LDA预测手写数字集

import sklearn.datasets as sk
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score

digitals = sk.load_digits()

print(type(digitals.target))

print('手写数据集中有以下类型数据：')
print(digitals.keys())
print('\n\ndata为其各像素块数据:')
print(digitals.data)
print('\n\ntarget为其各图片的标签(实际数字):')
print(digitals.target)
print('\n\nfeature_names为其各各像素块的名称(列名):')
print(digitals.feature_names)
print('\n\ntarget_names全部标签都是什么:')
print(digitals.target_names)

print(digitals.data)
exit(0)
x_train, x_test, y_train, y_test = train_test_split(digitals.data, digitals.target,test_size=0.2)
model = LinearDiscriminantAnalysis(n_components = 2)
model.fit(x_train, y_train)
print("\n\nX_test为:")
print(model.predict(x_test))
print("\n\nY_test值为:")
print(y_test)
print("\n\n准确度为:")
print(accuracy_score(model.predict(x_test),y_test))