public IActionResult CreateRoutine(CreateRoutineReceiver data)
{
//
var db = Database.GetDatabase(_env.ContentRootPath);
List <(Guid exerciseId, decimal amount, TimeSpan ts)> ps = new List <(Guid exerciseId, decimal amount, TimeSpan ts)>();
for (int i = 0; i < data.exercises.Length; i++)
{
var result = data.exercises[i].Split("_");
ps.Add((Guid.Parse(result[0]), decimal.Parse(result[1]), TimeSpan.FromSeconds(double.Parse(data.times[i]))));
}
var user = db.Users[0];
var newRoutine = new Routine()
{
Name = data.name,
Exercises = new List <(Guid ExerciseId, decimal amountTypeDependent, TimeSpan timeTaken)>(ps),
RoutineId = Guid.NewGuid()
};
var newFitH = new FitnessHistory()
{
EventDateTime = DateTime.Now,
RoutinePerformed = newRoutine,
UserId = user.Id
};
db.Users[0].History.Add(newFitH);
Database.SaveDatabase(db, _env.ContentRootPath);
return(Json(new { id = newRoutine.RoutineId }));
}
public FitnessHistoryGraphItem(FitnessHistory fH, List <Exercise> exercises)
{
decimal cal = 0;
Exercises = new List <string>();
foreach (var(ExerciseId, amountTypeDependent, timeTaken) in fH.RoutinePerformed.Exercises)
{
Exercise e = exercises.Single(ex => ex.ExerciseId == ExerciseId);
//get ingredient name
Exercises.Add(e.Name);
cal += e.CaloriesPerUnit * amountTypeDependent;
TimeTaken += timeTaken;
}
Calories = cal;
Date = fH.EventDateTime;
Routine = fH.RoutinePerformed;
}
public double[] Solve(ObjectiveFunction ObjFunc)
{
if (Validation())
{
PopulationsHistory.Clear();
FitnessHistory.Clear();
#region Initial Population and its Fitness
double[][] initilaPopulation = initializePopulation();
PopulationsHistory.Add(initilaPopulation);
Fitness initialFitness = new Fitness(GA_Settings.PopulationSize);
Parallel.For(0, GA_Settings.PopulationSize, i =>
{
initialFitness.FitnessPopulation[i] = ObjFunc(initilaPopulation[i]);
});
initialFitness.SetFitenessData();
initialFitness.BestFeature = (double[])initilaPopulation[initialFitness.MaxFitnessIndex].Clone();;
FitnessHistory.Add(initialFitness);
OPTHistoryResult.Add(new Result
{
Parameters = (double[])initialFitness.BestFeature.Clone(),
target = new double[2] {
initialFitness.MaxFitness, initialFitness.MeanFitness
}
});
#endregion
int gen = 0;
#region While Loop for Genereations
while (gen < GA_Settings.Generations - 1)
{
double[][] currentPopulation = PopulationsHistory.Last();
Fitness currentFitness = FitnessHistory.Last();
double[][] newPopulation = new double[GA_Settings.PopulationSize][];
#region GA
newPopulation = Selection(currentPopulation, currentFitness.FitnessPopulation);
newPopulation[0] = (double[])currentFitness.BestFeature.Clone();
newPopulation = Crossover(newPopulation);
newPopulation = Mutation(newPopulation);
#endregion
Fitness newFitness = new Fitness(GA_Settings.PopulationSize);
Parallel.For(0, GA_Settings.PopulationSize, i =>
{
newFitness.FitnessPopulation[i] = ObjFunc(newPopulation[i]);
});
newFitness.SetFitenessData();
newFitness.BestFeature = (double[])newPopulation[newFitness.MaxFitnessIndex].Clone();
#region Saving Best fearture NEW Vs CURRENT
if (newFitness.MaxFitness <= currentFitness.MaxFitness)
{
newPopulation[0] = (double[])currentFitness.BestFeature.Clone();
newFitness.BestFeature = (double[])currentFitness.BestFeature.Clone();
newFitness.FitnessPopulation[0] = currentFitness.MaxFitness;
newFitness.SetFitenessData();
}
#endregion
PopulationsHistory.Add(newPopulation);
FitnessHistory.Add(newFitness);
OPTHistoryResult.Add(new Result
{
Parameters = (double[])newFitness.BestFeature.Clone(),
target = new double[2] {
newFitness.MaxFitness, newFitness.MeanFitness
}
});
gen++;
}
#endregion
OPTResult.Parameters = OPTHistoryResult.Last().Parameters;
OPTResult.target = OPTHistoryResult.Last().target;
}
return(OPTHistoryResult.Last().Parameters);
}