# -*- coding: utf-8 -*-
"""
Created on Sat Oct 13 10:25:38 2018
@author: fengjuan
"""
#从sklearn.datasets导入数字加载器
from sklearn.datasets import load_digits
from sklearn.cross_validation import train_test_split
digits=load_digits()
#从通过数据加载器获得的手写体数码图像数据存储在digits变量中
print(digits.data.shape)
#将数据分割，25%作为测试集，75%作为训练集
X_train,X_test,y_train,y_test=train_test_split(digits.data,digits.target,
test_size=0.25,random_state=33)
print(y_train.shape)
print(y_test.shape)
#sklearn.preprocessing中导入标准化模块
from sklearn.preprocessing import StandardScaler
#从sklearn.svm导入基于线性假设的支持向量机分类器LinearSVC
from sklearn.svm import LinearSVC
#对特征数据进行标准化
ss = StandardScaler()
X_train = ss.fit_transform(X_train)
X_test = ss.transform(X_test)
#初始化基于线性假设的支持向量机分类器LinearSVC
lsvc=LinearSVC()
#模型训练
lsvc.fit(X_train,y_train)
#用训练好的模型进行预测，并将预测结果存储在变量y_predict中
y_predict=lsvc.predict(X_test)
#识别能力的评估
from sklearn.metrics import classification_report
print('Accuracy of Linear SVC is:',lsvc.score(X_test,y_test))
print(classification_report(y_test,y_predict,target_names=digits.target_names.astype(str)))
#结果'''Accuracy of Linear SVC is: 0.9533333333333334
'''
precision recall f1-score support
0 0.92 1.00 0.96 35
1 0.96 0.98 0.97 54
2 0.98 1.00 0.99 44
3 0.93 0.93 0.93 46
4 0.97 1.00 0.99 35
5 0.94 0.94 0.94 48
6 0.96 0.98 0.97 51
7 0.92 1.00 0.96 35
8 0.98 0.84 0.91 58
9 0.95 0.91 0.93 44
avg / total 0.95 0.95 0.95 450'''