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Use get and set in regression classes #3734

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merged 1 commit into from Mar 24, 2017
Merged

Use get and set in regression classes #3734

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6 changes: 3 additions & 3 deletions src/shogun/regression/KernelRidgeRegression.cpp
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Expand Up @@ -30,15 +30,15 @@ CKernelRidgeRegression::CKernelRidgeRegression(float64_t tau, CKernel* k, CLabel
{
init();

m_tau=tau;
set_tau(tau);
set_labels(lab);
set_kernel(k);
}

void CKernelRidgeRegression::init()
{
m_tau=1e-6;
m_epsilon=0.0001;
set_tau(1e-6);
set_epsilon(0.0001);
SG_ADD(&m_tau, "tau", "Regularization parameter", MS_AVAILABLE);
}

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19 changes: 10 additions & 9 deletions src/shogun/regression/LeastAngleRegression.cpp
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Expand Up @@ -28,7 +28,7 @@ CLeastAngleRegression::CLeastAngleRegression(bool lasso) :
CLinearMachine(), m_lasso(lasso),
m_max_nonz(0), m_max_l1_norm(0)
{
m_epsilon = CMath::MACHINE_EPSILON;
set_epsilon(CMath::MACHINE_EPSILON);
SG_ADD(&m_epsilon, "epsilon", "Epsilon for early stopping", MS_AVAILABLE);
SG_ADD(&m_max_nonz, "max_nonz", "Max number of non-zero variables", MS_AVAILABLE);
SG_ADD(&m_max_l1_norm, "max_l1_norm", "Max l1-norm of estimator", MS_AVAILABLE);
Expand Down Expand Up @@ -185,7 +185,7 @@ bool CLeastAngleRegression::train_machine_templated(CDenseFeatures<ST> * data)
// main loop
//========================================
int32_t nloop=0;
while (m_num_active < max_active_allowed && max_corr/n_vec > m_epsilon && !stop_cond)
while (m_num_active < max_active_allowed && max_corr/n_vec > get_epsilon() && !stop_cond)
{
// corr = X' * (y-mu) = - X'*mu + Xy
typename SGVector<ST>::EigenVectorXtMap map_corr(&corr[0], n_fea);
Expand Down Expand Up @@ -298,17 +298,18 @@ bool CLeastAngleRegression::train_machine_templated(CDenseFeatures<ST> * data)
// update estimator
for (index_t i=0; i < m_num_active; ++i)
beta[m_active_set[i]] += gamma * wA(i);

// early stopping on max l1-norm
if (m_max_l1_norm > 0)
if (get_max_l1_norm() > 0)
{
ST l1 = SGVector<ST>::onenorm(&beta[0], n_fea);
if (l1 > m_max_l1_norm)
if (l1 > get_max_l1_norm())
{
// stopping with interpolated beta
stop_cond = true;
lasso_cond = false;
ST l1_prev = (ST) SGVector<ST>::onenorm(&m_beta_path_t[nloop][0], n_fea);
ST s = (m_max_l1_norm-l1_prev)/(l1-l1_prev);
ST s = (get_max_l1_norm()-l1_prev)/(l1-l1_prev);

typename SGVector<ST>::EigenVectorXtMap map_beta(&beta[0], n_fea);
typename SGVector<ST>::EigenVectorXtMap map_beta_prev(&m_beta_path_t[nloop][0], n_fea);
Expand All @@ -333,13 +334,13 @@ bool CLeastAngleRegression::train_machine_templated(CDenseFeatures<ST> * data)

nloop++;
m_beta_path_t.push_back(beta);
if (size_t(m_num_active) >= m_beta_idx.size())
if (size_t(m_num_active) >= get_path_size())
m_beta_idx.push_back(nloop);
else
m_beta_idx[m_num_active] = nloop;

// early stopping with max number of non-zero variables
if (m_max_nonz > 0 && m_num_active >= m_max_nonz)
if (get_max_non_zero() > 0 && m_num_active >= get_max_non_zero())
stop_cond = true;
SG_DEBUG("Added : %d , Dropped %d, Active set size %d max_corr %.17f \n", i_max_corr, i_kick, m_num_active, max_corr);
}
Expand All @@ -356,8 +357,8 @@ bool CLeastAngleRegression::train_machine_templated(CDenseFeatures<ST> * data)

// assign default estimator
set_w(SGVector<float64_t>(n_fea));
switch_w(m_beta_idx.size()-1);

switch_w(get_path_size()-1);
return true;
}

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4 changes: 2 additions & 2 deletions src/shogun/regression/LinearRidgeRegression.cpp
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Expand Up @@ -29,14 +29,14 @@ CLinearRidgeRegression::CLinearRidgeRegression(float64_t tau, CDenseFeatures<flo
{
init();

m_tau=tau;
set_tau(tau);
set_labels(lab);
set_features(data);
}

void CLinearRidgeRegression::init()
{
m_tau=1e-6;
set_tau(1e-6);

SG_ADD(&m_tau, "tau", "Regularization parameter", MS_AVAILABLE);
}
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16 changes: 8 additions & 8 deletions src/shogun/regression/svr/LibLinearRegression.cpp
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Expand Up @@ -77,13 +77,13 @@ bool CLibLinearRegression::train_machine(CFeatures* data)
}

SGVector<float64_t> w;
if (m_use_bias)
if (get_use_bias())
w=SGVector<float64_t>(SG_MALLOC(float64_t, num_feat+1), num_feat);
else
w=SGVector<float64_t>(num_feat);

liblinear_problem prob;
if (m_use_bias)
if (get_use_bias())
{
prob.n=w.vlen+1;
memset(w.vector, 0, sizeof(float64_t)*(w.vlen+1));
Expand All @@ -103,10 +103,10 @@ bool CLibLinearRegression::train_machine(CFeatures* data)
{
double* Cs = SG_MALLOC(double, prob.l);
for(int i = 0; i < prob.l; i++)
Cs[i] = m_C;
Cs[i] = get_C();

function *fun_obj=new l2r_l2_svr_fun(&prob, Cs, m_tube_epsilon);
CTron tron_obj(fun_obj, m_epsilon);
function *fun_obj=new l2r_l2_svr_fun(&prob, Cs, get_tube_epsilon());
CTron tron_obj(fun_obj, get_epsilon());
tron_obj.tron(w.vector, m_max_train_time);
delete fun_obj;
SG_FREE(Cs);
Expand Down Expand Up @@ -160,10 +160,10 @@ bool CLibLinearRegression::train_machine(CFeatures* data)
void CLibLinearRegression::solve_l2r_l1l2_svr(SGVector<float64_t>& w, const liblinear_problem *prob)
{
int l = prob->l;
double C = m_C;
double p = m_tube_epsilon;
double C = get_C();
double p = get_tube_epsilon();
int w_size = prob->n;
double eps = m_epsilon;
double eps = get_epsilon();
int i, s, iter = 0;
int max_iter = 1000;
int active_size = l;
Expand Down