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Конвертация 7 функций из кода C++ в MQL5

MQL5 Outros Conversão C++

Termos de Referência

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∑n​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.
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∑n​weight⋅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∑n​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.		 
void Quant (int Type_Quant,int N, double &arr_In[],float &arr_Out[])
{
}


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