当前位置：网站首页>机器学习之支持向量机实例，线性核函数 多项式核函数 RBF高斯核函数 sigmoid核函数
机器学习之支持向量机实例，线性核函数 多项式核函数 RBF高斯核函数 sigmoid核函数
2022-08-04 18:41:00 【51CTO】
支持向量机实例
1.线性核函数
def
test_SVC_linear():
'''
测试 SVC 的用法。这里使用的是最简单的线性核
:param data: 可变参数。它是一个元组，这里要求其元素依次为训练样本集、测试样本集、训练样本的标记、测试样本的标记
:return: None
'''
iris
=
datasets.
load_iris()
X_train,
X_test,
y_train,
y_test
=
train_test_split(
iris.
data,
iris.
target,
test_size
=
0.25,
random_state
=
0,
stratify
=
iris.
target)
cls
=
SVC(
kernel
=
'linear')
cls.
fit(
X_train,
y_train)
print(
'Coefficients:%s, intercept %s'
%(
cls.
coef_,
cls.
intercept_))
print(
'Score: %.2f'
%
cls.
score(
X_test,
y_test))
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2.多项式核函数
def
test_SVC_poly():
'''
测试多项式核的 SVC 的预测性能随 degree、gamma、coef0 的影响.
:param data: 可变参数。它是一个元组，这里要求其元素依次为训练样本集、测试样本集、训练样本的标记、测试样本的标记
:return: None
'''
iris
=
datasets.
load_iris()
X_train,
X_test,
y_train,
y_test
=
train_test_split(
iris.
data,
iris.
target,
test_size
=
0.25,
random_state
=
0,
stratify
=
iris.
target)
fig
=
plt.
figure()
### 测试 degree ####
degrees
=
range(
1,
20)
train_scores
=[]
test_scores
=[]
for
degree
in
degrees:
cls
=
SVC(
kernel
=
'poly',
degree
=
degree,
gamma
=
'auto')
cls.
fit(
X_train,
y_train)
train_scores.
append(
cls.
score(
X_train,
y_train))
test_scores.
append(
cls.
score(
X_test,
y_test))
ax
=
fig.
add_subplot(
1,
3,
1)
# 一行三列
ax.
plot(
degrees,
train_scores,
label
=
"Training score ",
marker
=
'+' )
ax.
plot(
degrees,
test_scores,
label
=
" Testing score ",
marker
=
'o' )
ax.
set_title(
"SVC_poly_degree ")
ax.
set_xlabel(
"p")
ax.
set_ylabel(
"score")
ax.
set_ylim(
0,
1.05)
ax.
legend(
loc
=
"best",
framealpha
=
0.5)
### 测试 gamma ，此时 degree 固定为 3####
gammas
=
range(
1,
20)
train_scores
=[]
test_scores
=[]
for
gamma
in
gammas:
cls
=
SVC(
kernel
=
'poly',
gamma
=
gamma,
degree
=
3)
cls.
fit(
X_train,
y_train)
train_scores.
append(
cls.
score(
X_train,
y_train))
test_scores.
append(
cls.
score(
X_test,
y_test))
ax
=
fig.
add_subplot(
1,
3,
2)
ax.
plot(
gammas,
train_scores,
label
=
"Training score ",
marker
=
'+' )
ax.
plot(
gammas,
test_scores,
label
=
" Testing score ",
marker
=
'o' )
ax.
set_title(
"SVC_poly_gamma ")
ax.
set_xlabel(
r"$\gamma$")
ax.
set_ylabel(
"score")
ax.
set_ylim(
0,
1.05)
ax.
legend(
loc
=
"best",
framealpha
=
0.5)
### 测试 r ，此时 gamma固定为10 ， degree 固定为 3######
rs
=
range(
0,
20)
train_scores
=[]
test_scores
=[]
for
r
in
rs:
cls
=
SVC(
kernel
=
'poly',
gamma
=
10,
degree
=
3,
coef0
=
r)
cls.
fit(
X_train,
y_train)
train_scores.
append(
cls.
score(
X_train,
y_train))
test_scores.
append(
cls.
score(
X_test,
y_test))
ax
=
fig.
add_subplot(
1,
3,
3)
ax.
plot(
rs,
train_scores,
label
=
"Training score ",
marker
=
'+' )
ax.
plot(
rs,
test_scores,
label
=
" Testing score ",
marker
=
'o' )
ax.
