This page lists ready to run shogun examples for the C++ libshogun interface.
To run the examples you will need to manually compile them via
g++ name_of_example.cpp -lshogun
in case you installed libshogun to a nonstandard directory you will need to specify the appropriate library and include paths, e.g.
g++ -I/path/to/libshogun/includes name_of_example.cpp -L/path/to/libshogun/sofile -lshogun
Then the examples are standard binary executables and can be started via
./name_of_example
respectively if the libraries are in nonstandard locations (such that they cannot be found by the dynamic linker)
LD_LIBRARY_PATH=path/to/libshogun ./name_of_example
/*
* This program is free software; you can redistribute it and/or modify
* it under the terms of the GNU General Public License as published by
* the Free Software Foundation; either version 3 of the License, or
* (at your option) any later version.
*
* Written (W) 2011 Heiko Strathmann
* Copyright (C) 2011 Berlin Institute of Technology and Max-Planck-Society
*/
#include <shogun/base/init.h>
#include <shogun/base/ParameterMap.h>
using namespace shogun;
void print_message(FILE* target, const char* str)
{
fprintf(target, "%s", str);
}
void print_value(SGParamInfo* key, ParameterMap* map)
{
SGParamInfo* current=map->get(key);
key->print_param_info();
SG_SPRINT("value: ");
if (current)
current->print_param_info();
else
SG_SPRINT("no element\n");
SG_SPRINT("\n");
}
int main(int argc, char **argv)
{
init_shogun(&print_message, &print_message, &print_message);
ParameterMap* map=new ParameterMap();
EContainerType cfrom=CT_SCALAR;
EContainerType cto=CT_MATRIX;
EStructType sfrom=ST_NONE;
EStructType sto=ST_STRING;
EPrimitiveType pfrom=PT_BOOL;
EPrimitiveType pto=PT_SGOBJECT;
map->put(new SGParamInfo("2", cfrom, sfrom, pfrom),
new SGParamInfo("zwei", cto, sto, pto));
map->put(new SGParamInfo("1", cfrom, sfrom, pfrom),
new SGParamInfo("eins", cto, sto, pto));
map->put(new SGParamInfo("4", cfrom, sfrom, pfrom),
new SGParamInfo("vier", cto, sto, pto));
map->put(new SGParamInfo("3", cfrom, sfrom, pfrom),
new SGParamInfo("drei", cto, sto, pto));
SG_SPRINT("before finalization:\n");
map->print_map();
map->finalize_map();
SG_SPRINT("\n\nafter finalization:\n");
map->print_map();
SGParamInfo* key;
SG_SPRINT("\n\ntesting map\n");
key=new SGParamInfo("1", cfrom, sfrom, pfrom);
print_value(key, map);
delete key;
key=new SGParamInfo("2", cfrom, sfrom, pfrom);
print_value(key, map);
delete key;
key=new SGParamInfo("2", cto, sfrom, pfrom);
print_value(key, map);
delete key;
key=new SGParamInfo("2", cfrom, sto, pfrom);
print_value(key, map);
delete key;
key=new SGParamInfo("2", cfrom, sfrom, pto);
print_value(key, map);
delete key;
key=new SGParamInfo("5", cfrom, sfrom, pfrom);
print_value(key, map);
delete key;
delete map;
exit_shogun();
return 0;
}
#include <shogun/base/init.h>
using namespace shogun;
int main(int argc, char** argv)
{
init_shogun();
exit_shogun();
return 0;
}
/*
* This program is free software; you can redistribute it and/or modify
* it under the terms of the GNU General Public License as published by
* the Free Software Foundation; either version 3 of the License, or
* (at your option) any later version.
*
* Written (W) 2008-2009 Soeren Sonnenburg
* Copyright (C) 2008-2009 Fraunhofer Institute FIRST and Max Planck Society
*/
#include <shogun/kernel/GaussianKernel.h>
#include <shogun/features/SimpleFeatures.h>
#include <shogun/classifier/svm/LibSVM.h>
#include <shogun/mathematics/Math.h>
#include <shogun/lib/common.h>
#include <shogun/base/init.h>
#include <stdlib.h>
#include <stdio.h>
using namespace shogun;
#define NUM 100
#define DIMS 2
#define DIST 0.5
float64_t* lab;
float64_t* feat;
void gen_rand_data()
{
lab=SG_MALLOC(float64_t, NUM);
feat=SG_MALLOC(float64_t, NUM*DIMS);
for (int32_t i=0; i<NUM; i++)
{
if (i<NUM/2)
{
lab[i]=-1.0;
for (int32_t j=0; j<DIMS; j++)
feat[i*DIMS+j]=CMath::random(0.0,1.0)+DIST;
}
else
{
lab[i]=1.0;
for (int32_t j=0; j<DIMS; j++)
feat[i*DIMS+j]=CMath::random(0.0,1.0)-DIST;
}
}
CMath::display_vector(lab,NUM);
CMath::display_matrix(feat,DIMS, NUM);
}
int main()
{
const int32_t feature_cache=0;
const int32_t kernel_cache=0;
const float64_t rbf_width=10;
const float64_t svm_C=10;
const float64_t svm_eps=0.001;
init_shogun();
gen_rand_data();
// create train labels
CLabels* labels=new CLabels(SGVector<float64_t>(lab, NUM));
SG_REF(labels);
// create train features
CSimpleFeatures<float64_t>* features = new CSimpleFeatures<float64_t>(feature_cache);
SG_REF(features);
features->set_feature_matrix(feat, DIMS, NUM);
// create gaussian kernel
CGaussianKernel* kernel = new CGaussianKernel(kernel_cache, rbf_width);
SG_REF(kernel);
kernel->init(features, features);
// create svm via libsvm and train
CLibSVM* svm = new CLibSVM(svm_C, kernel, labels);
SG_REF(svm);
svm->set_epsilon(svm_eps);
svm->train();
printf("num_sv:%d b:%f\n", svm->get_num_support_vectors(), svm->get_bias());
// classify + display output
CLabels* out_labels=svm->apply();
for (int32_t i=0; i<NUM; i++)
printf("out[%d]=%f\n", i, out_labels->get_label(i));
SG_UNREF(labels);
SG_UNREF(out_labels);
SG_UNREF(kernel);
SG_UNREF(features);
SG_UNREF(svm);
exit_shogun();
return 0;
}
#include <shogun/features/Labels.h>
#include <shogun/features/SimpleFeatures.h>
#include <shogun/kernel/GaussianKernel.h>
#include <shogun/classifier/svm/LibSVM.h>
#include <shogun/base/init.h>
#include <shogun/lib/common.h>
#include <shogun/io/SGIO.h>
using namespace shogun;
void print_message(FILE* target, const char* str)
{
fprintf(target, "%s", str);
}
int main(int argc, char** argv)
{
init_shogun(&print_message);
// create some data
float64_t* matrix = SG_MALLOC(float64_t, 6);
for (int32_t i=0; i<6; i++)
matrix[i]=i;
// create three 2-dimensional vectors
// shogun will now own the matrix created
CSimpleFeatures<float64_t>* features= new CSimpleFeatures<float64_t>();
features->set_feature_matrix(matrix, 2, 3);
// create three labels
CLabels* labels=new CLabels(3);
labels->set_label(0, -1);
labels->set_label(1, +1);
labels->set_label(2, -1);
// create gaussian kernel with cache 10MB, width 0.5
CGaussianKernel* kernel = new CGaussianKernel(10, 0.5);
kernel->init(features, features);
// create libsvm with C=10 and train
CLibSVM* svm = new CLibSVM(10, kernel, labels);
svm->train();
// classify on training examples
for (int32_t i=0; i<3; i++)
SG_SPRINT("output[%d]=%f\n", i, svm->apply(i));
// free up memory
SG_UNREF(svm);
exit_shogun();
return 0;
}
/*
* This program is free software; you can redistribute it and/or modify
* it under the terms of the GNU General Public License as published by
* the Free Software Foundation; either version 3 of the License, or
* (at your option) any later version.
*
* Written (W) 2009 Alexander Binder
* Copyright (C) 2009 Fraunhofer Institute FIRST and Max-Planck-Society
*/
#include <iostream>
#include <shogun/io/SGIO.h>
#include <shogun/lib/ShogunException.h>
#include <shogun/kernel/CustomKernel.h>
#include <shogun/kernel/CombinedKernel.h>
#include <shogun/classifier/mkl/MKLMultiClass.h>
// g++ -Wall -O3 classifier_mklmulticlass.cpp -I /home/theseus/private/alx/shoguntrunk/compiledtmp/include -L/home/theseus/private/alx/shoguntrunk/compiledtmp/lib -lshogun
using namespace shogun;
void print_message(FILE* target, const char* str)
{
fprintf(target, "%s", str);
}
void print_warning(FILE* target, const char* str)
{
fprintf(target, "%s", str);
}
void print_error(FILE* target, const char* str)
{
fprintf(target, "%s", str);
}
void getgauss(float64_t & y1, float64_t & y2)
{
float x1, x2, w;
do {
x1 = 2.0 * rand()/(float64_t)RAND_MAX - 1.0;
x2 = 2.0 * rand()/(float64_t)RAND_MAX - 1.0;
w = x1 * x1 + x2 * x2;
} while ( (w >= 1.0)|| (w<1e-9) );
w = sqrt( (-2.0 * log( w ) ) / w );
y1 = x1 * w;
y2 = x2 * w;
}
void gendata(std::vector<float64_t> & x,std::vector<float64_t> & y,
CLabels*& lab)
{
int32_t totalsize=240;
int32_t class1size=80;
int32_t class2size=70;
//generating three class data set
x.resize(totalsize);
y.resize(totalsize);
for(size_t i=0; i< x.size();++i)
getgauss(x[i], y[i]);
for(size_t i=0; i< x.size();++i)
{
if((int32_t)i < class1size)
{
x[i]+=0;
y[i]+=0;
}
else if( (int32_t)i< class1size+class2size)
{
x[i]+=+1;
y[i]+=-1;
}
else
{
x[i]+=-1;
y[i]+=+1;
}
}
//set labels
lab=new CLabels(x.size());
for(size_t i=0; i< x.size();++i)
{
if((int32_t)i < class1size)
lab->set_int_label(i,0);
else if( (int32_t)i< class1size+class2size)
lab->set_int_label(i,1);
else
lab->set_int_label(i,2);
}
}
void gentrainkernel(float64_t * & ker1 ,float64_t * & ker2, float64_t * & ker3 ,float64_t &
autosigma,float64_t & n1,float64_t & n2, float64_t & n3,
const std::vector<float64_t> & x,
const std::vector<float64_t> & y)
{
autosigma=0;
for(size_t l=0; l< x.size();++l)
{
for(size_t r=0; r<= l;++r)
{
float64_t dist=((x[l]-x[r])*(x[l]-x[r]) + (y[l]-y[r])*(y[l]-y[r]));
autosigma+=dist*2.0/(float64_t)x.size()/((float64_t)x.size()+1);
}
}
float64_t fm1=0, mean1=0,fm2=0, mean2=0,fm3=0, mean3=0;
ker1=SG_MALLOC(float64_t, x.size()*x.size());
ker2=SG_MALLOC(float64_t, x.size()*x.size());
ker3=SG_MALLOC(float64_t, x.size()*x.size());
for(size_t l=0; l< x.size();++l)
{
for(size_t r=0; r< x.size();++r)
{
float64_t dist=((x[l]-x[r])*(x[l]-x[r]) + (y[l]-y[r])*(y[l]-y[r]));
ker1[l +r*x.size()]= exp( -dist/autosigma/autosigma) ;
//ker2[l +r*x.size()]= exp( -dist/sigma2/sigma2) ;
ker2[l +r*x.size()]= x[l]*x[r] + y[l]*y[r];
ker3[l +r*x.size()]= (x[l]*x[r] + y[l]*y[r]+1)*(x[l]*x[r] + y[l]*y[r]+1);
fm1+=ker1[l +r*x.size()]/(float64_t)x.size()/((float64_t)x.size());
fm2+=ker2[l +r*x.size()]/(float64_t)x.size()/((float64_t)x.size());
fm3+=ker3[l +r*x.size()]/(float64_t)x.size()/((float64_t)x.size());
if(l==r)
{
mean1+=ker1[l +r*x.size()]/(float64_t)x.size();
mean2+=ker2[l +r*x.size()]/(float64_t)x.size();
mean3+=ker3[l +r*x.size()]/(float64_t)x.size();
}
}
}
n1=(mean1-fm1);
n2=(mean2-fm2);
n3=(mean3-fm3);
for(size_t l=0; l< x.size();++l)
{
for(size_t r=0; r< x.size();++r)
{
ker1[l +r*x.size()]=ker1[l +r*x.size()]/n1;
ker2[l +r*x.size()]=ker2[l +r*x.size()]/n2;
ker3[l +r*x.size()]=ker3[l +r*x.size()]/n3;
}
}
}
void gentestkernel(float64_t * & ker1 ,float64_t * & ker2,float64_t * & ker3,
const float64_t autosigma,const float64_t n1,const float64_t n2, const float64_t n3,
const std::vector<float64_t> & x,const std::vector<float64_t> & y,
const std::vector<float64_t> & tx,const std::vector<float64_t> & ty)
{
ker1=SG_MALLOC(float64_t, x.size()*tx.size());
ker2=SG_MALLOC(float64_t, x.size()*tx.size());
ker3=SG_MALLOC(float64_t, x.size()*tx.size());
for(size_t l=0; l< x.size();++l)
{
for(size_t r=0; r< tx.size();++r)
{
float64_t dist=((x[l]-tx[r])*(x[l]-tx[r]) + (y[l]-ty[r])*(y[l]-ty[r]));
ker1[l +r*x.size()]= exp( -dist/autosigma/autosigma) ;
ker2[l +r*x.size()]= x[l]*tx[r] + y[l]*ty[r];
ker3[l +r*x.size()]= (x[l]*tx[r] + y[l]*ty[r]+1)*(x[l]*tx[r] + y[l]*ty[r]+1);
}
}
for(size_t l=0; l< x.size();++l)
{
for(size_t r=0; r< tx.size();++r)
{
ker1[l +r*x.size()]=ker1[l +r*x.size()]/n1;
ker2[l +r*x.size()]=ker2[l +r*x.size()]/n2;
ker3[l +r*x.size()]=ker3[l +r*x.size()]/n2;
}
}
}
void tester()
{
CLabels* lab=NULL;
std::vector<float64_t> x,y;
gendata(x,y, lab);
SG_REF(lab);
float64_t* ker1=NULL;
float64_t* ker2=NULL;
float64_t* ker3=NULL;
float64_t autosigma=1;
float64_t n1=0;
float64_t n2=0;
float64_t n3=0;
int32_t numdata=0;
gentrainkernel( ker1 , ker2, ker3 , autosigma, n1, n2, n3,x,y);
numdata=x.size();
CCombinedKernel* ker=new CCombinedKernel();
CCustomKernel* kernel1=new CCustomKernel();
CCustomKernel* kernel2=new CCustomKernel();
CCustomKernel* kernel3=new CCustomKernel();
kernel1->set_full_kernel_matrix_from_full(SGMatrix<float64_t>(ker1, numdata,numdata));
kernel2->set_full_kernel_matrix_from_full(SGMatrix<float64_t>(ker2, numdata,numdata));
kernel3->set_full_kernel_matrix_from_full(SGMatrix<float64_t>(ker3, numdata,numdata));
ker->append_kernel(kernel1);
ker->append_kernel(kernel2);
ker->append_kernel(kernel3);
//here comes the core stuff
float64_t regconst=1.0;
CMKLMultiClass* tsvm =new CMKLMultiClass(regconst, ker, lab);
tsvm->set_epsilon(0.0001); // SVM epsilon
// MKL parameters
tsvm->set_mkl_epsilon(0.01); // subkernel weight L2 norm termination criterion
tsvm->set_max_num_mkliters(120); // well it will be just three iterations
tsvm->set_mkl_norm(1.5); // mkl norm
//starting svm training
tsvm->train();
SG_SPRINT("finished svm training\n");
//starting svm testing on training data
CLabels* res=tsvm->apply();
ASSERT(res);
float64_t err=0;
for(int32_t i=0; i<numdata;++i)
{
ASSERT(i< res->get_num_labels());
if (lab->get_int_label(i)!=res->get_int_label(i))
err+=1;
}
err/=(float64_t)res->get_num_labels();
SG_SPRINT("prediction error on training data (3 classes): %f ",err);
SG_SPRINT("random guess error would be: %f \n",2/3.0);
SG_FREE(ker1);
SG_FREE(ker2);
SG_FREE(ker3);
//generate test data
CLabels* tlab=NULL;
std::vector<float64_t> tx,ty;
gendata( tx,ty,tlab);
SG_REF(tlab);
float64_t* tker1=NULL;
float64_t* tker2=NULL;
float64_t* tker3=NULL;
gentestkernel(tker1,tker2,tker3, autosigma, n1,n2,n3, x,y, tx,ty);
int32_t numdatatest=tx.size();
CCombinedKernel* tker=new CCombinedKernel();
SG_REF(tker);
CCustomKernel* tkernel1=new CCustomKernel();
CCustomKernel* tkernel2=new CCustomKernel();
CCustomKernel* tkernel3=new CCustomKernel();
tkernel1->set_full_kernel_matrix_from_full(SGMatrix<float64_t>(tker1,numdata, numdatatest));
tkernel2->set_full_kernel_matrix_from_full(SGMatrix<float64_t>(tker2,numdata, numdatatest));
tkernel3->set_full_kernel_matrix_from_full(SGMatrix<float64_t>(tker2,numdata, numdatatest));
tker->append_kernel(tkernel1);
tker->append_kernel(tkernel2);
tker->append_kernel(tkernel3);
int32_t numweights;
float64_t* weights=tsvm->getsubkernelweights(numweights);
SG_SPRINT("test kernel weights\n");
for(int32_t i=0; i< numweights;++i)
SG_SPRINT("%f ", weights[i]);
SG_SPRINT("\n");
//set kernel
tker->set_subkernel_weights(weights, numweights);
tsvm->set_kernel(tker);
//compute classification error, check mem
CLabels* tres=tsvm->apply();
float64_t terr=0;
for(int32_t i=0; i<numdatatest;++i)
{
ASSERT(i< tres->get_num_labels());
if(tlab->get_int_label(i)!=tres->get_int_label(i))
terr+=1;
}
terr/=(float64_t) tres->get_num_labels();
SG_SPRINT("prediction error on test data (3 classes): %f ",terr);
SG_SPRINT("random guess error would be: %f \n",2/3.0);
SG_FREE(tker1);
SG_FREE(tker2);
SG_FREE(tker3);
SG_UNREF(tsvm);
SG_UNREF(res);
SG_UNREF(tres);
SG_UNREF(lab);
SG_UNREF(tlab);
SG_UNREF(tker);
SG_FREE(weights);
weights=NULL;
SG_SPRINT( "finished \n");
}
namespace shogun
{
extern Version* sg_version;
extern SGIO* sg_io;
}
int main()
{
init_shogun(&print_message, &print_warning,
&print_error);
try
{
sg_version->print_version();
sg_io->set_loglevel(MSG_INFO);
tester();
}
catch(ShogunException & sh)
{
printf("%s",sh.get_exception_string());
}
exit_shogun();
return 0;
}
/*
* This program is free software; you can redistribute it and/or modify
* it under the terms of the GNU General Public License as published by
* the Free Software Foundation; either version 3 of the License, or
* (at your option) any later version.
