import sys
import numpy as np
from copy import deepcopy
from lib.PEDA.modt.hypervolume import InnerHyperVolume
def compute_hypervolume(ep_objs_batch):
    n = len(ep_objs_batch[0])
    HV = InnerHyperVolume(np.zeros(n))
    return HV.compute(ep_objs_batch)

def compute_sparsity(ep_objs_batch):
    if len(ep_objs_batch) < 2:
        return 0.0

    sparsity = 0.0
    m = len(ep_objs_batch[0])
    ep_objs_batch_np = np.array(ep_objs_batch)
    for dim in range(m):
        objs_i = np.sort(deepcopy(ep_objs_batch_np.T[dim]))
        for i in range(1, len(objs_i)):
            sparsity += np.square(objs_i[i] - objs_i[i - 1])
    sparsity /= (len(ep_objs_batch) - 1)
    return sparsity

def check_dominated(obj_batch, obj, tolerance=0):
    return (np.logical_and((obj_batch * (1-tolerance) >= obj).all(axis=1), (obj_batch * (1-tolerance) > obj).any(axis=1))).any()

# return sorted indices of nondominated objs
def undominated_indices(obj_batch_input, tolerance=0):
    obj_batch = np.array(obj_batch_input)
    sorted_indices = np.argsort(obj_batch.T[0])
    indices = []
    for idx in sorted_indices:
        if not check_dominated(obj_batch, obj_batch[idx], tolerance):
            indices.append(idx)
    return indices