 growing rule
        equation 6 from Cohen &amp; Singer (1999)
        &#34;&#34;&#34;
        W_plus, W_minus = self._get_design_matrices(X, y, rule)
        # C_R = self._sample_weight(W_plus, W_minus)
        return np.sqrt(W_plus) - np.sqrt(W_minus)

    def _sample_weight(self, plus, minus):
        &#34;&#34;&#34; Calculate learner sample weight
        in paper this is C_R, which is confidence of learner
        &#34;&#34;&#34;
        return 0.5 * np.log((plus + (1 / (2 * len(self.D)))) /
         