*
* Written (W) 2011 Heiko Strathmann
* Copyright (C) 2011 Berlin Institute of Technology and Max-Planck-Society
*/
#include <shogun/base/init.h>
#include <shogun/evaluation/CrossValidation.h>
#include <shogun/evaluation/ContingencyTableEvaluation.h>
#include <shogun/evaluation/StratifiedCrossValidationSplitting.h>
#include <shogun/modelselection/GridSearchModelSelection.h>
#include <shogun/modelselection/ModelSelectionParameters.h>
#include <shogun/modelselection/ParameterCombination.h>
#include <shogun/features/Labels.h>
#include <shogun/features/SimpleFeatures.h>
#include <shogun/clustering/KMeans.h>
#include <shogun/distance/EuclidianDistance.h>
#include <shogun/distance/MinkowskiMetric.h>
using namespace shogun;
void print_message(FILE* target, const char* str)
{
fprintf(target, "%s", str);
}
int main(int argc, char **argv)
{
init_shogun(&print_message, &print_message, &print_message);
int32_t num_clusters=4;
int32_t num_features=11;
int32_t dim_features=3;
int32_t num_vectors_per_cluster=5;
float64_t cluster_std_dev=2.0;
/* build random cluster centers */
SGMatrix<float64_t> cluster_centers(dim_features, num_clusters);
CMath::random_vector(cluster_centers.matrix, dim_features*num_clusters,
-10.0, 10.0);
CMath::display_matrix(cluster_centers.matrix, cluster_centers.num_rows,
cluster_centers.num_cols, "cluster centers");
/* create data around clusters */
SGMatrix<float64_t> data(dim_features, num_clusters*num_vectors_per_cluster);
for (index_t i=0; i<num_clusters; ++i)
{
for (index_t j=0; j<dim_features; ++j)
{
for (index_t k=0; k<num_vectors_per_cluster; ++k)
{
index_t idx=i*dim_features*num_vectors_per_cluster;
idx+=j;
idx+=k*dim_features;
float64_t entry=cluster_centers.matrix[i*dim_features+j];
data.matrix[idx]=CMath::normal_random(entry, cluster_std_dev);
}
}
}
/* create features, SG_REF to avoid deletion */
CSimpleFeatures<float64_t>* features=new CSimpleFeatures<float64_t> ();
features->set_feature_matrix(data);
SG_REF(features);
/* create labels for cluster centers */
CLabels* labels=new CLabels(num_features);
for (index_t i=0; i<num_features; ++i)
labels->set_label(i, i%2==0 ? 1 : -1);
/* create distance */
CEuclidianDistance* distance=new CEuclidianDistance(features, features);
/* create distance machine */
CKMeans* clustering=new CKMeans(num_clusters, distance);
clustering->train(features);
/* build clusters */
CLabels* result=clustering->apply();
for (index_t i=0; i<result->get_num_labels(); ++i)
SG_SPRINT("cluster index of vector %i: %f\n", i, result->get_label(i));
/* print cluster centers */
CSimpleFeatures<float64_t>* centers=
(CSimpleFeatures<float64_t>*)distance->get_lhs();
SGMatrix<float64_t> centers_matrix=centers->get_feature_matrix();
CMath::display_matrix(centers_matrix.matrix, centers_matrix.num_rows,
centers_matrix.num_cols, "learned centers");
CMath::display_matrix(cluster_centers.matrix, cluster_centers.num_rows,
cluster_centers.num_cols, "real centers");
/* clean up */
SG_UNREF(result);
SG_UNREF(centers);
SG_UNREF(clustering);
SG_UNREF(labels);
SG_UNREF(features);
cluster_centers.destroy_matrix();
exit_shogun();
return 0;
}
/*
* This program is free software; you can redistribute it and/or modify
* it under the terms of the GNU General Public License as published by
* the Free Software Foundation; either version 3 of the License, or
* (at your option) any later version.
*
* Written (W) 2011 Heiko Strathmann
* Copyright (C) 2011 Berlin Institute of Technology and Max-Planck-Society
*/
#include <shogun/base/init.h>
#include <shogun/features/SimpleFeatures.h>
#include <shogun/features/Subset.h>
using namespace shogun;
void print_message(FILE* target, const char* str)
{
fprintf(target, "%s", str);
}
int main(int argc, char **argv)
{
init_shogun(&print_message, &print_message, &print_message);
SGMatrix<float64_t> data(3, 10);
CSimpleFeatures<float64_t>* f=new CSimpleFeatures<float64_t>(data);
CMath::range_fill_vector(data.matrix, data.num_cols*data.num_rows, 1.0);
CMath::display_matrix(data.matrix, data.num_rows, data.num_cols,
"original feature data");
index_t offset_subset=1;
SGVector<index_t> feature_subset(8);
CMath::range_fill_vector(feature_subset.vector, feature_subset.vlen,
offset_subset);
CMath::display_vector(feature_subset.vector, feature_subset.vlen,
"feature subset");
f->set_subset(new CSubset(feature_subset));
SG_SPRINT("feature vectors after setting subset on original data:\n");
for (index_t i=0; i<f->get_num_vectors(); ++i)
{
SGVector<float64_t> vec=f->get_feature_vector(i);
SG_SPRINT("%i: ", i);
CMath::display_vector(vec.vector, vec.vlen);
f->free_feature_vector(vec, i);
}
index_t offset_copy=2;
SGVector<index_t> feature_copy_subset(4);
CMath::range_fill_vector(feature_copy_subset.vector,
feature_copy_subset.vlen, offset_copy);
CMath::display_vector(feature_copy_subset.vector, feature_copy_subset.vlen,
"indices that are to be copied");
CSimpleFeatures<float64_t>* subset_copy=
(CSimpleFeatures<float64_t>*)f->copy_subset(feature_copy_subset);
SGMatrix<float64_t> subset_copy_matrix=subset_copy->get_feature_matrix();
CMath::display_matrix(subset_copy_matrix.matrix,
subset_copy_matrix.num_rows, subset_copy_matrix.num_cols,
"copy matrix");
index_t num_its=subset_copy_matrix.num_rows*subset_copy_matrix.num_cols;
for (index_t i=0; i<num_its; ++i)
{
index_t idx=i+(offset_copy+offset_subset)*subset_copy_matrix.num_rows;
ASSERT(subset_copy_matrix.matrix[i]==data.matrix[idx]);
}
SG_UNREF(f);
SG_UNREF(subset_copy);
SG_FREE(feature_copy_subset.vector);
SG_SPRINT("\nEND\n");
exit_shogun();
return 0;
}
/*
* This program is free software; you can redistribute it and/or modify
* it under the terms of the GNU General Public License as published by
* the Free Software Foundation; either version 3 of the License, or
* (at your option) any later version.
*
* Written (W) 2011 Heiko Strathmann
* Copyright (C) 2011 Berlin Institute of Technology and Max-Planck-Society
*/
#include <shogun/base/init.h>
#include <shogun/features/SparseFeatures.h>
#include <shogun/features/Subset.h>
using namespace shogun;
void print_message(FILE* target, const char* str)
{
fprintf(target, "%s", str);
}
int main(int argc, char **argv)
{
init_shogun(&print_message, &print_message, &print_message);
index_t num_vectors=10;
index_t num_features=3;
/* create some sparse data */
SGSparseMatrix<float64_t> data=SGSparseMatrix<float64_t>(num_vectors,
num_features);
for (index_t i=0; i<num_vectors; ++i)
{
/* put elements only at even indices */
data.sparse_matrix[i]=SGSparseVector<float64_t>(num_features, 2*i);
/* fill */
for (index_t j=0; j<num_features; ++j)
{
data.sparse_matrix[i].features[j].entry=i+j;
data.sparse_matrix[i].features[j].feat_index=3*j;
}
}
CSparseFeatures<float64_t>* f=new CSparseFeatures<float64_t>(data);
/* display sparse matrix */
SG_SPRINT("original data\n");
for (index_t i=0; i<num_vectors; ++i)
{
SG_SPRINT("sparse vector at %i: [", data.sparse_matrix[i].vec_index);
for (index_t j=0; j<num_features; ++j)
SG_SPRINT("%f, ", data.sparse_matrix[i].features[j].entry);
SG_SPRINT("]\n");
}
/* indices for a subset */
index_t offset_subset=1;
SGVector<index_t> feature_subset(8);
CMath::range_fill_vector(feature_subset.vector, feature_subset.vlen,
offset_subset);
CMath::display_vector(feature_subset.vector, feature_subset.vlen,
"feature subset");
/* set subset and print data */
f->set_subset(new CSubset(feature_subset));
SG_SPRINT("feature vectors after setting subset on original data:\n");
for (index_t i=0; i<f->get_num_vectors(); ++i)
{
SGSparseVector<float64_t> vec=f->get_sparse_feature_vector(i);
SG_SPRINT("sparse vector at %i: ", vec.vec_index);
for (index_t j=0; j<num_features; ++j)
SG_SPRINT("%f, ", vec.features[j].entry);
SG_SPRINT("]\n");
f->free_sparse_feature_vector(vec, i);
}
/* indices that are to copy */
index_t offset_copy=2;
SGVector<index_t> feature_copy_subset(4);
CMath::range_fill_vector(feature_copy_subset.vector,
feature_copy_subset.vlen, offset_copy);
CMath::display_vector(feature_copy_subset.vector, feature_copy_subset.vlen,
"indices that are to be copied");
/* copy a subset of features */
CSparseFeatures<float64_t>* subset_copy=
(CSparseFeatures<float64_t>*)f->copy_subset(feature_copy_subset);
/* print copied subset */
SG_SPRINT("copied features:\n");
for (index_t i=0; i<subset_copy->get_num_vectors(); ++i)
{
SGSparseVector<float64_t> vec=subset_copy->get_sparse_feature_vector(i);
SG_SPRINT("sparse vector at %i: ", vec.vec_index);
for (index_t j=0; j<num_features; ++j)
SG_SPRINT("%f, ", vec.features[j].entry);
SG_SPRINT("]\n");
subset_copy->free_sparse_feature_vector(vec, i);
}
/* test if all elements are copied correctly */
for (index_t i=0; i<subset_copy->get_num_vectors(); ++i)
{
SGSparseVector<float64_t> vec=subset_copy->get_sparse_feature_vector(i);
index_t ind=i+offset_copy+offset_subset;
for (index_t j=0; j<vec.num_feat_entries; ++j)
{
float64_t a_entry=vec.features[j].entry;
float64_t b_entry=data.sparse_matrix[ind].features[j].entry;
index_t a_idx=vec.features[j].feat_index;
index_t b_idx=data.sparse_matrix[ind].features[j].feat_index;
ASSERT(a_entry==b_entry);
ASSERT(a_idx==b_idx);
}
subset_copy->free_sparse_feature_vector(vec, i);
}
SG_UNREF(f);
SG_UNREF(subset_copy);
feature_copy_subset.destroy_vector();
exit_shogun();
return 0;
}
/*
* This program is free software; you can redistribute it and/or modify
* it under the terms of the GNU General Public License as published by
* the Free Software Foundation; either version 3 of the License, or
* (at your option) any later version.
*
* Written (W) 2011 Heiko Strathmann
* Copyright (C) 2011 Berlin Institute of Technology and Max-Planck-Society
*/
#include <shogun/base/init.h>
#include <shogun/features/SimpleFeatures.h>
#include <shogun/features/Subset.h>
using namespace shogun;
void print_message(FILE* target, const char* str)
{
fprintf(target, "%s", str);
}
int main(int argc, char **argv)
{
init_shogun(&print_message, &print_message, &print_message);
index_t num_strings=10;
index_t max_string_length=20;
index_t min_string_length=max_string_length/2;
SGStringList<char> strings(num_strings, max_string_length);
SG_SPRINT("original string data:\n");
for (index_t i=0; i<num_strings; ++i)
{
index_t len=CMath::random(min_string_length, max_string_length);
SGString<char> current(len);
SG_SPRINT("[%i]: \"", i);
/* fill with random uppercase letters (ASCII) */
for (index_t j=0; j<len; ++j)
{
current.string[j]=(char)CMath::random('A', 'Z');
/* attach \0 to print letter */
char* string=SG_MALLOC(char, 2);
string[0]=current.string[j];
string[1]='\0';
SG_SPRINT("%s", string);
SG_FREE(string);
}
SG_SPRINT("\"\n");
strings.strings[i]=current;
}
/* create num_feautres 2-dimensional vectors */
CStringFeatures<char>* f=new CStringFeatures<char>(strings, ALPHANUM);
index_t offset_subset=1;
SGVector<index_t> feature_subset(8);
CMath::range_fill_vector(feature_subset.vector, feature_subset.vlen,
offset_subset);
CMath::display_vector(feature_subset.vector, feature_subset.vlen,
"feature subset");
f->set_subset(new CSubset(feature_subset));
SG_SPRINT("feature vectors after setting subset on original data:\n");
for (index_t i=0; i<f->get_num_vectors(); ++i)
{
SGVector<char> vec=f->get_feature_vector(i);
SG_SPRINT("%i: ", i);
for (index_t j=0; j<vec.vlen; ++j)
SG_SPRINT("%c", vec.vector[j]);
SG_SPRINT("\n");
f->free_feature_vector(vec.vector, i, vec.do_free);
}
index_t offset_copy=2;
SGVector<index_t> feature_copy_subset(4);
CMath::range_fill_vector(feature_copy_subset.vector,
feature_copy_subset.vlen, offset_copy);
CMath::display_vector(feature_copy_subset.vector, feature_copy_subset.vlen,
"indices that are to be copied");
CStringFeatures<char>* subset_copy=(CStringFeatures<char>*)f->copy_subset(
feature_copy_subset);
for (index_t i=0; i<subset_copy->get_num_vectors(); ++i)
{
SGVector<char> vec=subset_copy->get_feature_vector(i);
SG_SPRINT("%i: ", i);
for (index_t j=0; j<vec.vlen; ++j)
SG_SPRINT("%c", vec.vector[j]);
SG_SPRINT("\n");
subset_copy->free_feature_vector(vec.vector, i, vec.do_free);
}
for (index_t i=0; i<subset_copy->get_num_vectors(); ++i)
{
SGVector<char> vec=subset_copy->get_feature_vector(i);
for (index_t j=0; j<vec.vlen; ++j)
{
index_t offset_idx=i+(offset_copy+offset_subset);
ASSERT(vec.vector[j]==strings.strings[offset_idx].string[j]);
}
subset_copy->free_feature_vector(vec.vector, i, vec.do_free);
}
SG_UNREF(f);
SG_UNREF(subset_copy);
SG_FREE(feature_copy_subset.vector);
exit_shogun();
return 0;
}
/*
* This program is free software; you can redistribute it and/or modify
* it under the terms of the GNU General Public License as published by
* the Free Software Foundation; either version 3 of the License, or
* (at your option) any later version.
*
* Written (W) 2011 Heiko Strathmann
* Copyright (C) 2011 Berlin Institute of Technology and Max-Planck-Society
*/
#include <shogun/base/init.h>
#include <shogun/features/Labels.h>
#include <shogun/mathematics/Math.h>
using namespace shogun;
void print_message(FILE* target, const char* str)
{
fprintf(target, "%s", str);
}
const int32_t num_labels=10;
const int32_t num_classes=3;
int main(int argc, char **argv)
{
init_shogun(&print_message, &print_message, &print_message);
const int32_t num_subset_idx=CMath::random(1, num_labels);
/* create labels */
CLabels* labels=new CLabels(num_labels);
for (index_t i=0; i<num_labels; ++i)
labels->set_label(i, i%num_classes);
SG_REF(labels);
/* print labels */
SGVector<float64_t> labels_data=labels->get_labels();
CMath::display_vector(labels_data.vector, labels_data.vlen, "labels");
/* create subset indices */
SGVector<index_t> subset_idx(CMath::randperm(num_subset_idx),
num_subset_idx);
/* print subset indices */
CMath::display_vector(subset_idx.vector, subset_idx.vlen, "subset indices");
/* apply subset to features */
SG_SPRINT("\n\n-------------------\n"
"applying subset to features\n"
"-------------------\n");
labels->set_subset(new CSubset(subset_idx));
/* do some stuff do check and output */
ASSERT(labels->get_num_labels()==num_subset_idx);
SG_SPRINT("labels->get_num_labels(): %d\n", labels->get_num_labels());
for (index_t i=0; i<labels->get_num_labels(); ++i)
{
float64_t label=labels->get_label(i);
SG_SPRINT("label %f:\n", label);
ASSERT(label==labels_data.vector[labels->subset_idx_conversion(i)]);
}
/* remove features subset */SG_SPRINT("\n\n-------------------\n"
"removing subset from features\n"
"-------------------\n");
labels->remove_subset();
ASSERT(labels->get_num_labels()==num_labels);
SG_SPRINT("labels->get_num_labels(): %d\n", labels->get_num_labels());
for (index_t i=0; i<labels->get_num_labels(); ++i)
{
float64_t label=labels->get_label(i);
SG_SPRINT("label %f:\n", label);
ASSERT(label==labels_data.vector[i]);
}
SG_UNREF(labels);
SG_SPRINT("\nEND\n");
exit_shogun();
return 0;
}
/*
* This program is free software; you can redistribute it and/or modify
* it under the terms of the GNU General Public License as published by
* the Free Software Foundation; either version 3 of the License, or
* (at your option) any later version.
