public PredictionControl(IModel model, List<Property> properties, List<LoggedValue> loggedValues)
{
_logger = new LogIt("", loggedValues); // TODO Add Path
// Variables from properties list
M = (int) Convert.ToDouble(properties.Find(p => p.Name.Equals("M")).Value);
_horizonSize = (int)Convert.ToDouble(properties.Find(p => p.Name.Equals("Horizon")).Value);
_predictionHorizonSize = (int)Convert.ToDouble(properties.Find(p => p.Name.Equals("PredictionHorizon")).Value); // TODO
_discount = Convert.ToDouble(properties.Find(p => p.Name.Equals("Discount")).Value);
_startSigma = Convert.ToDouble(properties.Find(p => p.Name.Equals("StartSigma")).Value);
_sigmaMin = Convert.ToDouble(properties.Find(p => p.Name.Equals("SigmaMin")).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);
_iterationLimitOptimisation = (int)Convert.ToDouble(properties.Find(p => p.Name.Equals("OptimisationIterationLimit")).Value);
_iterationsLimit = (int)(TimeLimit / externalDiscretization);
_model = model;
_model.SetDiscretizations(externalDiscretization, internalDiscretization);
_state = new List<double>(_model.GetInitialState());
_minAction = _model.MinActionValues();
_maxAction = _model.MaxActionValues();
VestSum = 0;
VestAv = 0;
GenerateHorizon();
_iterationNumExternal = 0;
_episodeNr = 0;
}
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);
}
