Treat data dropwear using a characteristic vector PCA using a covariance matrix.

Take 2-dimensional features to facilitate graphic display

import matplotlib.pyplot as plt

from sklearn.decomposition import PCA

from sklearn.datasets import load_iris



data = load_iris()

y = data.target

X = data.data

pca = PCA(n_components=2)

reduced_X = pca.fit_transform(X)



red_x, red_y = [], []

blue_x, blue_y = [], []

green_x, green_y = [], []

for i in range(len(reduced_X)):

 if y[i] == 0:

 red_x.append(reduced_X[i][0])

 red_y.append(reduced_X[i][1])

 elif y[i] == 1:

 blue_x.append(reduced_X[i][0])

 blue_y.append(reduced_X[i][1])

 else:

 green_x.append(reduced_X[i][0])

 green_y.append(reduced_X[i][1])

plt.scatter(red_x, red_y, c='r', marker='x')

plt.scatter(blue_x, blue_y, c='b', marker='D')

plt.scatter(green_x, green_y, c='g', marker='.')

plt.show()

import matplotlib.pyplot as plt

from sklearn.decomposition import PCA

from sklearn.datasets import load_iris



data = load_iris()

y = data.target

X = data.data

pca = PCA(n_components=2)

reduced_X = pca.fit_transform(X)



red_x, red_y = [], []

blue_x, blue_y = [], []

green_x, green_y = [], []

for i in range(len(reduced_X)):

 if y[i] == 0:

 red_x.append(reduced_X[i][0])

 red_y.append(reduced_X[i][1])

 elif y[i] == 1:

 blue_x.append(reduced_X[i][0])

 blue_y.append(reduced_X[i][1])

 else:

 green_x.append(reduced_X[i][0])

 green_y.append(reduced_X[i][1])

plt.scatter(red_x, red_y, c='r', marker='x')

plt.scatter(blue_x, blue_y, c='b', marker='D')

plt.scatter(green_x, green_y, c='g', marker='.')

plt.show()