theme;
trs.UpdateTraining(tr);
return(RedirectToAction("Index"));
}
public bool AddTrainings(string userId, string trainings, int TPID)
{
trainer trainer = db.trainer.FirstOrDefault(x => x.ID == userId);
trainingProgram trainingProgram = db.trainingProgram.FirstOrDefault(x => x.TPID == TPID);
if (trainer.TRID != trainingProgram.trainer_TRID)
{
return(false);
}
else
{
try
{
List <int> TIDList = new List <int>();
var trainingArray = JsonConvert.DeserializeObject <List <TrainingDTO> >(trainings);
foreach (TrainingDTO x in trainingArray)
{
var t = new training
{
exercise_EID = x.exercise_EID,
numberOfSets = x.numberOfSets == null ? null : x.numberOfSets,
numberOfReps = x.numberOfReps == null ? null : x.numberOfReps,
durationMin = x.durationMin == null ? null : x.durationMin,
restBetweenMin = x.restBetweenMin == null ? null : x.restBetweenMin,
sunday = x.sunday,
monday = x.monday,
tuesday = x.tuesday,
wednesday = x.wednesday,
thursday = x.thursday,
friday = x.friday,
saturday = x.saturday,
className = x.className,
timeOfDay = x.timeOfDay
};
db.training.Add(t);
db.SaveChanges();
TIDList.Add(t.TID);
}
foreach (int x in TIDList)
{
db.trainingProgramTraining.Add(new trainingProgramTraining {
trainingProgram_TPID = TPID, training_TID = x
});
}
db.SaveChanges();
return(true);
}
catch (Exception)
{
return(false);
}
}
}
public ActionResult Edit(int id, Training model)
{
training t = trainingService.GetById(id);
t.Former = model.Former;
t.Start_date = model.Start_date;
t.End_date = model.End_date;
t.Place = model.Place;
t.Price = model.Price;
trainingService.Update(t);
trainingService.Commit();
return(RedirectToAction("Index"));
}
// GET: Training/Edit/5
public ActionResult Edit(int id)
{
if (id == null)
{
return(new HttpStatusCodeResult(HttpStatusCode.BadRequest));
}
training train = trs.getOneTraining(id);
if (train == null)
{
return(HttpNotFound());
}
return(View(train));
}
// GET: Training/Edit/5
public ActionResult Edit(int id)
{
training t = trainingService.GetById(id);
Training model = new Training
{
Former = t.Former,
Start_date = t.Start_date,
End_date = t.End_date,
Place = t.Place,
Price = t.Price
};
return(View(model));
}
public ActionResult Create(Training model)
{
training t = new training
{
Former = model.Former,
Start_date = model.Start_date,
End_date = model.End_date,
Place = model.Place,
Price = model.Price
};
trainingService.Add(t);
trainingService.Commit();
return(RedirectToAction("Index"));
}
// GET: Project/Delete/5
public ActionResult Delete(int id)
{
HttpClient client = new HttpClient();
client.BaseAddress = new Uri("http://localhost:9080");
client.DefaultRequestHeaders.Accept.Add(new System.Net.Http.Headers.MediaTypeWithQualityHeaderValue("application/json"));
HttpResponseMessage response = client.GetAsync("Advyteam-web/api/training" + id).Result;
training training = new training();
if (response.IsSuccessStatusCode)
{
training = response.Content.ReadAsAsync <training>().Result;
}
else
{
ViewBag.training = "erreur";
}
return(View(training));
}
private void button3_Click(object sender, EventArgs e)
{
this.Close();
training t = new training();
t.Show();
}
public void UpdateTraining(training tr)
{
utwk.TrainingRepository.Update(tr);
}
public void addTraining(training t)
{
utwk.TrainingRepository.Add(t);
utwk.Commit();
}
public int Change(training t)
{
return(dao.Change(t));
}
public int Add(training t)
{
return(dao.Add(t));
}
public int Del(training t)
{
return(dao.Del(t));
}
public void sendDeath(int id, float score) // Nous informe qu'une voiture est morte
{
this.aliveCars--;
updateTextInfo();
genes[id].score = score;
if (this.aliveCars == 0) // Toutes les voitures sont mortes
{
if (currentBatch < batches)
{
newBatch();
return;
}
}
else
{
return; // On attend que tout le monde meurt
}
// On repère les 3 meilleurs
int size = batches * carsPerBatch;
int[] bestIds = { 0, 0, 0 };
float[] bestScores = { 0f, 0f, 0f };
for (int i = 0; i < size; i++)
{
bool done = false;
for (int iBest = 0; iBest < 3; iBest++)
{
if (!done && (genes[i].score > bestScores[iBest]))
{
shiftIntArray(bestIds, iBest);
bestIds[iBest] = i;
shiftFloatArray(bestScores, iBest);
bestScores[iBest] = genes[i].score;
done = true;
}
}
}
// Premiers candidats: les 3 meilleurs de la dernière génération
training[] nextGen = new training[size];
for (int i = 0; i < 3; i++)
{
nextGen[i] = new training(genes[bestIds[i]].brain);
}
// Quetrième: gènes avec la moyenne de toutes les voitures, pondérée avec leur score.
// On est obligé de séparer en 2 tableaux
gene[] genesOnly = new gene[size];
for (int i = 0; i < size; i++)
{
genesOnly[i] = genes[i].brain;
}
float[] scoresOnly = new float[size];
for (int i = 0; i < size; i++)
{
scoresOnly[i] = genes[i].score;
}
nextGen[3] = new training(gene.weightedAverage(genesOnly, scoresOnly));
// Cinquième: gènes avec la moyenne non pondérée de toutes les coitures
float[] one = new float[size];
for (int i = 0; i < size; i++)
{
one[i] = 1f;
}
nextGen[4] = new training(gene.weightedAverage(genesOnly, one));
// Six et septième: idem mais avec les 3 premiers seulement
gene[] bo3Genes = new gene[3];
float[] bo3one = { 1f, 1f, 1f };
for (int i = 0; i < 3; i++)
{
bo3Genes[i] = genes[bestIds[i]].brain;
}
nextGen[5] = new training(gene.weightedAverage(bo3Genes, bestScores));
nextGen[6] = new training(gene.weightedAverage(bo3Genes, bo3one));
// Pour les autres, soit on:
// - Met un nouveau gène aléatoire
// - Fait une moyenne sur tous les gènes pondérée aléatoirement
for (int i = 7; i < size; i++)
{
// Pour les 2 cas, on regarde la parité de i
if ((i % 2) == 1)
{
// Nouveau gène aléatoire
nextGen[i] = new training(layers);
}
else
{
// Moyenne pondérée aléatoire
float[] randomWeights = new float[size];
for (int iWeight = 0; iWeight < size; iWeight++)
{
randomWeights[iWeight] = Random.Range(0f, 100f);
}
nextGen[i] = new training(gene.weightedAverage(genesOnly, randomWeights));
}
}
generation++;
genes = nextGen;
currentBatch = 0;
// On scale le cooldown checkpoint
cpCooldown *= generationCooldownScale;
if (cpCooldown < finalCpCooldown)
{
cpCooldown = finalCpCooldown;
}
newBatch();
}
public training SelectOne(training us)
{
return(ist.SelectBy(e => e.tra_id == us.tra_id)[0]);
}
public bool Upd(training us)
{
return(ist.Upd(us));
}
public bool Add(training us)
{
return(ist.Add(us));
}
public bool Del(training us)
{
return(ist.Del(us));
}
void Start()
{
training = GameObject.Find("Training Canvas").GetComponent <training>();
}
