#SVM的使用
（结合具体代码说明，代码参考邹博老师的代码）
1、使用numpy中的loadtxt读入数据文件
data：鸢尾花数据
5.1,3.5,1.4,0.2,Iris-setosa
4.9,3.0,1.4,0.2,Iris-setosa
4.7,3.2,1.3,0.2,Iris-setosa
4.6,3.1,1.5,0.2,Iris-setosa
5.0,3.6,1.4,0.2,Iris-setosa
读取：
- def iris_type(s):
- it = {b'Iris-setosa': 0, b'Iris-versicolor': 1, b'Iris-virginica': 2}
- return it[s]
- path = 'iris.data' # 数据文件路径
- data = np.loadtxt(path, dtype=float, delimiter=',', converters={4: iris_type})
：path路径
：dtype读取类型
：delimiter分隔符
：converters- A dictionary mapping column number to a function that will parse the
column string into the desired value. E.g., if column 0 is a date
string: ``converters = {0: datestr2num}``. Converters can also be
used to provide a default value for missing data (but see also
`genfromtxt`): ``converters = {3: lambda s: float(s.strip() or 0)}``.
Default: None.
#data
[[5.1, 3.5, 1.4, 0.2, 0. ],
[4.9, 3. , 1.4, 0.2, 0. ],
[4.7, 3.2, 1.3, 0.2, 0. ],
[4.6, 3.1, 1.5, 0.2, 0. ],
[5. , 3.6, 1.4, 0.2, 0. ]]
2、数据分训练测试集
#split用法
def split(ary,indices_or_sections,axis = 0):
'''
Split an array into multiple sub-arrays.
'''
Parameters-------------
ary : ndarray---Array to be divided into sub-arrays.
indices_or_sections---int or 1-D array
If `indices_or_sections` is an integer, N, the array will be divided
into N equal arrays along `axis`. If such a split is not possible,
an error is raised.
If `indices_or_sections` is a 1-D array of sorted integers, the entries
indicate where along `axis` the array is split. For example,
``[2, 3]`` would, for ``axis=0``, result in
- ary[:2]
- ary[2:3]
- ary[3:]
If an index exceeds the dimension of the array along `axis`,
an empty sub-array is returned correspondingly.
axis:int,optional---The axis along which to split,default is 0.
0按列分割，1按行分割
Return:sub-array:list of ndarrays
A list of sub-arrays
example:
x,y = numpy.split(data,(4,),axis = 1)
x:[[5.1, 3.5, 1.4, 0.2],
[4.9, 3. , 1.4, 0.2],
[4.7, 3.2, 1.3, 0.2],
[4.6, 3.1, 1.5, 0.2],
[5. , 3.6, 1.4, 0.2]]
y:[[0.],
[0.],
[0.],
[0.],
[0.]]
x = x[:, :2] #一种说法是为了后期可视化，经过本人测试，3的时候训练集准确率为100%，但正确率却远远低于2的时候。（具体原因没想明白，知道的小伙伴欢迎留言告知）
x_train, x_test, y_train, y_test = train_test_split(x, y, random_state=1, train_size=0.6) #random_state随机种子，train_size训练集占的百分比。
3、训练SVM
# clf = svm.SVC(C=0.1, kernel='linear', decision_function_shape='ovr')
【训练集正确率： 0.8 测试集正确率： 0.8】
clf = svm.SVC(C=0.8, kernel='rbf', gamma=20, decision_function_shape='ovr')
【训练集正确率：0.86666 测试集正确率：0.65】
clf.fit(x_train, y_train.ravel())
kernel='linear'时，为线性核，C越大分类效果越好，但有可能出现过拟合;
kernel='rbf'时，为高斯核，gamma越小，分类界面越连续；gamma越大，分类界面越分散，分类效果越好（训练集），但是有可能会过拟合。
decision_function_shape='ovr'时(one v rest)，即一个类别与其他类别进行划分;
decision_function_shape='ovo'时(one v one)，即将类别两两之间进行划分，用二分类的方法模拟多分类的结果。
# 准确率计算方式
print ( clf.score(x_train, y_train) ) # 精度
y_hat = clf.predict(x_train)
show_accuracy(y_hat, y_train, '训练集')
print ( clf.score(x_test, y_test) )
y_hat = clf.predict(x_test)
show_accuracy(y_hat, y_test, '测试集')