From c8bc446438ead22f54ec86c8d221931d3d9da4db Mon Sep 17 00:00:00 2001 From: Davis King Date: Sat, 5 Jul 2008 16:26:22 +0000 Subject: [PATCH] Added a rank_features() example. --HG-- extra : convert_revision : svn%3Afdd8eb12-d10e-0410-9acb-85c331704f74/trunk%402372 --- examples/CMakeLists.txt | 1 + examples/rank_features_ex.cpp | 140 ++++++++++++++++++++++++++++++++++ 2 files changed, 141 insertions(+) create mode 100644 examples/rank_features_ex.cpp diff --git a/examples/CMakeLists.txt b/examples/CMakeLists.txt index e557c9e9a..852450c8d 100644 --- a/examples/CMakeLists.txt +++ b/examples/CMakeLists.txt @@ -45,6 +45,7 @@ add_example(mlp_ex) add_example(multithreaded_object_ex) add_example(pipe_ex) add_example(queue_ex) +add_example(rank_features_ex) add_example(server_http_ex) add_example(sockets_ex) add_example(sockets_ex_2) diff --git a/examples/rank_features_ex.cpp b/examples/rank_features_ex.cpp new file mode 100644 index 000000000..32fc1a150 --- /dev/null +++ b/examples/rank_features_ex.cpp @@ -0,0 +1,140 @@ +/* + + This is an example illustrating the use of the rank_features() function + from the dlib C++ Library. + + This example creates a simple set of data and then shows + you how to use the rank_features() function to find a good + set of features (where "good" means the feature set will probably + work well with a classification algorithm). + + The data used in this example will be 4 dimensional data and will + come from a distribution where points with a distance less than 10 + from the origin are labeled +1 and all other points are labeled + as -1. Note that this data is conceptually 2 dimensional but we + will add two extra features for the purpose of showing what + the rank_features() function does. +*/ + + +#include +#include "dlib/svm.h" +#include "dlib/rand.h" +#include + +using namespace std; +using namespace dlib; + + +int main() +{ + + // This first typedef declares a matrix with 4 rows and 1 column. It will be the + // object that contains each of our 4 dimensional samples. + typedef matrix sample_type; + + + + // Now lets make some vector objects that can hold our samples + std::vector samples; + std::vector labels; + + dlib::rand::float_1a rnd; + + for (int x = -20; x <= 20; ++x) + { + for (int y = -20; y <= 20; ++y) + { + sample_type samp; + + // the first two features are just the (x,y) position of our points and so + // we expect them to be good features since our two classes here are points + // close to the origin and points far away from the origin. + samp(0) = x; + samp(1) = y; + + // This is a worthless feature since it is just random noise. It should + // be indicated as worthless by the rank_features() function below. + samp(2) = rnd.get_random_double(); + + // 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; + + // 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. + // otherwise it is a -1 class point + if (sqrt((double)x*x + y*y) <= 10) + labels.push_back(+1); + else + labels.push_back(-1); + } + } + + + // Here we normalize all the samples by subtracting the mean and dividing by the standard deviation. + // This is generally a good idea since it often heads off numerical stability problems and also + // prevents one large feature from smothering others. + const sample_type m(mean(vector_to_matrix(samples))); // compute a mean vector + const sample_type sd(reciprocal(sqrt(variance(vector_to_matrix(samples))))); // compute a standard deviation vector + // now normalize each sample + for (unsigned long i = 0; i < samples.size(); ++i) + samples[i] = pointwise_multiply(samples[i] - m, sd); + + // This is another thing that is often good to do from a numerical stability point of view. + // In our case it doesn't really matter. + randomize_samples(samples,labels); + + + + // This is a typedef for the type of kernel we are going to use in this example. + // In this case I have selected the radial basis kernel that can operate on our + // 4D sample_type objects. In general, I would suggest using the same kernel for + // classification and feature ranking. + typedef radial_basis_kernel kernel_type; + + // This line here declares the kcentroid object we want to use for feature ranking. Note that there + // are two numbers in it. The first is the argument to the kernel. The second is a tolerance argument + // for the kcentroid object. This tolerance is basically a control on the number of support vectors it + // will use, with a smaller tolerance giving better accuracy but longer running times. Generally + // something in the range 0.01 to 0.001 is a good choice. + kcentroid kc(kernel_type(0.05), 0.001); + + // And finally we get to the feature ranking. Here we call rank_features() with the kcentroid we just made, + // the samples and labels we made above, and the number of features we want it to rank. Note that + // rank_features() operates on dlib::matrix objects so we need to use the vector_to_matrix() function + // to cast the std::vector objects to dlib::matrix. Also note that the vector_to_matrix() doesn't actually + // copy the std::vector, but instead it uses a template expression technique to recast it as a dlib::matrix + // object. (see the dlib::matrix example and documentation for more details on template expressions). + cout << rank_features(kc, vector_to_matrix(samples), vector_to_matrix(labels), 4) << endl; + + // The output is: + /* + 0 0.452251 + 1 0.259739 + 3 0.28801 + 2 -0.0347664 + */ + + // The first column is a list of the features in order of decreasing goodness. So the rank_features() function + // is telling us that the samples[i](0) and samples[i](1) (i.e. the x and y) features are the best two. Then + // after that the next best feature is the samples[i](3) (i.e. the y corrupted by noise) and finally the worst + // feature is the one that is just random noise. So in this case rank_features did exactly what we would + // 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. + + // 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 + +} +