added current c++ code
This commit is contained in:
@@ -1,431 +1,135 @@
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//#include "usingneuralnet.h"
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#include "usingpca.h"
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#include <omp.h>
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#include "pca/TrainPCA.h"
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#include <KLib/misc/gnuplot/Gnuplot.h>
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#include <KLib/misc/gnuplot/GnuplotSplot.h>
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#include <KLib/misc/gnuplot/GnuplotSplotElementLines.h>
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#include "pca/KNN.h"
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#include "pca/aKNN.h"
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#include <vector>
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std::vector<std::string> COLORS = {"#000000", "#0000ff", "#00ff00", "#ff0000", "#00ffff"};
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std::string getClass(const std::vector<ClassifiedFeature>& nns) {
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std::unordered_map<std::string, int> map;
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for(const ClassifiedFeature& nn : nns) { map[nn.className] += 1; }
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for (auto& it : map) {
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if (it.second > nns.size() * 0.75) {return it.first;}
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}
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return "";
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}
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struct Stats{
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int match;
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int error;
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int unknown;
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Stats() : match(0), error(0), unknown(0) {;}
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float getSum() {return match+error+unknown;}
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};
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int main(void) {
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omp_set_dynamic(false);
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omp_set_num_threads(3);
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const int numFeatures = 3;
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std::vector<ClassifiedPattern> patTrain = TrainPCA::getTrainData();
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TrainPCA::Matrices m = TrainPCA::getMatrices(patTrain, numFeatures);
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std::vector<ClassifiedPattern> patTest = TrainPCA::getTestData();
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// construct knn
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aKNN<ClassifiedFeature, 3> knn;
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for (const ClassifiedPattern& pat : patTrain) {
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K::DynColVector<float> vec = m.A1 * K::PCAHelper<float>::toVector(pat.pattern);
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const std::vector<float> arr = {vec(0), vec(1), vec(2)};
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knn.add(ClassifiedFeature(pat.className, arr));
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}
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knn.build();
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K::Gnuplot gp;
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K::GnuplotSplot splot;
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K::GnuplotSplotElementLines lines[5];
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Stats stats;
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int xx = 0;
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for (const ClassifiedPattern& pat : patTest) {
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const int idx = Settings::classToInt(pat.className);
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K::DynColVector<float> vec = m.A1 * K::PCAHelper<float>::toVector(pat.pattern);
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// get KNN's answer
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std::vector<float> arr = {vec(0), vec(1), vec(2)};
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std::vector<ClassifiedFeature> neighbors = knn.get(arr.data(), 10);
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std::string gotClass = getClass(neighbors);
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if (pat.className == gotClass) {stats.match++;}
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else if (gotClass == "") {stats.unknown++;}
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else {stats.error++;}
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if (++xx % 16 == 0) {
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std::cout << pat.className << " -> " << gotClass << std::endl;
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std::cout << stats.getSum() << ":" << stats.match << ":" << stats.error << ":" << stats.unknown << std::endl;
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std::cout << stats.match/stats.getSum() << ":" << stats.error/stats.getSum() << ":" << stats.unknown/stats.getSum() << std::endl;
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}
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// plot
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K::GnuplotPoint3 p3(vec(0), vec(1), vec(2));
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lines[idx].add(p3);
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}
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for (int i = 0; i < 5; ++i) {lines[i].setColorHex(COLORS[i]);}
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for (int i = 0; i < 5; ++i) {splot.add(&lines[i]);}
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gp.setDebugOutput(false);
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gp.draw(splot);
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gp.flush();
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sleep(10000);
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//#include "sensors/SensorReader.h"
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//#include "Interpolator.h"
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//#include <KLib/misc/gnuplot/Gnuplot.h>
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//#include <KLib/misc/gnuplot/GnuplotPlot.h>
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//#include <KLib/misc/gnuplot/GnuplotPlotElementLines.h>
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//#include <KLib/misc/gnuplot/GnuplotMultiplot.h>
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//#include <KLib/math/neuralnet/NeuralNetIHO.h>
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//#include <KLib/math/optimization/NumOptAlgoGenetic.h>
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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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// REJECT,
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//};
