Python_sklearn机器学习库学习笔记（六） dimensionality-reduction-with-pca

# 用PCA降维

#计算协方差矩阵
import numpy as np
X=[[2,0,-1.4],
  [2.2,0.2,-1.5],
  [2.4,0.1,-1],
  [1.9,0,-1.2]]

np.cov(np.array(X).T)

#计算特征向量
import numpy as np
w,v=np.linalg.eig(np.array([[1,-2],[2,-3]]))
print w,v

# 降维可视化

%matplotlib inline
import matplotlib.pyplot as plt
from matplotlib.font_manager import FontProperties
font = FontProperties(fname=r"c:windowsfontsmsyh.ttc", size=10)

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()