示例#1
0
def id3(data):
    if all(x['analito'] == 'glicose' for x in data):
        return Leaf('glicose')

    if all(x['analito'] == 'triglicerideo' for x in data):
        return Leaf('triglicerideo')

    if len([k for k in data[0].keys() if k != 'analito']) <= 0:
        return Leaf(most_common([d['analito'] for d in data]))

    # Entropia dos atributos
    splited = {k: entropy([x[k] for x in data.copy()]) for k in data[0].keys()}
    splited.pop('analito')

    # Atributo separador
    separator = min(splited.items(), key=lambda x: x[1])[0]
    node = Node(separator)

    if is_continuous(data[0][separator]):
        for x in data:
            x[separator] = round(x[separator], PRECISION)

    for v in set(x[separator] for x in data):
        aux = [x for x in data.copy() if separator in x and x[separator] == v]
        for i in aux:
            i.pop(separator)

        node.add_son({v: id3(aux)})

    return node
示例#2
0
def predict(element, tree):
    if type(tree) is Leaf:
        return tree.answer

    if is_continuous(element[tree.attr]):
        return predict(element, tree.sons[nearest(element[tree.attr],
                                                  list(tree.sons.keys()),
                                                  precision=PRECISION)])

    return predict(element, tree.sons[element[tree.attr]])