public Dictionary <object, object> predict(Dictionary <string, dynamic> inputData,
bool byName = true,
Combiner combiner = Combiner.Plurality,
int missing_strategy = 0,
bool addDistribution = true)
{
mv = new MultiVote();
if (_models.Count > 1)
{
inputData = _models[0].prepareInputData(inputData);
}
for (i = 0; i < this._models.Count; i++)
{
_modelsPredictions[i] = this._models[i].predict(inputData, byName, missing_strategy);
mv.append(_modelsPredictions[i].toDictionary(addDistribution));
}
return(mv.combine((int)combiner, addDistribution: addDistribution));
}
public Dictionary <object, object> predict(Dictionary <string, dynamic> inputData,
bool byName = true,
Combiner combiner = Combiner.Plurality,
int missing_strategy = 0,
bool addDistribution = true)
{
mv = new MultiVote();
bool addConfidence = areBoostedTrees;
if (_models.Count > 1)
{
inputData = _models[0].prepareInputData(inputData);
byName = false;
}
mv.areBoostedTrees = areBoostedTrees;
Dictionary <object, object> vote;
for (i = 0; i < this._models.Count; i++)
{
if (areBoostedTrees)
{
_modelsBoostedPredictions[i] = this._models[i].predictBoosted(inputData, byName, missing_strategy);
vote = _modelsBoostedPredictions[i].toDictionary();
vote["weight"] = this._models[i].Boosting["weight"];
if (this._models[i].Boosting.ContainsKey("objective_class"))
{
vote["class"] = this._models[i].Boosting["objective_class"];
}
mv.append(vote);
}
else
{
_modelsPredictions[i] = this._models[i].predict(inputData, byName, missing_strategy);
mv.append(_modelsPredictions[i].toDictionary(addDistribution));
}
}
return(mv.combine((int)combiner, addDistribution: addDistribution, addConfidence: addConfidence));
}