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///** interpolate the output for the given position using the provided range */
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//template <typename T> T blur(K::Interpolator<uint64_t, T>& interpol, const uint64_t ms, const int s = 3) {
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// return interpol.get(ms-s*2) * 0.1 +
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// interpol.get(ms-s) * 0.2 +
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// interpol.get(ms) * 0.4 +
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// interpol.get(ms+s) * 0.2 +
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// interpol.get(ms+s*2) * 0.1;
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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() {
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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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//static constexpr int NUM_IN = 60;
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//static constexpr int NUM_HID = 16;
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//static constexpr int NUM_OUT = 4;
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//static constexpr int NUM_ARGS = NUM_IN*NUM_HID + NUM_HID*NUM_OUT;
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//static std::vector<float> getNetworkInput(K::Interpolator<uint64_t, SensorGyro>& interpol, const uint64_t pos) {
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// std::vector<float> val;
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// val.resize(NUM_IN);
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// int idx = 0;
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// for (int offset = -500; offset < 500; offset += 50) {
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// SensorGyro gyro = interpol.get(pos + offset);
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// val[idx++] = gyro.x;
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// val[idx++] = gyro.y;
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// val[idx++] = gyro.z;
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// assert(idx <= NUM_IN);
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// }
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// return val;
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//}
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///** get the index of the largest element within vec */
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//static int getMaxIdx(const K::NeuralNetResultIHO<NUM_OUT>& vec) {
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// float max = 0;
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// int idx = 0;
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// for (int i = 0; i < NUM_OUT; ++i) {
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// if (vec.values[i] > max) {
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// max = vec.values[i];
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// idx = i;
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// }
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// }
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// return idx;
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//}
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//struct TMP {int index; float value;};
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//static std::vector<TMP> getSorted(const K::NeuralNetResultIHO<NUM_OUT>& vec) {
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// std::vector<TMP> tmp;
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// for (int i = 0; i < NUM_OUT; ++i) {tmp.push_back( TMP{i, vec.values[i]} );}
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// auto comp = [] (const TMP& t1, const TMP& t2) {return t2.value < t1.value;};
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// std::sort(tmp.begin(), tmp.end(), comp);
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// return tmp;
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//}
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//static void debug(Practice& p, K::NeuralNetResultIHO<NUM_OUT>& res) {
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// const int maxIdx = getMaxIdx(res);
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// const char max = (res.values[maxIdx] > 0.5) ? (maxIdx + '0') : ('?');
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// std::cout << "practice was: " << (int)p.type;
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// std::cout << " network says: " << max << "\t";
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// std::cout << "[";
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// for (int i = 0; i < NUM_OUT; ++i) {
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// std::cout << res.values[i] << ", ";
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// }
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// std::cout << "]" << std::endl;
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//}
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//static void debugPlot(Practice& p) {
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// static K::Gnuplot gp;
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// K::GnuplotPlot plot;
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// K::GnuplotPlotElementLines line[3];
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// line[0].setColorHex("#ff0000"); line[0].setTitle("x");
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// line[1].setColorHex("#00ff00"); line[1].setTitle("y");
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// line[2].setColorHex("#0000ff"); line[2].setTitle("z");
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// plot.add(&line[0]);
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// plot.add(&line[1]);
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// plot.add(&line[2]);
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// K::Interpolator<uint64_t, SensorGyro> interpol = p.getInterpol();
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// for (int ms = 0; ms < 20000; ms += 50) {
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// SensorGyro s = interpol.get(ms);
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// line[0].add(K::GnuplotPoint2(ms, s.x));
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// line[1].add(K::GnuplotPoint2(ms, s.y));
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// line[2].add(K::GnuplotPoint2(ms, s.z));
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// }
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// gp.setDebugOutput(true);
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// gp.draw(plot);
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// gp.flush();
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//}
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//int main(void) {
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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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// practices.push_back(
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// Practice {
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// PracticeType::REST,
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// SensorReader::read("/mnt/firma/kunden/HandyGames/daten/idle/restposition_gl_24.txt"),