*
* Written (W) 2011 Heiko Strathmann
* Copyright (C) 2011 Berlin Institute of Technology and Max-Planck-Society
*/
#include <shogun/base/init.h>
#include <shogun/features/SimpleFeatures.h>
#include <shogun/features/Subset.h>
using namespace shogun;
void print_message(FILE* target, const char* str)
{
fprintf(target, "%s", str);
}
void check_transposed(CSimpleFeatures<int32_t>* features)
{
CSimpleFeatures<int32_t>* transposed=features->get_transposed();
CSimpleFeatures<int32_t>* double_transposed=transposed->get_transposed();
for (index_t i=0; i<features->get_num_vectors(); ++i)
{
SGVector<int32_t> orig_vec=features->get_feature_vector(i);
SGVector<int32_t> new_vec=double_transposed->get_feature_vector(i);
ASSERT(orig_vec.vlen==new_vec.vlen);
for (index_t j=0; j<orig_vec.vlen; j++)
ASSERT(orig_vec.vector[j]==new_vec.vector[j]);
/* not necessary since feature matrix is in memory. for documentation */
features->free_feature_vector(orig_vec,i);
double_transposed->free_feature_vector(new_vec, i);
}
SG_UNREF(transposed);
SG_UNREF(double_transposed);
}
const int32_t num_vectors=6;
const int32_t dim_features=6;
int main(int argc, char **argv)
{
init_shogun(&print_message, &print_message, &print_message);
const int32_t num_subset_idx=CMath::random(1, num_vectors);
/* create feature data matrix */
SGMatrix<int32_t> data(dim_features, num_vectors);
/* fill matrix with random data */
for (index_t i=0; i<num_vectors; ++i)
{
for (index_t j=0; j<dim_features; ++j)
data.matrix[i*dim_features+j]=CMath::random(-5, 5);
}
/* create simple features */
CSimpleFeatures<int32_t>* features=new CSimpleFeatures<int32_t> (data);
SG_REF(features);
/* print feature matrix */
CMath::display_matrix(data.matrix, data.num_rows, data.num_cols,
"feature matrix");
/* create subset indices */
SGVector<index_t> subset_idx(CMath::randperm(num_subset_idx),
num_subset_idx);
/* print subset indices */
CMath::display_vector(subset_idx.vector, subset_idx.vlen, "subset indices");
/* apply subset to features */
SG_SPRINT("\n\n-------------------\n"
"applying subset to features\n"
"-------------------\n");
features->set_subset(new CSubset(subset_idx));
/* do some stuff do check and output */
ASSERT(features->get_num_vectors()==num_subset_idx);
/* check get_Transposed method */
SG_SPRINT("checking transpose...");
check_transposed(features);
SG_SPRINT("does work\n");
SG_SPRINT("features->get_num_vectors(): %d\n", features->get_num_vectors());
for (index_t i=0; i<features->get_num_vectors(); ++i)
{
SGVector<int32_t> vec=features->get_feature_vector(i);
SG_SPRINT("vector %d: ", i);
CMath::display_vector(vec.vector, vec.vlen);
for (index_t j=0; j<dim_features; ++j)
ASSERT(vec.vector[j]==data.matrix[features->subset_idx_conversion(
i)*num_vectors+j]);
/* not necessary since feature matrix is in memory. for documentation */
features->free_feature_vector(vec, i);
}
/* remove features subset */
SG_SPRINT("\n\n-------------------\n"
"removing subset from features\n"
"-------------------\n");
features->remove_subset();
/* do some stuff do check and output */
ASSERT(features->get_num_vectors()==num_vectors);
SG_SPRINT("features->get_num_vectors(): %d\n", features->get_num_vectors());
/* check get_Transposed method */
SG_SPRINT("checking transpose...");
check_transposed(features);
SG_SPRINT("does work\n");
for (index_t i=0; i<features->get_num_vectors(); ++i)
{
SGVector<int32_t> vec=features->get_feature_vector(i);
SG_SPRINT("vector %d: ", i);
CMath::display_vector(vec.vector, vec.vlen);
for (index_t j=0; j<dim_features; ++j)
ASSERT(vec.vector[j]==data.matrix[features->subset_idx_conversion(i)
*num_vectors+j]);
/* not necessary since feature matrix is in memory. for documentation */
features->free_feature_vector(vec, i);
}
SG_UNREF(features);
SG_SPRINT("\nEND\n");
exit_shogun();
return 0;
}
/*
* This program is free software; you can redistribute it and/or modify
* it under the terms of the GNU General Public License as published by
* the Free Software Foundation; either version 3 of the License, or
* (at your option) any later version.
*
* Written (W) 2011 Heiko Strathmann
* Copyright (C) 2011 Berlin Institute of Technology and Max-Planck-Society
*/
#include <shogun/base/init.h>
#include <shogun/features/SparseFeatures.h>
#include <shogun/features/Subset.h>
using namespace shogun;
void print_message(FILE* target, const char* str)
{
fprintf(target, "%s", str);
}
const int32_t num_vectors=6;
const int32_t dim_features=6;
void check_transposed(CSparseFeatures<int32_t>* features)
{
CSparseFeatures<int32_t>* transposed=features->get_transposed();
CSparseFeatures<int32_t>* double_transposed=transposed->get_transposed();
for (index_t i=0; i<features->get_num_vectors(); ++i)
{
SGSparseVector<int32_t> orig_vec=features->get_sparse_feature_vector(i);
SGSparseVector<int32_t> new_vec=
double_transposed->get_sparse_feature_vector(i);
for (index_t j=0; j<dim_features; j++)
ASSERT(orig_vec.features[j].entry==new_vec.features[j].entry);
/* not necessary since feature matrix is in memory. for documentation */
features->free_sparse_feature_vector(orig_vec, i);
double_transposed->free_sparse_feature_vector(new_vec, i);
}
SG_UNREF(transposed);
SG_UNREF(double_transposed);
}
int main(int argc, char **argv)
{
init_shogun(&print_message, &print_message, &print_message);
const int32_t num_subset_idx=CMath::random(1, num_vectors);
/* create feature data matrix */
SGMatrix<int32_t> data(dim_features, num_vectors);
/* fill matrix with random data */
for (index_t i=0; i<num_vectors*dim_features; ++i)
data.matrix[i]=CMath::random(1, 9);
/* create sparse features */
CSparseFeatures<int32_t>* features=new CSparseFeatures<int32_t>(data);
/* print dense feature matrix */
CMath::display_matrix(data.matrix, data.num_rows, data.num_cols,
"dense feature matrix");
/* create subset indices */
SGVector<index_t> subset_idx(CMath::randperm(num_subset_idx),
num_subset_idx);
/* print subset indices */
CMath::display_vector(subset_idx.vector, subset_idx.vlen, "subset indices");
/* apply subset to features */
SG_SPRINT("\n-------------------\n"
"applying subset to features\n"
"-------------------\n");
features->set_subset(new CSubset(subset_idx));
/* do some stuff do check and output */
ASSERT(features->get_num_vectors()==num_subset_idx);
SG_SPRINT("features->get_num_vectors(): %d\n", features->get_num_vectors());
/* check get_Transposed method */
SG_SPRINT("checking transpose...");
check_transposed(features);
SG_SPRINT("does work\n");
for (index_t i=0; i<features->get_num_vectors(); ++i)
{
SGSparseVector<int32_t> vec=features->get_sparse_feature_vector(i);
SG_SPRINT("sparse_vector[%d]=", i);
for (index_t j=0; j<vec.num_feat_entries; ++j)
{
SG_SPRINT("%d", vec.features[j].entry);
if (j<vec.num_feat_entries-1)
SG_SPRINT(",");
}
SG_SPRINT("\n");
for (index_t j=0; j<vec.num_feat_entries; ++j)
{
int32_t a=vec.features[j].entry;
index_t ind=features->subset_idx_conversion(i)*num_vectors+j;
int32_t b=data.matrix[ind];
ASSERT(a==b);
}
features->free_sparse_feature_vector(vec, i);
}
/* remove features subset */
SG_SPRINT("\n-------------------\n"
"removing subset from features\n"
"-------------------\n");
features->remove_subset();
/* do some stuff do check and output */
ASSERT(features->get_num_vectors()==num_vectors);
SG_SPRINT("features->get_num_vectors(): %d\n", features->get_num_vectors());
/* check get_Transposed method */
SG_SPRINT("checking transpose...");
check_transposed(features);
SG_SPRINT("does work\n");
for (index_t i=0; i<features->get_num_vectors(); ++i)
{
SGSparseVector<int32_t> vec=features->get_sparse_feature_vector(i);
SG_SPRINT("sparse_vector[%d]=", i);
for (index_t j=0; j<vec.num_feat_entries; ++j)
{
SG_SPRINT("%d", vec.features[j].entry);
if (j<vec.num_feat_entries-1)
SG_SPRINT(",");
}
SG_SPRINT("\n");
for (index_t j=0; j<vec.num_feat_entries; ++j)
ASSERT(vec.features[j].entry==data.matrix[i*num_vectors+j]);
features->free_sparse_feature_vector(vec, i);
}
SG_UNREF(features);
SG_FREE(data.matrix);
exit_shogun();
return 0;
}
#include <shogun/features/SimpleFeatures.h>
#include <shogun/kernel/GaussianKernel.h>
#include <shogun/base/init.h>
#include <shogun/lib/common.h>
#include <shogun/io/SGIO.h>
#include <stdio.h>
using namespace shogun;
void print_message(FILE* target, const char* str)
{
fprintf(target, "%s", str);
}
int main(int argc, char** argv)
{
init_shogun(&print_message);
// create some data
float64_t* matrix = SG_MALLOC(float64_t, 6);
for (int32_t i=0; i<6; i++)
matrix[i]=i;
// create three 2-dimensional vectors
// shogun will now own the matrix created
CSimpleFeatures<float64_t>* features= new CSimpleFeatures<float64_t>();
features->set_feature_matrix(matrix, 2, 3);
// create gaussian kernel with cache 10MB, width 0.5
CGaussianKernel* kernel = new CGaussianKernel(features, features, 10, 0.5);
// print kernel matrix
for (int32_t i=0; i<3; i++)
{
for (int32_t j=0; j<3; j++)
{
SG_SPRINT("%f ", kernel->kernel(i,j));
}
SG_SPRINT("\n");
}
// free up memory
SG_UNREF(kernel);
exit_shogun();
return 0;
}
#include <shogun/features/SimpleFeatures.h>
#include <shogun/kernel/DotKernel.h>
#include <shogun/base/init.h>
#include <shogun/lib/common.h>
#include <shogun/io/SGIO.h>
#include <stdio.h>
using namespace shogun;
class CReverseLinearKernel : public CDotKernel
{
public:
/** default constructor */
CReverseLinearKernel() : CDotKernel(0)
{
}
/** destructor */
virtual ~CReverseLinearKernel()
{
}
/** initialize kernel
*
* @param l features of left-hand side
* @param r features of right-hand side
* @return if initializing was successful
*/
virtual bool init(CFeatures* l, CFeatures* r)
{
CDotKernel::init(l, r);
return init_normalizer();
}
/** load kernel init_data
*
* @param src file to load from
* @return if loading was successful
*/
virtual bool load_init(FILE* src)
{
return false;
}
/** save kernel init_data
*
* @param dest file to save to
* @return if saving was successful
*/
virtual bool save_init(FILE* dest)
{
return false;
}
/** return what type of kernel we are
*
* @return kernel type UNKNOWN (as it is not part
* officially part of shogun)
*/
virtual EKernelType get_kernel_type()
{
return K_UNKNOWN;
}
/** return the kernel's name
*
* @return name "Reverse Linear"
*/
inline virtual const char* get_name() const
{
return "ReverseLinear";
}
protected:
/** compute kernel function for features a and b
* idx_{a,b} denote the index of the feature vectors
* in the corresponding feature object
*
* @param idx_a index a
* @param idx_b index b
* @return computed kernel function at indices a,b
*/
virtual float64_t compute(int32_t idx_a, int32_t idx_b)
{
int32_t alen, blen;
bool afree, bfree;
float64_t* avec=
((CSimpleFeatures<float64_t>*) lhs)->get_feature_vector(idx_a, alen, afree);
float64_t* bvec=
((CSimpleFeatures<float64_t>*) rhs)->get_feature_vector(idx_b, blen, bfree);
ASSERT(alen==blen);
float64_t result=0;
for (int32_t i=0; i<alen; i++)
result+=avec[i]*bvec[alen-i-1];
((CSimpleFeatures<float64_t>*) lhs)->free_feature_vector(avec, idx_a, afree);
((CSimpleFeatures<float64_t>*) rhs)->free_feature_vector(bvec, idx_b, bfree);
return result;
}
};
void print_message(FILE* target, const char* str)
{
fprintf(target, "%s", str);
}
int main(int argc, char** argv)
{
init_shogun(&print_message);
// create some data
float64_t* matrix = SG_MALLOC(float64_t, 6);
for (int32_t i=0; i<6; i++)
matrix[i]=i;
// create three 2-dimensional vectors
// shogun will now own the matrix created
CSimpleFeatures<float64_t>* features= new CSimpleFeatures<float64_t>();
features->set_feature_matrix(matrix, 2, 3);
// create reverse linear kernel
CReverseLinearKernel* kernel = new CReverseLinearKernel();
kernel->init(features,features);
// print kernel matrix
for (int32_t i=0; i<3; i++)
{
for (int32_t j=0; j<3; j++)
SG_SPRINT("%f ", kernel->kernel(i,j));
SG_SPRINT("\n");
}
// free up memory
SG_UNREF(kernel);
exit_shogun();
return 0;
}
/*
* This program is free software; you can redistribute it and/or modify
* it under the terms of the GNU General Public License as published by
* the Free Software Foundation; either version 3 of the License, or
* (at your option) any later version.
*
* Written (W) 2009 Soeren Sonnenburg
* Copyright (C) 2009 Fraunhofer Institute FIRST and Max-Planck-Society
*/
#include <shogun/io/SGIO.h>
#include <shogun/lib/Time.h>
#include <shogun/lib/ShogunException.h>
#include <shogun/mathematics/Math.h>
#include <shogun/lib/DynInt.h>
using namespace shogun;
void print_message(FILE* target, const char* str)
{
fprintf(target, "%s", str);
}
void print_warning(FILE* target, const char* str)
{
fprintf(target, "%s", str);
}
void print_error(FILE* target, const char* str)
{
fprintf(target, "%s", str);
}
void gen_ints(uint256_t* &a, uint32_t* &b, uint32_t len)
{
a=SG_MALLOC(uint256_t, len);
b=SG_MALLOC(uint32_t, len);
CMath::init_random(17);
for (uint32_t i=0; i<len; i++)
{
uint64_t r[4]={(uint64_t) CMath::random() << 32 | CMath::random(),
(uint64_t) CMath::random() << 32 | CMath::random(),
(uint64_t) CMath::random() << 32 | CMath::random(),
(uint64_t) CMath::random() << 32 | CMath::random()};
a[len-i-1]=r;
b[len-i-1]=i;
}
}
const int LEN = 5*1024*1024;
int main()
{
init_shogun(&print_message, &print_warning,
&print_error);
try
{
uint256_t* a;
uint32_t* b;
CTime t;
t.io->set_loglevel(MSG_DEBUG);
SG_SPRINT("gen data..");
t.start();
gen_ints(a,b, LEN);
t.cur_time_diff(true);
SG_SPRINT("qsort..");
t.start();
CMath::qsort_index(a, b, LEN);
t.cur_time_diff(true);
SG_SPRINT("\n\n");
for (uint32_t i=0; i<10; i++)
{
SG_SPRINT("a[%d]=", i);
a[i].print_hex();
SG_SPRINT("\n");
}
SG_SPRINT("\n\n");
a[0]=(uint64_t[4]) {1,2,3,4};
uint64_t val[4]={5,6,7,8};
a[1]=val;
a[2]=a[0];
CMath::swap(a[0],a[1]);
printf("a[0]==a[1] %d\n", (int) (a[0] == a[1]));
printf("a[0]<a[1] %d\n", (int) (a[0] < a[1]));
printf("a[0]<=a[1] %d\n", (int) (a[0] <= a[1]));
printf("a[0]>a[1] %d\n", (int) (a[0] > a[1]));
printf("a[0]>=a[1] %d\n", (int) (a[0] >= a[1]));
printf("a[0]==a[0] %d\n", (int) (a[0] == a[0]));
printf("a[0]<a[0] %d\n", (int) (a[0] < a[0]));
printf("a[0]<=a[0] %d\n", (int) (a[0] <= a[0]));
printf("a[0]>a[0] %d\n", (int) (a[0] > a[0]));
printf("a[0]>=a[0] %d\n", (int) (a[0] >= a[0]));
SG_SPRINT("\n\n");
for (uint32_t i=0; i<10 ; i++)
{
SG_SPRINT("a[%d]=", i);
a[i].print_hex();
printf("\n");
}
SG_FREE(a);
SG_FREE(b);
}
catch(ShogunException & sh)
{
SG_SPRINT("%s",sh.get_exception_string());
}
exit_shogun();
return 0;
}
#include <shogun/lib/FibonacciHeap.h>
#include <stdio.h>
using namespace shogun;
int main(int argc, char** argv)
{
init_shogun();
double v[8] = {0.0,0.1,0.2,0.3,0.4,0.5,0.6,0.7};
int k[8] = {0,1,2,3,4,5,6,7};
CFibonacciHeap* heap = new CFibonacciHeap(8);
for (int i=0; i<8; i++)
heap->insert(k[i],v[i]);
int k_extract;
double v_extract;
for (int i=0; i<8; i++)
{
k_extract = heap->extract_min(v_extract);
if (v[k_extract]!=v_extract)
{
printf("Fibonacci heap goes wrong.\n");
}
}
delete heap;
exit_shogun();
return 0;
}
#include <shogun/base/init.h>
#include <shogun/lib/common.h>
#include <shogun/lib/GCArray.h>
#include <shogun/kernel/Kernel.h>
#include <shogun/kernel/GaussianKernel.h>
#include <stdio.h>
using namespace shogun;
const int l=10;
int main(int argc, char** argv)
{
init_shogun();
// create array a
CGCArray<CKernel*> kernels(l);
for (int i=0; i<l; i++)
kernels.set(new CGaussianKernel(10, 1.0), i);
for (int i=0; i<l; i++)
printf("kernels[%d]=%p\n", i, kernels.get(i));
exit_shogun();
return 0;
}
#include <shogun/lib/Hash.h>
#include <stdio.h>
using namespace shogun;
int main(int argc, char** argv)
{
init_shogun();
uint8_t array[4]={0,1,2,3};
printf("hash(0)=%0x\n", CHash::MurmurHash2(&array[0], 1, 0xDEADBEAF));
printf("hash(1)=%0x\n", CHash::MurmurHash2(&array[1], 1, 0xDEADBEAF));
printf("hash(2)=%0x\n", CHash::MurmurHash2(&array[0], 2, 0xDEADBEAF));
printf("hash(3)=%0x\n", CHash::MurmurHash2(&array[0], 4, 0xDEADBEAF));
uint32_t h=CHash::IncrementalMurmurHash2(array[0], 0xDEADBEAF);
printf("inc_hash(0)=%0x\n", h);
h=CHash::IncrementalMurmurHash2(array[1], h);
printf("inc_hash(1)=%0x\n", h);
h=CHash::IncrementalMurmurHash2(array[2], h);
printf("inc_hash(2)=%0x\n", h);
h=CHash::IncrementalMurmurHash2(array[3], h);
printf("inc_hash(3)=%0x\n", h);
exit_shogun();
return 0;
}
#include <shogun/lib/HashSet.h>
#include <stdio.h>
using namespace shogun;
int main(int argc, char** argv)
{
init_shogun();
double v[8] = {0.0,0.0,0.1,0.1,0.2,0.2,0.3,0.3};
CHashSet* set = new CHashSet(8);
for (int i=0; i<8; i++)
set->insert_key(i,v[i]);
delete set;
exit_shogun();
return 0;
}
#include <shogun/base/init.h>
#include <shogun/lib/common.h>
#include <shogun/lib/memory.h>
#include <shogun/lib/IndirectObject.h>
#include <shogun/mathematics/Math.h>
#include <shogun/base/SGObject.h>
#include <stdio.h>
using namespace shogun;
const int l=10;
int main(int argc, char** argv)
{
init_shogun();
// create array a
int32_t* a=SG_MALLOC(int32_t, l);
for (int i=0; i<l; i++)
a[i]=l-i;
typedef CIndirectObject<int32_t, int32_t**> INDIRECT;
// create array of indirect objects pointing to array a
INDIRECT::set_array(&a);
INDIRECT* x = SG_MALLOC(INDIRECT, l);
INDIRECT::init_slice(x, l);
printf("created array a and indirect object array x pointing to a.\n\n");
for (int i=0; i<l; i++)
printf("a[%d]=%d x[%d]=%d\n", i, a[i], i, int32_t(x[i]));
//sort the array
CMath::qsort(x, l);
printf("\n\nvoila! sorted indirect object array x, keeping a const.\n\n");
for (int i=0; i<l; i++)
printf("a[%d]=%d x[%d]=%d\n", i, a[i], i, int32_t(x[i]));
exit_shogun();
return 0;
}
/*
* This program is free software; you can redistribute it and/or modify
* it under the terms of the GNU General Public License as published by
* the Free Software Foundation; either version 3 of the License, or
* (at your option) any later version.
