code
import pandas as pd
import numpy as np
from sklearn.decomposition import PCA
from matplotlib import pyplot as plt
digits_train = pd.read_csv('http://archive.ics.uci.edu/ml/machine-learning-databases/optdigits/optdigits.tra', header=None)
digits_test = pd.read_csv('http://archive.ics.uci.edu/ml/machine-learning-databases/optdigits/optdigits.tes', header=None)
X_digits = digits_train[np.arange(64)]
y_digits = digits_train[64]
estimator = PCA(n_components=2)
X_pca = estimator.fit_transform(X_digits)
print(type(X_pca))
def plot_pca_scatter():
colors = ['black', 'blue', 'purple', 'yellow', 'white', 'red', 'lime', 'cyan', 'orange', 'gray']
for i in range(len(colors)):
px = X_pca[:, 0][y_digits.as_matrix() == i]
py = X_pca[:, 1][y_digits.as_matrix() == i]
plt.scatter(px, py, c=colors[i])
plt.legend(np.arange(0, 10).astype(str))
plt.xlabel('First Principal Component')
plt.ylabel('Second Principal Component')
plt.show()
plot_pca_scatter()
result
由下图可以看出原本64维特征向量压缩至2个维度后,同一类型的digits基本上分布在同一块区域。