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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 new file mode 100644 index 000000000..548db4be7 --- /dev/null +++ b/ml/dlib/examples/rank_features_ex.cpp @@ -0,0 +1,152 @@ +// 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 + + +} + |