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Port classifier meta examples #4361

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merged 1 commit into from Jul 8, 2018
Merged

Port classifier meta examples #4361

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2 changes: 2 additions & 0 deletions cmake/FindMetaExamples.cmake
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Expand Up @@ -35,6 +35,8 @@ function(get_excluded_meta_examples)
statistical_testing/quadratic_time_maximum_mean_discrepancy.sg
gaussian_process/classifier.sg
gaussian_process/regression.sg
binary/svmlin.sg
binary/svmsgd.sg
)
ENDIF()

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2 changes: 1 addition & 1 deletion data
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Submodule data updated 2 files
+14 −0 testsuite/meta/binary/svmlin.dat
+14 −0 testsuite/meta/binary/svmsgd.dat
5 changes: 2 additions & 3 deletions examples/meta/src/binary/mpdsvm.sg
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Expand Up @@ -5,16 +5,15 @@ File f_labels_test = csv_file("../../data/classifier_binary_2d_linear_labels_tes

Features feats_train = features(f_feats_train)
Features feats_test = features(f_feats_test)
BinaryLabels labels_train(f_labels_train)
Labels labels_train = labels(f_labels_train)
Labels labels_test = labels(f_labels_test)

Kernel gaussian = kernel("GaussianKernel", log_width=0.01)
gaussian.init(feats_train, feats_train)

real C=1.0
Machine svm = machine("MPDSVM", C1=C, C2=C, kernel=gaussian, epsilon=0.00001)
Machine svm = machine("MPDSVM", C1=C, C2=C, kernel=gaussian, epsilon=0.00001, labels=labels_train)

svm.set_labels(labels_train)
svm.train()

Labels labels_predict = svm.apply(feats_test)
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29 changes: 0 additions & 29 deletions examples/undocumented/python/classifier_svmlin.py
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28 changes: 0 additions & 28 deletions examples/undocumented/python/classifier_svmsgd.py
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2 changes: 1 addition & 1 deletion src/gpl
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Submodule gpl updated 3 files
+14 −0 shogun/classifier/svm/SVMLin.cpp
+3 −0 shogun/classifier/svm/SVMLin.h
+15 −13 shogun/classifier/svm/SVMSGD.cpp
17 changes: 9 additions & 8 deletions src/shogun/classifier/svm/MPDSVM.cpp
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Expand Up @@ -30,13 +30,13 @@ CMPDSVM::~CMPDSVM()

bool CMPDSVM::train_machine(CFeatures* data)
{
ASSERT(m_labels)
ASSERT(m_labels->get_label_type() == LT_BINARY)
auto labels = binary_labels(m_labels);

ASSERT(kernel)

if (data)
{
if (m_labels->get_num_labels() != data->get_num_vectors())
if (labels->get_num_labels() != data->get_num_vectors())
SG_ERROR("Number of training vectors does not match number of labels\n")
kernel->init(data, data);
}
Expand All @@ -48,7 +48,7 @@ bool CMPDSVM::train_machine(CFeatures* data)
const int64_t maxiter = 1L<<30;
//const bool nustop=false;
//const int32_t k=2;
const int32_t n=m_labels->get_num_labels();
const int32_t n = labels->get_num_labels();
ASSERT(n>0)
//const float64_t d = 1.0/n/nu; //NUSVC
const float64_t d = get_C1(); //CSVC
Expand Down Expand Up @@ -84,9 +84,9 @@ bool CMPDSVM::train_machine(CFeatures* data)
for (int32_t i=0; i<n; i++)
{
alphas[i]=0;
F[i]=((CBinaryLabels*) m_labels)->get_label(i);
F[i] = labels->get_label(i);
//F[i+n]=-1;
hessres[i]=((CBinaryLabels*) m_labels)->get_label(i);
hessres[i] = labels->get_label(i);
//hessres[i+n]=-1;
//dalphas[i]=F[i+n]*etas[1]; //NUSVC
dalphas[i]=-1; //CSVC
Expand Down Expand Up @@ -149,7 +149,8 @@ bool CMPDSVM::train_machine(CFeatures* data)
{
obj-=alphas[i];
for (int32_t j=0; j<n; j++)
obj+=0.5*((CBinaryLabels*) m_labels)->get_label(i)*((CBinaryLabels*) m_labels)->get_label(j)*alphas[i]*alphas[j]*kernel->kernel(i,j);
obj += 0.5 * labels->get_label(i) * labels->get_label(j) *
alphas[i] * alphas[j] * kernel->kernel(i, j);
}

SG_DEBUG("obj:%f pviol:%f dviol:%f maxpidx:%d iter:%d\n", obj, maxpviol, maxdviol, maxpidx, niter)
Expand Down Expand Up @@ -250,7 +251,7 @@ bool CMPDSVM::train_machine(CFeatures* data)
if (alphas[i]>0)
{
//set_alpha(j, alphas[i]*labels->get_label(i)/etas[1]);
set_alpha(j, alphas[i]*((CBinaryLabels*) m_labels)->get_label(i));
set_alpha(j, alphas[i] * labels->get_label(i));
set_support_vector(j, i);
j++;
}
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