python线性判别分析（LDA）小实例

from sklearm.datasets import load_iris
df = load_iris()
print(df.DESCR)

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)

import numpy as np
from sklearn.model_selection import train_test_split
X, y = np.arange(10).reshape((5, 2)), range(5)
X
    array([[0, 1],
           [2, 3],
           [4, 5],
           [6, 7],
           [8, 9]])
list(y)
    [0, 1, 2, 3, 4]

X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size=0.33, random_state=42)
    ...
X_train
    array([[4, 5],
           [0, 1],
           [6, 7]])
y_train
    [2, 0, 3]
X_test
    array([[2, 3],
           [8, 9]])
y_test
    [1, 4]

train_test_split(y, shuffle=False)
    [[0, 1, 2], [3, 4]]

from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
LDA = LinearDiscriminantAnalysis()
LDA.fit(X_train,y_train)
Out[18]: 
LinearDiscriminantAnalysis(n_components=None, priors=None, shrinkage=None,
              solver='svd', store_covariance=False, tol=0.0001)
LDA.score(X_train,y_train)
Out[19]: 0.9732142857142857

LDA.predict(X_test)
Out[20]: 
array([0, 0, 0, 0, 1, 1, 1, 0, 1, 2, 2, 2, 1, 2, 1, 0, 0, 2, 0, 1, 2, 1, 1,
       0, 2, 0, 0, 1, 2, 1, 0, 1, 2, 2, 0, 1, 2, 2])

LDA.score(X_test,y_test)
Out[21]: 1.0

dir(LDA)

import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D

X = np.vstack((X_train,X_test))
Y = np.vstack((y_train.reshape(y_train.size,1),\
    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()