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Merged SLEP updates
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@lisitsyn
lisitsyn committed Jun 16, 2012
2 parents 740c2c5 + f621fdf commit afe730c
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Showing 14 changed files with 263 additions and 338 deletions.
1 change: 1 addition & 0 deletions examples/undocumented/libshogun/Makefile
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Expand Up @@ -81,6 +81,7 @@ TARGETS = basic_minimal \
kernel_machine_train_locked \
statistics \
statistics_quadratic_time_mmd \
transfer_multitasklsregression

all: $(TARGETS)

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50 changes: 50 additions & 0 deletions examples/undocumented/libshogun/transfer_multitasklsregression.cpp
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@@ -0,0 +1,50 @@
#include <shogun/labels/RegressionLabels.h>
#include <shogun/features/DenseFeatures.h>
#include <shogun/transfer/multitask/Task.h>
#include <shogun/transfer/multitask/TaskGroup.h>
#include <shogun/transfer/multitask/MultitaskLSRegression.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
SGMatrix<float64_t> matrix(2,4);
for (int32_t i=0; i<2*4; i++)
matrix.matrix[i]=i;

CDenseFeatures<float64_t>* features= new CDenseFeatures<float64_t>(matrix);

// create three labels
CRegressionLabels* labels=new CRegressionLabels(4);
labels->set_label(0, -1);
labels->set_label(1, +1);
labels->set_label(2, -1);
labels->set_label(3, +1);

CTask* first_task = new CTask(0,2);
CTask* second_task = new CTask(2,4);

CTaskGroup* task_group = new CTaskGroup();
task_group->add_task(first_task);
task_group->add_task(second_task);

CMultitaskLSRegression* regressor = new CMultitaskLSRegression(0.5,features,labels,task_group);
regressor->train();

regressor->set_current_task(0);
regressor->get_w().display_vector();
SG_UNREF(regressor);
exit_shogun();
return 0;
}
53 changes: 31 additions & 22 deletions src/shogun/lib/slep/slep_mt_lsr.cpp
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Expand Up @@ -28,7 +28,7 @@ SGMatrix<double> slep_mt_lsr(
double lambda, lambda_max, beta;
double funcp = 0.0, func = 0.0;

int n_tasks;
int n_tasks = options.n_nodes;

int iter = 1;
bool done = false;
Expand All @@ -37,47 +37,55 @@ SGMatrix<double> slep_mt_lsr(
double* ATy = SG_CALLOC(double, n_feats*n_tasks);
for (t=0; t<n_tasks; t++)
{
int task_ind_start = options.ind[t]+1;
int task_ind_start = options.ind[t];
int task_ind_end = options.ind[t+1];
for (i=task_ind_start; i<task_ind_end; i++)
features->add_to_dense_vec(y[i],i,ATy+t*n_feats,n_feats);
}

if (options.regularization!=0)
{
/*
if (options.general)
lambda_max = findLambdaMax_mt(ATy, n_vecs, n_tasks, options.ind, options.n_nodes);
if (z<0 || z>1)
SG_SERROR("z is not in range [0,1]");

double q_bar = 0.0;
if (options.q==1)
q_bar = CMath::ALMOST_INFTY;
else if (options.q>1e-6)
q_bar = 1;
else
lambda_max = general_findLambdaMax_mt(ATy, n_vecs, n_tasks, options.G,
options.ind, options.n_nodes);
q_bar = options.q/(options.q-1);
lambda_max = 0.0;

for (t=0; t<n_tasks; t++)
{
lambda_max =
CMath::max(lambda_max,
SGVector<float64_t>::qnorm(ATy+t*n_feats, n_feats, q_bar));
}

lambda = z*lambda_max;
*/
}
else
lambda = z;