*
* Written (W) 2011 Sergey Lisitsyn
* Copyright (C) 2011 Berlin Institute of Technology and Max-Planck-Society
*/
#include <shogun/base/init.h>
#include <shogun/lib/config.h>
#include <shogun/mathematics/arpack.h>
using namespace shogun;
int main(int argc, char** argv)
{
init_shogun();
#ifdef HAVE_ARPACK
int N = 100;
int nev = 2;
double* matrix = new double[N*N];
double* eigenvalues = new double[nev];
double* eigenvectors = new double[nev*N];
for (int i=0; i<N; i++)
{
for (int j=0; j<N; j++)
matrix[i*N+j] = i*i+j*j;
}
int status = 0;
arpack_dsaeupd_wrap(matrix, NULL, N, 2, "LM", 1, false, 0.0, 0.0,
eigenvalues, eigenvectors, status);
if (status!=0)
return -1;
arpack_dsaeupd_wrap(matrix, NULL, N, 2, "BE", 3, false, 1.0, 0.0,
eigenvalues, eigenvectors, status);
if (status!=0)
return -1;
delete[] eigenvalues;
delete[] eigenvectors;
delete[] matrix;
#endif // HAVE_ARPACK
exit_shogun();
return 0;
}
/*
* This program is free software; you can redistribute it and/or modify
* it under the terms of the GNU General Public License as published by
* the Free Software Foundation; either version 3 of the License, or
* (at your option) any later version.
*
* Written (W) 2011 Heiko Strathmann
* Copyright (C) 2011 Berlin Institute of Technology and Max-Planck-Society
*/
#include <shogun/base/init.h>
#include <shogun/mathematics/Statistics.h>
#include <shogun/mathematics/Math.h>
using namespace shogun;
void print_message(FILE* target, const char* str)
{
fprintf(target, "%s", str);
}
int main(int argc, char **argv)
{
init_shogun(&print_message, &print_message, &print_message);
SGVector<float64_t> data(10, true);
CMath::range_fill_vector(data.vector, data.vlen, 1.0);
float64_t low, up, mean;
float64_t error_prob=0.05;
mean=CStatistics::confidence_intervals_mean(data, error_prob, low, up);
SG_SPRINT("sample mean: %f. True mean lies in [%f,%f] with %f%%\n",
mean, low, up, 100*(1-error_prob));
data.free_vector();
SG_SPRINT("\nEND\n");
exit_shogun();
return 0;
}
/*
* This program is free software; you can redistribute it and/or modify
* it under the terms of the GNU General Public License as published by
* the Free Software Foundation; either version 3 of the License, or
* (at your option) any later version.
*
* Written (W) 2011 Sergey Lisitsyn
* Copyright (C) 2011 Berlin Institute of Technology and Max-Planck-Society
*/
#include <shogun/base/init.h>
#include <shogun/lib/config.h>
#include <shogun/mathematics/lapack.h>
using namespace shogun;
int main(int argc, char** argv)
{
init_shogun();
#ifdef HAVE_LAPACK
// size of square matrix
int N = 100;
// square matrix
double* matrix = new double[N*N];
// for storing eigenpairs
double* eigenvalues = new double[N];
double* eigenvectors = new double[N*N];
// for SVD
double* U = new double[N*N];
double* s = new double[N*N];
double* Vt = new double[N*N];
// status (should be zero)
int status;
// DSYGVX
for (int i=0; i<N; i++)
{
for (int j=0; j<N; j++)
matrix[i*N+j] = (i-j)/(i+j+1);
matrix[i*N+i] += 10;
}
status = 0;
wrap_dsygvx(1,'V','U',N,matrix,N,matrix,N,1,3,eigenvalues,eigenvectors,&status);
if (status!=0)
{
printf("DSYGVX failed with code %d\n",status);
return -1;
}
delete[] eigenvectors;
// DGEQRF+DORGQR
status = 0;
double* tau = new double[N];
wrap_dgeqrf(N,N,matrix,N,tau,&status);
wrap_dorgqr(N,N,N,matrix,N,tau,&status);
if (status!=0)
{
printf("DGEQRF/DORGQR failed with code %d\n",status);
return -1;
}
delete[] tau;
// DGESVD
for (int i=0; i<N; i++)
{
for (int j=0; j<N; j++)
matrix[i*N+j] = i*i+j*j;
}
status = 0;
wrap_dgesvd('A','A',N,N,matrix,N,s,U,N,Vt,N,&status);
if (status!=0)
{
printf("DGESVD failed with code %d\n",status);
return -1;
}
delete[] s;
delete[] U;
delete[] Vt;
// DSYEV
for (int i=0; i<N; i++)
{
for (int j=0; j<N; j++)
matrix[i*N+j] = i*i+j*j;
}
status = 0;
wrap_dsyev('V','U',N,matrix,N,eigenvalues,&status);
if (status!=0)
{
printf("DSYEV failed with code %d\n",status);
return -1;
}
delete[] eigenvalues;
delete[] matrix;
#endif // HAVE_LAPACK
exit_shogun();
return 0;
}
/*
* This program is free software; you can redistribute it and/or modify
* it under the terms of the GNU General Public License as published by
* the Free Software Foundation; either version 3 of the License, or
* (at your option) any later version.
*
* Written (W) 2011 Heiko Strathmann
* Copyright (C) 2011 Berlin Institute of Technology and Max-Planck-Society
*/
#include <shogun/base/init.h>
#include <shogun/modelselection/ModelSelectionParameters.h>
#include <shogun/modelselection/ParameterCombination.h>
#include <shogun/kernel/GaussianKernel.h>
#include <shogun/features/Labels.h>
#include <shogun/features/SimpleFeatures.h>
#include <shogun/classifier/svm/LibSVM.h>
using namespace shogun;
void print_message(FILE* target, const char* str)
{
fprintf(target, "%s", str);
}
CModelSelectionParameters* create_param_tree()
{
CModelSelectionParameters* root=new CModelSelectionParameters();
CModelSelectionParameters* c=new CModelSelectionParameters("C1");
root->append_child(c);
c->build_values(1.0, 2.0, R_EXP);
CGaussianKernel* gaussian_kernel=new CGaussianKernel();
CModelSelectionParameters* param_gaussian_kernel=
new CModelSelectionParameters("kernel", gaussian_kernel);
root->append_child(param_gaussian_kernel);
CModelSelectionParameters* param_gaussian_kernel_width=
new CModelSelectionParameters("width");
param_gaussian_kernel_width->build_values(1.0, 2.0, R_EXP);
param_gaussian_kernel->append_child(param_gaussian_kernel_width);
return root;
}
void apply_parameter_tree(CDynamicObjectArray<CParameterCombination>* combinations)
{
/* create some data */
float64_t* matrix=SG_MALLOC(float64_t, 6);
for (index_t i=0; i<6; i++)
matrix[i]=i;
/* create three 2-dimensional vectors
* to avoid deleting these, REF now and UNREF when finished */
CSimpleFeatures<float64_t>* features=new CSimpleFeatures<float64_t> ();
SG_REF(features);
features->set_feature_matrix(matrix, 2, 3);
/* create three labels, will be handed to svm and automaticall deleted */
CLabels* labels=new CLabels(3);
SG_REF(labels);
labels->set_label(0, -1);
labels->set_label(1, +1);
labels->set_label(2, -1);
/* create libsvm with C=10 and train */
CLibSVM* svm=new CLibSVM();
SG_REF(svm);
svm->set_labels(labels);
for (index_t i=0; i<combinations->get_num_elements(); ++i)
{
SG_SPRINT("applying:\n");
CParameterCombination* current_combination=combinations->get_element(i);
current_combination->print_tree();
Parameter* current_parameters=svm->m_parameters;
current_combination->apply_to_modsel_parameter(current_parameters);
SG_UNREF(current_combination);
/* get kernel to set features, get_kernel SG_REF's the kernel */
CKernel* kernel=svm->get_kernel();
kernel->init(features, features);
svm->train();
/* classify on training examples */
for (index_t i=0; i<3; i++)
SG_SPRINT("output[%d]=%f\n", i, svm->apply(i));
/* unset features and SG_UNREF kernel */
kernel->cleanup();
SG_UNREF(kernel);
SG_SPRINT("----------------\n\n");
}
/* free up memory */
SG_UNREF(features);
SG_UNREF(labels);
SG_UNREF(svm);
}
int main(int argc, char **argv)
{
init_shogun(&print_message, &print_message, &print_message);
/* create example tree */
CModelSelectionParameters* tree=create_param_tree();
tree->print_tree();
SG_SPRINT("----------------------------------\n");
/* build combinations of parameter trees */
CDynamicObjectArray<CParameterCombination>* combinations=tree->get_combinations();
apply_parameter_tree(combinations);
/* print and directly delete them all */
for (index_t i=0; i<combinations->get_num_elements(); ++i)
{
CParameterCombination* combination=combinations->get_element(i);
SG_UNREF(combination);
}
SG_UNREF(combinations);
/* delete example tree (after processing of combinations because CSGObject
* (namely the kernel) of the tree is SG_UNREF'ed (and not REF'ed anywhere
* else) */
SG_UNREF(tree);
exit_shogun();
return 0;
}
/*
* This program is free software; you can redistribute it and/or modify
* it under the terms of the GNU General Public License as published by
* the Free Software Foundation; either version 3 of the License, or
* (at your option) any later version.
*
* Written (W) 2011 Heiko Strathmann
* Copyright (C) 2011 Berlin Institute of Technology and Max-Planck-Society
*/
#include <shogun/base/init.h>
#include <shogun/evaluation/CrossValidation.h>
#include <shogun/evaluation/ContingencyTableEvaluation.h>
#include <shogun/evaluation/StratifiedCrossValidationSplitting.h>
#include <shogun/modelselection/GridSearchModelSelection.h>
#include <shogun/modelselection/ModelSelectionParameters.h>
#include <shogun/modelselection/ParameterCombination.h>
#include <shogun/features/Labels.h>
#include <shogun/features/SimpleFeatures.h>
#include <shogun/classifier/svm/LibSVM.h>
#include <shogun/kernel/GaussianKernel.h>
#include <shogun/kernel/PowerKernel.h>
#include <shogun/distance/MinkowskiMetric.h>
using namespace shogun;
void print_message(FILE* target, const char* str)
{
fprintf(target, "%s", str);
}
CModelSelectionParameters* create_param_tree()
{
CModelSelectionParameters* root=new CModelSelectionParameters();
CModelSelectionParameters* c1=new CModelSelectionParameters("C1");
root->append_child(c1);
c1->build_values(-1.0, 1.0, R_EXP);
CModelSelectionParameters* c2=new CModelSelectionParameters("C2");
root->append_child(c2);
c2->build_values(-1.0, 1.0, R_EXP);
CGaussianKernel* gaussian_kernel=new CGaussianKernel();
CModelSelectionParameters* param_gaussian_kernel=
new CModelSelectionParameters("kernel", gaussian_kernel);
CModelSelectionParameters* gaussian_kernel_width=
new CModelSelectionParameters("width");
gaussian_kernel_width->build_values(-1.0, 1.0, R_EXP, 1.0, 2.0);
param_gaussian_kernel->append_child(gaussian_kernel_width);
root->append_child(param_gaussian_kernel);
CPowerKernel* power_kernel=new CPowerKernel();
CModelSelectionParameters* param_power_kernel=
new CModelSelectionParameters("kernel", power_kernel);
root->append_child(param_power_kernel);
CModelSelectionParameters* param_power_kernel_degree=
new CModelSelectionParameters("degree");
param_power_kernel_degree->build_values(1.0, 2.0, R_LINEAR);
param_power_kernel->append_child(param_power_kernel_degree);
CMinkowskiMetric* m_metric=new CMinkowskiMetric(10);
CModelSelectionParameters* param_power_kernel_metric1=
new CModelSelectionParameters("distance", m_metric);
param_power_kernel->append_child(param_power_kernel_metric1);
CModelSelectionParameters* param_power_kernel_metric1_k=
new CModelSelectionParameters("k");
param_power_kernel_metric1_k->build_values(1.0, 2.0, R_LINEAR);
param_power_kernel_metric1->append_child(param_power_kernel_metric1_k);
return root;
}
int main(int argc, char **argv)
{
init_shogun(&print_message, &print_message, &print_message);
int32_t num_subsets=3;
int32_t num_vectors=20;
int32_t dim_vectors=3;
/* create some data and labels */
float64_t* matrix=SG_MALLOC(float64_t, num_vectors*dim_vectors);
CLabels* labels=new CLabels(num_vectors);
for (int32_t i=0; i<num_vectors*dim_vectors; i++)
matrix[i]=CMath::randn_double();
/* create num_feautres 2-dimensional vectors */
CSimpleFeatures<float64_t>* features=new CSimpleFeatures<float64_t> ();
features->set_feature_matrix(matrix, dim_vectors, num_vectors);
/* create labels, two classes */
for (index_t i=0; i<num_vectors; ++i)
labels->set_label(i, i%2==0 ? 1 : -1);
/* create svm */
CLibSVM* classifier=new CLibSVM();
/* splitting strategy */
CStratifiedCrossValidationSplitting* splitting_strategy=
new CStratifiedCrossValidationSplitting(labels, num_subsets);
/* accuracy evaluation */
CContingencyTableEvaluation* evaluation_criterium=
new CContingencyTableEvaluation(ACCURACY);
/* cross validation class for evaluation in model selection */
CCrossValidation* cross=new CCrossValidation(classifier, features, labels,
splitting_strategy, evaluation_criterium);
cross->set_num_runs(1);
/* model parameter selection, deletion is handled by modsel class (SG_UNREF) */
CModelSelectionParameters* param_tree=create_param_tree();
param_tree->print_tree();
/* handles all of the above structures in memory */
CGridSearchModelSelection* grid_search=new CGridSearchModelSelection(
param_tree, cross);
CParameterCombination* best_combination=grid_search->select_model();
SG_SPRINT("best parameter(s):\n");
best_combination->print_tree();
best_combination->apply_to_machine(classifier);
/* larger number of runs to have tighter confidence intervals */
cross->set_num_runs(10);
cross->set_conf_int_alpha(0.01);
CrossValidationResult result=cross->evaluate();
SG_SPRINT("result: ");
result.print_result();
/* clean up destroy result parameter */
SG_UNREF(best_combination);
SG_UNREF(grid_search);
exit_shogun();
return 0;
}
/*
* This program is free software; you can redistribute it and/or modify
* it under the terms of the GNU General Public License as published by
* the Free Software Foundation; either version 3 of the License, or
* (at your option) any later version.
*
* Written (W) 2011 Heiko Strathmann
* Copyright (C) 2011 Berlin Institute of Technology and Max-Planck-Society
*/
#include <shogun/base/init.h>
#include <shogun/lib/config.h>
#include <shogun/evaluation/CrossValidation.h>
#include <shogun/evaluation/ContingencyTableEvaluation.h>
#include <shogun/evaluation/StratifiedCrossValidationSplitting.h>
#include <shogun/modelselection/GridSearchModelSelection.h>
#include <shogun/modelselection/ModelSelectionParameters.h>
#include <shogun/modelselection/ParameterCombination.h>
#include <shogun/features/Labels.h>
#include <shogun/features/SimpleFeatures.h>
#include <shogun/classifier/svm/LibLinear.h>
using namespace shogun;
void print_message(FILE* target, const char* str)
{
fprintf(target, "%s", str);
}
CModelSelectionParameters* create_param_tree()
{
CModelSelectionParameters* root=new CModelSelectionParameters();
CModelSelectionParameters* c1=new CModelSelectionParameters("C1");
root->append_child(c1);
c1->build_values(-2.0, 2.0, R_EXP);
CModelSelectionParameters* c2=new CModelSelectionParameters("C2");
root->append_child(c2);
c2->build_values(-2.0, 2.0, R_EXP);
return root;
}
int main(int argc, char **argv)
{
init_shogun(&print_message, &print_message, &print_message);
#ifdef HAVE_LAPACK
int32_t num_subsets=5;
int32_t num_features=11;
/* create some data */
float64_t* matrix=SG_MALLOC(float64_t, num_features*2);
for (int32_t i=0; i<num_features*2; i++)
matrix[i]=i;
/* create num_feautres 2-dimensional vectors */
CSimpleFeatures<float64_t>* features=new CSimpleFeatures<float64_t> ();
features->set_feature_matrix(matrix, 2, num_features);
/* create three labels */
CLabels* labels=new CLabels(num_features);
for (index_t i=0; i<num_features; ++i)
labels->set_label(i, i%2==0 ? 1 : -1);
/* create linear classifier (use -s 2 option to avoid warnings) */
CLibLinear* classifier=new CLibLinear(L2R_L2LOSS_SVC);
/* splitting strategy */
CStratifiedCrossValidationSplitting* splitting_strategy=
new CStratifiedCrossValidationSplitting(labels, num_subsets);
/* accuracy evaluation */
CContingencyTableEvaluation* evaluation_criterium=
new CContingencyTableEvaluation(ACCURACY);
/* cross validation class for evaluation in model selection */
CCrossValidation* cross=new CCrossValidation(classifier, features, labels,
splitting_strategy, evaluation_criterium);
/* model parameter selection, deletion is handled by modsel class (SG_UNREF) */
CModelSelectionParameters* param_tree=create_param_tree();
param_tree->print_tree();
/* handles all of the above structures in memory */
CGridSearchModelSelection* grid_search=new CGridSearchModelSelection(
param_tree, cross);
CParameterCombination* best_combination=grid_search->select_model();
SG_SPRINT("best parameter(s):\n");
best_combination->print_tree();
best_combination->apply_to_machine(classifier);
CrossValidationResult result=cross->evaluate();
result.print_result();
/* clean up */
SG_UNREF(best_combination);
SG_UNREF(grid_search);
#endif // HAVE_LAPACK
exit_shogun();
return 0;
}
/*
* This program is free software; you can redistribute it and/or modify
* it under the terms of the GNU General Public License as published by
* the Free Software Foundation; either version 3 of the License, or
* (at your option) any later version.