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// {1000, 2000, 3000, 4000, 5000, 6000, 7000, 8000, 9000}
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// }
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// );
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// practices.push_back(
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// Practice {
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// PracticeType::SITUPS,
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// SensorReader::read("/mnt/firma/kunden/HandyGames/daten/situps/situps_gl_12_subject_1_left.txt"),
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// {1850, 3250, 4750, 6150, 7550, 8950, 10350, 11600, 13000}
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// }
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// );
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// std::vector<std::vector<float>> vecs = {vec1, vec2};
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// std::cout << K::PCAHelper<float>::getR(vecs) << std::endl;
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// std::cout << K::PCAHelper<float>::getM(vecs) << std::endl;
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// practices.push_back(
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// Practice {
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// PracticeType::PUSHUPS,
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// SensorReader::read("/mnt/firma/kunden/HandyGames/daten/pushups/pushups_gl_8_subject_4_right.txt"),
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// {2750, 4200, 5850, 7400, 9000, 10650}
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// //{3500, 5000, 8300, 9900, 11550}
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// }
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// );
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// K::PCAHelper<float>::R r;
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// r.add(vec1); r.add(vec2); std::cout << r.get() << std::endl;
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// Eigen::Vector3f v1; v1 << 1,2,3;
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// Eigen::Vector3f v2; v2 << 3,4,5;
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// std::vector<Eigen::Vector3f> vecs2 = {v1, v2};
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// std::cout << K::PCAHelper<float>::getR(vecs2) << std::endl;
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// std::cout << K::PCAHelper<float>::getM(vecs2) << std::endl;
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// UsingNeuralNet::run();
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//UsingPCA::run();
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// practices.push_back(
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// Practice {
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// PracticeType::REST,
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// SensorReader::read("/mnt/firma/kunden/HandyGames/daten/jumpingjack/jumpingjack_gl_5_subject_3_left.txt"),
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// {1950+500, 2900+500, 3850+500, 4850+500, 5850+500, 6850+500, 7850+500, 8850+500, 9800+500, 10800+500, 11850+500}
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// }
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// );
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//// practices.push_back(
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//// Practice {
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//// PracticeType::REST,
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//// SensorReader::read("/mnt/firma/kunden/HandyGames/daten/pushups/pushups_gl_8_subject_4_right.txt"),
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//// //{2750, 4200, 5850, 7400, 9000, 10650}
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//// {3500, 5000, 8300, 9900, 11550}
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//// }
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//// );
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// practices.push_back(
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// Practice {
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// PracticeType::REST,
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// SensorReader::read("/mnt/firma/kunden/HandyGames/daten/situps/situps_gl_12_subject_1_left.txt"),
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// {1850+600, 3250+600, 4750+600, 6150+600, 7550+600, 8950+600, 10350+600, 11600+600, 13000+600}
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// }
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// );
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// debugPlot(practices.back());
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// sleep(100);
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// class MyOpt : public K::NumOptFunction<NUM_ARGS> {
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// public:
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// std::vector<Practice>& practices;
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// K::NeuralNetIHO<NUM_IN, NUM_HID, NUM_OUT>& net;
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// /** ctor */
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// MyOpt(std::vector<Practice>& practices, K::NeuralNetIHO<NUM_IN, NUM_HID, NUM_OUT>& net) : practices(practices), net(net) {
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// ;
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// }
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// double getValue(const K::NumOptVector<NUM_ARGS>& args) const {
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// // configure the network
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// std::vector<float> vals;
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// for(int i = 0; i < NUM_ARGS; ++i) {vals.push_back(args[i]);}
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// net.setAll(vals);
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// // temporals
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// float points = 0;
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// // process every practice
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// for (Practice& p : practices) {
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// // get the values for the neural-net-input
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// K::Interpolator<uint64_t, SensorGyro> interpol = p.getInterpol();
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// // process 4 (positive) occurences within the practice
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// for (int key = 0; key < 4; ++key) {
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// for (int o = -100; o <= +100; o +=50) {
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// const uint64_t ts = p.keyGyro[key] + o;
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// const std::vector<float> values = getNetworkInput(interpol, ts);
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// // calculate the output
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// const K::NeuralNetResultIHO<NUM_OUT> res = net.getOutput(values.data());
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// // largest value matches the desired type -> good!