set_title(
"SVC_poly_r ")
ax.
set_xlabel(
r"r")
ax.
set_ylabel(
"score")
ax.
set_ylim(
0,
1.05)
ax.
legend(
loc
=
"best",
framealpha
=
0.5)
plt.
show()
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3.RBF高斯核函数
ef
test_SVC_rbf():
'''
测试 高斯核的 SVC 的预测性能随 gamma 参数的影响
:param data: 可变参数。它是一个元组，这里要求其元素依次为训练样本集、测试样本集、训练样本的标记、测试样本的标记
:return: None
'''
iris
=
datasets.
load_iris()
X_train,
X_test,
y_train,
y_test
=
train_test_split(
iris.
data,
iris.
target,
test_size
=
0.25,
random_state
=
0,
stratify
=
iris.
target)
gammas
=
range(
1,
20)
train_scores
=[]
test_scores
=[]
for
gamma
in
gammas:
cls
=
SVC(
kernel
=
'rbf',
gamma
=
gamma)
cls.
fit(
X_train,
y_train)
train_scores.
append(
cls.
score(
X_train,
y_train))
test_scores.
append(
cls.
score(
X_test,
y_test))
fig
=
plt.
figure()
ax
=
fig.
add_subplot(
1,
1,
1)
ax.
plot(
gammas,
train_scores,
label
=
"Training score ",
marker
=
'+' )
ax.
plot(
gammas,
test_scores,
label
=
" Testing score ",
marker
=
'o' )
ax.
set_title(
"SVC_rbf")
ax.
set_xlabel(
r"$\gamma$")
ax.
set_ylabel(
"score")
ax.
set_ylim(
0,
1.05)
ax.
legend(
loc
=
"best",
framealpha
=
0.5)
plt.
show()
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4.sigmoid核函数
def
test_SVC_sigmoid():
'''
测试 sigmoid 核的 SVC 的预测性能随 gamma、coef0 的影响.
:param data: 可变参数。它是一个元组，这里要求其元素依次为训练样本集、测试样本集、训练样本的标记、测试样本的标记
:return: None
'''
iris
=
datasets.
load_iris()
X_train,
X_test,
y_train,
y_test
=
train_test_split(
iris.
data,
iris.
target,
test_size
=
0.25,
random_state
=
0,
stratify
=
iris.
target)
fig
=
plt.
figure()
### 测试 gamma ，固定 coef0 为 0 ####
gammas
=
np.
logspace(
-
2,
1)
train_scores
=[]
test_scores
=[]
for
gamma
in
gammas:
cls
=
SVC(
kernel
=
'sigmoid',
gamma
=
gamma,
coef0
=
0)
cls.
fit(
X_train,
y_train)
train_scores.
append(
cls.
score(
X_train,
y_train))
test_scores.
append(
cls.
score(
X_test,
y_test))
ax
=
fig.
add_subplot(
1,
2,
1)
ax.
plot(
gammas,
train_scores,
label
=
"Training score ",
marker
=
'+' )
ax.
plot(
gammas,
test_scores,
label
=
" Testing score ",
marker
=
'o' )
ax.
set_title(
"SVC_sigmoid_gamma ")
ax.
set_xscale(
"log")
ax.
set_xlabel(
r"$\gamma$")
ax.
set_ylabel(
"score")
ax.
set_ylim(
0,
1.05)
ax.
legend(
loc
=
"best",
framealpha
=
0.5)
### 测试 r，固定 gamma 为 0.01 ######
rs
=
np.
linspace(
0,
5)
train_scores
=[]
test_scores
=[]
for
r
in
rs:
cls
=
SVC(
kernel
=
'sigmoid',
coef0
=
r,
gamma
=
0.01)
cls.
fit(
X_train,
y_train)
train_scores.
append(
cls.
score(
X_train,
y_train))
test_scores.
append(
cls.
score(
X_test,
y_test))
ax
=
fig.
add_subplot(
1,
2,
2)
ax.
plot(
rs,
train_scores,
label
=
"Training score ",
marker
=
'+' )
ax.
plot(
rs,
test_scores,
label
=
" Testing score ",
marker
=
'o' )
ax.
set_title(
"SVC_sigmoid_r ")
ax.
set_xlabel(
r"r")
ax.
set_ylabel(
"score")
ax.
set_ylim(
0,
1.05)
ax.
legend(
loc
=
"best",
framealpha
=
0.5)
plt.
