Analyse en Composante Principale

import numpy as np import matplotlib.pyplot as plt from sklearn.decomposition import PCA

from sklearn.datasets import load_digits digits = load_digits() X = digits.data y = digits.target f, axarr = plt.subplots(1,2) axarr[0].imshow(X[0].reshape(8,8)) axarr[1].imshow(X[1].reshape(8,8)) plt.show()

model = PCA(n_components=2) X_2D = model.fit_transform(X) plt.scatter(X_2D[:,0], X_2D[:,1], c=y) plt.colorbar() plt.show()

model = PCA(n_components=64) X_reduced = model.fit_transform(X) plt.plot(np.cumsum(model.explained_variance_ratio_)) plt.show()

n_comp = np.argmax(np.cumsum(model.explained_variance_ratio_) > 0.97)

model = PCA(n_components=n_comp) X_opti = model.fit_transform(X) X_opti_img = model.inverse_transform(X_opti)

f, axarr = plt.subplots(1,2) axarr[0].imshow(X_opti_img[0].reshape(8,8)) axarr[1].imshow(X[0].reshape(8,8)) plt.show()