[PYTHON/SKLEARN] 주성분 분석(PCA, Principle Component Analysis) 시각화 알고리즘 사용하기

■ 주성분 분석(PCA, Principle Component Analysis) 시각화 알고리즘을 사용하는 방법을 보여준다.

▶ 예제 코드 (PY)


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

mnistBunch   = datasets.load_digits(n_class = 6)
imageNDArray = mnistBunch.data
labelNDArray = mnistBunch.target
pcaNDArray   = decomposition.TruncatedSVD(n_components = 2).fit_transform(imageNDArray)

figure, axesSubplot = pp.subplots()

axesSubplot.scatter(pcaNDArray[:, 0], pcaNDArray[:, 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.decomposition as decomposition

mnistBunch = datasets.load_digits(n_class = 6)

imageNDArray = mnistBunch.data

labelNDArray = mnistBunch.target

pcaNDArray = decomposition.TruncatedSVD(n_components = 2).fit_transform(imageNDArray)

figure, axesSubplot = pp.subplots()

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

axesSubplot.set_xticks(())

axesSubplot.set_yticks(())

pp.show()