QFD2/QFD/algorithm/EntropyWeights.cpp

//
// Created by Austin on 2023/10/10.
//

#include "EntropyWeights.h"
#include <QDebug>
#include <numeric>
#include <cmath>

/**
    QVector<QVector<double>> pdata = { { 4.1, 3.9, 5.9, 4.9, 4,   6.1, 8.2,  6.5,  7.5,  6.8,
                                         7.4, 5.7, 2.4, 2.2, 1.9, 1.8, 15.9, 18.9, 18.8, 16.8 },
                                       { 0.04, 0.08, 0.07, 0.23, 0.14, 0.23, 0.21, 0.13, 0.16, 0.04,
                                         0.05, 0.08, 0.14, 0.24, 0.11, 0.06, 0.2,  0.09, 0.12, 0.27 },
                                       { 0.19, 0.38, 0.23, 1.03, 0.57, 1.19, 0.88, 0.8,  1.28, 0.17,
                                         0.33, 0.6,  0.23, 0.35, 0.3,  0.28, 1.05, 0.82, 0.8,  1.74 },
                                       { 0.01, 0.18, 0.04, 0.3,  0.11, 0.47, 0.2,  0.26, 0.88, 0.02,
                                         0.06, 0.47, 0.04, 0.06, 0.1,  0.1,  0.21, 0.34, 0.39, 1.51 },
                                       { 0.36, 0.19, 0.36, 1.4,  1.12, 0.93, 0.97, 0.52, 0.8,  0.24,
                                         0.39, 0.46, 0.94, 1.78, 0.5,  0.29, 1.84, 0.58, 0.76, 1.78 },
                                       { 0.05, 0.04, 0.07, 0.09, 0.05, 0.07, 0.1,  0.09, 0.12, 0.11,
                                         0.1,  0.08, 0.02, 0.02, 0.04, 0.03, 0.12, 0.36, 0.26, 0.32 } };

    EntropyWeights ew(pdata);

    QVector<double> weights;
    QVector<double> score;
    ew.compute(weights, score);
    qDebug() << score;
    qDebug() << weights;
 */

EntropyWeights::EntropyWeights(const EntropyMat &mat, const QVector<bool> &direction)
    : ymin_(0.002), ymax_(0.996), mat_(mat), direction_(direction)
{
    index_num_ = mat_.count();
    if (index_num_ == 0) {
        sample_num_ = 0;
    } else {
        sample_num_ = mat_.at(0).count();
    }
}

void EntropyWeights::setYMin(double ymin)
{
    if (ymin < 0.002) {
        ymin_ = 0.002;
    } else if (ymin > ymax_) {
        return;
    }

    ymin_ = ymin;
}

void EntropyWeights::setYMax(double ymax)
{
    if (ymax > 0.996) {
        ymax_ = 0.996;
    } else if (ymax < ymin_) {
        return;
    }

    ymax_ = ymax;
}

void EntropyWeights::getWeights(QVector<double> &weights, QVector<double> &score)
{
    // 计算第j个指标下，第i个样本占该指标的比重p
    EntropyMat pMat;
    for (int i = 0; i < mat_.count(); i++) {
        pMat.append(QVector<double>(mat_.at(i).count(), 0));
        double sum = std::accumulate(mat_.at(i).begin(), mat_.at(i).end(), 0.0);
        for (int j = 0; j < mat_.at(i).count(); j++) {
            pMat[i][j] = mat_[i][j] / sum;
        }
    }

    double k = 1 / std::log(sample_num_);

    // 计算第j个指标熵值
    QVector<double> e(mat_.count(), 0);
    for (int i = 0; i < mat_.count(); i++) {
        //        QVector<double> f(mat_.at(i).count(), 0.0);
        double fSum = 0;
        for (int j = 0; j < mat_.at(i).count(); j++) {
            fSum += pMat[i][j] * std::log(pMat[i][j]);
        }
        e[i] = -k * fSum;
    }
    //    qDebug() << e;

    // 计算信息熵冗余度
    QVector<double> d(mat_.count(), 0);
    for (int i = 0; i < e.count(); i++) {
        d[i] = 1 - e[i];
    }

    // 求权值
    double dSum = std::accumulate(d.begin(), d.end(), 0.0);

    for (auto v : d) {
        weights.append(v / dSum);
    }

    for (int i = 0; i < sample_num_; ++i) {
        double s = 0;
        for (int j = 0; j < index_num_; j++) {
            s += pMat[j][i] * weights[j];
        }
        score.append(s * 100);
    }
}

/**
 * 实现正向或负向指标归一化，返回归一化后的数据矩阵
 */
void EntropyWeights::normalization()
{
    for (int i = 0; i < mat_.count(); i++) {
        double minValue = 0;
        double maxValue = 0;

        getMinMax(mat_.at(i), minValue, maxValue);

        bool dir = true;
        if (!direction_.isEmpty()) {
            dir = direction_.at(i);
        }
        if (dir) {
            for (int j = 0; j < mat_.at(i).count(); j++) {
                mat_[i][j] = (ymax_ - ymin_) * (mat_.at(i).at(j) - minValue) / (maxValue - minValue) + ymin_;
            }
        } else {
            for (int j = 0; j < mat_.at(i).count(); j++) {
                mat_[i][j] = (ymax_ - ymin_) * (maxValue - mat_.at(i).at(j)) / (maxValue - minValue) + ymin_;
            }
        }
    }
}

void EntropyWeights::getMinMax(const QVector<double> &in, double &min, double &max)
{
    if (in.count() > 0) {
        min = max = in.at(0);

        for (int i = 1; i < in.count(); i++) {
            if (in.at(i) < min) {
                min = in.at(i);
            } else if (in.at(i) > max) {
                max = in.at(i);
            }
        }
    }
}

void EntropyWeights::compute(QVector<double> &weights, QVector<double> &score)
{
    normalization();
    //    qDebug() << mat_;
    getWeights(weights, score);
    //    qDebug() << "mat_" << mat_;
}