*
* Written (W) 2011 Heiko Strathmann
* Copyright (C) 2011 Berlin Institute of Technology and Max-Planck-Society
*/
#include <shogun/base/init.h>
#include <shogun/evaluation/CrossValidation.h>
#include <shogun/evaluation/ContingencyTableEvaluation.h>
#include <shogun/evaluation/StratifiedCrossValidationSplitting.h>
#include <shogun/modelselection/GridSearchModelSelection.h>
#include <shogun/modelselection/ModelSelectionParameters.h>
#include <shogun/modelselection/ParameterCombination.h>
#include <shogun/features/Labels.h>
#include <shogun/features/StringFeatures.h>
#include <shogun/classifier/svm/LibSVM.h>
#include <shogun/kernel/DistantSegmentsKernel.h>
using namespace shogun;
void print_message(FILE* target, const char* str)
{
fprintf(target, "%s", str);
}
CModelSelectionParameters* create_param_tree()
{
CModelSelectionParameters* root=new CModelSelectionParameters();
CModelSelectionParameters* c1=new CModelSelectionParameters("C1");
root->append_child(c1);
c1->build_values(1.0, 2.0, R_EXP);
CModelSelectionParameters* c2=new CModelSelectionParameters("C2");
root->append_child(c2);
c2->build_values(1.0, 2.0, R_EXP);
CDistantSegmentsKernel* ds_kernel=new CDistantSegmentsKernel();
CModelSelectionParameters* param_ds_kernel=
new CModelSelectionParameters("kernel", ds_kernel);
root->append_child(param_ds_kernel);
CModelSelectionParameters* ds_kernel_delta=
new CModelSelectionParameters("delta");
ds_kernel_delta->build_values(1, 2, R_LINEAR);
param_ds_kernel->append_child(ds_kernel_delta);
CModelSelectionParameters* ds_kernel_theta=
new CModelSelectionParameters("theta");
ds_kernel_theta->build_values(1, 2, R_LINEAR);
param_ds_kernel->append_child(ds_kernel_theta);
return root;
}
int main(int argc, char **argv)
{
init_shogun(&print_message, &print_message, &print_message);
index_t num_strings=10;
index_t max_string_length=20;
index_t min_string_length=max_string_length/2;
index_t num_subsets=num_strings/3;
SGStringList<char> strings(num_strings, max_string_length);
for (index_t i=0; i<num_strings; ++i)
{
index_t len=CMath::random(min_string_length, max_string_length);
SGString<char> current(len);
SG_SPRINT("string %i: \"", i);
/* fill with random uppercase letters (ASCII) */
for (index_t j=0; j<len; ++j)
{
current.string[j]=(char)CMath::random('A', 'Z');
char* string=new char[2];
string[0]=current.string[j];
string[1]='\0';
SG_SPRINT("%s", string);
delete[] string;
}
SG_SPRINT("\"\n");
strings.strings[i]=current;
}
/* create num_feautres 2-dimensional vectors */
CStringFeatures<char>* features=new CStringFeatures<char>(strings, ALPHANUM);
/* create labels, two classes */
CLabels* labels=new CLabels(num_strings);
for (index_t i=0; i<num_strings; ++i)
labels->set_label(i, i%2==0 ? 1 : -1);
/* create svm classifier */
CLibSVM* classifier=new CLibSVM();
/* splitting strategy */
CStratifiedCrossValidationSplitting* splitting_strategy=
new CStratifiedCrossValidationSplitting(labels, num_subsets);
/* accuracy evaluation */
CContingencyTableEvaluation* evaluation_criterium=
new CContingencyTableEvaluation(ACCURACY);
/* cross validation class for evaluation in model selection */
CCrossValidation* cross=new CCrossValidation(classifier, features, labels,
splitting_strategy, evaluation_criterium);
cross->set_num_runs(2);
/* model parameter selection, deletion is handled by modsel class (SG_UNREF) */
CModelSelectionParameters* param_tree=create_param_tree();
param_tree->print_tree();
/* handles all of the above structures in memory */
CGridSearchModelSelection* grid_search=new CGridSearchModelSelection(
param_tree, cross);
CParameterCombination* best_combination=grid_search->select_model();
SG_SPRINT("best parameter(s):\n");
best_combination->print_tree();
best_combination->apply_to_machine(classifier);
/* larger number of runs to have tighter confidence intervals */
cross->set_num_runs(10);
cross->set_conf_int_alpha(0.01);
CrossValidationResult result=cross->evaluate();
SG_SPRINT("result: ");
result.print_result();
/* clean up */
SG_UNREF(best_combination);
SG_UNREF(grid_search);
exit_shogun();
return 0;
}
/*
* This program is free software; you can redistribute it and/or modify
* it under the terms of the GNU General Public License as published by
* the Free Software Foundation; either version 3 of the License, or
* (at your option) any later version.
*
* Written (W) 2011 Heiko Strathmann
* Copyright (C) 2011 Berlin Institute of Technology and Max-Planck-Society
*/
#include <shogun/base/init.h>
#include <shogun/modelselection/ModelSelectionParameters.h>
#include <shogun/modelselection/ParameterCombination.h>
#include <shogun/kernel/GaussianKernel.h>
#include <shogun/kernel/PowerKernel.h>
#include <shogun/distance/MinkowskiMetric.h>
#include <shogun/kernel/DistantSegmentsKernel.h>
using namespace shogun;
void print_message(FILE* target, const char* str)
{
fprintf(target, "%s", str);
}
CModelSelectionParameters* build_complex_example_tree()
{
CModelSelectionParameters* root=new CModelSelectionParameters();
CModelSelectionParameters* c=new CModelSelectionParameters("C");
root->append_child(c);
c->build_values(1.0, 1.0, R_EXP);
CPowerKernel* power_kernel=new CPowerKernel();
CModelSelectionParameters* param_power_kernel=
new CModelSelectionParameters("kernel", power_kernel);
root->append_child(param_power_kernel);
CModelSelectionParameters* param_power_kernel_degree=
new CModelSelectionParameters("degree");
param_power_kernel_degree->build_values(1.0, 1.0, R_EXP);
param_power_kernel->append_child(param_power_kernel_degree);
CMinkowskiMetric* m_metric=new CMinkowskiMetric(10);
CModelSelectionParameters* param_power_kernel_metric1=
new CModelSelectionParameters("distance", m_metric);
param_power_kernel->append_child(param_power_kernel_metric1);
CModelSelectionParameters* param_power_kernel_metric1_k=
new CModelSelectionParameters("k");
param_power_kernel_metric1_k->build_values(1.0, 12.0, R_LINEAR);
param_power_kernel_metric1->append_child(param_power_kernel_metric1_k);
CGaussianKernel* gaussian_kernel=new CGaussianKernel();
CModelSelectionParameters* param_gaussian_kernel=
new CModelSelectionParameters("kernel", gaussian_kernel);
root->append_child(param_gaussian_kernel);
CModelSelectionParameters* param_gaussian_kernel_width=
new CModelSelectionParameters("width");
param_gaussian_kernel_width->build_values(1.0, 2.0, R_EXP);
param_gaussian_kernel->append_child(param_gaussian_kernel_width);
CDistantSegmentsKernel* ds_kernel=new CDistantSegmentsKernel();
CModelSelectionParameters* param_ds_kernel=new CModelSelectionParameters("kernel",
ds_kernel);
root->append_child(param_ds_kernel);
CModelSelectionParameters* param_ds_kernel_delta=
new CModelSelectionParameters("delta");
param_ds_kernel_delta->build_values(1.0, 2.0, R_EXP);
param_ds_kernel->append_child(param_ds_kernel_delta);
CModelSelectionParameters* param_ds_kernel_theta=
new CModelSelectionParameters("theta");
param_ds_kernel_theta->build_values(1.0, 2.0, R_EXP);
param_ds_kernel->append_child(param_ds_kernel_theta);
return root;
}
CModelSelectionParameters* build_sgobject_no_childs_tree()
{
CPowerKernel* power_kernel=new CPowerKernel();
CModelSelectionParameters* param_power_kernel=
new CModelSelectionParameters("kernel", power_kernel);
return param_power_kernel;
}
CModelSelectionParameters* build_leaf_node_tree()
{
CModelSelectionParameters* c_1=new CModelSelectionParameters("C1");
c_1->build_values(1.0, 1.0, R_EXP);
return c_1;
}
CModelSelectionParameters* build_root_no_childs_tree()
{
return new CModelSelectionParameters();
}
CModelSelectionParameters* build_root_value_childs_tree()
{
CModelSelectionParameters* root=new CModelSelectionParameters();
CModelSelectionParameters* c_1=new CModelSelectionParameters("C1");
root->append_child(c_1);
c_1->build_values(1.0, 1.0, R_EXP);
CModelSelectionParameters* c_2=new CModelSelectionParameters("C2");
root->append_child(c_2);
c_2->build_values(1.0, 1.0, R_EXP);
return root;
}
CModelSelectionParameters* build_root_sg_object_child_tree()
{
CModelSelectionParameters* root=new CModelSelectionParameters();
CPowerKernel* power_kernel=new CPowerKernel();
CModelSelectionParameters* param_power_kernel=
new CModelSelectionParameters("kernel", power_kernel);
root->append_child(param_power_kernel);
return root;
}
CModelSelectionParameters* build_root_sg_object_child_value_child_tree()
{
CModelSelectionParameters* root=new CModelSelectionParameters();
CPowerKernel* power_kernel=new CPowerKernel();
CModelSelectionParameters* param_power_kernel=
new CModelSelectionParameters("kernel", power_kernel);
CModelSelectionParameters* c=new CModelSelectionParameters("C");
root->append_child(c);
c->build_values(1.0, 1.0, R_EXP);
root->append_child(param_power_kernel);
return root;
}
void test_get_combinations(CModelSelectionParameters* tree)
{
tree->print_tree();
/* build combinations of parameter trees */
CDynamicObjectArray<CParameterCombination>* combinations=tree->get_combinations();
/* print and directly delete them all */
SG_SPRINT("----------------------------------\n");
for (index_t i=0; i<combinations->get_num_elements(); ++i)
{
CParameterCombination* combination=combinations->get_element(i);
combination->print_tree();
SG_UNREF(combination);
}
SG_UNREF(combinations);
}
int main(int argc, char **argv)
{
init_shogun(&print_message, &print_message, &print_message);
CModelSelectionParameters* tree;
tree=build_root_no_childs_tree();
SG_REF(tree);
test_get_combinations(tree);
SG_UNREF(tree);
tree=build_leaf_node_tree();
SG_REF(tree);
test_get_combinations(tree);
SG_UNREF(tree);
tree=build_sgobject_no_childs_tree();
SG_REF(tree);
test_get_combinations(tree);
SG_UNREF(tree);
tree=build_root_value_childs_tree();
SG_REF(tree);
test_get_combinations(tree);
SG_UNREF(tree);
tree=build_root_sg_object_child_tree();
SG_REF(tree);
test_get_combinations(tree);
SG_UNREF(tree);
tree=build_root_sg_object_child_value_child_tree();
SG_REF(tree);
test_get_combinations(tree);
SG_UNREF(tree);
tree=build_complex_example_tree();
SG_REF(tree);
test_get_combinations(tree);
SG_UNREF(tree);
exit_shogun();
return 0;
}
/*
* This program is free software; you can redistribute it and/or modify
* it under the terms of the GNU General Public License as published by
* the Free Software Foundation; either version 3 of the License, or
* (at your option) any later version.
*
* Written (W) 2011 Heiko Strathmann
* Copyright (C) 2011 Berlin Institute of Technology and Max-Planck-Society
*/
#include <shogun/base/init.h>
#include <shogun/modelselection/ParameterCombination.h>
#include <shogun/lib/DynamicObjectArray.h>
#include <stdlib.h>
using namespace std;
using namespace shogun;
void print_message(FILE* target, const char* str)
{
fprintf(target, "%s", str);
}
void test_parameter_set_multiplication()
{
SG_SPRINT("\ntest_parameter_set_multiplication()\n");
DynArray<Parameter*> set1;
DynArray<Parameter*> set2;
SGVector<float64_t> param_vector(8, true);
CMath::range_fill_vector(param_vector.vector, param_vector.vlen);
SGVector<Parameter*> parameters(4, true);
parameters.vector[0]=new Parameter();
parameters.vector[0]->add(¶m_vector.vector[0], "0");
parameters.vector[0]->add(¶m_vector.vector[1], "1");
set1.append_element(parameters.vector[0]);
parameters.vector[1]=new Parameter();
parameters.vector[1]->add(¶m_vector.vector[2], "2");
parameters.vector[1]->add(¶m_vector.vector[3], "3");
set1.append_element(parameters.vector[1]);
parameters.vector[2]=new Parameter();
parameters.vector[2]->add(¶m_vector.vector[4], "4");
parameters.vector[2]->add(¶m_vector.vector[5], "5");
set2.append_element(parameters.vector[2]);
parameters.vector[3]=new Parameter();
parameters.vector[3]->add(¶m_vector.vector[6], "6");
parameters.vector[3]->add(¶m_vector.vector[7], "7");
set2.append_element(parameters.vector[3]);
DynArray<Parameter*>* result=new DynArray<Parameter*>();//CParameterCombination::parameter_set_multiplication(set1, set2);
for (index_t i=0; i<result->get_num_elements(); ++i)
{
Parameter* p=result->get_element(i);
for (index_t j=0; j<p->get_num_parameters(); ++j)
SG_SPRINT("%s ", p->get_parameter(j)->m_name);
SG_SPRINT("\n");
delete p;
}
delete result;
for (index_t i=0; i<4; ++i)
delete parameters.vector[i];
param_vector.free_vector();
parameters.free_vector();
}
void test_leaf_sets_multiplication()
{
SG_SPRINT("\ntest_leaf_sets_multiplication()\n");
SGVector<float64_t> param_vector(6, true);
CMath::range_fill_vector(param_vector.vector, param_vector.vlen);
CDynamicObjectArray<CDynamicObjectArray<CParameterCombination> > sets;
CParameterCombination* new_root=new CParameterCombination();
SG_REF(new_root);
CDynamicObjectArray<CParameterCombination>* current=new CDynamicObjectArray<
CParameterCombination>();
sets.append_element(current);
Parameter* p=new Parameter();
p->add(¶m_vector.vector[0], "0");
CParameterCombination* pc=new CParameterCombination(p);
current->append_element(pc);
p=new Parameter();
p->add(¶m_vector.vector[1], "1");
pc=new CParameterCombination(p);
current->append_element(pc);
/* first case: one element */
CDynamicObjectArray<CParameterCombination>* result_simple=
CParameterCombination::leaf_sets_multiplication(sets, new_root);
SG_SPRINT("one set\n");
for (index_t i=0; i<result_simple->get_num_elements(); ++i)
{
CParameterCombination* current=result_simple->get_element(i);
current->print_tree();
SG_UNREF(current);
}
SG_UNREF(result_simple);
/* now more elements are created */
current=new CDynamicObjectArray<CParameterCombination>();
sets.append_element(current);
p=new Parameter();
p->add(¶m_vector.vector[2], "2");
pc=new CParameterCombination(p);
current->append_element(pc);
p=new Parameter();
p->add(¶m_vector.vector[3], "3");
pc=new CParameterCombination(p);
current->append_element(pc);
current=new CDynamicObjectArray<CParameterCombination>();
sets.append_element(current);
p=new Parameter();
p->add(¶m_vector.vector[4], "4");
pc=new CParameterCombination(p);
current->append_element(pc);
p=new Parameter();
p->add(¶m_vector.vector[5], "5");
pc=new CParameterCombination(p);
current->append_element(pc);
/* second case: more element */
CDynamicObjectArray<CParameterCombination>* result_complex=
CParameterCombination::leaf_sets_multiplication(sets, new_root);
SG_SPRINT("more sets\n");
for (index_t i=0; i<result_complex->get_num_elements(); ++i)
{
CParameterCombination* current=result_complex->get_element(i);
current->print_tree();
SG_UNREF(current);
}
SG_UNREF(result_complex);
SG_UNREF(new_root);
param_vector.free_vector();
}
int main(int argc, char **argv)
{
init_shogun(&print_message, &print_message, &print_message);
test_parameter_set_multiplication();
test_leaf_sets_multiplication();
exit_shogun();
return 0;
}
/*
* This program is free software; you can redistribute it and/or modify
* it under the terms of the GNU General Public License as published by
* the Free Software Foundation; either version 3 of the License, or
* (at your option) any later version.
