# encoding=utf-8
import time
import numpy as np
import pandas as pd
from sklearn.cross_validation import train_test_split
from sklearn.metrics import accuracy_score
from sklearn import datasets
from sklearn import svm
if __name__ == \'__main__\':
print(\'prepare datasets...\')
# Iris数据集
# iris=datasets.load_iris()
# features=iris.data
# labels=iris.target
# MINST数据集
raw_data = pd.read_csv(\'../data/train_binary.csv\', header=0) # 读取csv数据，并将第一行视为表头，返回DataFrame类型
data = raw_data.values
features = data[::, 1::]
labels = data[::, 0] # 选取33%数据作为测试集，剩余为训练集
train_features, test_features, train_labels, test_labels = train_test_split(features, labels, test_size=0.33, random_state=0)
time_2=time.time()
print(\'Start training...\')
clf = svm.SVC() # svm class
clf.fit(train_features, train_labels) # training the svc model
time_3 = time.time()
print(\'training cost %f seconds\' % (time_3 - time_2))
print(\'Start predicting...\')
test_predict=clf.predict(test_features)
time_4 = time.time()
print(\'predicting cost %f seconds\' % (time_4 - time_3))
score = accuracy_score(test_labels, test_predict)
print("The accruacy score is %f" % score)
测试数据集为经过二分类处理后的MNIST数据集，获取地址train_binary.csv
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原文链接：https://blog.csdn.net/fuqiuai/article/details/79483057