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#pragma once
#include <cmath>
#include <base/arithmeticOverflow.h>
#include <IO/WriteHelpers.h>
#include <IO/ReadHelpers.h>
#include <AggregateFunctions/IAggregateFunction.h>
#include <AggregateFunctions/Moments.h>
#include <AggregateFunctions/Helpers.h>
#include <AggregateFunctions/FactoryHelpers.h>
#include <DataTypes/DataTypesNumber.h>
#include <DataTypes/DataTypesDecimal.h>
#include <Columns/ColumnVector.h>
#include <Columns/ColumnDecimal.h>
/** This is simple, not numerically stable
* implementations of variance/covariance/correlation functions.
*
* It is about two times faster than stable variants.
* Numerical errors may occur during summation.
*
* This implementation is selected as default,
* because "you don't pay for what you don't need" principle.
*
* For more sophisticated implementation, look at AggregateFunctionStatistics.h
*/
namespace DB
{
struct Settings;
enum class StatisticsFunctionKind
{
varPop, varSamp,
stddevPop, stddevSamp,
skewPop, skewSamp,
kurtPop, kurtSamp,
covarPop, covarSamp,
corr
};
template <typename T, size_t _level>
struct StatFuncOneArg
{
using Type1 = T;
using Type2 = T;
using ResultType = std::conditional_t<std::is_same_v<T, Float32>, Float32, Float64>;
using Data = VarMoments<ResultType, _level>;
static constexpr UInt32 num_args = 1;
};
template <typename T1, typename T2, template <typename> typename Moments>
struct StatFuncTwoArg
{
using Type1 = T1;
using Type2 = T2;
using ResultType = std::conditional_t<std::is_same_v<T1, T2> && std::is_same_v<T1, Float32>, Float32, Float64>;
using Data = Moments<ResultType>;
static constexpr UInt32 num_args = 2;
};
template <typename StatFunc>
class AggregateFunctionVarianceSimple final
: public IAggregateFunctionDataHelper<typename StatFunc::Data, AggregateFunctionVarianceSimple<StatFunc>>
{
public:
using T1 = typename StatFunc::Type1;
using T2 = typename StatFunc::Type2;
using ColVecT1 = ColumnVectorOrDecimal<T1>;
using ColVecT2 = ColumnVectorOrDecimal<T2>;
using ResultType = typename StatFunc::ResultType;
using ColVecResult = ColumnVector<ResultType>;
explicit AggregateFunctionVarianceSimple(const DataTypes & argument_types_, StatisticsFunctionKind kind_)
: IAggregateFunctionDataHelper<typename StatFunc::Data, AggregateFunctionVarianceSimple<StatFunc>>(argument_types_, {}, std::make_shared<DataTypeNumber<ResultType>>())
, src_scale(0), kind(kind_)
{
chassert(!argument_types_.empty());
if (isDecimal(argument_types_.front()))
src_scale = getDecimalScale(*argument_types_.front());
}
String getName() const override
{
return String(magic_enum::enum_name(kind));
}
bool allocatesMemoryInArena() const override { return false; }
void add(AggregateDataPtr __restrict place, const IColumn ** columns, size_t row_num, Arena *) const override
{
if constexpr (StatFunc::num_args == 2)
this->data(place).add(
static_cast<ResultType>(static_cast<const ColVecT1 &>(*columns[0]).getData()[row_num]),
static_cast<ResultType>(static_cast<const ColVecT2 &>(*columns[1]).getData()[row_num]));
else
{
if constexpr (is_decimal<T1>)
{
this->data(place).add(
convertFromDecimal<DataTypeDecimal<T1>, DataTypeFloat64>(
static_cast<const ColVecT1 &>(*columns[0]).getData()[row_num], src_scale));
}
else
this->data(place).add(
static_cast<ResultType>(static_cast<const ColVecT1 &>(*columns[0]).getData()[row_num]));
}
}
void merge(AggregateDataPtr __restrict place, ConstAggregateDataPtr rhs, Arena *) const override
{
this->data(place).merge(this->data(rhs));
}
void serialize(ConstAggregateDataPtr __restrict place, WriteBuffer & buf, std::optional<size_t> /* version */) const override
{
this->data(place).write(buf);
}