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// std::vector<TMP> resSort = getSorted(res);
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// if (resSort[0].index == (int) p.type) {
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// //if ( (resSort[0].value - resSort[1].value) > 0.25 ) {
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// ++points;
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// points += resSort[0].value;
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// points -= resSort[1].value;
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// //}
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// //points += resSort[0].value;
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// //points += (resSort[0].value - resSort[1].value);
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// } else {
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// --points;
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// }
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//// // update the score
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//// for (int i = 0; i < NUM_OUT; ++i) {
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//// if (i == (int) p.type) {
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//// points += 3 * res.values[i]; // matches
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//// } else {
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//// points -= res.values[i]; // does not match
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//// }
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//// }
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//// int maxIdx = getMaxIdx(res);
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//// if (maxIdx == (int) p.type) {
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//// ++points;
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//// }
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// }
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// UsingNeuralNet::debugPlot(
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// Practice {
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// PracticeType::KNEEBEND,
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// SensorReader::read("/mnt/firma/kunden/HandyGames/daten/kneebend/kneebend_gl_0_subject_0_right.txt"),
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// {2650, 4750, 6750, 8800, 10800, 12800}
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// //{3500, 5000, 8300, 9900, 11550}
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// }
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// }
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// std::cout << points << std::endl;
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// return -points;
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// }
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// );
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// };
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//sleep(1000);
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// K::NumOptAlgoGenetic<NUM_ARGS> opt;
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// K::NumOptVector<NUM_ARGS> vec;
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// K::NeuralNetIHO<NUM_IN, NUM_HID, NUM_OUT> net;
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// MyOpt func(practices, net);
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// opt.setElitism(0.025f);
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// opt.setPopulationSize(300);
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// opt.setMaxIterations(100);
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// opt.setMutation(0.10f);
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// opt.setValRange(0.5);
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// opt.calculateOptimum(func, vec);
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//// // process every practice
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//// for (Practice& p : practices) {
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//// // get the values for the neural-net-input
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//// K::Interpolator<uint64_t, SensorGyro> interpol = p.getInterpol();
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//// // process every (positive) occurence within the practice
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//// for (uint64_t ts : p.keyGyro) {
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//// std::vector<float> values = getNetworkInput(interpol, ts);
