[英]How to train in Matlab a model, save it to disk, and load in C++ program?
I am using libsvm version 3.16. 我使用libsvm版本3.16。 I have done some training in Matlab, and created a model.
我在Matlab做过一些训练,并创建了一个模型。 Now I would like to save this model to disk and load this model in my C++ program.
现在我想将此模型保存到磁盘并在我的C ++程序中加载此模型。 So far I have found the following alternatives:
到目前为止,我发现了以下替代方案:
Thus, both of these options are not satisfactory, 因此,这两种选择都不令人满意,
Does anyone have an idea? 有没有人有想法?
My solution was to retrain in C++ because I couldn't find a nice way to directly save the model. 我的解决方案是在C ++中重新训练,因为我找不到直接保存模型的好方法。 Here's my code.
这是我的代码。 You'll need to adapt it and clean it up a bit.
你需要调整它并清理一下。 The biggest change you'll have to make it not hard coding the
svm_parameter
values like I did. 你要做的最大的改变就是不要像我那样对
svm_parameter
值进行硬编码。 You'll also have to replace FilePath
with std::string
. 您还必须使用
std::string
替换FilePath
。 I'm copying, pasting and making small edits here in SO so the formatting won't e perfect: 我在这里复制,粘贴和进行小编辑,所以格式化不完美:
Used like this: 像这样使用:
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(¶m_);
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, ¶m_);
}
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_;
};
Option 1 is actually pretty reasonable. 选项1实际上非常合理。 If you save the model in libsvm's C format through matlab, then it is straightforward to work with the model in C/C++ using functions provided by libsvm.
如果通过matlab以libsvm的C格式保存模型,那么使用libsvm提供的函数可以直接在C / C ++中使用模型。 Trying to work with matlab-formatted data in C++ will probably be much more difficult.
尝试在C ++中使用matlab格式的数据可能会困难得多。
The main
function in "svm-predict.c" (located in the root directory of the libsvm package) probably has most of what you need: “svm-predict.c”中的
main
功能(位于libsvm包的根目录中)可能具有您需要的大部分功能:
if((model=svm_load_model(argv[i+1]))==0)
{
fprintf(stderr,"can't open model file %s\n",argv[i+1]);
exit(1);
}
To predict a label for example x
using the model, you can run 要使用模型预测例如
x
的标签,您可以运行
int predict_label = svm_predict(model,x);
The trickiest part of this will be to transfer your data into the libsvm format (unless your data is in the libsvm text file format, in which case you can just use the predict
function in "svm-predict.c"). 最棘手的部分是将数据转换为libsvm格式(除非您的数据采用libsvm文本文件格式,在这种情况下,您只需使用“svm-predict.c”中的
predict
函数)。
A libsvm vector, x
, is an array of struct svm_node
that represents a sparse array of data. libsvm向量
x
是struct svm_node
的数组,表示稀疏的数据数组。 Each svm_node has an index and a value, and the vector must be terminated by an index that is set to -1. 每个svm_node都有一个索引和一个值,并且该向量必须由一个设置为-1的索引终止。 For instance, to encode the vector
[0,1,0,5]
, you could do the following: 例如,要对矢量
[0,1,0,5]
进行编码,您可以执行以下操作:
struct svm_node *x = (struct svm_node *) malloc(3*sizeof(struct svm_node));
x[0].index=2; //NOTE: libsvm indices start at 1
x[0].value=1.0;
x[1].index=4;
x[1].value=5.0;
x[2].index=-1;
For SVM types other than the classifier (C_SVC), look at the predict
function in "svm-predict.c". 对于分类器(C_SVC)以外的SVM类型,请查看“svm-predict.c”中的
predict
函数。
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