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Diffstat (limited to 'ml/dlib/examples/rank_features_ex.cpp')
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diff --git a/ml/dlib/examples/rank_features_ex.cpp b/ml/dlib/examples/rank_features_ex.cpp deleted file mode 100644 index 548db4be7..000000000 --- a/ml/dlib/examples/rank_features_ex.cpp +++ /dev/null @@ -1,152 +0,0 @@ -// The contents of this file are in the public domain. See LICENSE_FOR_EXAMPLE_PROGRAMS.txt -/* - - 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 <iostream> -#include <dlib/svm.h> -#include <dlib/rand.h> -#include <vector> - -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<double, 4, 1> sample_type; - - - - // Now let's make some vector objects that can hold our samples - std::vector<sample_type> samples; - std::vector<double> labels; - - dlib::rand rnd; - - for (int x = -30; x <= 30; ++x) - { - for (int y = -30; y <= 30; ++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*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 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) <= 15) - labels.push_back(+1); - else - labels.push_back(-1); - } - } - - - // Here we normalize all the samples by subtracting their mean and dividing by their 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(mat(samples))); // compute a mean vector - const sample_type sd(reciprocal(stddev(mat(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. - // However, in our case it doesn't really matter. It's just here to show you how to do it. - 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<sample_type> kernel_type; - - // The radial_basis_kernel has a parameter called gamma that we need to set. Generally, - // you should try the same gamma that you are using for training. But if you don't - // have a particular gamma in mind then you can use the following function to - // find a reasonable default gamma for your data. Another reasonable way to pick a gamma - // is often to use 1.0/compute_mean_squared_distance(randomly_subsample(samples, 2000)). - // It computes the mean squared distance between 2000 randomly selected samples and often - // works quite well. - const double gamma = verbose_find_gamma_with_big_centroid_gap(samples, labels); - - // Next we declare an instance of the kcentroid object. It is used by rank_features() - // two represent the centroids of the two classes. The kcentroid has 3 parameters - // you need to set. The first argument to the constructor is the kernel we wish to - // use. The second is a parameter that determines the numerical accuracy with which - // the object will perform part of the ranking algorithm. Generally, smaller values - // give better results but cause the algorithm to attempt to use more dictionary vectors - // (and thus run slower and use more memory). The third argument, however, is the - // maximum number of dictionary vectors a kcentroid is allowed to use. So you can use - // it to put an upper limit on the runtime complexity. - kcentroid<kernel_type> kc(kernel_type(gamma), 0.001, 25); - - // 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. - cout << rank_features(kc, samples, labels) << endl; - - // The output is: - /* - 0 0.749265 - 1 1 - 3 0.933378 - 2 0.825179 - */ - - // 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 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. The max value is always 1. In the case below we see that the bad - // features actually make the class separation go down. - - // So to break it down a little more. - // 0 0.749265 <-- class separation of feature 0 all by itself - // 1 1 <-- class separation of feature 0 and 1 - // 3 0.933378 <-- class separation of feature 0, 1, and 3 - // 2 0.825179 <-- class separation of feature 0, 1, 3, and 2 - - -} - |