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Updated the rank features example

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--HG--
extra : convert_revision : svn%3Afdd8eb12-d10e-0410-9acb-85c331704f74/trunk%402542
This commit is contained in:
Davis King 2008-10-07 02:24:23 +00:00
parent 770368705b
commit c4ab461065
1 changed files with 17 additions and 16 deletions
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33
examples/rank_features_ex.cpp
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@ -41,9 +41,9 @@ int main()
dlib::rand::float_1a rnd;
for (int x = -20; x <= 20; ++x)
for (int x = -30; x <= 30; ++x)
{
for (int y = -20; y <= 20; ++y)
for (int y = -30; y <= 30; ++y)
{
sample_type samp;
@ -60,14 +60,14 @@ int main()
// This is a version of the y feature that is corrupted by random noise. It
// should be ranked as less useful than features 0, and 1, but more useful
// than the above feature.
samp(3) = y - rnd.get_random_double()*10;
samp(3) = y*0.2 + (rnd.get_random_double()-0.5)*10;
// add this sample into our vector of samples.
samples.push_back(samp);
// if this point is less than 10 from the origin then label it as a +1 class point.
// if this point is less than 15 from the origin then label it as a +1 class point.
// otherwise it is a -1 class point
if (sqrt((double)x*x + y*y) <= 10)
if (sqrt((double)x*x + y*y) <= 15)
labels.push_back(+1);
else
labels.push_back(-1);
@ -109,10 +109,10 @@ int main()
// The output is:
/*
0 0.452251
1 0.259739
3 0.28801
2 -0.0347664
1 0.514169
0 0.810535
3 1
2 0.966936
*/
// The first column is a list of the features in order of decreasing goodness. So the rank_features() function
@ -122,15 +122,16 @@ int main()
// intuitively expect.
// The second column of the matrix is a number that indicates how much that feature contributes to the
// separation of the two classes. So a bigger number is better and smaller is worse. What we see above is that
// the first 3 features all help separate the data and the last one actually hurts us in terms of this metric.
// The second column of the matrix is a number that indicates how much the features up to that point
// contribute to the separation of the two classes. So bigger numbers are better since they
// indicate a larger separation.
// So to break it down a little more.
// 0 0.452251 <-- class separation of feature 0 all by itself
// 1 0.259739 <-- Additional separation gained from feature 1 if classification is done with features 1 and 0
// 3 0.28801 <-- Additional separation gained from feature 3 if classification is done with features 3, 0, and 1
// 2 -0.0347664 <-- Additional separation gained from feature 2 if classification is done with features 2, 3, 0, and 1
// 1 0.514169 <-- class separation of feature 1 all by itself
// 0 0.810535 <-- class separation of feature 1 and 0
// 3 1 <-- class separation of feature 1, 0, and 3
// 2 0.966936 <-- class separation of feature 1, 0, 3, and 2
}