Как обучить в Matlab модель, сохранить ее на диск и загрузить в C ++ программу?

Мое решение состояло в том, чтобы переучиться на C ++, потому что я не мог найти хороший способ напрямую сохранить модель. Вот мой код Вам нужно будет адаптировать его и немного почистить. Самое большое изменение, которое вы должны сделать, это не трудно кодировать svm_parameter ценности, как я сделал. Вам также придется заменить FilePath с std::string, Я копирую, вставляю и делаю небольшие правки здесь, в SO, поэтому форматирование не будет идеальным:

Используется так:

    auto targetsPath = FilePath("targets.txt");
auto observationsPath = FilePath("observations.txt");

auto targetsMat = MatlabMatrixFileReader::Read(targetsPath, ',');
auto observationsMat = MatlabMatrixFileReader::Read(observationsPath, ',');
auto v = MiscVector::ConvertVecOfVecToVec(targetsMat);
auto model = SupportVectorRegressionModel{ observationsMat, v };

std::vector<double> observation{ { // 32 feature observation
0.883575729725847,0.919446119013878,0.95359403450317,
0.968233630936732,0.91891307107125,0.887897763183844,
0.937588566544751,0.920582702918882,0.888864454119387,
0.890066735260163,0.87911085669864,0.903745573664995,
0.861069296586979,0.838606194934074,0.856376230548304,
0.863011311537075,0.807688936997926,0.740434984165146,
0.738498042748759,0.736410940165691,0.697228384912424,
0.608527698289016,0.632994967880269,0.66935784966765,
0.647761430696238,0.745961037635717,0.560761134660957,
0.545498063585615,0.590854855113663,0.486827902942118,
0.187128866890822,- 0.0746523069562551
} };

double prediction = model.Predict(observation);

miscvector.h

    static vector<double> ConvertVecOfVecToVec(const vector<vector<double>> &mat)
{
vector<double> targetsVec;
targetsVec.reserve(mat.size());
for (size_t i = 0; i < mat.size(); i++)
{
targetsVec.push_back(mat[i][0]);
}
return targetsVec;
}

libsvmtargetobjectconvertor.h

#pragma once

#include "machinelearning.h"
struct svm_node;

class LibSvmTargetObservationConvertor
{
public:
svm_node ** LibSvmTargetObservationConvertor::ConvertObservations(const vector<MlObservation> &observations, size_t numFeatures) const
{
svm_node **svmObservations = (svm_node **)malloc(sizeof(svm_node *) * observations.size());
for (size_t rowI = 0; rowI < observations.size(); rowI++)
{
svm_node *row = (svm_node *)malloc(sizeof(svm_node) * numFeatures);
for (size_t colI = 0; colI < numFeatures; colI++)
{
row[colI].index = colI;
row[colI].value = observations[rowI][colI];
}
row[numFeatures].index = -1; // apparently needed
svmObservations[rowI] = row;
}
return svmObservations;
}

svm_node* LibSvmTargetObservationConvertor::ConvertMatToSvmNode(const MlObservation &observation) const
{
size_t numFeatures = observation.size();
svm_node *obsNode = (svm_node *)malloc(sizeof(svm_node) * numFeatures);
for (size_t rowI = 0; rowI < numFeatures; rowI++)
{
obsNode[rowI].index = rowI;
obsNode[rowI].value = observation[rowI];
}
obsNode[numFeatures].index = -1; // apparently needed
return obsNode;
}
};

machinelearning.h

#pragma once

#include <vector>
using std::vector;

using MlObservation = vector<double>;
using MlTarget = double;

//machinelearningmodel.h
#pragma once

#include <vector>
#include "machinelearning.h"class MachineLearningModel
{
public:
virtual ~MachineLearningModel() {}
virtual double Predict(const MlObservation &observation) const = 0;
};

matlabmatrixfilereader.h

#pragma once

#include <vector>
using std::vector;

class FilePath;
// Matrix created with command:
// dlmwrite('my_matrix.txt', somematrix, 'delimiter', ',', 'precision', 15);
// In these files, each row is a matrix row. Commas separate elements on a row.
// There is no space at the end of a row. There is a blank line at the bottom of the file.
// File format:
// 0.4,0.7,0.8
// 0.9,0.3,0.5
// etc.
static class MatlabMatrixFileReader
{
public:
static vector<vector<double>> Read(const FilePath &asciiFilePath, char delimiter)
{

vector<vector<double>> values;
vector<double> valueline;
std::ifstream fin(asciiFilePath.Path());
string item, line;
while (getline(fin, line))
{
std::istringstream in(line);

while (getline(in, item, delimiter))
{
valueline.push_back(atof(item.c_str()));
}
values.push_back(valueline);
valueline.clear();
}
fin.close();
return values;
}

};

supportvectorregressionmodel.h

#pragma once

#include <vector>
using std::vector;
#include "machinelearningmodel.h"
#include "svm.h" // libsvm

class FilePath;

class SupportVectorRegressionModel : public MachineLearningModel
{
public:
SupportVectorRegressionModel::~SupportVectorRegressionModel()
{
svm_free_model_content(model_);
svm_destroy_param(&param_);
svm_free_and_destroy_model(&model_);
}

SupportVectorRegressionModel::SupportVectorRegressionModel(const vector<MlObservation>& observations, const vector<MlTarget>& targets)
{
// assumes all observations have same number of features
size_t numFeatures = observations[0].size();

//setup targets
//auto v = ConvertVecOfVecToVec(targetsMat);
double *targetsPtr = const_cast<double *>(&targets[0]); // why aren't the targets const?

LibSvmTargetObservationConvertor conv;
svm_node **observationsPtr = conv.ConvertObservations(observations, numFeatures);

// setup observations
//svm_node **observations = BuildObservations(observationsMat, numFeatures);

// setup problem
svm_problem problem;
problem.l = targets.size();
problem.y = targetsPtr;
problem.x = observationsPtr;

// specific to out training sets
// TODO:    This is hard coded.
//          Bust out these values for use in constructor
param_.C = 0.4;                 // cost
param_.svm_type = 4;            // SVR
param_.kernel_type = 2;         // radial
param_.nu = 0.6;                // SVR nu
// These values are the defaults used in the Matlab version
// as found in svm_model_matlab.c
param_.gamma = 1.0 / (double)numFeatures;
param_.coef0 = 0;
param_.cache_size = 100;        // in MB
param_.shrinking = 1;
param_.probability = 0;
param_.degree = 3;
param_.eps = 1e-3;
param_.p = 0.1;
param_.shrinking = 1;
param_.probability = 0;
param_.nr_weight = 0;
param_.weight_label = NULL;
param_.weight = NULL;

// suppress command line output
svm_set_print_string_function([](auto c) {});

model_ = svm_train(&problem, &param_);
}

double SupportVectorRegressionModel::Predict(const vector<double>& observation) const
{
LibSvmTargetObservationConvertor conv;
svm_node *obsNode = conv.ConvertMatToSvmNode(observation);
double prediction = svm_predict(model_, obsNode);
return prediction;
}

SupportVectorRegressionModel::SupportVectorRegressionModel(const FilePath & modelFile)
{
model_ = svm_load_model(modelFile.Path().c_str());
}
private:
svm_model *model_;
svm_parameter param_;
};