*
* Written (W) 2011 Heiko Strathmann
* Copyright (C) 2011 Berlin Institute of Technology and Max-Planck-Society
*/
#include <shogun/base/init.h>
#include <shogun/modelselection/ModelSelectionParameters.h>
#include <shogun/modelselection/ParameterCombination.h>
#include <shogun/kernel/GaussianKernel.h>
#include <shogun/kernel/PowerKernel.h>
#include <shogun/distance/MinkowskiMetric.h>
#include <shogun/kernel/DistantSegmentsKernel.h>
using namespace shogun;
void print_message(FILE* target, const char* str)
{
fprintf(target, "%s", str);
}
CModelSelectionParameters* create_param_tree()
{
CModelSelectionParameters* root=new CModelSelectionParameters();
CModelSelectionParameters* c=new CModelSelectionParameters("C");
root->append_child(c);
c->build_values(1, 2, R_EXP);
CPowerKernel* power_kernel=new CPowerKernel();
CModelSelectionParameters* param_power_kernel=
new CModelSelectionParameters("kernel", power_kernel);
root->append_child(param_power_kernel);
CModelSelectionParameters* param_power_kernel_degree=
new CModelSelectionParameters("degree");
param_power_kernel_degree->build_values(1, 2, R_EXP);
param_power_kernel->append_child(param_power_kernel_degree);
CMinkowskiMetric* m_metric=new CMinkowskiMetric(10);
CModelSelectionParameters* param_power_kernel_metric1=
new CModelSelectionParameters("distance", m_metric);
param_power_kernel->append_child(param_power_kernel_metric1);
CModelSelectionParameters* param_power_kernel_metric1_k=
new CModelSelectionParameters("k");
param_power_kernel_metric1_k->build_values(1, 2, R_LINEAR);
param_power_kernel_metric1->append_child(param_power_kernel_metric1_k);
CGaussianKernel* gaussian_kernel=new CGaussianKernel();
CModelSelectionParameters* param_gaussian_kernel=
new CModelSelectionParameters("kernel", gaussian_kernel);
root->append_child(param_gaussian_kernel);
CModelSelectionParameters* param_gaussian_kernel_width=
new CModelSelectionParameters("width");
param_gaussian_kernel_width->build_values(1, 2, R_EXP);
param_gaussian_kernel->append_child(param_gaussian_kernel_width);
CDistantSegmentsKernel* ds_kernel=new CDistantSegmentsKernel();
CModelSelectionParameters* param_ds_kernel=new CModelSelectionParameters("kernel",
ds_kernel);
root->append_child(param_ds_kernel);
CModelSelectionParameters* param_ds_kernel_delta=
new CModelSelectionParameters("delta");
param_ds_kernel_delta->build_values(1, 2, R_EXP);
param_ds_kernel->append_child(param_ds_kernel_delta);
CModelSelectionParameters* param_ds_kernel_theta=
new CModelSelectionParameters("theta");
param_ds_kernel_theta->build_values(1, 2, R_EXP);
param_ds_kernel->append_child(param_ds_kernel_theta);
return root;
}
int main(int argc, char **argv)
{
init_shogun(&print_message, &print_message, &print_message);
/* create example tree */
CModelSelectionParameters* tree=create_param_tree();
SG_REF(tree);
tree->print_tree();
/* build combinations of parameter trees */
CDynamicObjectArray<CParameterCombination>* combinations=tree->get_combinations();
/* print and directly delete them all */
SG_SPRINT("----------------------------------\n");
for (index_t i=0; i<combinations->get_num_elements(); ++i)
{
CParameterCombination* combination=combinations->get_element(i);
combination->print_tree();
SG_UNREF(combination);
}
SG_UNREF(combinations);
/* delete example tree */
SG_UNREF(tree);
exit_shogun();
return 0;
}
/*
* This program is free software; you can redistribute it and/or modify
* it under the terms of the GNU General Public License as published by
* the Free Software Foundation; either version 3 of the License, or
* (at your option) any later version.
*
* Written (W) 2011 Heiko Strathmann
* Copyright (C) 2011 Berlin Institute of Technology and Max-Planck-Society
*/
#include <shogun/features/Labels.h>
#include <shogun/features/SimpleFeatures.h>
#include <shogun/kernel/GaussianKernel.h>
#include <shogun/classifier/svm/LibSVM.h>
#include <shogun/base/init.h>
#include <shogun/lib/common.h>
#include <shogun/io/SGIO.h>
using namespace shogun;
void print_message(FILE* target, const char* str)
{
fprintf(target, "%s", str);
}
/* number of features and their dimension */
int32_t n=6;
int main(int argc, char** argv)
{
init_shogun(&print_message);
/* create some random data */
float64_t* matrix = SG_MALLOC(float64_t, n*n);
for(int32_t i=0; i<n*n; ++i)
matrix[i]=CMath::random((float64_t)-n,(float64_t)n);
CMath::display_matrix(matrix, n, n);
/* create n n-dimensional feature vectors */
CSimpleFeatures<float64_t>* features= new CSimpleFeatures<float64_t>();
features->set_feature_matrix(matrix, n, n);
/* create gaussian kernel with cache 10MB, width will be changed later */
CGaussianKernel* kernel = new CGaussianKernel(10, 0);
kernel->init(features, features);
/* create n labels (+1,-1,+1,-1,...) */
CLabels* labels=new CLabels(n);
for (int32_t i=0; i<n; ++i)
labels->set_label(i, i%2==0 ? +1 : -1);
/* create libsvm with C=10 and produced labels */
CLibSVM* svm=new CLibSVM(10, kernel, labels);
/* iterate over different width parameters */
for (int32_t i=0; i<10; ++i)
{
SG_SPRINT("\n\ncurrent kernel width: 2^%d=%f\n", i, CMath::pow(2.0,i));
float64_t width=CMath::pow(2.0,i);
/* create parameter to change current kernel width */
Parameter* param=new Parameter();
param->add(&width, "width", "");
/* tell kernel to use the newly produced parameter */
kernel->m_parameters->set_from_parameters(param);
/* print kernel matrix */
for (int32_t i=0; i<n; i++)
{
for (int32_t j=0; j<n; j++)
SG_SPRINT("%f ", kernel->kernel(i,j));
SG_SPRINT("\n");
}
/* train and classify */
svm->train();
for (int32_t i=0; i<n; ++i)
SG_SPRINT("output[%d]=%f\treal[%d]=%f\n", i,
svm->apply(i), i, labels->get_label(i));
delete param;
}
/* free up memory */
SG_UNREF(svm);
exit_shogun();
return 0;
}
/*
* This program is free software; you can redistribute it and/or modify
* it under the terms of the GNU General Public License as published by
* the Free Software Foundation; either version 3 of the License, or
* (at your option) any later version.
*
* Written (W) 2011 Heiko Strathmann
* Copyright (C) 2011 Berlin Institute of Technology and Max-Planck-Society
*/
#include <shogun/features/Labels.h>
#include <shogun/features/SimpleFeatures.h>
#include <shogun/kernel/GaussianKernel.h>
#include <shogun/classifier/svm/LibSVM.h>
#include <shogun/base/init.h>
#include <shogun/lib/common.h>
#include <shogun/io/SGIO.h>
using namespace shogun;
void print_message(FILE* target, const char* str)
{
fprintf(target, "%s", str);
}
/* number of features and their dimension, number of kernels */
const int32_t n=7;
int main(int argc, char** argv)
{
init_shogun(&print_message);
/* create some random data and hand it to each kernel */
float64_t* matrix=SG_MALLOC(float64_t, n*n);
for (int32_t k=0; k<n*n; ++k)
matrix[k]=CMath::random((float64_t) -n, (float64_t) n);
SG_SPRINT("feature data:\n");
CMath::display_matrix(matrix, n, n);
CSimpleFeatures<float64_t>* features=new CSimpleFeatures<float64_t> ();
features->set_feature_matrix(matrix, n, n);
/* create n kernels with n features each */
CGaussianKernel** kernels=SG_MALLOC(CGaussianKernel*, n);
for (int32_t i=0; i<n; ++i)
{
kernels[i]=new CGaussianKernel(10, CMath::random(0.0, (float64_t)n*n));
/* hand data to kernel */
kernels[i]->init(features, features);
}
/* create n parameter instances, each with one kernel */
Parameter** parameters=SG_MALLOC(Parameter*, n);
for (int32_t i=0; i<n; ++i)
{
parameters[i]=new Parameter();
parameters[i]->add((CSGObject**)&kernels[i], "kernel", "");
}
/* create n labels (+1,-1,+1,-1,...) */
CLabels* labels=new CLabels(n);
for (int32_t i=0; i<n; ++i)
labels->set_label(i, i%2==0 ? +1 : -1);
/* create libsvm with C=10 and produced labels */
CLibSVM* svm=new CLibSVM(10, NULL, labels);
/* iterate over all parameter instances and set them as subkernel */
for (int32_t i=0; i<n; ++i)
{
SG_SPRINT("\nkernel %d has width %f\n", i, kernels[i]->get_width());
/* change kernel, old one is UNREF'ed, new one is REF'ed */
svm->m_parameters->set_from_parameters(parameters[i]);
/* train and classify with the different kernels */
svm->train();
for (int32_t i=0; i<n; ++i)
SG_SPRINT("output[%d]=%f\treal[%d]=%f\n", i,
svm->apply(i), i, labels->get_label(i));
}
/* free up memory: delete all Parameter instances */
for (int32_t i=0; i<n; ++i)
delete parameters[i];
/* delete created arrays */
SG_FREE(kernels);
SG_FREE(parameters);
/* this also handles features, labels, and last kernel in kernels[n-1] */
SG_UNREF(svm);
exit_shogun();
return 0;
}
/*
* This program is free software; you can redistribute it and/or modify
* it under the terms of the GNU General Public License as published by
* the Free Software Foundation; either version 3 of the License, or
* (at your option) any later version.
*
* Written (W) 2011 Heiko Strathmann
* Copyright (C) 2011 Berlin Institute of Technology and Max-Planck-Society
*/
#include <shogun/lib/config.h>
#include <shogun/classifier/svm/LibSVM.h>
#include <shogun/classifier/svm/LibLinear.h>
#include <shogun/kernel/DistantSegmentsKernel.h>
#include <shogun/kernel/GaussianKernel.h>
#include <shogun/kernel/PowerKernel.h>
#include <shogun/distance/MinkowskiMetric.h>
using namespace shogun;
void print_message(FILE* target, const char* str)
{
fprintf(target, "%s", str);
}
void print_modsel_parameters(CSGObject* object)
{
SGVector<char*> modsel_params=object->get_modelsel_names();
SG_SPRINT("Parameters of %s available for model selection:\n",
object->get_name());
char* type_string=SG_MALLOC(char, 100);
for (index_t i=0; i<modsel_params.vlen; ++i)
{
/* extract current name, ddescription and type, and print them */
const char* name=modsel_params.vector[i];
index_t index=object->get_modsel_param_index(name);
TSGDataType type=object->m_model_selection_parameters->get_parameter(
index)->m_datatype;
type.to_string(type_string, 100);
SG_SPRINT("\"%s\": \"%s\", %s\n", name,
object->get_modsel_param_descr(name), type_string);
}
SG_FREE(type_string);
modsel_params.destroy_vector();
SG_SPRINT("\n");
}
int main(int argc, char** argv)
{
init_shogun(&print_message);
#ifndef HAVE_LAPACK
CSGObject* object;
object=new CLibSVM();
print_modsel_parameters(object);
SG_UNREF(object);
object=new CLibLinear();
print_modsel_parameters(object);
SG_UNREF(object);
object=new CDistantSegmentsKernel();
print_modsel_parameters(object);
SG_UNREF(object);
object=new CGaussianKernel();
print_modsel_parameters(object);
SG_UNREF(object);
object=new CPowerKernel();
print_modsel_parameters(object);
SG_UNREF(object);
object=new CMinkowskiMetric();
print_modsel_parameters(object);
SG_UNREF(object);
#endif // HAVE_LAPACK
exit_shogun();
return 0;
}
/*
* This program is free software; you can redistribute it and/or modify
* it under the terms of the GNU General Public License as published by
* the Free Software Foundation; either version 3 of the License, or
* (at your option) any later version.
*
* Written (W) 2011 Heiko Strathmann
* DS-Kernel implementation Written (W) 2008 Sébastien Boisvert under GPLv3
* Copyright (C) 2011 Berlin Institute of Technology and Max-Planck-Society
*/
#include <shogun/io/SGIO.h>
#include <shogun/mathematics/Math.h>
#include <shogun/base/Parameter.h>
#include <shogun/kernel/DistantSegmentsKernel.h>
#include <shogun/kernel/GaussianKernel.h>
using namespace shogun;
int32_t max=3;
const float64_t initial_value=1;
const float64_t another_value=2;
void print_message(FILE* target, const char* str)
{
fprintf(target, "%s", str);
}
bool test_float_scalar()
{
bool result=true;
Parameter* original_parameter_list=new Parameter();
float64_t original_parameter=initial_value;
original_parameter_list->add(&original_parameter, "param", "");
float64_t new_parameter=another_value;
Parameter* new_parameter_list=new Parameter();
new_parameter_list->add(&new_parameter, "param", "");
original_parameter_list->set_from_parameters(new_parameter_list);
result&=original_parameter==another_value;
delete original_parameter_list;
delete new_parameter_list;
return result;
}
bool test_float_vector()
{
bool result=true;
Parameter* original_parameter_list=new Parameter();
float64_t* original_parameter=SG_MALLOC(float64_t, max);
CMath::fill_vector(original_parameter, max, initial_value);
original_parameter_list->add_vector(&original_parameter, &max, "param", "");
float64_t* new_parameter=SG_MALLOC(float64_t, max);
CMath::fill_vector(new_parameter, max, another_value);
Parameter* new_parameter_list=new Parameter();
new_parameter_list->add_vector(&new_parameter, &max, "param", "");
original_parameter_list->set_from_parameters(new_parameter_list);
for (int32_t i=0; i<max; ++i)
result&=original_parameter[i]==another_value;
delete original_parameter;
delete new_parameter;
delete original_parameter_list;
delete new_parameter_list;
return result;
}
bool test_float_matrix()
{
bool result=true;
Parameter* original_parameter_list=new Parameter();
float64_t* original_parameter=SG_MALLOC(float64_t, max*max);
CMath::fill_vector(original_parameter, max*max, initial_value);
original_parameter_list->add_matrix(&original_parameter, &max, &max, "param", "");
float64_t* new_parameter=SG_MALLOC(float64_t, max*max);
CMath::fill_vector(new_parameter, max*max, another_value);
Parameter* new_parameter_list=new Parameter();
new_parameter_list->add_matrix(&new_parameter, &max, &max, "param", "");
original_parameter_list->set_from_parameters(new_parameter_list);
for (int32_t i=0; i<max*max; ++i)
result&=original_parameter[i]==another_value;
delete original_parameter;
delete new_parameter;
delete original_parameter_list;
delete new_parameter_list;
return result;
}
bool test_sgobject_scalar()
{
bool result=true;
Parameter* original_parameter_list=new Parameter();
CSGObject* original_parameter=new CGaussianKernel(10, 10);
SG_REF(original_parameter);
original_parameter_list->add(&original_parameter, "kernel", "");
CSGObject* new_parameter=new CDistantSegmentsKernel(10, 10, 10);
Parameter* new_parameter_list=new Parameter();
new_parameter_list->add(&new_parameter, "kernel", "");
/* note: old_parameter is SG_UNREF'ed, new one SG_REF'ed */
original_parameter_list->set_from_parameters(new_parameter_list);
result&=original_parameter==new_parameter;
/* old original kernel was deleted by shogun's SG_UNREF */
SG_UNREF(new_parameter);
delete original_parameter_list;
delete new_parameter_list;
return result;
}
bool test_sgobject_vector()
{
bool result=true;
Parameter* original_parameter_list=new Parameter();
CSGObject** original_parameter=SG_MALLOC(CSGObject*, max);
for (int32_t i=0; i<max; ++i)
{
original_parameter[i]=new CDistantSegmentsKernel(1, 1, 1);
SG_REF(original_parameter[i]);
}
original_parameter_list->add_vector(&original_parameter, &max, "param", "");
CSGObject** new_parameter=SG_MALLOC(CSGObject*, max);
for (int32_t i=0; i<max; ++i)
new_parameter[i]=new CDistantSegmentsKernel(2, 2, 2);
Parameter* new_parameter_list=new Parameter();
new_parameter_list->add_vector(&new_parameter, &max, "param", "");
/* note: old_parameters are SG_UNREF'ed, new ones SG_REF'ed */
original_parameter_list->set_from_parameters(new_parameter_list);
for (int32_t i=0; i<max; ++i)
result&=original_parameter[i]==new_parameter[i];
/* old original kernels were deleted by shogun's SG_UNREF */
delete original_parameter;
for (int32_t i=0; i<max; ++i)
SG_UNREF(new_parameter[i]);
delete new_parameter;
delete original_parameter_list;
delete new_parameter_list;
return result;
}
bool test_sgobject_matrix()
{
bool result=true;
Parameter* original_parameter_list=new Parameter();
CSGObject** original_parameter=SG_MALLOC(CSGObject*, max*max);
for (int32_t i=0; i<max; ++i)
{
for (int32_t j=0; j<max; ++j)
{
original_parameter[j*max+i]=new CDistantSegmentsKernel(1, 1, 1);
SG_REF(original_parameter[j*max+i]);
}
}
original_parameter_list->add_matrix(&original_parameter, &max, &max, "param", "");
CSGObject** new_parameter=SG_MALLOC(CSGObject*, max*max);
for (int32_t i=0; i<max; ++i)
{
for (int32_t j=0; j<max; ++j)
new_parameter[j*max+i]=new CDistantSegmentsKernel(1, 1, 1);
}
Parameter* new_parameter_list=new Parameter();
new_parameter_list->add_matrix(&new_parameter, &max, &max, "param", "");
/* note: old_parameters are SG_UNREF'ed, new ones SG_REF'ed */
original_parameter_list->set_from_parameters(new_parameter_list);
for (int32_t i=0; i<max; ++i)
{
for (int32_t j=0; j<max; ++j)
result&=original_parameter[j*max+i]==new_parameter[j*max+i];
}
/* old original kernels were deleted by shogun's SG_UNREF */
delete original_parameter;
for (int32_t i=0; i<max*max; ++i)
SG_UNREF(new_parameter[i]);
delete new_parameter;
delete original_parameter_list;
delete new_parameter_list;
return result;
}
int main(int argc, char **argv)
{
init_shogun(&print_message, &print_message, &print_message);
bool result=true;
/* test wheater set_from_parameters works for these types */
result&=test_float_scalar();
result&=test_sgobject_scalar();
result&=test_sgobject_vector();
result&=test_sgobject_matrix();
result&=test_float_matrix();
result&=test_float_vector();
if (result)
SG_SPRINT("SUCCESS!\n");
else
SG_SPRINT("FAILURE!\n");
exit_shogun();
return 0;
}
/*
* This program is free software; you can redistribute it and/or modify
* it under the terms of the GNU General Public License as published by
* the Free Software Foundation; either version 3 of the License, or
* (at your option) any later version.
