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299 lines
8.6 KiB
C++
299 lines
8.6 KiB
C++
#pragma once
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#include <DB/Common/HashTable/HashMap.h>
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#include <DB/DataTypes/DataTypesNumberFixed.h>
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#include <DB/DataTypes/DataTypeArray.h>
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#include <DB/AggregateFunctions/IBinaryAggregateFunction.h>
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#include <DB/AggregateFunctions/QuantilesCommon.h>
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#include <DB/Columns/ColumnArray.h>
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namespace DB
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{
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/** В качестве состояния используется хэш-таблица вида: значение -> сколько раз встретилось.
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*/
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template <typename T>
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struct AggregateFunctionQuantileExactWeightedData
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{
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using Key = T;
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using Weight = UInt64;
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/// При создании, хэш-таблица должна быть небольшой.
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using Map = HashMap<
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Key, Weight,
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HashCRC32<Key>,
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HashTableGrower<4>,
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HashTableAllocatorWithStackMemory<sizeof(std::pair<Key, Weight>) * (1 << 3)>
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>;
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Map map;
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};
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/** Точно вычисляет квантиль по множеству значений, для каждого из которых задан вес - сколько раз значение встречалось.
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* Можно рассматривать набор пар value, weight - как набор гистограмм,
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* в которых value - значение, округлённое до середины столбика, а weight - высота столбика.
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* В качестве типа аргумента может быть только числовой тип (в том числе, дата и дата-с-временем).
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* Тип результата совпадает с типом аргумента.
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*/
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template <typename ValueType, typename WeightType>
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class AggregateFunctionQuantileExactWeighted final
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: public IBinaryAggregateFunction<
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AggregateFunctionQuantileExactWeightedData<ValueType>,
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AggregateFunctionQuantileExactWeighted<ValueType, WeightType>>
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{
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private:
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double level;
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DataTypePtr type;
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public:
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AggregateFunctionQuantileExactWeighted(double level_ = 0.5) : level(level_) {}
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String getName() const override { return "quantileExactWeighted"; }
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DataTypePtr getReturnType() const override
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{
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return type;
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}
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void setArgumentsImpl(const DataTypes & arguments)
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{
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type = arguments[0];
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}
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void setParameters(const Array & params) override
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{
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if (params.size() != 1)
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throw Exception("Aggregate function " + getName() + " requires exactly one parameter.", ErrorCodes::NUMBER_OF_ARGUMENTS_DOESNT_MATCH);
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level = apply_visitor(FieldVisitorConvertToNumber<Float64>(), params[0]);
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}
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void addImpl(AggregateDataPtr place, const IColumn & column_value, const IColumn & column_weight, size_t row_num) const
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{
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this->data(place)
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.map[static_cast<const ColumnVector<ValueType> &>(column_value).getData()[row_num]]
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+= static_cast<const ColumnVector<WeightType> &>(column_weight).getData()[row_num];
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}
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void merge(AggregateDataPtr place, ConstAggregateDataPtr rhs) const override
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{
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auto & map = this->data(place).map;
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const auto & rhs_map = this->data(rhs).map;
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for (const auto & pair : rhs_map)
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map[pair.first] += pair.second;
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}
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void serialize(ConstAggregateDataPtr place, WriteBuffer & buf) const override
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{
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this->data(place).map.write(buf);
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}
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void deserialize(AggregateDataPtr place, ReadBuffer & buf) const override
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{
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typename AggregateFunctionQuantileExactWeightedData<ValueType>::Map::Reader reader(buf);
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auto & map = this->data(place).map;
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while (reader.next())
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{
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const auto & pair = reader.get();
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map[pair.first] = pair.second;
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}
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}
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void insertResultInto(ConstAggregateDataPtr place, IColumn & to) const override
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{
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auto & map = this->data(place).map;
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size_t size = map.size();
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if (0 == size)
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{
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static_cast<ColumnVector<ValueType> &>(to).getData().push_back(ValueType());
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return;
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}
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/// Копируем данные во временный массив, чтобы получить нужный по порядку элемент.
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using Pair = typename AggregateFunctionQuantileExactWeightedData<ValueType>::Map::value_type;
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std::unique_ptr<Pair[]> array_holder(new Pair[size]);
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Pair * array = array_holder.get();
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size_t i = 0;
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UInt64 sum_weight = 0;
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for (const auto & pair : map)
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{
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sum_weight += pair.second;
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array[i] = pair;
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++i;
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}
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std::sort(array, array + size, [](const Pair & a, const Pair & b) { return a.first < b.first; });
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UInt64 threshold = std::ceil(sum_weight * level);
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UInt64 accumulated = 0;
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const Pair * it = array;
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const Pair * end = array + size;
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while (it < end)
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{
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accumulated += it->second;
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if (accumulated >= threshold)
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break;
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++it;
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}
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if (it == end)
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--it;
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static_cast<ColumnVector<ValueType> &>(to).getData().push_back(it->first);
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}
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};
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/** То же самое, но позволяет вычислить сразу несколько квантилей.
