def draw2D(self):
model = TSNE(n_components=2)
result = model.fit_transform(self.__wordVector)
model = KMeans(5)
lable = model.fit_predict(result)
cm_subsection = linspace(0, 1, 5)
colors = [cm.rainbow(x) for x in cm_subsection]
random.shuffle(colors)
fig = plt.figure(figsize=(20, 12))
for i, word in enumerate(self.__showWords):
plt.scatter(result[i, 0], result[i, 1], color=colors[lable[i]])
plt.annotate(word, xy=(result[i, 0], result[i, 1]))
fig.show()
self.__saveImag(plt, '2D')
import numpy as np
from sklearn.manifold import TSNE
data = np.load('tsne_data.npy')
print("Dimensions of the data: ", data.shape)
kmeans_1 = TSNE(n_clusters=2, perplexity=30)
transformed_data = kmeans_1.fit_predict(data)
