DSCAnalysisTool/src/data/lowesssmoother.cpp

#include "LowessSmoother.h"
#include <numeric>

namespace Lowess {

void validateConfig(const Config& config) {
    if (config.smoothingFactor <= 0 || config.smoothingFactor > 1) {
        throw std::invalid_argument("Smoothing factor must be between 0 and 1");
    }
    if (config.robustnessIterations < 1) {
        throw std::invalid_argument("Robustness iterations must be at least 1");
    }
}

double weightFunction(double x) {
    if (x < 0 || x > 1) return 0;
    return std::pow(1 - std::pow(x, 3), 3);
}

double medianAbsoluteDeviation(const std::vector<double>& residuals) {
    if (residuals.empty()) return 0;

    std::vector<double> absResiduals(residuals.size());
    for (size_t i = 0; i < residuals.size(); ++i) {
        absResiduals[i] = std::abs(residuals[i]);
    }

    std::sort(absResiduals.begin(), absResiduals.end());
    return absResiduals[absResiduals.size() / 2];
}

std::vector<double> computeBandwidths(const std::vector<double>& x, double smoothingFactor) {
    size_t n = x.size();
    size_t r = static_cast<size_t>(std::ceil(smoothingFactor * n));
    std::vector<double> h(n);

    for (size_t i = 0; i < n; ++i) {
        std::vector<double> distances(n);
        for (size_t j = 0; j < n; ++j) {
            distances[j] = std::abs(x[i] - x[j]);
        }
        std::sort(distances.begin(), distances.end());
        h[i] = distances[r];
    }

    return h;
}

std::vector<double> smooth(
    const std::vector<double>& x,
    const std::vector<double>& y,
    const Config& config
) {
    validateConfig(config);

    if (x.size() != y.size()) {
        throw std::invalid_argument("x and y must have the same size");
    }
    if (x.size() < 2) {
        throw std::invalid_argument("At least 2 data points required");
    }

    size_t n = x.size();
    std::vector<double> yest(n, 0);
    std::vector<double> delta(n, 1);
    std::vector<double> residuals(n);

    // 计算带宽
    std::vector<double> h = computeBandwidths(x, config.smoothingFactor);

    for (int iteration = 0; iteration < config.robustnessIterations; ++iteration) {
        for (size_t i = 0; i < n; ++i) {
            // 计算权重
            std::vector<double> weights(n);
            for (size_t j = 0; j < n; ++j) {
                double u = std::abs(x[j] - x[i]) / h[i];
                weights[j] = delta[j] * weightFunction(u);
            }

            // 加权最小二乘
            double sumW = 0, sumWx = 0, sumWx2 = 0, sumWy = 0, sumWxy = 0;
            for (size_t j = 0; j < n; ++j) {
                sumW += weights[j];
                sumWx += weights[j] * x[j];
                sumWx2 += weights[j] * x[j] * x[j];
                sumWy += weights[j] * y[j];
                sumWxy += weights[j] * x[j] * y[j];
            }

            double det = sumW * sumWx2 - sumWx * sumWx;
            if (std::abs(det) < 1e-10) {  // 防止奇异矩阵
                yest[i] = sumWy / sumW;   // 退化为加权平均
            } else {
                double a = (sumWx2 * sumWy - sumWx * sumWxy) / det;
                double b = (sumW * sumWxy - sumWx * sumWy) / det;
                yest[i] = a + b * x[i];
            }
        }

        // 计算残差和鲁棒权重
        for (size_t i = 0; i < n; ++i) {
            residuals[i] = y[i] - yest[i];
        }

        double s = medianAbsoluteDeviation(residuals);
        if (std::abs(s) < 1e-10) s = 1;  // 防止除以0

        for (size_t i = 0; i < n; ++i) {
            double u = residuals[i] / (6.0 * s);
            if (u < -1) u = -1;
            if (u > 1) u = 1;
            delta[i] = std::pow(1 - u * u, 2);
        }
    }

    return yest;
}

} // namespace Lowess