show()
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代码：
import
numpy
as
np
from
sklearn
import
datasets
from
sklearn.
model_selection
import
train_test_split
from
sklearn.
svm
import
SVC
import
matplotlib.
pyplot
as
plt
def
test_SVC_linear():
'''
测试 SVC 的用法。这里使用的是最简单的线性核
:param data: 可变参数。它是一个元组，这里要求其元素依次为训练样本集、测试样本集、训练样本的标记、测试样本的标记
:return: None
'''
iris
=
datasets.
load_iris()
X_train,
X_test,
y_train,
y_test
=
train_test_split(
iris.
data,
iris.
target,
test_size
=
0.25,
random_state
=
0,
stratify
=
iris.
target)
cls
=
SVC(
kernel
=
'linear')
cls.
fit(
X_train,
y_train)
print(
'Coefficients:%s, intercept %s'
%(
cls.
coef_,
cls.
intercept_))
print(
'Score: %.2f'
%
cls.
score(
X_test,
y_test))
def
test_SVC_poly():
'''
测试多项式核的 SVC 的预测性能随 degree、gamma、coef0 的影响.
:param data: 可变参数。它是一个元组，这里要求其元素依次为训练样本集、测试样本集、训练样本的标记、测试样本的标记
:return: None
'''
iris
=
datasets.
load_iris()
X_train,
X_test,
y_train,
y_test
=
train_test_split(
iris.
data,
iris.
target,
test_size
=
0.25,
random_state
=
0,
stratify
=
iris.
target)
fig
=
plt.
figure()
### 测试 degree ####
degrees
=
range(
1,
20)
train_scores
=[]
test_scores
=[]
for
degree
in
degrees:
cls
=
SVC(
kernel
=
'poly',
degree
=
degree,
gamma
=
'auto')
cls.
fit(
X_train,
y_train)
train_scores.
append(
cls.
score(
X_train,
y_train))
test_scores.
append(
cls.
score(
X_test,
y_test))
ax
=
fig.
add_subplot(
1,
3,
1)
# 一行三列
ax.
plot(
degrees,
train_scores,
label
=
"Training score ",
marker
=
'+' )
ax.
plot(
degrees,
test_scores,
label
=
" Testing score ",
marker
=
'o' )
ax.
set_title(
"SVC_poly_degree ")
ax.
set_xlabel(
"p")
ax.
set_ylabel(
"score")
ax.
set_ylim(
0,
1.05)
ax.
legend(
loc
=
"best",
framealpha
=
0.5)
### 测试 gamma ，此时 degree 固定为 3####
gammas
=
range(
1,
20)
train_scores
=[]
test_scores
=[]
for
gamma
in
gammas:
cls
=
SVC(
kernel
=
'poly',
gamma
=
gamma,
degree
=
3)
cls.
fit(
X_train,
y_train)
train_scores.
append(
cls.
score(
X_train,
y_train))
test_scores.
append(
cls.
score(
X_test,
y_test))
ax
=
fig.
add_subplot(
1,
3,
2)
ax.
plot(
gammas,
train_scores,
label
=
"Training score ",
marker
=
'+' )
ax.
plot(
gammas,
test_scores,
label
=
" Testing score ",
marker
=
'o' )
ax.
set_title(
"SVC_poly_gamma ")
ax.
set_xlabel(
r"$\gamma$")
ax.
set_ylabel(
"score")
ax.
set_ylim(
0,
1.05)
ax.
legend(
loc
=
"best",
framealpha
=
0.5)
### 测试 r ，此时 gamma固定为10 ， degree 固定为 3######
rs
=
range(
0,
20)
train_scores
=[]
test_scores
=[]
for
r
in
rs:
cls
=
SVC(
kernel
=
'poly',
gamma
=
10,
degree
=
3,
coef0
=
r)
cls.
fit(
X_train,
y_train)
train_scores.
append(
cls.
score(
X_train,
y_train))
test_scores.
append(
cls.
score(
X_test,
y_test))
ax
=
fig.
add_subplot(
1,
3,
3)
ax.
plot(
rs,
train_scores,
label
=
"Training score ",
marker
=
'+' )
ax.
plot(
rs,
test_scores,
label
=
" Testing score ",
marker
=
'o' )
ax.
set_title(
"SVC_poly_r ")
ax.
set_xlabel(
r"r")
ax.
set_ylabel(
"score")
ax.
set_ylim(
0,
1.05)
ax.
