public Advisor(IModel model, List<Property> properties, List<LoggedValue> loggedValues)
{
_model = model;
MODEL_ACTION_VAR_COUNT = _model.GenerateControlVariables().Count;
MODEL_STATE_VAR_COUNT = _model.GetInitialState().Count;
HORIZON_SIZE = (int)Convert.ToDouble(properties.Find(p => p.Name.Equals("Horizon")).Value);
Vsize = (int)Convert.ToDouble(properties.Find(p => p.Name.Equals("Neurons Number")).Value);
BetaV = Convert.ToDouble(properties.Find(p => p.Name.Equals("BetaV")).Value);
Gamma = Convert.ToDouble(properties.Find(p => p.Name.Equals("Discount")).Value);;
Sigma = Convert.ToDouble(properties.Find(p => p.Name.Equals("Sigma")).Value);
SigmaMin = Convert.ToDouble(properties.Find(p => p.Name.Equals("SigmaMin")).Value);
TimesToAdjust = (int)Convert.ToDouble(properties.Find(p => p.Name.Equals("TimesToAdjust")).Value);
var externalDiscretization =
Convert.ToDouble(properties.Find(p => p.Name.Equals("ExternalDiscretization")).Value);
var internalDiscretization =
Convert.ToDouble(properties.Find(p => p.Name.Equals("InternalDiscretization")).Value);
var TimeLimit = Convert.ToDouble(properties.Find(p => p.Name.Equals("TimeLimit")).Value);
TimesToTeach = (int)Convert.ToDouble(properties.Find(p => p.Name.Equals("TimesToTeach")).Value);
TimesToAdjustPastActions = (int)Convert.ToDouble(properties.Find(p => p.Name.Equals("TimesToAdjustPastActions")).Value);
_logger = new LogIt("", loggedValues);
_model.SetDiscretizations(externalDiscretization, internalDiscretization);
TimeIndex = 0;
_episodeNr = 0;
AllVisits = new ArrayList();
Sampler = new ASampler();
IterationsLimit = (int) (TimeLimit / externalDiscretization);
VestSum = 0;
StartInState(_model.GetInitialState().ToArray());
MinAction = new Vector(_model.MinActionValues());
MaxAction = new Vector(_model.MaxActionValues());
double[] stateAverage = _model.GetStateValuesAverageNN().ToArray();
double[] stateStandardDeviation = _model.GetStateValuesStandardDeviationNN().ToArray();
Init(stateAverage, stateStandardDeviation, MinAction.Table, Vsize);
}
public List<Double> MeddleWithGoalAndStartingState()
{
var state = GetInitialState();
state[1] = new ASampler().NextDouble() * 2 * Math.PI;
return state;
}
public List<Double> MeddleWithGoalAndStartingState()
{
var rand = new ASampler();
_setpoint = rand.Next(28000 - 20000) + 20000.5;
var state = GetInitialState();
while (GetValue(state)[0] < 25000.5 || GetValue(state)[0] > 38000.0)
{
state[0] = rand.NextDouble() * 5.506774;
state[1] = rand.NextDouble() * 0.132906;
state[2] = rand.NextDouble() * 0.0019752;
state[3] = rand.NextDouble() * 49.38182;
}
return state;
}
public CellSet()
{
TheSampler = new ASampler();
}
