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dlib
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This change was just to avoid a warning from gcc. The code still 

computes exactly the same thing as before.

--HG--
extra : convert_revision : svn%3Afdd8eb12-d10e-0410-9acb-85c331704f74/trunk%403519
This commit is contained in:
Davis King 2010-03-01 04:39:42 +00:00
parent e5d719e20a
commit b77ee4307a
1 changed files with 44 additions and 39 deletions
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83
dlib/svm/svm_c_linear_trainer.h
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@ -16,6 +16,45 @@
namespace dlib
{
// ----------------------------------------------------------------------------------------
template <typename T>
typename enable_if<is_matrix<typename T::type>,unsigned long>::type num_dimensions_in_samples (
const T& samples
)
{
if (samples.size() > 0)
return samples(0).size();
else
return 0;
}
template <typename T>
typename disable_if<is_matrix<typename T::type>,unsigned long>::type num_dimensions_in_samples (
const T& samples
)
/*!
T must be a sparse vector with an integral key type
!*/
{
typedef typename T::type sample_type;
// You are getting this error because you are attempting to use sparse sample vectors with
// the svm_c_linear_trainer object but you aren't using an unsigned integer as your key type
// in the sparse vectors.
COMPILE_TIME_ASSERT(sparse_vector::has_unsigned_keys<sample_type>::value);
// these should be sparse samples so look over all them to find the max dimension.
unsigned long max_dim = 0;
for (long i = 0; i < samples.size(); ++i)
{
if (samples(i).size() > 0)
max_dim = std::max<unsigned long>(max_dim, (--samples(i).end())->first + 1);
}
return max_dim;
}
// ----------------------------------------------------------------------------------------
template <
@ -203,7 +242,7 @@ namespace dlib
const matrix_exp<EXP>& sample
) const
{
return dot(colm(w,0,w.size()-1), sample);
return dot(colm(w,0,sample.size()), sample);
}
template <typename T>
@ -219,43 +258,6 @@ namespace dlib
return temp;
}
template <typename T>
typename enable_if<is_matrix<typename T::type>,unsigned long>::type num_dimensions_in_samples (
const T& samples
) const
{
if (samples.size() > 0)
return samples(0).size();
else
return 0;
}
template <typename T>
typename disable_if<is_matrix<typename T::type>,unsigned long>::type num_dimensions_in_samples (
const T& samples
) const
/*!
T must be a sparse vector with an integral key type
!*/
{
typedef typename T::type sample_type;
// You are getting this error because you are attempting to use sparse sample vectors with
// the svm_c_linear_trainer object but you aren't using an unsigned integer as your key type
// in the sparse vectors.
COMPILE_TIME_ASSERT(sparse_vector::has_unsigned_keys<sample_type>::value);
// these should be sparse samples so look over all them to find the max dimension.
unsigned long max_dim = 0;
for (long i = 0; i < samples.size(); ++i)
{
if (samples(i).size() > 0)
max_dim = std::max<unsigned long>(max_dim, (--samples(i).end())->first + 1);
}
return max_dim;
}
// -----------------------------------------------------
// -----------------------------------------------------
@ -619,7 +621,10 @@ namespace dlib
df.basis_vectors.set_size(1);
// Copy the plane normal into the output basis vector. The output vector might be a
// sparse vector container so we need to use this special kind of copy to handle that case.
sparse_vector::assign_dense_to_sparse(df.basis_vectors(0), matrix_cast<scalar_type>(colm(w, 0, w.size()-1)));
// As an aside, the reason for using num_dimensions_in_samples() and not just w.size()-1 is because
// doing it this way avoids an inane warning from gcc that can occur in some cases.
const long out_size = num_dimensions_in_samples(x);
sparse_vector::assign_dense_to_sparse(df.basis_vectors(0), matrix_cast<scalar_type>(colm(w, 0, out_size)));
df.alpha.set_size(1);
df.alpha(0) = 1;