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Improved example a little. 

--HG--
extra : convert_revision : svn%3Afdd8eb12-d10e-0410-9acb-85c331704f74/trunk%403631
This commit is contained in:
Davis King 2010-05-16 17:54:19 +00:00
parent 5c05797532
commit 812057913e
1 changed files with 4 additions and 7 deletions
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11
examples/empirical_kernel_map_ex.cpp
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@ -214,10 +214,8 @@ void test_empirical_kernel_map (
// Now lets do something more interesting. The following loop finds the centroids
// of the two classes of data.
sample_type class1_center(ekm.out_vector_size());
sample_type class2_center(ekm.out_vector_size());
class1_center = 0;
class2_center = 0;
sample_type class1_center;
sample_type class2_center;
for (unsigned long i = 0; i < projected_samples.size(); ++i)
{
if (labels[i] == 1)
@ -234,9 +232,8 @@ void test_empirical_kernel_map (
// Now classify points by which center they are nearest. Recall that the data
// is made up of two concentric circles. Normally you can't separate two concentric
// circles by checking which points are nearest to each center since they have the same
// centers. All the points would just associate to the smallest circle. However,
// the kernel trick makes the data separable and the loop below will perfectly
// classify each data point.
// centers. However, the kernel trick makes the data separable and the loop below will
// perfectly classify each data point.
for (unsigned long i = 0; i < projected_samples.size(); ++i)
{
double distance_to_class1 = length(projected_samples[i] - class1_center);