Method make_psi

python_examples/svm_struct.py:215–261

Compute PSI(x,label).

(self, x, label)

        self.labels = labels

    def make_psi(self, x, label):
        """Compute PSI(x,label)."""
        # All we are doing here is taking x, which is a 3 dimensional sample
        # vector in this example program, and putting it into one of 3 places in
        # a 9 dimensional PSI vector, which we then return.  So this function
        # returns PSI(x,label).  To see why we setup PSI like this, recall how
        # predict_label() works.  It takes in a 9 dimensional weight vector and
        # breaks the vector into 3 pieces.  Each piece then defines a different
        # classifier and we use them in a one-vs-all manner to predict the
        # label.  So now that we are in the structural SVM code we have to
        # define the PSI vector to correspond to this usage.  That is, we need
        # to setup PSI so that argmax_y dot(weights,PSI(x,y)) ==
        # predict_label(weights,x).  This is how we tell the structural SVM
        # solver what kind of problem we are trying to solve.
        #
        # It's worth emphasizing that the single biggest step in using a
        # structural SVM is deciding how you want to represent PSI(x,label).  It
        # is always a vector, but deciding what to put into it to solve your
        # problem is often not a trivial task. Part of the difficulty is that
        # you need an efficient method for finding the label that makes
        # dot(w,PSI(x,label)) the biggest.  Sometimes this is easy, but often
        # finding the max scoring label turns into a difficult combinatorial
        # optimization problem.  So you need to pick a PSI that doesn't make the
        # label maximization step intractable but also still well models your
        # problem.
        #
        # Create a dense vector object (note that you can also use unsorted
        # sparse vectors (i.e.  dlib.sparse_vector objects) to represent your
        # PSI vector.  This is useful if you have very high dimensional PSI
        # vectors that are mostly zeros.  In the context of this example, you
        # would simply return a dlib.sparse_vector at the end of make_psi() and
        # the rest of the example would still work properly. ).
        psi = dlib.vector()
        # Set it to have 9 dimensions.  Note that the elements of the vector
        # are 0 initialized.
        psi.resize(self.num_dimensions)
        dims = len(x)
        if label == 0:
            for i in range(0, dims):
                psi[i] = x[i]
        elif label == 1:
            for i in range(dims, 2 * dims):
                psi[i] = x[i - dims]
        else:  # the label must be 2
            for i in range(2 * dims, 3 * dims):
                psi[i] = x[i - 2 * dims]
        return psi

    # Now we get to the two member functions that are directly called by
    # dlib.solve_structural_svm_problem().