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//// K::NeuralNetResultIHO<NUM_OUT> res = net.getOutput(values.data());
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//// debug(p, res);
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//// {
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//// std::vector<float> values = getNetworkInput(interpol, ts+500);
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//// K::NeuralNetResultIHO<NUM_OUT> res = net.getOutput(values.data());
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//// std::cout << "###"; debug(p, res);
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//// }
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//// }getMaxIdx
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//// }
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// K::Gnuplot gp1;
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// K::Gnuplot gp2;
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// K::GnuplotPlot plot1;
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// K::GnuplotPlot plot2;
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// K::GnuplotMultiplot plot(2,1);
|
||||
// plot.add(&plot1);
|
||||
// plot.add(&plot2);
|
||||
|
||||
// K::GnuplotPlotElementLines line[3];
|
||||
// line[0].setColorHex("#ff0000"); line[0].setTitle("x");
|
||||
// line[1].setColorHex("#00ff00"); line[1].setTitle("y");
|
||||
// line[2].setColorHex("#0000ff"); line[2].setTitle("z");
|
||||
// plot1.add(&line[0]);
|
||||
// plot1.add(&line[1]);
|
||||
// plot1.add(&line[2]);
|
||||
|
||||
// K::GnuplotPlotElementLines netLines[NUM_OUT];
|
||||
// netLines[0].setColorHex("#ff0000"); netLines[0].setTitle("REST"); netLines[0].setLineWidth(2);
|
||||
// netLines[1].setColorHex("#00ff00"); netLines[1].setTitle("JUMPING_JACK"); netLines[1].setLineWidth(2);
|
||||
// netLines[2].setColorHex("#0000ff"); netLines[2].setTitle("SITUPS"); netLines[2].setLineWidth(2);
|
||||
// netLines[3].setColorHex("#ffff00"); netLines[3].setTitle("PUSBACKS"); netLines[3].setLineWidth(2);
|
||||
|
||||
// for (int i = 0; i < NUM_OUT; ++i) {
|
||||
// plot2.add(&netLines[i]);
|
||||
// }
|
||||
|
||||
// // process every practice
|
||||
// for (Practice& p : practices) {
|
||||
|
||||
// // get the values for the neural-net-input
|
||||
|
||||
// K::Interpolator<uint64_t, SensorGyro> interpol = p.getInterpol();
|
||||
|
||||
// line[0].clear();
|
||||
// line[1].clear();
|
||||
// line[2].clear();
|
||||
|
||||
// for (int i = 0; i < NUM_OUT; ++i) {
|
||||
// netLines[i].clear();
|
||||
// }
|
||||
|
||||
// for (int ms = 0; ms < 20000; ms += 50) {
|
||||
// SensorGyro s = interpol.get(ms);
|
||||
// line[0].add(K::GnuplotPoint2(ms, s.x));
|
||||
// line[1].add(K::GnuplotPoint2(ms, s.y));
|
||||
// line[2].add(K::GnuplotPoint2(ms, s.z));
|
||||
// }
|
||||
|
||||
// // process every (positive) occurence within the practice
|
||||
// for (int ts = 1000; ts < 10000; ts += 50) {
|
||||
|
||||
// std::vector<float> values = getNetworkInput(interpol, ts);
|
||||
// K::NeuralNetResultIHO<NUM_OUT> res = net.getOutput(values.data());
|
||||
// debug(p, res);
|
||||
|
||||
// for (int i = 0; i < NUM_OUT; ++i) {
|
||||
// netLines[i].add(K::GnuplotPoint2(ts, res.values[i]));
|
||||
// }
|
||||
|
||||
// gp1 << "set arrow 1 from " << ts-500 << ",-10 to " << ts-500 << ",+10\n";
|
||||
// gp1 << "set arrow 2 from " << ts+500 << ",-10 to " << ts+500 << ",+10\n";
|
||||
// gp1.draw(plot1);
|
||||
// gp1.flush();
|
||||
|
||||
// gp2.draw(plot2);
|
||||
// gp2.flush();
|
||||
|
||||
// usleep(1000*33);
|
||||
|
||||
|
||||
// }
|
||||
|
||||
// }
|
||||
|
||||
|
||||
|
||||
//// K::Gnuplot gp;
|
||||
//// K::GnuplotPlot plot;
|
||||
//// K::GnuplotPlotElementLines line[3];
|
||||
//// line[0].setColorHex("#ff0000"); line[0].setTitle("x");
|
||||
//// line[1].setColorHex("#00ff00"); line[1].setTitle("y");
|
||||
//// line[2].setColorHex("#0000ff"); line[2].setTitle("z");
|
||||
|
||||
//// Practice p1 = practices[0];
|
||||
|
||||
//// auto interpol = p1.getInterpol();
|
||||
//// for (int ms = 0; ms < 20000; ms += 50) {
|
||||
//// SensorGyro s = blur(interpol, ms, 10);
|
||||
//// line[0].add(K::GnuplotPoint2(ms, s.x));
|
||||
//// line[1].add(K::GnuplotPoint2(ms, s.y));
|
||||
//// line[2].add(K::GnuplotPoint2(ms, s.z));
|
||||
//// }
|
||||
|
||||
//// plot.add(&line[0]);
|
||||
//// plot.add(&line[1]);
|
||||
//// plot.add(&line[2]);
|
||||
//// gp.draw(plot);
|
||||
//// for (uint64_t ts : p1.keyGyro) {
|
||||
//// gp << "set arrow from " << ts << ",-10 to " << ts << ",+10\n";
|
||||
//// }
|
||||
//// gp.flush();
|
||||
|
||||
|
||||
// sleep(1000);
|
||||
|
||||
//}
|
||||
}
|
||||
|
||||
Reference in New Issue
Block a user