skipthoughts.py 文件源码

def word_features(table):
    """
    Extract word features into a normalized matrix
    """
    features = numpy.zeros((len(table), 620), dtype='float32')
    keys = table.keys()
    for i in range(len(table)):
        f = table[keys[i]]
        features[i] = f / norm(f)
    return features