// Licensed to the Apache Software Foundation (ASF) under one

// or more contributor license agreements.  See the NOTICE file

// distributed with this work for additional information

// regarding copyright ownership.  The ASF licenses this file

// to you under the Apache License, Version 2.0 (the

// "License"); you may not use this file except in compliance

// with the License.  You may obtain a copy of the License at

//

//   http://www.apache.org/licenses/LICENSE-2.0

//

// Unless required by applicable law or agreed to in writing,

// software distributed under the License is distributed on an

// "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY

// KIND, either express or implied.  See the License for the

// specific language governing permissions and limitations

// under the License.

#pragma once

#include <math.h>

#include "core/assert_cast.h"

#include "core/column/column.h"

#include "core/data_type/data_type.h"

#include "core/data_type/data_type_decimal.h"

#include "core/data_type/define_primitive_type.h"

#include "core/data_type/primitive_type.h"

#include "core/types.h"

#include "core/value/decimalv2_value.h"

#include "exprs/aggregate/aggregate_function.h"

namespace doris {

class Arena;

class BufferReadable;

class BufferWritable;

/**

 * SEM = sqrt(variance / count) = sqrt(m2 / (count * (count - 1)))

 * It uses Welford’s method for numerically stable one-pass computation of the mean and variance.

 */

struct AggregateFunctionSemData {

    double mean {};

    double m2 {}; // Cumulative sum of squares

    UInt64 count = 0;

    //  Let the old mean be mean_{n-1} and we receive a new value x_n.

    //  The new mean should be: mean_n = mean_{n-1} + (x_n - mean_{n-1}) / n

    //  The new M2 should capture total squared deviations from the new mean:

    //      M2_n = M2_{n-1} + (x_n - mean_{n-1}) * (x_n - mean_n)

    void add(const double& value) {

        count++;

        double delta = value - mean;

        mean += delta / static_cast<double>(count);

        double delta2 = value - mean;

        m2 += delta * delta2;

    }

    // Suppose we have dataset A (count_a, mean_a, M2_a) and B (count_b, mean_b, M2_b).

    // When merging:

    //   - The total count is count_a + count_b

    //   - The new mean is the weighted average of the two means

    //   - The new M2 accumulates both variances and an adjustment term to account for

    //     the difference in means between A and B:

    //        M2 = M2_a + M2_b + delta^2 * count_a * count_b / total_count

    // where delta = mean_b - mean_a

    void merge(const AggregateFunctionSemData& rhs) {

        UInt64 total_count = count + rhs.count;

        double delta = rhs.mean - mean;

        mean = (mean * static_cast<double>(count) + rhs.mean * static_cast<double>(rhs.count)) /

               static_cast<double>(total_count);

        m2 += rhs.m2 + delta * delta * static_cast<double>(count) * static_cast<double>(rhs.count) /

                               static_cast<double>(total_count);

        count = total_count;

    }

    void write(BufferWritable& buf) const {

        buf.write_binary(mean);

        buf.write_binary(m2);

        buf.write_binary(count);

    }

    void read(BufferReadable& buf) {

        buf.read_binary(mean);

        buf.read_binary(m2);

        buf.read_binary(count);

    }

    void reset() {

        mean = {};

        m2 = {};

        count = 0;

    }

    double result() const {

        if (count < 2) {

            return 0;

        }

        double dCount = static_cast<double>(count);

        double result = std::sqrt(m2 / (dCount * (dCount - 1.0)));

        return result;

    }

};

template <typename Data>

class AggregateFunctionSem final

        : public IAggregateFunctionDataHelper<Data, AggregateFunctionSem<Data>>,

          UnaryExpression,

          NullableAggregateFunction {

public:

    AggregateFunctionSem(const DataTypes& argument_types_)

            : IAggregateFunctionDataHelper<Data, AggregateFunctionSem<Data>>(argument_types_) {}

    String get_name() const override { return "sem"; }

    DataTypePtr get_return_type() const override { return std::make_shared<DataTypeFloat64>(); }

    void add(AggregateDataPtr __restrict place, const IColumn** columns, ssize_t row_num,

             Arena&) const override {

        const auto& column =

                assert_cast<const ColumnFloat64&, TypeCheckOnRelease::DISABLE>(*columns[0]);

        this->data(place).add((double)column.get_data()[row_num]);

    }

    void reset(AggregateDataPtr place) const override { this->data(place).reset(); }

    void merge(AggregateDataPtr __restrict place, ConstAggregateDataPtr rhs,

               Arena&) const override {

        this->data(place).merge(this->data(rhs));

    }

    void serialize(ConstAggregateDataPtr __restrict place, BufferWritable& buf) const override {

        this->data(place).write(buf);

    }

    void deserialize(AggregateDataPtr __restrict place, BufferReadable& buf,

                     Arena&) const override {

        this->data(place).read(buf);

    }

    void insert_result_into(ConstAggregateDataPtr __restrict place, IColumn& to) const override {

        auto& column = assert_cast<ColumnFloat64&>(to);

        column.get_data().push_back(this->data(place).result());

    }

};

} // namespace doris