支持向量机svm入门：使用svm实现sklearn自带的手写数据集识别

 直接附上代码，代码中有注释

# -*- coding:utf-8 -*-
import sys
from sklearn.datasets import load_digits  # 加载手写数字识别数据
import pylab as pl
from sklearn.model_selection import train_test_split  # 训练测试数据分割
from sklearn.preprocessing import StandardScaler  # 标准化工具
from sklearn.svm import LinearSVC
from sklearn.metrics import classification_report  # 预测结果分析工具

digits = load_digits()
# 数据维度，1797幅图，8*8
print(digits.data.shape)
#长度为64的一维向量
print(digits.data[0])
# 分割数据
X_train, X_test, Y_train, Y_test = train_test_split(digits.data, digits.target, test_size=0.25, random_state=33)

print(X_train.shape)#(1347,64)
print(Y_test.shape)#(450,)
print(Y_test)

ss = StandardScaler()
# fit是实例方法，必须由实例调用
X_train = ss.fit_transform(X_train)
X_test = ss.transform(X_test)
 
lsvc = LinearSVC()
lsvc.fit(X_train, Y_train)

Y_predict = lsvc.predict(X_test)

print(classification_report(Y_test, Y_predict, target_names=digits.target_names.astype(str)))