[PYTHON/SKLEARN] t-SNE 시각화 알고리즘 사용하기

■ t-SNE 시각화 알고리즘을 사용하는 방법을 보여준다.

▶ 예제 코드 (PY)


import matplotlib.pyplot as pp
import numpy as np
import sklearn.datasets as datasets
import sklearn.manifold as manifold

mnistBunch   = datasets.load_digits(n_class = 6)
imageNDArray = mnistBunch.data
labelNDArray = mnistBunch.target

tsneNDArray = manifold.TSNE(n_components = 2, init = "pca", random_state = 0).fit_transform(imageNDArray)

figure, axesSubplot = pp.subplots()

axesSubplot.scatter(tsneNDArray[:, 0], tsneNDArray[:, 1], c = labelNDArray)
axesSubplot.set_xticks(())
axesSubplot.set_yticks(())

pp.show()

import matplotlib.pyplot as pp

import numpy as np

import sklearn.datasets as datasets

import sklearn.manifold as manifold

mnistBunch = datasets.load_digits(n_class = 6)

imageNDArray = mnistBunch.data

labelNDArray = mnistBunch.target

tsneNDArray = manifold.TSNE(n_components = 2, init = "pca", random_state = 0).fit_transform(imageNDArray)

figure, axesSubplot = pp.subplots()

axesSubplot.scatter(tsneNDArray[:, 0], tsneNDArray[:, 1], c = labelNDArray)

axesSubplot.set_xticks(())

axesSubplot.set_yticks(())

pp.show()