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153 lines
6.7 KiB
153 lines
6.7 KiB
// The contents of this file are in the public domain. See LICENSE_FOR_EXAMPLE_PROGRAMS.txt
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/*
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This is an example illustrating the use of the rank_features() function
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from the dlib C++ Library.
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This example creates a simple set of data and then shows
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you how to use the rank_features() function to find a good
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set of features (where "good" means the feature set will probably
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work well with a classification algorithm).
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The data used in this example will be 4 dimensional data and will
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come from a distribution where points with a distance less than 10
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from the origin are labeled +1 and all other points are labeled
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as -1. Note that this data is conceptually 2 dimensional but we
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will add two extra features for the purpose of showing what
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the rank_features() function does.
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*/
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#include <iostream>
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#include <dlib/svm.h>
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#include <dlib/rand.h>
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#include <vector>
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using namespace std;
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using namespace dlib;
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int main()
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{
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// This first typedef declares a matrix with 4 rows and 1 column. It will be the
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// object that contains each of our 4 dimensional samples.
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typedef matrix<double, 4, 1> sample_type;
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// Now let's make some vector objects that can hold our samples
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std::vector<sample_type> samples;
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std::vector<double> labels;
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dlib::rand rnd;
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for (int x = -30; x <= 30; ++x)
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{
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for (int y = -30; y <= 30; ++y)
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{
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sample_type samp;
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// the first two features are just the (x,y) position of our points and so
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// we expect them to be good features since our two classes here are points
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// close to the origin and points far away from the origin.
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samp(0) = x;
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samp(1) = y;
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// This is a worthless feature since it is just random noise. It should
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// be indicated as worthless by the rank_features() function below.
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samp(2) = rnd.get_random_double();
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// This is a version of the y feature that is corrupted by random noise. It
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// should be ranked as less useful than features 0, and 1, but more useful
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// than the above feature.
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samp(3) = y*0.2 + (rnd.get_random_double()-0.5)*10;
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// add this sample into our vector of samples.
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samples.push_back(samp);
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// if this point is less than 15 from the origin then label it as a +1 class point.
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// otherwise it is a -1 class point
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if (sqrt((double)x*x + y*y) <= 15)
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labels.push_back(+1);
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else
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labels.push_back(-1);
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}
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}
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// Here we normalize all the samples by subtracting their mean and dividing by their standard deviation.
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// This is generally a good idea since it often heads off numerical stability problems and also
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// prevents one large feature from smothering others.
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const sample_type m(mean(mat(samples))); // compute a mean vector
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const sample_type sd(reciprocal(stddev(mat(samples)))); // compute a standard deviation vector
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// now normalize each sample
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for (unsigned long i = 0; i < samples.size(); ++i)
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samples[i] = pointwise_multiply(samples[i] - m, sd);
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// This is another thing that is often good to do from a numerical stability point of view.
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// However, in our case it doesn't really matter. It's just here to show you how to do it.
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randomize_samples(samples,labels);
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// This is a typedef for the type of kernel we are going to use in this example.
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// In this case I have selected the radial basis kernel that can operate on our
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// 4D sample_type objects. In general, I would suggest using the same kernel for
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// classification and feature ranking.
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typedef radial_basis_kernel<sample_type> kernel_type;
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// The radial_basis_kernel has a parameter called gamma that we need to set. Generally,
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// you should try the same gamma that you are using for training. But if you don't
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// have a particular gamma in mind then you can use the following function to
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// find a reasonable default gamma for your data. Another reasonable way to pick a gamma
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// is often to use 1.0/compute_mean_squared_distance(randomly_subsample(samples, 2000)).
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// It computes the mean squared distance between 2000 randomly selected samples and often
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// works quite well.
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const double gamma = verbose_find_gamma_with_big_centroid_gap(samples, labels);
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// Next we declare an instance of the kcentroid object. It is used by rank_features()
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// two represent the centroids of the two classes. The kcentroid has 3 parameters
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// you need to set. The first argument to the constructor is the kernel we wish to
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// use. The second is a parameter that determines the numerical accuracy with which
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// the object will perform part of the ranking algorithm. Generally, smaller values
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// give better results but cause the algorithm to attempt to use more dictionary vectors
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// (and thus run slower and use more memory). The third argument, however, is the
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// maximum number of dictionary vectors a kcentroid is allowed to use. So you can use
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// it to put an upper limit on the runtime complexity.
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kcentroid<kernel_type> kc(kernel_type(gamma), 0.001, 25);
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// And finally we get to the feature ranking. Here we call rank_features() with the kcentroid we just made,
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// the samples and labels we made above, and the number of features we want it to rank.
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cout << rank_features(kc, samples, labels) << endl;
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// The output is:
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/*
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0 0.749265
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1 1
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3 0.933378
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2 0.825179
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*/
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// The first column is a list of the features in order of decreasing goodness. So the rank_features() function
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// is telling us that the samples[i](0) and samples[i](1) (i.e. the x and y) features are the best two. Then
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// after that the next best feature is the samples[i](3) (i.e. the y corrupted by noise) and finally the worst
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// feature is the one that is just random noise. So in this case rank_features did exactly what we would
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// intuitively expect.
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// The second column of the matrix is a number that indicates how much the features up to that point
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// contribute to the separation of the two classes. So bigger numbers are better since they
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// indicate a larger separation. The max value is always 1. In the case below we see that the bad
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// features actually make the class separation go down.
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// So to break it down a little more.
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// 0 0.749265 <-- class separation of feature 0 all by itself
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// 1 1 <-- class separation of feature 0 and 1
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// 3 0.933378 <-- class separation of feature 0, 1, and 3
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// 2 0.825179 <-- class separation of feature 0, 1, 3, and 2
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}
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