Sometimes self.relevance_vectors_.shape[0] == 0, which breaks fit() and predict()

[wvxvw]

Sorry, I'm far from machine learning, just helping a friend to figure out problems with the code left by someone else.

After some debugging, I found that:

    def _prune(self):
        """Remove basis functions based on alpha values."""
        keep_alpha = self.alpha_ < self.threshold_alpha

        if not np.any(keep_alpha):
            keep_alpha[0] = True

        if self.bias_used:
            if not keep_alpha[0]:
                self.bias_used = False
            if self.kernel != "precomputed":
                self.relevance_vectors_ = self.relevance_vectors_[
                    keep_alpha[1:]]
            # Breakpoint added by me
            if not self.relevance_vectors_.shape[0]:
                import pdb
                pdb.set_trace()
            self.relevance_ = self.relevance_[keep_alpha[1:]]
        else:
            if self.kernel != "precomputed":
                self.relevance_vectors_ = self.relevance_vectors_[keep_alpha]
            self.relevance_ = self.relevance_[keep_alpha]

        self.alpha_ = self.alpha_[keep_alpha]
        self._alpha_old = self._alpha_old[keep_alpha]
        self.gamma_ = self.gamma_[keep_alpha]
        self.Phi_ = self.Phi_[:, keep_alpha]
        self.Sigma_ = self.Sigma_[np.ix_(keep_alpha, keep_alpha)]
        self.mu_ = self.mu_[keep_alpha]

taken from em_rvm.py is responsible for "nonsense" values in self.relevance_vectors_. This happens when keep_alpha is [True, False, ..., False]. Unfortunately, variables like "alpha" or other Greek letters don't help me understand why the values are the way they are, so I have to leave this report in the state that it is. The bottom line: the particular value of self.alpha_, when it's causing creation of zero-size self.relevance_vectors_ should be treated exceptionally. It's either an error (Why?) or, if not an error, then it should be made to do something sensible.