decisionboundaryplot.py 文件源码

def _find_decision_boundary_on_hypersphere(self, centroid, R, penalize_known=False):
        def objective(phi, grad=0):
            # search on hypersphere surface in polar coordinates - map back to cartesian
            cx = centroid + polar_to_cartesian(phi, R)
            try:
                cx2d = self.dimensionality_reduction.transform([cx])[0]
                error = self.decision_boundary_distance(cx)
                if penalize_known:
                    # slight penalty for being too close to already known decision boundary
                    # keypoints
                    db_distances = [euclidean(cx2d, self.decision_boundary_points_2d[k])
                                    for k in range(len(self.decision_boundary_points_2d))]
                    error += 1e-8 * ((self.mean_2d_dist - np.min(db_distances)) /
                                     self.mean_2d_dist)**2
                return error
            except (Exception, ex):
                print("Error in objective function:", ex)
                return np.infty

        optimizer = self._get_optimizer(
            D=self.X.shape[1] - 1, upper_bound=2 * np.pi, iteration_budget=self.hypersphere_iteration_budget)
        optimizer.set_min_objective(objective)
        db_phi = optimizer.optimize([rnd.random() * 2 * np.pi for k in range(self.X.shape[1] - 1)])
        db_point = centroid + polar_to_cartesian(db_phi, R)
        return db_point