#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：

：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:

3、训练SVM

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