İş Gereklilikleri
Необходимо конвертировать функции из кода C++ по этой ссылке в MQL5.
Список функций:
THolder<IBinarizer> MakeBinarizer(const EBorderSelectionType type) { switch (type) { case EBorderSelectionType::UniformAndQuantiles: return MakeHolder<TMedianPlusUniformBinarizer>(); case EBorderSelectionType::GreedyLogSum: return MakeHolder<TGreedyBinarizer<EPenaltyType::MaxSumLog>>(); case EBorderSelectionType::GreedyMinEntropy: return MakeHolder<TGreedyBinarizer<EPenaltyType::MinEntropy>>(); case EBorderSelectionType::MaxLogSum: return MakeHolder<TExactBinarizer<EPenaltyType::MaxSumLog>>(); case EBorderSelectionType::MinEntropy: return MakeHolder<TExactBinarizer<EPenaltyType::MinEntropy>>(); case EBorderSelectionType::Median: return MakeHolder<TMedianBinarizer>(); case EBorderSelectionType::Uniform: return MakeHolder<TUniformBinarizer>(); }
Описание методов можно посмотреть по ссылке.
Результатом работы должны быть такая функция
Mode | How splits are chosen |
---|---|
Median | Include an approximately equal number of objects in every bucket. |
Uniform | Generate splits by dividing the [min_feature_value, max_feature_value] segment into subsegments of equal length. Absolute values of the feature are used in this case. |
UniformAndQuantiles | Combine the splits obtained in the following modes, after first halving the quantization size provided by the starting parameters for each of them: - Median. - Uniform. |
MaxLogSum | Maximize the value of the following expression inside each bucket: ∑ i = 1 n log ( w e i g h t ) , w h e r e \sum\limits_{i=1}^{n}\log(weight){ , where} i=1∑nlog(weight),where - n n n — The number of distinct objects in the bucket. - w e i g h t weight weight — The number of times an object in the bucket is repeated. |
MinEntropy | Minimize the value of the following expression inside each bucket: ∑ i = 1 n w e i g h t ⋅ l o g ( w e i g h t ) , < b r / > w h e r e \sum \limits_{i=1}^{n} weight \cdot log (weight) { ,<br/> where} i=1∑nweight⋅log(weight), where - n n n — The number of distinct objects in the bucket. - w e i g h t weight weight — The number of times an object in the bucket is repeated. |
GreedyLogSum | Maximize the greedy approximation of the following expression inside every bucket: ∑ i = 1 n log ( w e i g h t ) , w h e r e \sum\limits_{i=1}^{n}\log(weight){ , where} i=1∑nlog(weight),where - n n n — The number of distinct objects in the bucket. - w e i g h t weight weight — The number of times an object in the bucket is repeated. |
void Quant (int Type_Quant,int N, double &arr_In[],float &arr_Out[]) { }
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