from sklearn.decomposition import PCA
pca = PCA(n_components=1)
pca.fit(X)
X_reduction = pca.transform(X)
X_restore = pca.inverse_transform(X_reduction)
plt.scatter(X[:, 0], X[:, 1], color='b', alpha=0.5)
plt.scatter(X_restore[:, 0], X_restore[:, 1], color='r', alpha=0.5)
plt.show()
import numpy as np
import matplotlib.pyplot as plt
from sklearn import datasets
digits = datasets.load_digits()
X = digits.data
y = digits.target
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=666)
pca = PCA(n_components=X_train.shape[1])
pca.fit(X_train)
pca.explained_variance_ratio_
plt.plot([i for i in range(X_train.shape[1])],
[np.sum(pca.explained_variance_ratio_[:i+1]) for i in range(X_train.shape[1])])
plt.show()
pca = PCA(0.95)
pca.fit(X_train)
pca.explained_variance_ratio_
pca = PCA(n_components=2)
pca.fit(X)
X_reduction = pca.transform(X)
for i in range(10):
plt.scatter(X_reduction[y==i, 0], X_reduction[y==i,1], alpha=0.8)
plt.show()
