Function find_analogies

rnn_class/batch_wiki.py:150–176

(w1, w2, w3, we_file='word_embeddings.npy', w2i_file='wikipedia_word2idx.json')

def find_analogies(w1, w2, w3, we_file='word_embeddings.npy', w2i_file='wikipedia_word2idx.json'):
    We = np.load(we_file)
    with open(w2i_file) as f:
        word2idx = json.load(f)

    king = We[word2idx[w1]]
    man = We[word2idx[w2]]
    woman = We[word2idx[w3]]
    v0 = king - man + woman

    def dist1(a, b):
        return np.linalg.norm(a - b)
    def dist2(a, b):
        return 1 - a.dot(b) / (np.linalg.norm(a) * np.linalg.norm(b))

    for dist, name in [(dist1, 'Euclidean'), (dist2, 'cosine')]:
        min_dist = float('inf')
        best_word = ''
        for word, idx in iteritems(word2idx):
            if word not in (w1, w2, w3):
                v1 = We[idx]
                d = dist(v0, v1)
                if d < min_dist:
                    min_dist = d
                    best_word = word
        print("closest match by", name, "distance:", best_word)
        print(w1, "-", w2, "=", best_word, "-", w3)