利用TSNE将word2vec词向量降维并显示

import numpy as np
from sklearn.manifold import TSNE
import matplotlib.pyplot as plt
import gensim
import matplotlib as mpl

# 若要显示中文字体则取消注释下面两行
# mpl.rcParams['font.sans-serif'] = ['FangSong'] # 指定中文字体
# mpl.rcParams['axes.unicode_minus'] = False # 解决保存图像是负号'-'显示为方块的问题

def plot_with_labels(low_dim_embs, labels, filename):   # 绘制词向量图
    assert low_dim_embs.shape[0] >= len(labels), 'More labels than embeddings'
    print('绘制词向量中......')
    plt.figure(figsize=(10, 10))  # in inches
    for i, label in enumerate(labels):
        x, y = low_dim_embs[i, :]
        plt.scatter(x, y)	# 画点，对应low_dim_embs中每个词向量
        plt.