How to use the adaptive.learner.triangulation.point_in_simplex function in adaptive

The multiplication to apply to the simplex before choosing
        the new point.

    Returns
    -------
    point : numpy array of length N
        The coordinates of the suggested new point.
    """

    if transform is not None:
        simplex = np.dot(simplex, transform)

    # choose center if and only if the shape of the simplex is nice,
    # otherwise: the center the longest edge
    center, _radius = circumsphere(simplex)
    if point_in_simplex(center, simplex):
        point = np.average(simplex, axis=0)
    else:
        distances = scipy.spatial.distance.pdist(simplex)
        distance_matrix = scipy.spatial.distance.squareform(distances)
        i, j = np.unravel_index(np.argmax(distance_matrix), distance_matrix.shape)
        point = (simplex[i, :] + simplex[j, :]) / 2

    if transform is not None:
        point = np.linalg.solve(transform, point)  # undo the transform

    return point

The linear transform to apply to the simplex vertices
        before determining which point to choose. Must be
        invertible.

    Returns
    -------
    point : (n,) array
        The point that was chosen in the simplex
    """
    if transform is not None:
        simplex = np.dot(simplex, transform)

    # Choose center only if the shape of the simplex is nice,
    # otherwise: the center the longest edge
    center, _radius = circumsphere(simplex)
    if point_in_simplex(center, simplex):
        point = np.average(simplex, axis=0)
    else:
        distances = scipy.spatial.distance.pdist(simplex)
        distance_matrix = scipy.spatial.distance.squareform(distances)
        i, j = np.unravel_index(np.argmax(distance_matrix), distance_matrix.shape)
        point = (simplex[i, :] + simplex[j, :]) / 2

    if transform is not None:
        point = np.linalg.solve(transform, point)  # undo the transform

    return tuple(point)