legend(
loc
=
"best",
framealpha
=
0.5)
plt.
show()
def
test_SVC_rbf():
'''
测试 高斯核的 SVC 的预测性能随 gamma 参数的影响
:param data: 可变参数。它是一个元组，这里要求其元素依次为训练样本集、测试样本集、训练样本的标记、测试样本的标记
:return: None
'''
iris
=
datasets.
load_iris()
X_train,
X_test,
y_train,
y_test
=
train_test_split(
iris.
data,
iris.
target,
test_size
=
0.25,
random_state
=
0,
stratify
=
iris.
target)
gammas
=
range(
1,
20)
train_scores
=[]
test_scores
=[]
for
gamma
in
gammas:
cls
=
SVC(
kernel
=
'rbf',
gamma
=
gamma)
cls.
fit(
X_train,
y_train)
train_scores.
append(
cls.
score(
X_train,
y_train))
test_scores.
append(
cls.
score(
X_test,
y_test))
fig
=
plt.
figure()
ax
=
fig.
add_subplot(
1,
1,
1)
ax.
plot(
gammas,
train_scores,
label
=
"Training score ",
marker
=
'+' )
ax.
plot(
gammas,
test_scores,
label
=
" Testing score ",
marker
=
'o' )
ax.
set_title(
"SVC_rbf")
ax.
set_xlabel(
r"$\gamma$")
ax.
set_ylabel(
"score")
ax.
set_ylim(
0,
1.05)
ax.
legend(
loc
=
"best",
framealpha
=
0.5)
plt.
show()
def
test_SVC_sigmoid():
'''
测试 sigmoid 核的 SVC 的预测性能随 gamma、coef0 的影响.
:param data: 可变参数。它是一个元组，这里要求其元素依次为训练样本集、测试样本集、训练样本的标记、测试样本的标记
:return: None
'''
iris
=
datasets.
load_iris()
X_train,
X_test,
y_train,
y_test
=
train_test_split(
iris.
data,
iris.
target,
test_size
=
0.25,
random_state
=
0,
stratify
=
iris.
target)
fig
=
plt.
figure()
### 测试 gamma ，固定 coef0 为 0 ####
gammas
=
np.
logspace(
-
2,
1)
train_scores
=[]
test_scores
=[]
for
gamma
in
gammas:
cls
=
SVC(
kernel
=
'sigmoid',
gamma
=
gamma,
coef0
=
0)
cls.
fit(
X_train,
y_train)
train_scores.
append(
cls.
score(
X_train,
y_train))
test_scores.
append(
cls.
score(
X_test,
y_test))
ax
=
fig.
add_subplot(
1,
2,
1)
ax.
plot(
gammas,
train_scores,
label
=
"Training score ",
marker
=
'+' )
ax.
plot(
gammas,
test_scores,
label
=
" Testing score ",
marker
=
'o' )
ax.
set_title(
"SVC_sigmoid_gamma ")
ax.
set_xscale(
"log")
ax.
set_xlabel(
r"$\gamma$")
ax.
set_ylabel(
"score")
ax.
set_ylim(
0,
1.05)
ax.
legend(
loc
=
"best",
framealpha
=
0.5)
### 测试 r，固定 gamma 为 0.01 ######
rs
=
np.
linspace(
0,
5)
train_scores
=[]
test_scores
=[]
for
r
in
rs:
cls
=
SVC(
kernel
=
'sigmoid',
coef0
=
r,
gamma
=
0.01)
cls.
fit(
X_train,
y_train)
train_scores.
append(
cls.
score(
X_train,
y_train))
test_scores.
append(
cls.
score(
X_test,
y_test))
ax
=
fig.
add_subplot(
1,
2,
2)
ax.
plot(
rs,
train_scores,
label
=
"Training score ",
marker
=
'+' )
ax.
plot(
rs,
test_scores,
label
=
" Testing score ",
marker
=
'o' )
ax.
set_title(
"SVC_sigmoid_r ")
ax.
set_xlabel(
r"r")
ax.
set_ylabel(
"score")
ax.
set_ylim(
0,
1.05)
ax.
legend(
loc
=
"best",
framealpha
=
0.5)
plt.
show()
if
__name__
==
"__main__":
test_SVC_linear()
test_SVC_poly()
test_SVC_rbf()
test_SVC_sigmoid()
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结果：
线性核函数
多项式核函数
RBF高斯核函数
sigmoid核函数
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