neuagent.py 文件源码

def load_wemb(params, vocab):
    wemb = pkl.load(open(prm.wordemb_path, 'rb'))
    dim_emb_orig = wemb.values()[0].shape[0]

    W = 0.01 * np.random.randn(prm.n_words, dim_emb_orig).astype(config.floatX)
    for word, pos in vocab.items():
        if word in wemb:
            W[pos,:] = wemb[word]

    if prm.dim_emb < dim_emb_orig:
        pca =PCA(n_components=prm.dim_emb, copy=False, whiten=True)
        W = pca.fit_transform(W)

    params['W'] = W

    return params