Quaternion InitialTilt()
{
var gaussian = BoxMuller.Gaussian();
var tilt = Quaternion.Euler(tiltPower * gaussian.x, Random.Range(0f, ROUND_IN_DEG), tiltPower * gaussian.y);
return(tilt);
}
public void ExecuteBoxMullerToCsvFile()
{
var results = new double[users, iterations + cellsBefore, paramsGen];
// Go over all the users
for (var i = 0; i < users; i++)
{
_boxMuller = new BoxMuller(i);
// for each param
for (var j = 0; j < paramsGen; j++)
{
results[i, 0, j] = mean[i];
results[i, 1, j] = stnDev[i];
// Iterations
for (var k = cellsBefore; k < iterations + cellsBefore; k++)
{
var r = (int)_boxMuller.GetNormalWithLimits(mean[i], stnDev[i], 1, 10);
results[i, k, j] = r;
}
}
}
WriteToCSV(results);
}
public void ExecuteBoxMullerToCsvFile2()
{
var results = new double[users, iterations + cellsBefore, paramsGen];
for (var i = 0; i < users; i++)
{
_boxMuller = new BoxMuller(i);
for (var j = 0; j < paramsGen; j++)
{
results[i, 0, j] = mean[i];
results[i, 1, j] = stnDev[i];
for (var k = cellsBefore; k < iterations + cellsBefore; k++)
{
// r_i = N(M_i,SD_i)
var r = (int)_boxMuller.GetNormalWithLimits(mean[i], stnDev[i], 1, 10);
results[i, k, j] = r;
// Don't continue on last iteration
if (k == iterations + cellsBefore - 1)
{
break;
}
// M_i+1 = N(M_i,SD_i) > M_i ? M_i + 0.5 : M_i
mean[i] = (r > mean[i] + Limit) ? mean[i] + Value : mean[i];
mean[i] = (r < mean[i] - Limit) ? mean[i] - Value : mean[i];
}
}
}
WriteToCSV(results);
}
private void InitializeMovesetScore(List <ActionBase> actions, float precision)
{
_movesetScore = new SortedSet <Tuple <float, ActionBase> >(new ActionWeight());
foreach (ActionBase action in actions)
{
float weight = precision * (BoxMuller.GetRandom() / 2 + 1);
if (!action.CheckCost(this._char))
{
weight = 0;
}
_movesetScore.Add(new Tuple <float, ActionBase>(weight, action));
}
}
public void PerturbScore()
{
SortedSet <Tuple <float, ActionBase> > newScore = new SortedSet <Tuple <float, ActionBase> >(new ActionWeight());
foreach (Tuple <float, ActionBase> action in _movesetScore)
{
float score = action.Item1 * (BoxMuller.GetRandom() / 2 + 1);
// Rubberband
score -= (score - _precision) * _weight;
newScore.Add(new Tuple <float, ActionBase>(score, action.Item2));
}
_movesetScore = newScore;
}