*
* Written (W) 2008-2010 Soeren Sonnenburg, Alexander Binder
* Copyright (C) 2008-2009 Fraunhofer Institute FIRST and Max Planck Society
* Copyright (C) 2010 Berlin Institute of Technology
*/
#include <shogun/kernel/GaussianKernel.h>
#include <shogun/kernel/LinearKernel.h>
#include <shogun/preproc/RandomFourierGaussPreproc.h>
#include <shogun/features/SimpleFeatures.h>
#include <shogun/classifier/svm/LibSVM.h>
#include <shogun/lib/Mathematics.h>
#include <shogun/lib/common.h>
#include <shogun/base/init.h>
#include <stdlib.h>
#include <stdio.h>
#include <vector>
#include <iostream>
#include <algorithm>
#include <ctime>
using namespace shogun;
void gen_rand_data(float64_t* & feat, float64_t* & lab,const int32_t num,const int32_t dims,const float64_t dist)
{
lab=SG_MALLOC(float64_t, num);
feat=SG_MALLOC(float64_t, num*dims);
for (int32_t i=0; i<num; i++)
{
if (i<num/2)
{
lab[i]=-1.0;
for (int32_t j=0; j<dims; j++)
feat[i*dims+j]=CMath::random(0.0,1.0)+dist;
}
else
{
lab[i]=1.0;
for (int32_t j=0; j<dims; j++)
feat[i*dims+j]=CMath::random(0.0,1.0)-dist;
}
}
CMath::display_vector(lab,num);
CMath::display_matrix(feat,dims, num);
}
int main()
{
time_t a,b;
int32_t dims=6000;
float64_t dist=0.5;
int32_t randomfourier_featurespace_dim=500; // the typical application of the below preprocessor are cases with high input dimensionalities of some thousands
int32_t numtr=3000;
int32_t numte=3000;
const int32_t feature_cache=0;
const int32_t kernel_cache=0;
// important trick for RFgauss to work: kernel width is set such that average inner kernel distance is close one
// the rfgauss approximation breaks down if average inner kernel distances (~~ kernel width to small compared to variance of data) are too large
// try rbf_width=0.1 to see how it fails! - you will see the problem in the large number of negative kernel entries (numnegratio) for the rfgauss linear kernel
const float64_t rbf_width=4000;
const float64_t svm_C=10;
const float64_t svm_eps=0.001;
init_shogun();
float64_t* feattr(NULL);
float64_t* labtr(NULL);
a=time(NULL);
std::cout << "generating train data"<<std::endl;
gen_rand_data(feattr,labtr,numtr,dims,dist);
float64_t* feattr2=SG_MALLOC(float64_t, numtr*dims);
std::copy(feattr,feattr+numtr*dims,feattr2);
std::cout << "finished"<<std::endl;
b=time(NULL);
std::cout<< "elapsed time in seconds "<<b-a <<std::endl;
float64_t* featte(NULL);
float64_t* labte(NULL);
a=time(NULL);
std::cout << "generating test data"<<std::endl;
gen_rand_data(featte,labte,numte,dims,dist);
float64_t* featte2=SG_MALLOC(float64_t, numtr*dims);
std::copy(featte,featte+numtr*dims,featte2);
float64_t* featte3=SG_MALLOC(float64_t, numtr*dims);
std::copy(featte,featte+numtr*dims,featte3);
std::cout << "finished"<<std::endl;
b=time(NULL);
std::cout<< "elapsed time in seconds "<<b-a <<std::endl;
// create train labels
CLabels* labelstr=new CLabels();
labelstr->set_labels(labtr, numtr);
SG_REF(labelstr);
// create train features
a=time(NULL);
std::cout << "initializing shogun train feature"<<std::endl;
CSimpleFeatures<float64_t>* featurestr1 = new CSimpleFeatures<float64_t>(feature_cache);
SG_REF(featurestr1);
featurestr1->set_feature_matrix(feattr, dims, numtr);
std::cout << "finished"<<std::endl;
//b=time(NULL);
//std::cout<< "elapsed time in seconds "<<b-a <<std::endl;
// create gaussian kernel
// std::cout << "computing gaussian train kernel"<<std::endl;
CGaussianKernel* kerneltr1 = new CGaussianKernel(kernel_cache, rbf_width);
SG_REF(kerneltr1);
kerneltr1->init(featurestr1, featurestr1);
// create svm via libsvm and train
CLibSVM* svm1 = new CLibSVM(svm_C, kerneltr1, labelstr);
SG_REF(svm1);
svm1->set_epsilon(svm_eps);
a=time(NULL);
std::cout << "training SVM over gaussian kernel"<<std::endl;
svm1->train();
std::cout << "finished"<<std::endl;
b=time(NULL);
std::cout<< "elapsed time in seconds "<<b-a <<std::endl;
printf("num_sv:%d b:%f\n", svm1->get_num_support_vectors(), svm1->get_bias());
a=time(NULL);
std::cout << "initializing shogun test feature"<<std::endl;
CSimpleFeatures<float64_t>* featureste1 = new CSimpleFeatures<float64_t>(feature_cache);
SG_REF(featureste1);
featureste1->set_feature_matrix(featte, dims, numte);
std::cout << "finished"<<std::endl;
//b=time(NULL);
//std::cout<< "elapsed time in seconds "<<b-a <<std::endl;
//std::cout << "computing gaussian test kernel"<<std::endl;
CGaussianKernel* kernelte1 = new CGaussianKernel(kernel_cache, rbf_width);
SG_REF(kernelte1);
kernelte1->init(featurestr1, featureste1);
svm1->set_kernel(kernelte1);
a=time(NULL);
std::cout << "scoring gaussian test kernel"<<std::endl;
std::vector<float64_t> scoreste1(numte);
float64_t err1=0;
for(int32_t i=0; i< numte ;++i)
{
scoreste1[i]=svm1->classify_example(i);
if(scoreste1[i]*labte[i]<0)
{
err1+=1.0/numte;
}
}
std::cout << "finished"<<std::endl;
b=time(NULL);
std::cout<< "elapsed time in seconds "<<b-a <<std::endl;
// ***************************************
// now WITH the preprocessor
a=time(NULL);
std::cout << "initializing preprocessor"<<std::endl;
CRandomFourierGaussPreproc *rfgauss=new CRandomFourierGaussPreproc;
SG_REF(rfgauss);
rfgauss->get_io()->set_loglevel(MSG_DEBUG);
// ************************************************************
// set parameters of the preprocessor
// ******************************** !!!!!!!!!!!!!!!!! CMath::sqrt(rbf_width/2.0)
rfgauss->set_kernelwidth( CMath::sqrt(rbf_width/2.0) );
rfgauss->set_dim_input_space(dims);
rfgauss->set_dim_feature_space(randomfourier_featurespace_dim);
std::cout << "finished"<<std::endl;
//b=time(NULL);
//std::cout<< "elapsed time in seconds "<<b-a <<std::endl;
// create train features
a=time(NULL);
std::cout << "initializing shogun train feature again"<<std::endl;
CSimpleFeatures<float64_t>* featurestr2 = new CSimpleFeatures<float64_t>(feature_cache);
SG_REF(featurestr2);
featurestr2->set_feature_matrix(feattr2, dims, numtr);
std::cout << "finished"<<std::endl;
//b=time(NULL);
//std::cout<< "elapsed time in seconds "<<b-a <<std::endl;
// ************************************************************
// use preprocessor
// **************************************************************
// add preprocessor
featurestr2->add_preproc(rfgauss);
// apply preprocessor
a=time(NULL);
std::cout << "applying preprocessor to train feature"<<std::endl;
featurestr2->apply_preproc();
std::cout << "finished"<<std::endl;
b=time(NULL);
std::cout<< "elapsed time in seconds "<<b-a <<std::endl;
// save random coefficients and state data of preprocessor for use with a new preprocessor object (see lines following "// now the same with a new preprocessor to show the usage of set_randomcoefficients"
// Alternative: use built-in serialization to load and save state data from/to a file!!!
float64_t *randomcoeff_additive2, * randomcoeff_multiplicative2;
int32_t dim_feature_space2,dim_input_space2;
float64_t kernelwidth2;
rfgauss->get_randomcoefficients(&randomcoeff_additive2,
&randomcoeff_multiplicative2,
&dim_feature_space2, &dim_input_space2, &kernelwidth2);
// create linear kernel
//std::cout << "computing linear train kernel over preprocessed features"<<std::endl;
CLinearKernel* kerneltr2 = new CLinearKernel();
SG_REF(kerneltr2);
kerneltr2->init(featurestr2, featurestr2);
// create svm via libsvm and train
CLibSVM* svm2 = new CLibSVM(svm_C, kerneltr2, labelstr);
SG_REF(svm2);
svm2->set_epsilon(svm_eps);
a=time(NULL);
std::cout << "training SVM over linear kernel over preprocessed features"<<std::endl;
svm2->train();
std::cout << "finished"<<std::endl;
b=time(NULL);
std::cout<< "elapsed time in seconds "<<b-a <<std::endl;
printf("num_sv:%d b:%f\n", svm2->get_num_support_vectors(), svm2->get_bias());
a=time(NULL);
std::cout << "initializing shogun test feature again"<<std::endl;
CSimpleFeatures<float64_t>* featureste2 = new CSimpleFeatures<float64_t>(feature_cache);
SG_REF(featureste2);
featureste2->set_feature_matrix(featte2, dims, numte);
std::cout << "finished"<<std::endl;
//b=time(NULL);
//std::cout<< "elapsed time in seconds "<<b-a <<std::endl;
// ************************************************************
// use preprocessor
// **************************************************************
CRandomFourierGaussPreproc *rfgauss2=new CRandomFourierGaussPreproc;
SG_REF(rfgauss2);
rfgauss2->get_io()->set_loglevel(MSG_DEBUG);
// add preprocessor
featureste2->add_preproc(rfgauss);
// apply preprocessor
a=time(NULL);
std::cout << "applying same preprocessor to test feature"<<std::endl;
featureste2->apply_preproc();
std::cout << "finished"<<std::endl;
b=time(NULL);
std::cout<< "elapsed time in seconds "<<b-a <<std::endl;
//std::cout << "computing linear test kernel over preprocessed features"<<std::endl;
CLinearKernel* kernelte2 = new CLinearKernel();
SG_REF(kernelte2);
kernelte2->init(featurestr2, featureste2);
//std::cout << "finished"<<std::endl;
//b=time(NULL);
//std::cout<< "elapsed time in seconds "<<b-a <<std::endl;
svm2->set_kernel(kernelte2);
a=time(NULL);
std::cout << "scoring linear test kernel over preprocessed features"<<std::endl;
std::vector<float64_t> scoreste2(numte);
float64_t err2=0;
for(int32_t i=0; i< numte ;++i)
{
scoreste2[i]=svm2->classify_example(i);
if(scoreste2[i]*labte[i]<0)
{
err2+=1.0/numte;
}
}
std::cout << "finished"<<std::endl;
b=time(NULL);
std::cout<< "elapsed time in seconds "<<b-a <<std::endl;
std::cout << "pausing 12 seconds"<<std::endl;
sleep(12);
// ************************************************************
// compare results
// **************************************************************
int32_t num_labeldiffs=0;
float64_t avg_scorediff=0;
for(int32_t i=0; i< numte ;++i)
{
if( (int32_t)CMath::sign(scoreste1[i]) != (int32_t)CMath::sign(scoreste2[i]))
{
++num_labeldiffs;
}
avg_scorediff+=CMath::abs(scoreste1[i]-scoreste2[i])/numte;
std::cout<< "at sample i"<< i <<" label 1= " << CMath::sign(scoreste1[i]) <<" label 2= " << CMath::sign(scoreste2[i])<< " scorediff " << scoreste1[i] << " - " <<scoreste2[i] <<" = " << CMath::abs(scoreste1[i]-scoreste2[i])<<std::endl;
}
std::cout << "usedwidth for rbf kernel"<< kerneltr1->get_width() << " " << kernelte1->get_width()<<std::endl;
std::cout<< "number of different labels between gaussian kernel and rfgauss "<< num_labeldiffs<< " out of "<< numte << " labels "<<std::endl;
std::cout<< "average test sample SVM output score difference between gaussian kernel and rfgauss "<< avg_scorediff<<std::endl;
std::cout<< "classification errors gaussian kernel and rfgauss "<< err1 << " " <<err2<<std::endl;
a=time(NULL);
std::cout << "computing effective kernel widths (means of inner distances)"<<std::endl;
int32_t m, n;
float64_t * kertr1;
kerneltr1->get_kernel_matrix ( &kertr1, &m, &n);
std::cout << "kernel size "<< m << " "<< n <<std::endl;
float64_t avgdist1=0;
for(int i=0; i<m ;++i)
{
for(int l=0; l<i ;++l)
{
avgdist1+= -CMath::log(kertr1[i+l*m])*2.0/m/(m+1.0);
}
}
float64_t * kertr2;
kerneltr2->get_kernel_matrix (&kertr2,&m, &n);
float64_t avgdist2=0;
float64_t numnegratio=0;
for(int i=0; i<m ;++i)
{
for(int l=0; l<i ;++l)
{
if(kertr2[i+l*m]<=0)
{
numnegratio+=2.0/m/(m+1.0);
}
else
{
avgdist2+= -CMath::log(std::max(kertr2[i+l*m],1e-10))*2.0/m/(m+1.0);
}
}
}
std::cout << "finished"<<std::endl;
b=time(NULL);
std::cout<< "elapsed time in seconds "<<b-a <<std::endl;
std::cout << "effective kernel width for gaussian kernel and RFgauss "<< avgdist1 << " " <<avgdist2/(1.0-numnegratio) << std::endl<< " numnegratio (negative entries in RFgauss approx kernel)"<< numnegratio<<std::endl;
// **********************************************
// now the same with a new preprocessor to show the usage of set_randomcoefficients
// ********************************************8
CSimpleFeatures<float64_t>* featureste3 = new CSimpleFeatures<float64_t>(feature_cache);
SG_REF(featureste3);
featureste3->set_feature_matrix(featte3, dims, numte);
std::cout << "finished"<<std::endl;
//b=time(NULL);
//std::cout<< "elapsed time in seconds "<<b-a <<std::endl;
// ************************************************************
// use preprocessor
// **************************************************************
rfgauss2->set_randomcoefficients(
randomcoeff_additive2,
randomcoeff_multiplicative2,
dim_feature_space2, dim_input_space2, kernelwidth2);
// add preprocessor
featureste3->add_preproc(rfgauss2);
// apply preprocessor
a=time(NULL);
std::cout << "applying same preprocessor to test feature"<<std::endl;
featureste3->apply_preproc();
std::cout << "finished"<<std::endl;
b=time(NULL);
std::cout<< "elapsed time in seconds "<<b-a <<std::endl;
//std::cout << "computing linear test kernel over preprocessed features"<<std::endl;
CLinearKernel* kernelte3 = new CLinearKernel();
SG_REF(kernelte3);
kernelte2->init(featurestr2, featureste3);
//std::cout << "finished"<<std::endl;
//b=time(NULL);
//std::cout<< "elapsed time in seconds "<<b-a <<std::endl;
svm2->set_kernel(kernelte3);
a=time(NULL);
std::cout << "scoring linear test kernel over preprocessed features"<<std::endl;
std::vector<float64_t> scoreste3(numte);
float64_t err3=0;
for(int32_t i=0; i< numte ;++i)
{
scoreste3[i]=svm2->classify_example(i);
if(scoreste3[i]*labte[i]<0)
{
err3+=1.0/numte;
}
}
std::cout << "finished"<<std::endl;
b=time(NULL);
std::cout<< "elapsed time in seconds "<<b-a <<std::endl;
std::cout << "pausing 12 seconds"<<std::endl;
sleep(12);
// ************************************************************
// compare results
// **************************************************************
num_labeldiffs=0;
avg_scorediff=0;
for(int32_t i=0; i< numte ;++i)
{
if( (int32_t)CMath::sign(scoreste1[i]) != (int32_t)CMath::sign(scoreste3[i]))
{
++num_labeldiffs;
}
avg_scorediff+=CMath::abs(scoreste1[i]-scoreste3[i])/numte;
std::cout<< "at sample i"<< i <<" label 1= " << CMath::sign(scoreste1[i]) <<" label 2= " << CMath::sign(scoreste3[i])<< " scorediff " << scoreste1[i] << " - " <<scoreste3[i] <<" = " << CMath::abs(scoreste1[i]-scoreste3[i])<<std::endl;
}
std::cout<< "number of different labels between gaussian kernel and rfgauss "<< num_labeldiffs<< " out of "<< numte << " labels "<<std::endl;
std::cout<< "average test sample SVM output score difference between gaussian kernel and rfgauss "<< avg_scorediff<<std::endl;
std::cout<< "classification errors gaussian kernel and rfgauss "<< err1 << " " <<err3<<std::endl;
SG_FREE(randomcoeff_additive2);
SG_FREE(randomcoeff_multiplicative2);
SG_FREE(labtr);
SG_FREE(labte);
SG_FREE(kertr1);
SG_FREE(kertr2);
SG_UNREF(labelstr);
SG_UNREF(kerneltr1);
SG_UNREF(kerneltr2);
SG_UNREF(kernelte1);
SG_UNREF(kernelte2);
SG_UNREF(kernelte3);
SG_UNREF(featurestr1);
SG_UNREF(featurestr2);
SG_UNREF(featureste1);
SG_UNREF(featureste2);
SG_UNREF(featureste3);
SG_UNREF(svm1);
SG_UNREF(svm2);
SG_UNREF(rfgauss);
SG_UNREF(rfgauss2);
exit_shogun();
return 0;
}
/*
* This program is free software; you can redistribute it and/or modify
* it under the terms of the GNU General Public License as published by
* the Free Software Foundation; either version 3 of the License, or
* (at your option) any later version.