void deserialize(AggregateDataPtr __restrict place, ReadBuffer & buf, std::optional<size_t> /* version */, Arena *) const override
{
this->data(place).read(buf);
}
void insertResultInto(AggregateDataPtr __restrict place, IColumn & to, Arena *) const override
{
const auto & data = this->data(place);
auto & dst = static_cast<ColVecResult &>(to).getData();
switch (kind)
{
case StatisticsFunctionKind::varPop:
{
dst.push_back(data.getPopulation());
break;
}
case StatisticsFunctionKind::varSamp:
{
dst.push_back(data.getSample());
break;
}
case StatisticsFunctionKind::stddevPop:
{
dst.push_back(sqrt(data.getPopulation()));
break;
}
case StatisticsFunctionKind::stddevSamp:
{
dst.push_back(sqrt(data.getSample()));
break;
}
case StatisticsFunctionKind::skewPop:
{
ResultType var_value = data.getPopulation();
if (var_value > 0)
dst.push_back(static_cast<ResultType>(data.getMoment3() / pow(var_value, 1.5)));
else
dst.push_back(std::numeric_limits<ResultType>::quiet_NaN());
break;
}
case StatisticsFunctionKind::skewSamp:
{
ResultType var_value = data.getSample();
if (var_value > 0)
dst.push_back(static_cast<ResultType>(data.getMoment3() / pow(var_value, 1.5)));
else
dst.push_back(std::numeric_limits<ResultType>::quiet_NaN());
break;
}
case StatisticsFunctionKind::kurtPop:
{
ResultType var_value = data.getPopulation();
if (var_value > 0)
dst.push_back(static_cast<ResultType>(data.getMoment4() / pow(var_value, 2)));
else
dst.push_back(std::numeric_limits<ResultType>::quiet_NaN());
break;
}
case StatisticsFunctionKind::kurtSamp:
{
ResultType var_value = data.getSample();
if (var_value > 0)
dst.push_back(static_cast<ResultType>(data.getMoment4() / pow(var_value, 2)));
else
dst.push_back(std::numeric_limits<ResultType>::quiet_NaN());
break;
}
case StatisticsFunctionKind::covarPop:
{
dst.push_back(data.getPopulation());
break;
}
case StatisticsFunctionKind::covarSamp:
{
dst.push_back(data.getSample());
break;
}
case StatisticsFunctionKind::corr:
{
dst.push_back(data.get());
break;
}
}
}
private:
UInt32 src_scale;
StatisticsFunctionKind kind;
};
struct Settings;
namespace ErrorCodes
{
extern const int ILLEGAL_TYPE_OF_ARGUMENT;
}
namespace
{
template <template <typename> typename FunctionTemplate, StatisticsFunctionKind kind>
AggregateFunctionPtr createAggregateFunctionStatisticsUnary(
const std::string & name, const DataTypes & argument_types, const Array & parameters, const Settings *)
{
assertNoParameters(name, parameters);
assertUnary(name, argument_types);
AggregateFunctionPtr res;
const DataTypePtr & data_type = argument_types[0];
if (isDecimal(data_type))
res.reset(createWithDecimalType<FunctionTemplate>(*data_type, argument_types, kind));
else
res.reset(createWithNumericType<FunctionTemplate>(*data_type, argument_types, kind));
if (!res)
throw Exception(ErrorCodes::ILLEGAL_TYPE_OF_ARGUMENT, "Illegal type {} of argument for aggregate function {}",
argument_types[0]->getName(), name);
return res;
}
template <template <typename, typename> typename FunctionTemplate, StatisticsFunctionKind kind>
AggregateFunctionPtr createAggregateFunctionStatisticsBinary(
const std::string & name, const DataTypes & argument_types, const Array & parameters, const Settings *)
{
assertNoParameters(name, parameters);
assertBinary(name, argument_types);
AggregateFunctionPtr res(createWithTwoBasicNumericTypes<FunctionTemplate>(*argument_types[0], *argument_types[1], argument_types, kind));
if (!res)
throw Exception(ErrorCodes::ILLEGAL_TYPE_OF_ARGUMENT, "Illegal types {} and {} of arguments for aggregate function {}",
argument_types[0]->getName(), argument_types[1]->getName(), name);
return res;
}
}
}
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