1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
|
#include <iostream>
#include <Eigen/Dense>
#include <boost/program_options.hpp>
#include <list>
#include "tools/easylogging++.h"
#include "model/ranksvmtn.h"
#include "tools/fileDataProvider.h"
#include "tools/matrixIO.h"
#include <fstream>
INITIALIZE_EASYLOGGINGPP
using namespace Eigen;
namespace po = boost::program_options;
po::variables_map vm;
int train() {
RSVM *rsvm;
rsvm = RSVM::loadModel(vm["model"].as<std::string>());
FileDP dp(vm["feature"].as<std::string>());
// Generic training operations
dp.open();
DataList D;
LOG(INFO)<<"Training started";
dp.getDataSet(D);
LOG(INFO)<<"Read "<<D.getSize()<<" entries with "<< D.getfSize()<<" features";
rsvm->train(D);
LOG(INFO)<<"Training finished,saving model";
dp.close();
rsvm->saveModel(vm["output"].as<std::string>().c_str());
delete rsvm;
return 0;
}
int predict() {
RSVM *rsvm;
rsvm = RSVM::loadModel(vm["model"].as<std::string>().c_str());
FileDP dp(vm["feature"].as<std::string>().c_str());
dp.open();
DataList D;
std::vector<double> L;
LOG(INFO)<<"Prediction started";
while (!dp.EOFile())
{
dp.getDataSet(D);
LOG(INFO)<<"Read "<<D.getSize()<<" entries with "<< D.getfSize()<<" features";
rsvm->predict(D,L);
}
LOG(INFO)<<"Training finished,saving prediction";
std::ofstream fout(vm["output"].as<std::string>().c_str());
for (int i=0; i<L.size();++i)
fout<<L[i]<<std::endl;
fout.close();
dp.close();
delete rsvm;
return 0;
}
int validate()
{
LOG(FATAL)<<"Not Implemented";
return 0;
}
int main(int argc, char **argv) {
// Defining program options
po::options_description desc("Allowed options");
desc.add_options()
("help,h", "produce help message")
("train,T", "training model")
("validate,V", "validate model")
("predict,P", "use model for prediction")
("model,m", po::value<std::string>(), "set input model file")
("output,o", po::value<std::string>(), "set output model/prediction file")
("feature,i", po::value<std::string>(), "set input feature file");
// Parsing program options
po::store(po::parse_command_line(argc, argv, desc), vm);
po::notify(vm);
/* Conjugate Gradient method test
MatrixXd A(3,3);
VectorXd b(3),x(3);
A<< 1,2,3,2,2,4,3,4,1;
b<< 1,1,1;
x<< 0,0,0;
cg_solve(A,b,x);
write_stream(std::cout,x);*/
// Print help if necessary
if (vm.count("help") || !(vm.count("train") || vm.count("validate") || vm.count("predict"))) {
std::cout << desc;
return 0;
}
if (vm.count("train")) {
LOG(INFO) << "Program option: training";
train();
}
else if (vm.count("validate")) {
LOG(INFO) << "Program option: validate";
validate();
}
else if (vm.count("predict")) {
LOG(INFO) << "Program option: predict";
predict();
}
return 0;
}
|
