from __future__ import division

import torch

from .degree import node_degree


def graclus_cluster(edge_index,
                    num_nodes=None,
                    edge_attr=None,
                    batch=None,
                    rid=None):

    num_nodes = edge_index.max() + 1 if num_nodes is None else num_nodes
    rid = torch.randperm(num_nodes) if rid is None else rid

    cut = normalized_cut(edge_index, num_nodes, edge_attr)

    print(cut)


def normalized_cut(edge_index, num_nodes, edge_attr=None):
    row, col = edge_index

    out = edge_attr.new() if edge_attr is not None else torch.Tensor()
    cut = node_degree(edge_index, num_nodes, out=out)
    cut = 1 / cut
    cut = cut[row] + cut[col]

    if edge_attr is None:
        return cut
    else:
        if edge_attr.dim() > 1 and edge_attr.size(1) > 1:
            edge_attr = torch.norm(edge_attr, 2, 1)
        return edge_attr.squeeze() * cut