private static double LocalSearchStep(ConnectionNetwork network, List <double> startingVector, List <double> stepVector, int coordiante)
{
Console.WriteLine("Optimizing over coordinate {0}", coordiante);
var best = -1;
var bestObjV = bigNumber;
for (int i = 0; i < candidate.Length; i++)
{
startingVector[coordiante] = startingVector[coordiante] + stepVector[coordiante] * candidate[i];
var temp = EstimateObjective(network, startingVector);
startingVector[coordiante] = startingVector[coordiante] - stepVector[coordiante] * candidate[i];
if (temp < bestObjV)
{
bestObjV = temp;
best = i;
}
}
if (candidate[best] == 0)
{
stepVector[coordiante] = stepVector[coordiante] / acceleration;
}
else
{
startingVector[coordiante] = startingVector[coordiante] + stepVector[coordiante] * candidate[best];
stepVector[coordiante] = stepVector[coordiante] * candidate[best];
}
Console.WriteLine("Best candidate: " + best.ToString() + ". Objective: " + bestObjV);
return(bestObjV);
}
private static double EstimateObjective(ConnectionNetwork network, List <double> startingVector)
{
var obj = new double[estimateRepetitions];
Parallel.For(0, estimateRepetitions, (i, state) =>
{
Console.WriteLine("Simulation: {0}", i);
obj[i] = ObjectiveFunction(network, startingVector);
});
return(obj.Average());
}
public static double SimpleLocalSearch(ConnectionNetwork network)
{
List <double> startingVector = new List <double>()
{
1.0 / 30000, 20, 0.2, 0.2, 0.2
};
List <double> stepVector = new List <double>();
foreach (var value in startingVector)
{
stepVector.Add(Math.Min(Math.Abs(value - 1), value) * 0.25);
}
return(SimpleLocalSearch(network, startingVector, stepVector));
}
static void Main(string[] args)
{
var diseaseNetwork = new ConnectionNetwork(Data.GenPopData(), Data.GenWaterData(), Data.GenAdminData());
var simulation = new DiseaseSimulation(diseaseNetwork);
Console.WriteLine(diseaseNetwork.numberOfPeople);
Console.WriteLine(diseaseNetwork.nodesByCoordAndCat.Count(x => x.Key.category == NodeCategory.People));
Console.WriteLine(diseaseNetwork.nodesByCoordAndCat.Count(x => x.Key.category == NodeCategory.WaterSource));
Console.WriteLine(diseaseNetwork.nodesByCoordAndCat.Count(x => x.Key.category == NodeCategory.River));
Console.WriteLine(diseaseNetwork.nodesByCoordAndCat.Values.Sum(x => x.successorNodes.Count));
//Intervention.CalculateR0(diseaseNetwork, simulation);
//RunSimulations();
Console.WriteLine("Press any key to continue.");
Console.ReadLine();
}
public static double SimpleLocalSearch(ConnectionNetwork network, List <double> startingVector, List <double> stepVector)
{
var prevBestObjV = bigNumber;
var newBestObjV = bigNumber;
var difference = bigNumber;
var iter = 0;
while (iter < maxIters)
{
Console.WriteLine("Iteration: {0}. Objective value: {1}", iter, newBestObjV);
prevBestObjV = newBestObjV;
for (int i = 0; i < startingVector.Count; i++)
{
newBestObjV = LocalSearchStep(network, startingVector, stepVector, i);
}
difference = Math.Abs(newBestObjV - prevBestObjV);
iter++;
}
string pathDesktop = Environment.GetFolderPath(Environment.SpecialFolder.Desktop);
string filePath = pathDesktop + "\\OptimalParameters" + ".csv";
if (!System.IO.File.Exists(filePath))
{
System.IO.File.Create(filePath).Close();
}
string delimter = ";";
using (System.IO.TextWriter writer = System.IO.File.CreateText(filePath))
{
foreach (var num in startingVector)
{
writer.WriteLine(string.Join(delimter, num));
}
writer.WriteLine(string.Join(delimter, newBestObjV));
}
return(newBestObjV);
}
static void ManualCalibration(ConnectionNetwork diseaseNetwork, DiseaseSimulation simulation)
{
var numSimulations = 1;
var tPop = diseaseNetwork.numberOfPeople;
string input = "R";
while (input != "S")
{
if (input == "N")
{
Console.WriteLine("Input new parametes separated by spaces: distContProb, K, infProb, hwProb, wwProb");
var newParams = Console.ReadLine();
var tokens = newParams.Split(' ');
var parameters = Array.ConvertAll(tokens, double.Parse).ToList();
simulation.SetSimulationParameters(parameters);
}
if (input == "C")
{
Console.WriteLine("Input new number of simulations: ");
var n = Console.ReadLine();
numSimulations = Convert.ToInt32(n);
}
var results = new double[numSimulations];
for (int i = 0; i < numSimulations; i++)
{
Console.WriteLine("Running simulation: " + (i + 1));
results[i] = ParameterFitting.ObjectiveFunction(simulation);
}
Console.WriteLine("Objective values: " + results.Average());
Console.WriteLine("Run another simulation (R), input new parameters (N), change number of simulations (C) or stop (S)");
input = Console.ReadLine();
}
}
static void RunSimulations()
{
var diseaseNetwork = new ConnectionNetwork(Data.GenPopData(), Data.GenWaterData(), Data.GenAdminData());
string file = Directory.GetCurrentDirectory();
file = Path.GetFullPath(Path.Combine(file, @"..\..\")) + "simResultsTimeUnscaled.csv";
string header = string.Join(",", Enumerable.Range(0, 496).Select(x => x.ToString()).ToArray()) + "\n";
for (int i = 0; i < 999; i++)
{
//Console.WriteLine(i);
var simulation = new DiseaseSimulation(diseaseNetwork);
var simResults = simulation.RunSimulation();
while (simResults[0].Last() < 1000)
{
simResults = simulation.RunSimulation();
}
if (!File.Exists(file))
{
File.WriteAllText(file, header);
}
Console.WriteLine(i.ToString() + ": " + simResults[0].Last().ToString());
//var line = string.Concat(simResults.Select(l => l.Last().ToString() + ","));
var line = string.Concat(simResults[0].Select(dInfectious => dInfectious.ToString() + ","));
line = line.Remove(line.Length - 1) + "\n";
File.AppendAllText(file, line);
}
}
public static double ObjectiveFunction(ConnectionNetwork network, List <double> parameters)
{
var simulation = new DiseaseSimulation(network);
return(ObjectiveFunction(simulation, parameters));
}
public static double ObjectiveFunction(ConnectionNetwork network)
{
var simulation = new DiseaseSimulation(network);
return(ObjectiveFunction(simulation));
}