*
* Written (W) 2011 Heiko Strathmann
* Copyright (C) 2011 Berlin Institute of Technology and Max-Planck-Society
*/
#include <shogun/base/init.h>
#include <shogun/base/Parameter.h>
#include <shogun/io/SerializableAsciiFile.h>
#include <shogun/io/SerializableJsonFile.h>
#include <shogun/io/SerializableXmlFile.h>
#include <shogun/io/SerializableHdf5File.h>
using namespace shogun;
void print_message(FILE* target, const char* str)
{
fprintf(target, "%s", str);
}
const char* filename="filename.txt";
void print(Parameter* p)
{
TParameter* param=p->get_parameter(0);
SGVector<float64_t>* v=(SGVector<float64_t>*)param->m_parameter;
CMath::display_vector(v->vector, v->vlen, "vector:");
param=p->get_parameter(1);
SGMatrix<float64_t>* m=(SGMatrix<float64_t>*)param->m_parameter;
CMath::display_matrix(m->matrix, m->num_rows, m->num_cols, "matrix:");
}
void check_content_equal(Parameter* save_param, Parameter* load_param)
{
TParameter* p;
p=save_param->get_parameter(0);
SGVector<float64_t>* sv=(SGVector<float64_t>*)p->m_parameter;
p=save_param->get_parameter(1);
SGMatrix<float64_t>* sm=(SGMatrix<float64_t>*)p->m_parameter;
p=load_param->get_parameter(0);
SGVector<float64_t>* lv=(SGVector<float64_t>*)p->m_parameter;
p=load_param->get_parameter(1);
SGMatrix<float64_t>* lm=(SGMatrix<float64_t>*)p->m_parameter;
ASSERT(sv->vlen==lv->vlen);
ASSERT(sm->num_rows==lm->num_rows);
ASSERT(sm->num_cols==lm->num_cols);
for (index_t i=0; i<sv->vlen; ++i)
ASSERT(sv->vector[i]==lv->vector[i]);
for (index_t i=0; i<sm->num_cols*sm->num_rows; ++i)
ASSERT(sm->matrix[i]==lm->matrix[i]);
}
void test_acsii(Parameter* save_param, Parameter* load_param)
{
SG_SPRINT("testing ascii serialization\n");
SG_SPRINT("to save:\n");
print(save_param);
SG_SPRINT("loaded before:\n");
print(load_param);
CSerializableAsciiFile* file;
file=new CSerializableAsciiFile(filename, 'w');
save_param->save(file);
file->close();
SG_UNREF(file);
file=new CSerializableAsciiFile(filename, 'r');
load_param->load(file);
file->close();
SG_UNREF(file);
SG_SPRINT("loaded after:\n");
print(load_param);
check_content_equal(save_param, load_param);
}
void test_hdf5(Parameter* save_param, Parameter* load_param)
{
/* TODO, HDF5 file leaks memory */
SG_SPRINT("testing hdf5 serialization\n");
SG_SPRINT("to save:\n");
print(save_param);
SG_SPRINT("loaded before:\n");
print(load_param);
CSerializableHdf5File* file;
file=new CSerializableHdf5File(filename, 'w');
save_param->save(file);
file->close();
SG_UNREF(file);
file=new CSerializableHdf5File(filename, 'r');
load_param->load(file);
file->close();
SG_UNREF(file);
SG_SPRINT("loaded after:\n");
print(load_param);
check_content_equal(save_param, load_param);
}
void test_json(Parameter* save_param, Parameter* load_param)
{
/* TODO, json file leaks memory, also save methods */
SG_SPRINT("testing json serialization\n");
SG_SPRINT("to save:\n");
print(save_param);
SG_SPRINT("loaded before:\n");
print(load_param);
CSerializableJsonFile* file;
file=new CSerializableJsonFile(filename, 'w');
save_param->save(file);
file->close();
SG_UNREF(file);
file=new CSerializableJsonFile(filename, 'r');
load_param->load(file);
file->close();
SG_UNREF(file);
SG_SPRINT("loaded after:\n");
print(load_param);
check_content_equal(save_param, load_param);
}
void test_xml(Parameter* save_param, Parameter* load_param)
{
/* TODO, xml file leaks memory and produces a read error */
SG_SPRINT("testing xml serialization\n");
SG_SPRINT("to save:\n");
print(save_param);
SG_SPRINT("loaded before:\n");
print(load_param);
CSerializableXmlFile* file;
file=new CSerializableXmlFile(filename, 'w');
save_param->save(file);
file->close();
SG_UNREF(file);
file=new CSerializableXmlFile(filename, 'r');
load_param->load(file);
file->close();
SG_UNREF(file);
SG_SPRINT("loaded after:\n");
print(load_param);
check_content_equal(save_param, load_param);
}
void reset_values(Parameter* save_param, Parameter* load_param)
{
TParameter* p;
p=save_param->get_parameter(0);
SGVector<float64_t>* sv=(SGVector<float64_t>*)p->m_parameter;
p=save_param->get_parameter(1);
SGMatrix<float64_t>* sm=(SGMatrix<float64_t>*)p->m_parameter;
p=load_param->get_parameter(0);
SGVector<float64_t>* lv=(SGVector<float64_t>*)p->m_parameter;
p=load_param->get_parameter(1);
SGMatrix<float64_t>* lm=(SGMatrix<float64_t>*)p->m_parameter;
sv->destroy_vector();
lv->destroy_vector();
sm->destroy_matrix();
lm->destroy_matrix();
*sv=SGVector<float64_t>(9);
*lv=SGVector<float64_t>(3);
*sm=SGMatrix<float64_t>(3, 3);
*lm=SGMatrix<float64_t>(4, 4);
CMath::range_fill_vector(sv->vector, sv->vlen);
CMath::range_fill_vector(sm->matrix, sm->num_rows*sm->num_cols);
CMath::fill_vector(lv->vector, lv->vlen, 0.0);
CMath::fill_vector(lm->matrix, lm->num_rows*lm->num_cols, 0.0);
}
int main(int argc, char **argv)
{
init_shogun(&print_message, &print_message, &print_message);
/* for serialization */
SGVector<float64_t> sv;
SGMatrix<float64_t> sm;
Parameter* sp=new Parameter();
sp->add(&sv, "vector", "description");
sp->add(&sm, "matrix", "description");
/* for deserialization */
SGVector<float64_t> lv;
SGMatrix<float64_t> lm;
Parameter* lp=new Parameter();
lp->add(&lv, "vector", "description");
lp->add(&lm, "matrix", "description");
/* still leaks memory TODO */
reset_values(sp, lp);
test_json(sp, lp);
reset_values(sp, lp);
test_acsii(sp, lp);
/* still leaks memory TODO */
reset_values(sp, lp);
test_hdf5(sp, lp);
/* still leaks memory TODO */
reset_values(sp, lp);
test_xml(sp, lp);
/* clean up */
sv.destroy_vector();
sm.destroy_matrix();
lv.destroy_vector();
lm.destroy_matrix();
delete sp;
delete lp;
exit_shogun();
return 0;
}
/*
* This program is free software; you can redistribute it and/or modify
* it under the terms of the GNU General Public License as published by
* the Free Software Foundation; either version 3 of the License, or
* (at your option) any later version.
*
* Written (W) 2011 Heiko Strathmann
* Copyright (C) 2011 Berlin Institute of Technology and Max-Planck-Society
*/
#include <shogun/base/init.h>
#include <shogun/evaluation/StratifiedCrossValidationSplitting.h>
#include <shogun/features/Labels.h>
using namespace shogun;
void print_message(FILE* target, const char* str)
{
fprintf(target, "%s", str);
}
int main(int argc, char **argv)
{
init_shogun(&print_message, &print_message, &print_message);
index_t num_labels, num_classes, num_subsets;
index_t runs=50;
while (runs-->0)
{
num_labels=CMath::random(5, 100);
num_classes=CMath::random(2, 10);
num_subsets=CMath::random(1, 10);
/* this will throw an error */
if (num_labels<num_subsets)
continue;
SG_SPRINT("num_labels=%d\nnum_classes=%d\nnum_subsets=%d\n\n",
num_labels, num_classes, num_subsets);
/* build labels */
CLabels* labels=new CLabels(num_labels);
for (index_t i=0; i<num_labels; ++i)
{
labels->set_label(i, CMath::random()%num_classes);
SG_SPRINT("label(%d)=%.18g\n", i, labels->get_label(i));
}
SG_SPRINT("\n");
/* print classes */
SGVector<float64_t> classes=labels->get_classes();
CMath::display_vector(classes.vector, classes.vlen, "classes");
classes.destroy_vector();
/* build splitting strategy */
CStratifiedCrossValidationSplitting* splitting=
new CStratifiedCrossValidationSplitting(labels, num_subsets);
for (index_t i=0; i<num_subsets; ++i)
{
SGVector<index_t> subset=splitting->generate_subset_indices(i);
SGVector<index_t> inverse=splitting->generate_subset_inverse(i);
SG_SPRINT("subset %d\n", i);
for (index_t j=0; j<subset.vlen; ++j)
SG_SPRINT("%d(%d),", subset.vector[j],
(int32_t)labels->get_label(j));
SG_SPRINT("\n");
SG_SPRINT("inverse %d\n", i);
for (index_t j=0; j<inverse.vlen; ++j)
SG_SPRINT("%d(%d),", inverse.vector[j],
(int32_t)labels->get_label(j));
SG_SPRINT("\n\n");
subset.destroy_vector();
inverse.destroy_vector();
}
/* check whether number of labels in every subset is nearly equal */
for (index_t i=0; i<num_classes; ++i)
{
SG_SPRINT("checking class %d\n", i);
/* count number of elements for this class */
SGVector<index_t> temp=splitting->generate_subset_indices(0);
int32_t count=0;
for (index_t j=0; j<temp.vlen; ++j)
{
if ((int32_t)labels->get_label(temp.vector[j])==i)
++count;
}
temp.destroy_vector();
/* check all subsets for same ratio */
for (index_t j=0; j<num_subsets; ++j)
{
SGVector<index_t> subset=splitting->generate_subset_indices(j);
int32_t temp_count=0;
for (index_t k=0; k<subset.vlen; ++k)
{
if ((int32_t)labels->get_label(subset.vector[k])==i)
++temp_count;
}
subset.destroy_vector();
/* at most one difference */
SG_SPRINT("number in subset %d: %d\n", j, temp_count);
ASSERT(CMath::abs(temp_count-count)<=1);
}
}
/* clean up */
SG_UNREF(splitting);
}
exit_shogun();
return 0;
}
/*
* This program is free software; you can redistribute it and/or modify
* it under the terms of the GNU General Public License as published by
* the Free Software Foundation; either version 3 of the License, or
* (at your option) any later version.
*
* Written (W) 2011 Shashwat Lal Das
* Copyright (C) 2011 Berlin Institute of Technology and Max-Planck-Society
*
* This file demonstrates how a regular CSimpleFeatures object can
* be used as input for the StreamingFeatures framework, effectively
* making it suitable for using online learning algorithms.
*/
#include <shogun/features/StreamingSimpleFeatures.h>
#include <shogun/io/StreamingFileFromSimpleFeatures.h>
#include <shogun/mathematics/Math.h>
#include <shogun/lib/common.h>
#include <shogun/io/SGIO.h>
#include <shogun/base/init.h>
#include <stdlib.h>
#include <stdio.h>
using namespace shogun;
#define NUM 100
#define DIMS 2
#define DIST 0.5
float32_t* feat;
float64_t* lab;
void gen_rand_data()
{
feat=SG_MALLOC(float32_t, NUM*DIMS);
lab=SG_MALLOC(float64_t, NUM);
for (int32_t i=0; i<NUM; i++)
{
if (i<NUM/2)
{
for (int32_t j=0; j<DIMS; j++)
feat[i*DIMS+j]=CMath::random(0.0,1.0)+DIST;
lab[i]=0;
}
else
{
for (int32_t j=0; j<DIMS; j++)
feat[i*DIMS+j]=CMath::random(0.0,1.0)-DIST;
lab[i]=1;
}
}
CMath::display_matrix(feat,DIMS, NUM);
}
int main()
{
init_shogun_with_defaults();
// Generate random data, features and labels
gen_rand_data();
// Create features
CSimpleFeatures<float32_t>* features = new CSimpleFeatures<float32_t>();
SG_REF(features);
features->set_feature_matrix(feat, DIMS, NUM);
// Create a StreamingSimpleFeatures object which uses the above as input; labels (float64_t*) are optional
CStreamingSimpleFeatures<float32_t>* streaming_simple = new CStreamingSimpleFeatures<float32_t>(features, lab);
SG_REF(streaming_simple);
// Start parsing of the examples; in this case, it is trivial - returns each vector from the SimpleFeatures object
streaming_simple->start_parser();
int32_t counter=0;
SG_SPRINT("Processing examples...\n\n");
// Run a while loop over all the examples. Note that since
// features are "streaming", there is no predefined
// number_of_vectors known to the StreamingFeatures object.
// Thus, this loop must be used to iterate over all the
// features
while (streaming_simple->get_next_example())
{
counter++;
// Get the current vector; no other vector is accessible
SGVector<float32_t> vec = streaming_simple->get_vector();
float64_t label = streaming_simple->get_label();
SG_SPRINT("Vector %d: [\t", counter);
for (int32_t i=0; i<vec.vlen; i++)
{
SG_SPRINT("%f\t", vec.vector[i]);
}
SG_SPRINT("Label=%f\t", label);
// Calculate dot product of the current vector (from
// the StreamingFeatures object) with itself (the
// vector passed as argument)
float32_t dot_prod = streaming_simple->dense_dot(vec.vector, vec.vlen);
SG_SPRINT("]\nDot product of the vector with itself: %f", dot_prod);
SG_SPRINT("\n\n");
// Free the example, since we are done with processing it.
streaming_simple->release_example();
}
// Now that all examples are used, end the parser.
streaming_simple->end_parser();
SG_FREE(lab);
SG_UNREF(streaming_simple);
SG_UNREF(features);
exit_shogun();
return 0;
}
/*
* This program is free software; you can redistribute it and/or modify
* it under the terms of the GNU General Public License as published by
* the Free Software Foundation; either version 3 of the License, or
* (at your option) any later version.
*
* Written (W) 2011 Shashwat Lal Das
* Copyright (C) 2011 Berlin Institute of Technology and Max-Planck-Society
*
* This example demonstrates use of the online variant of SGD which
* relies on the streaming features framework.
*/
#include <shogun/lib/common.h>
#include <shogun/io/StreamingAsciiFile.h>
#include <shogun/features/StreamingSparseFeatures.h>
#include <shogun/classifier/svm/OnlineSVMSGD.h>
using namespace shogun;
int main()
{
init_shogun_with_defaults();
// Create a StreamingAsciiFile from the training data
char* train_file_name = "../data/train_sparsereal.light";
CStreamingAsciiFile* train_file = new CStreamingAsciiFile(train_file_name);
SG_REF(train_file);
// Create a StreamingSparseFeatures from the StreamingAsciiFile.
// The bool value is true if examples are labelled.
// 1024 is a good standard value for the number of examples for the parser to hold at a time.
CStreamingSparseFeatures<float64_t>* train_features = new CStreamingSparseFeatures<float64_t>(train_file, true, 1024);
SG_REF(train_features);
// Create an OnlineSVMSGD object from the features. The first parameter is 'C'.
COnlineSVMSGD* sgd = new COnlineSVMSGD(1, train_features);
sgd->set_bias_enabled(false); // Enable/disable bias
sgd->set_lambda(0.1); // Choose lambda
sgd->train(); // Train
train_file->close();
// Now we want to test on other data
char* test_file_name = "../data/fm_test_sparsereal.dat";
CStreamingAsciiFile* test_file = new CStreamingAsciiFile(test_file_name);
SG_REF(test_file);
// Similar, but 'false' since the file contains unlabelled examples
CStreamingSparseFeatures<float64_t>* test_features = new CStreamingSparseFeatures<float64_t>(test_file, false, 1024);
SG_REF(test_features);
// Apply on all examples and return a CLabels*
CLabels* test_labels = sgd->apply(test_features);
for (int32_t i=0; i<test_labels->get_num_labels(); i++)
SG_SPRINT("For example %d, predicted label is %f.\n", i, test_labels->get_label(i));
SG_UNREF(test_features);
SG_UNREF(test_file);
SG_UNREF(train_features);
SG_UNREF(train_file);
SG_UNREF(sgd);
exit_shogun();
return 0;
}
// This example simply demonstrates the use/working of StreamingStringFeatures
#include <shogun/io/StreamingAsciiFile.h>
#include <shogun/features/StreamingStringFeatures.h>
using namespace shogun;
void display_vector(const SGString<char> &vec)
{
printf("\nNew Vector\n------------------\n");
printf("Length=%d.\n", vec.slen);
for (int32_t i=0; i<vec.slen; i++)
{
printf("%c", vec.string[i]);
}
printf("\n");
}
int main(int argc, char **argv)
{
init_shogun_with_defaults();
// Create a StreamingAsciiFile from our input file
CStreamingAsciiFile* file = new CStreamingAsciiFile("../data/fm_train_dna.dat");
// This file contains unlabelled data, so the second arg is `false'.
CStreamingStringFeatures<char>* feat = new CStreamingStringFeatures<char>(file, false, 1024);
// Alphabet to use is DNA
feat->use_alphabet(DNA);
// Loop over all examples and simply display each example
feat->start_parser();
while (feat->get_next_example())
{
SGString<char> vec = feat->get_vector();
display_vector(vec);
feat->release_example();
}
feat->end_parser();
// Get the alphabet and display the histogram
CAlphabet* alpha = feat->get_alphabet();
printf("\nThe histogram is:\n");
alpha->print_histogram();
SG_UNREF(alpha);
SG_UNREF(feat);
SG_UNREF(file);
exit_shogun();
return 0;
}
/*
* This program is free software; you can redistribute it and/or modify
* it under the terms of the GNU General Public License as published by
* the Free Software Foundation; either version 3 of the License, or
* (at your option) any later version.
*
* Written (W) 2011 Shashwat Lal Das
* Copyright (C) 2011 Berlin Institute of Technology and Max-Planck-Society
*
* This example demonstrates use of the Vowpal Wabbit learning algorithm.
*/
#include <shogun/lib/common.h>
#include <shogun/io/StreamingVwFile.h>
#include <shogun/features/StreamingVwFeatures.h>
#include <shogun/classifier/vw/VowpalWabbit.h>
using namespace shogun;
int main()
{
init_shogun_with_defaults();
char* train_file_name = "../data/train_sparsereal.light";
CStreamingVwFile* train_file = new CStreamingVwFile(train_file_name);
train_file->set_parser_type(T_SVMLIGHT); // Treat the file as SVMLight format
SG_REF(train_file);
CStreamingVwFeatures* train_features = new CStreamingVwFeatures(train_file, true, 1024);
SG_REF(train_features);
CVowpalWabbit* vw = new CVowpalWabbit(train_features);
vw->set_regressor_out("./vw_regressor_text.dat"); // Save regressor to this file
vw->set_adaptive(false); // Use adaptive learning
vw->train_machine();
SG_SPRINT("Weights have been output in text form to vw_regressor_text.dat.\n");
train_file->close();
SG_UNREF(train_features);
SG_UNREF(train_file);
SG_UNREF(vw);
exit_shogun();
return 0;
}
/*
* This program is free software; you can redistribute it and/or modify
* it under the terms of the GNU General Public License as published by
* the Free Software Foundation; either version 3 of the License, or
* (at your option) any later version.
*
* Written (W) 2011 Shashwat Lal Das
* Copyright (C) 2011 Berlin Institute of Technology and Max-Planck-Society
*
* This example demonstrates use of online SGD with CStreamingVwFeatures
* as the features object.
*/
#include <shogun/lib/common.h>
#include <shogun/io/StreamingVwFile.h>
#include <shogun/features/StreamingVwFeatures.h>
#include <shogun/classifier/svm/OnlineSVMSGD.h>
using namespace shogun;
int main()
{
init_shogun_with_defaults();
char* train_file_name = "../data/train_sparsereal.light";
CStreamingVwFile* train_file = new CStreamingVwFile(train_file_name);
train_file->set_parser_type(T_SVMLIGHT); // Treat the file as SVMLight format
SG_REF(train_file);
CStreamingVwFeatures* train_features = new CStreamingVwFeatures(train_file, true, 1024);
SG_REF(train_features);
COnlineSVMSGD* sgd = new COnlineSVMSGD(1, train_features);
sgd->set_bias_enabled(false);
sgd->set_lambda(0.1);
sgd->train();
train_file->close();
// Now we want to test on other data
char* test_file_name = "../data/fm_test_sparsereal.dat";
CStreamingVwFile* test_file = new CStreamingVwFile(test_file_name);
test_file->set_parser_type(T_SVMLIGHT);
SG_REF(test_file);
// Similar, but 'false' since the file contains unlabelled examples
CStreamingVwFeatures* test_features = new CStreamingVwFeatures(test_file, false, 1024);
SG_REF(test_features);
// Apply on all examples and return a CLabels*
CLabels* test_labels = sgd->apply(test_features);
for (int32_t i=0; i<test_labels->get_num_labels(); i++)
SG_SPRINT("For example %d, predicted label is %f.\n", i, test_labels->get_label(i));
SG_UNREF(test_features);
SG_UNREF(test_file);
SG_UNREF(train_features);
SG_UNREF(train_file);
SG_UNREF(sgd);
exit_shogun();
return 0;
}