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* Для этого, принимает в качестве параметров несколько уровней. Пример: quantilesExactWeighted(0.5, 0.8, 0.9, 0.95)(ConnectTiming, Weight).
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* Возвращает массив результатов.
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*/
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template <typename ValueType, typename WeightType>
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class AggregateFunctionQuantilesExactWeighted final
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: public IBinaryAggregateFunction<
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AggregateFunctionQuantileExactWeightedData<ValueType>,
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AggregateFunctionQuantilesExactWeighted<ValueType, WeightType>>
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{
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private:
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QuantileLevels<double> levels;
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DataTypePtr type;
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public:
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String getName() const override { return "quantilesExactWeighted"; }
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DataTypePtr getReturnType() const override
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{
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return std::make_shared<DataTypeArray>(type);
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}
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void setArgumentsImpl(const DataTypes & arguments)
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{
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type = arguments[0];
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}
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void setParameters(const Array & params) override
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{
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levels.set(params);
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}
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void addImpl(AggregateDataPtr place, const IColumn & column_value, const IColumn & column_weight, size_t row_num) const
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{
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this->data(place)
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.map[static_cast<const ColumnVector<ValueType> &>(column_value).getData()[row_num]]
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+= static_cast<const ColumnVector<WeightType> &>(column_weight).getData()[row_num];
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}
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void merge(AggregateDataPtr place, ConstAggregateDataPtr rhs) const override
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{
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auto & map = this->data(place).map;
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const auto & rhs_map = this->data(rhs).map;
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for (const auto & pair : rhs_map)
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map[pair.first] += pair.second;
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}
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void serialize(ConstAggregateDataPtr place, WriteBuffer & buf) const override
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{
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this->data(place).map.write(buf);
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}
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void deserialize(AggregateDataPtr place, ReadBuffer & buf) const override
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{
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typename AggregateFunctionQuantileExactWeightedData<ValueType>::Map::Reader reader(buf);
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auto & map = this->data(place).map;
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while (reader.next())
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{
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const auto & pair = reader.get();
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map[pair.first] = pair.second;
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}
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}
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void insertResultInto(ConstAggregateDataPtr place, IColumn & to) const override
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{
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auto & map = this->data(place).map;
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size_t size = map.size();
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ColumnArray & arr_to = static_cast<ColumnArray &>(to);
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ColumnArray::Offsets_t & offsets_to = arr_to.getOffsets();
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size_t num_levels = levels.size();
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offsets_to.push_back((offsets_to.size() == 0 ? 0 : offsets_to.back()) + num_levels);
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typename ColumnVector<ValueType>::Container_t & data_to = static_cast<ColumnVector<ValueType> &>(arr_to.getData()).getData();
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size_t old_size = data_to.size();
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data_to.resize(old_size + num_levels);
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if (0 == size)
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{
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for (size_t i = 0; i < num_levels; ++i)
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data_to[old_size + i] = ValueType();
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return;
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}
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/// Копируем данные во временный массив, чтобы получить нужный по порядку элемент.
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using Pair = typename AggregateFunctionQuantileExactWeightedData<ValueType>::Map::value_type;
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std::unique_ptr<Pair[]> array_holder(new Pair[size]);
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Pair * array = array_holder.get();
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size_t i = 0;
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UInt64 sum_weight = 0;
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for (const auto & pair : map)
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{
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sum_weight += pair.second;
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array[i] = pair;
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++i;
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}
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std::sort(array, array + size, [](const Pair & a, const Pair & b) { return a.first < b.first; });
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UInt64 accumulated = 0;
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const Pair * it = array;
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const Pair * end = array + size;
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size_t level_index = 0;
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UInt64 threshold = std::ceil(sum_weight * levels.levels[levels.permutation[level_index]]);
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while (it < end)
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{
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accumulated += it->second;
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while (accumulated >= threshold)
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{
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data_to[old_size + levels.permutation[level_index]] = it->first;
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++level_index;
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if (level_index == num_levels)
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return;
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threshold = std::ceil(sum_weight * levels.levels[levels.permutation[level_index]]);
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}
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++it;
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}
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while (level_index < num_levels)
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{
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data_to[old_size + levels.permutation[level_index]] = array[size - 1].first;
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++level_index;
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}
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}
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};
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}
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