114 lines
2.5 KiB
C++
114 lines
2.5 KiB
C++
#ifndef USINGPCA_H
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#define USINGPCA_H
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#include <vector>
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#include "sensors/SensorReader.h"
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#include "Interpolator.h"
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#include <eigen3/Eigen/Dense>
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enum class PracticeType {
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//REST,
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JUMPING_JACK,
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SITUPS,
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PUSHUPS,
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KNEEBEND,
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FORWARDBEND,
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};
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struct Practice {
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PracticeType type;
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Recording rec;
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std::vector<uint64_t> keyGyro;
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//Practice(const PracticeType p, const Recording& rec, const std::vector<uint64_t>& keyGyro) : p(p), rec(rec), keyGyro(keyGyro) {;}
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K::Interpolator<uint64_t, SensorGyro> getInterpol() const {
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K::Interpolator<uint64_t, SensorGyro> interpol;
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for (auto it : rec.gyro.values) {interpol.add(it.ts, it.val);}
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interpol.makeRelative();
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return interpol;
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}
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};
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class UsingPCA {
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public:
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static Eigen::VectorXf getWindow(Practice& p, uint64_t pos) {
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K::Interpolator<uint64_t, SensorGyro> interpol = p.getInterpol();
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Eigen::VectorXf vec(600/50*3, 1);
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int idx = 0;
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for (int offset = -300; offset < 300; offset += 50) {
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SensorGyro gyro = interpol.get(pos + offset);
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vec(idx++,0) = (gyro.x);
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vec(idx++,0) = (gyro.y);
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vec(idx++,0) = (gyro.z);
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}
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std::cout << vec << std::endl;
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return vec;
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}
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static std::vector<Eigen::VectorXf> getClassWindows(Practice& p) {
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std::vector<Eigen::VectorXf> windows;
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for (uint64_t pos = 1000; pos < 5000; pos += 500) {
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Eigen::VectorXf window = getWindow(p, pos);
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windows.push_back(window);
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}
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return windows;
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}
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static Eigen::MatrixXf getR(std::vector<Eigen::VectorXf>& vecs) {
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Eigen::MatrixXf mat = Eigen::MatrixXf::Zero(vecs[0].rows(), vecs[0].rows());
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for (const Eigen::VectorXf& vec : vecs) {
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mat += vec * vec.transpose();
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}
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mat /= vecs.size();
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return mat;
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}
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static Eigen::VectorXf getM(std::vector<Eigen::VectorXf>& vecs) {
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Eigen::MatrixXf mat = Eigen::MatrixXf::Zero(vecs[0].rows(), vecs[0].cols());
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for (const Eigen::VectorXf& vec : vecs) {
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mat += vec;
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}
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mat /= vecs.size();
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return mat;
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}
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static void run() {
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std::vector<Practice> practices;
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practices.push_back(
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Practice {
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PracticeType::JUMPING_JACK,
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SensorReader::read("/mnt/firma/kunden/HandyGames/daten/jumpingjack/jumpingjack_gl_5_subject_3_left.txt"),
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{1950, 2900, 3850, 4850, 5850, 6850, 7850, 8850, 9800, 10800, 11850}
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}
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);
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std::vector<Eigen::VectorXf> windows = getClassWindows(practices.back());
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Eigen::MatrixXf R = getR(windows);
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Eigen::MatrixXf m = getM(windows);
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Eigen::MatrixXf Q = R - (m * m.transpose());
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Eigen::SelfAdjointEigenSolver<Eigen::MatrixXf> es;
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es.compute(Q);
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int i = 0;
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}
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};
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#endif // USINGPCA_H
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