數(shù)據(jù)集使用的是鳶尾花數(shù)據(jù)
from sklearn.datasets import load_iris
df = load_iris()
print(df.DESCR)

將數(shù)據(jù)集拆分為訓(xùn)練集和測試集
from sklearn.model_selection import train_test_split
X_train, X_test,y_train,y_test = train_test_split(df.data,df.target,test_size=0.25,random_state=0,stratify=df.target)

使用help(train_test_split)查看幫助文檔
運(yùn)行幫助文檔中的例子
import numpy as np
from sklearn.model_selection import train_test_split
X, y = np.arange(10).reshape((5, 2)), range(5)
X

引入線性判別模型、擬合、預(yù)測
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
LDA = LinearDiscriminantAnalysis()
LDA.fit(X_train,y_train)

畫圖對擬合后的結(jié)果進(jìn)行展示
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D

X = np.vstack((X_train,X_test)) #定義參數(shù)X
Y = np.vstack((y_train.reshape(y_train.size,1),\ #定義參數(shù)Y
y_test.reshape(y_test.size,1)))
X.shape
Y.shape
converted_X = np.dot(X,np.transpose(LDA.coef_)) +
LDA.intercept_

fig = plt.figure()
ax = Axes3D(fig)
colors = ‘rgb’
markers = ‘o*s’
for target,color,marker in zip([0,1,2],colors,markers):
pos = (Y == target).ravel()
X = converted_X[pos,:]
ax.scatter(X[:,0],X[:,1],X[:,2],
color=color,marker=marker,
label = “Label%d”%target)
ax.legend(loc=“best”)
fig.suptitle(“Iris After LDA”)
plt.show()

總結(jié)

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