weighted_argumentation.py

"""
  source : https://www.irit.fr/~Leila.Amgoud/IJAR-2018.pdf
"""
from enum import auto


def h_product(x1, x2):
    return x1 * x2


def g_sum(neighbors, h):
    res = 0.0
    for (weight_node, weight_edge) in neighbors:
        res += h(weight_node, weight_edge)
    return res


def f_h_cat(poid_noeud, neighbors, g, h):
    return poid_noeud / (1 + g(neighbors, h))


class H(auto):
    PRODUCT = h_product


class G(auto):
    SUM = g_sum


class F(auto):
    H_Cat = f_h_cat


def calculate(graph):
    current_iter = {}
    # Init
    for node in list(graph.nodes):
        current_iter[node] = graph.nodes[node]['weight']

    for i in range(15):
        next_iter = {}
        print(current_iter)
        for node in list(graph.nodes):
            predecessors_weights = list(
                map(
                    # (node weight, edge weight) for each predecessor to node
                    lambda n: (current_iter[n], graph.edges[n, node]['weight']), graph.predecessors(node)
                )
            )
            next_iter[node] = F.H_Cat(graph.nodes[node]['weight'], predecessors_weights,
                                      G.SUM, H.PRODUCT)
        current_iter = next_iter


if __name__ == '__main__':
    import networkx as nx

    g = nx.DiGraph()
    g.add_node('a', weight=1)
    g.add_node('b', weight=1)
    g.add_node('c', weight=1)
    g.add_node('d', weight=1)
    g.add_node('e', weight=1)
    g.add_edge('a', 'b', weight=1)
    g.add_edge('a', 'e', weight=1)
    g.add_edge('e', 'a', weight=1)
    g.add_edge('e', 'c', weight=1)
    g.add_edge('e', 'd', weight=1)

    calculate(g)