SGMatrix<double> w(n_feats,n_tasks);
w.zero();
if (options.initial_w)
{
for (j=0; j<n_tasks; j++)
for (i=0; i<n_feats; i++)
w(i,j) = options.initial_w[j*n_feats+i];
}
else
{
for (j=0; j<n_tasks*n_feats; j++)
w[j] = 0.0;
}

double* s = SG_CALLOC(double, n_feats*n_tasks);
double* g = SG_CALLOC(double, n_feats*n_tasks);
double* v = SG_CALLOC(double, n_feats*n_tasks);
double* g = SG_CALLOC(double, n_feats*n_tasks*2);
double* v = SG_CALLOC(double, n_feats*n_tasks*2);

double* Aw = SG_CALLOC(double, n_vecs);
for (t=0; t<n_tasks; t++)
{
int task_ind_start = options.ind[t]+1;
int task_ind_start = options.ind[t];
int task_ind_end = options.ind[t+1];
for (i=task_ind_start; i<task_ind_end; i++)
Aw[i] = features->dense_dot(i,w.matrix+t*n_feats,n_feats);
Expand Down Expand Up @@ -118,7 +126,7 @@ SGMatrix<double> slep_mt_lsr(

for (t=0; t<n_tasks; t++)
{
int task_ind_start = options.ind[t]+1;
int task_ind_start = options.ind[t];
int task_ind_end = options.ind[t+1];
for (i=task_ind_start; i<task_ind_end; i++)
features->add_to_dense_vec(As[i],i,ATAs+t*n_feats,n_feats);
Expand All @@ -131,7 +139,7 @@ SGMatrix<double> slep_mt_lsr(
for (i=0; i<n_feats*n_tasks; i++)
wp[i] = w[i];

for (i=0; i<n_vecs*n_tasks; i++)
for (i=0; i<n_vecs; i++)
Awp[i] = Aw[i];

while (true)
Expand All @@ -140,15 +148,15 @@ SGMatrix<double> slep_mt_lsr(
for (i=0; i<n_feats*n_tasks; i++)
v[i] = s[i] - g[i]*(1.0/L);

eppMatrix(w.matrix, v, n_vecs, n_tasks, lambda/L, options.q);
eppMatrix(w.matrix, v, n_feats, n_tasks, lambda/L, options.q);

// v = x - s
for (i=0; i<n_feats*n_tasks; i++)
v[i] = w[i] - s[i];

for (t=0; t<n_tasks; t++)
{
int task_ind_start = options.ind[t]+1;
int task_ind_start = options.ind[t];
int task_ind_end = options.ind[t+1];
for (i=task_ind_start; i<task_ind_end; i++)
Aw[i] = features->dense_dot(i,w.matrix+t*n_feats,n_feats);
Expand Down Expand Up @@ -185,10 +193,10 @@ SGMatrix<double> slep_mt_lsr(
resid[i] = Aw[i] - y[i];

double w_norm = 0.0;
for (i=0; i<n_vecs; i++)
for (i=0; i<n_feats; i++)
{
double w_row_norm = 0.0;
for (t=0; t<n_tasks; i++)
for (t=0; t<n_tasks; t++)
w_row_norm += CMath::pow(w(i,t),options.q);
w_norm += CMath::pow(w_row_norm,1.0/options.q);
}
Expand Down Expand Up @@ -261,6 +269,7 @@ SGMatrix<double> slep_mt_lsr(
iter++;
}

// SG_SPRINT("Iteration = %d\n", iter);
// TODO Nemirovsky, Nesterov methods

SG_FREE(ATy);
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28 changes: 20 additions & 8 deletions src/shogun/lib/slep/slep_options.h
View file
Expand Up @@ -13,6 +13,8 @@

#define IGNORE_IN_CLASSLIST

#include <stdlib.h>

namespace shogun
{

Expand All @@ -26,18 +28,28 @@ IGNORE_IN_CLASSLIST struct slep_options
int restart_num;
int n_nodes;
int regularization;
double* ind;
int* ind;
double* ind_t;
double* G;
double* initial_w;
double q;

static bool get_default_general() { return false; }
static int get_default_termination() { return 2; }
static double get_default_tolerance() { return 1e-3; }
static int get_default_max_iter() { return 1000; }
static int get_default_restart_num() { return 100; }
static int get_default_regularization() { return 0; }
static double get_default_q() { return 2.0; }
static slep_options default_options()
{
slep_options opts;
opts.general = false;
opts.termination = 2;
opts.tolerance = 1e-3;
opts.max_iter = 1000;
opts.restart_num = 100;
opts.regularization = 0;
opts.q = 2.0;
opts.initial_w = NULL;
opts.ind = NULL;
opts.ind_t = NULL;
opts.G = NULL;
return opts;
}
};
#endif
}
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