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82 lines
2.4 KiB
C++
82 lines
2.4 KiB
C++
#pragma once
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#include <AggregateFunctions/ReservoirSamplerDeterministic.h>
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namespace DB
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{
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namespace ErrorCodes
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{
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extern const int NOT_IMPLEMENTED;
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}
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/** Quantile calculation with "reservoir sample" algorithm.
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* It collects pseudorandom subset of limited size from a stream of values,
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* and approximate quantile from it.
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* The function accept second argument, named "determinator"
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* and a hash function from it is calculated and used as a source for randomness
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* to apply random sampling.
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* The function is deterministic, but care should be taken with choose of "determinator" argument.
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*/
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template <typename Value>
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struct QuantileReservoirSamplerDeterministic
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{
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using Data = ReservoirSamplerDeterministic<Value, ReservoirSamplerDeterministicOnEmpty::RETURN_NAN_OR_ZERO>;
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Data data;
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void add(const Value &)
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{
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throw Exception("Method add without determinator is not implemented for ReservoirSamplerDeterministic", ErrorCodes::NOT_IMPLEMENTED);
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}
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template <typename Determinator>
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void add(const Value & x, const Determinator & determinator)
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{
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data.insert(x, determinator);
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}
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void merge(const QuantileReservoirSamplerDeterministic & rhs)
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{
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data.merge(rhs.data);
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}
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void serialize(WriteBuffer & buf) const
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{
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data.write(buf);
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}
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void deserialize(ReadBuffer & buf)
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{
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data.read(buf);
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}
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/// Get the value of the `level` quantile. The level must be between 0 and 1.
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Value get(Float64 level)
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{
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return data.quantileInterpolated(level);
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}
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/// Get the `size` values of `levels` quantiles. Write `size` results starting with `result` address.
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/// indices - an array of index levels such that the corresponding elements will go in ascending order.
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void getMany(const Float64 * levels, const size_t * indices, size_t size, Value * result)
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{
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for (size_t i = 0; i < size; ++i)
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result[indices[i]] = data.quantileInterpolated(levels[indices[i]]);
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}
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/// The same, but in the case of an empty state, NaN is returned.
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Float64 getFloat(Float64 level)
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{
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return data.quantileInterpolated(level);
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}
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void getManyFloat(const Float64 * levels, const size_t * indices, size_t size, Float64 * result)
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{
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for (size_t i = 0; i < size; ++i)
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result[indices[i]] = data.quantileInterpolated(levels[indices[i]]);
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}
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};
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}
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