def _posterior_mode(self, K, return_temporaries=False):
        """Mode-finding for binary Laplace GPC and fixed kernel.

        This approximates the posterior of the latent function values for given
        inputs and target observations with a Gaussian approximation and uses
        Newton's iteration to find the mode of this approximation.
        """
        # Based on Algorithm 3.1 of GPML

        # If warm_start are enabled, we reuse the last solution for the
        # posterior mode as initialization; otherwise, we initialize with 0
        if self.warm_start and hasattr(self, "f_cached") \
           and self.f_cached.shape == self.y_train_.shape:
            f = self.f_cached
        else:
            f = np.zeros_like(self.y_train_, dtype=np.float64)

        # Use Newton's iteration method to find mode of Laplace approximation
        log_marginal_likelihood = -np.inf
        for _ in range(self.max_iter_predict):
            # Line 4
            pi = 1 / (1 + np.exp(-f))
            W = pi * (1 - pi)
            # Line 5
            W_sr = np.sqrt(W)
            W_sr_K = W_sr[:, np.newaxis] * K
            B = np.eye(W.shape[0]) + W_sr_K * W_sr
            L = cholesky(B, lower=True)
            # Line 6
            b = W * f + (self.y_train_ - pi)
            # Line 7
            a = b - W_sr * cho_solve((L, True), W_sr_K.dot(b))
            # Line 8
            f = K.dot(a)

            # Line 10: Compute log marginal likelihood in loop and use as
            #          convergence criterion
            lml = -0.5 * a.T.dot(f) \
                - np.log(1 + np.exp(-(self.y_train_ * 2 - 1) * f)).sum() \
                - np.log(np.diag(L)).sum()
            # Check if we have converged (log marginal likelihood does
            # not decrease)
            # XXX: more complex convergence criterion
            if lml - log_marginal_likelihood < 1e-10:
                break
            log_marginal_likelihood = lml

        self.f_cached = f  # Remember solution for later warm-starts
        if return_temporaries:
            return log_marginal_likelihood, (pi, W_sr, L, b, a)
        else:
            return log_marginal_likelihood
dataset/ETHPy150Open scikit-learn/scikit-learn/sklearn/gaussian_process/gpc.py/_BinaryGaussianProcessClassifierLaplace._posterior_mode/original