LIEGE.py 文件源码

def train(train_dataTables, human_marks):
    global classifier
    samples =[]
    target = []

    for nn, dataTable in enumerate(train_dataTables):
        for i in xrange(dataTable.row):
            for j in xrange(dataTable.col):
                mention = dataTable[i][j]
                if mention.cid == -1:
                    continue
                eids = dataTable.get_eids(i, j)
                words = dataTable.get_words(i, j)
                entites = dataTable.get_entities(i ,j)
                true_id = human_marks[nn][i][j]['id']
                for ii, entity in enumerate(mention.candidates):
                    prior = entity.popular
                    SR = mention.getSR(ii, entites)
                    res = int(true_id == entity.id)
                    samples.append([prior, SR])
                    target.append(res)

    from sklearn import svm   
    classifier = svm.SVC(probability=True)
    classifier.fit(samples, target)