import numpy as np
import matplotlib.pyplot as plt from sklearn import datasets, linear_model,svm
from sklearn.model_selection import train_test_split def load_data_classfication():
'''
加载用于分类问题的数据集
'''
# 使用 scikit-learn 自带的 iris 数据集
iris=datasets.load_iris()
X_train=iris.data
y_train=iris.target
# 分层采样拆分成训练集和测试集,测试集大小为原始数据集大小的 1/4
return train_test_split(X_train, y_train,test_size=0.25,random_state=0,stratify=y_train) #支持向量机SVM非线性分类SVC模型
def test_SVC_linear(*data):
X_train,X_test,y_train,y_test=data
cls=svm.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)) # 生成用于分类的数据集
X_train,X_test,y_train,y_test=load_data_classfication()
# 调用 test_SVC_linear
test_SVC_linear(X_train,X_test,y_train,y_test)

def test_SVC_poly(*data):
'''
测试多项式核的 SVC 的预测性能随 degree、gamma、coef0 的影响.
'''
X_train,X_test,y_train,y_test=data
fig=plt.figure()
### 测试 degree ####
degrees=range(1,20)
train_scores=[]
test_scores=[]
for degree in degrees:
cls=svm.SVC(kernel='poly',degree=degree)
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=svm.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=svm.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() # 调用 test_SVC_poly
test_SVC_poly(X_train,X_test,y_train,y_test)

def test_SVC_rbf(*data):
'''
测试 高斯核的 SVC 的预测性能随 gamma 参数的影响
'''
X_train,X_test,y_train,y_test=data
gammas=range(1,20)
train_scores=[]
test_scores=[]
for gamma in gammas:
cls=svm.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() # 调用 test_SVC_rbf
test_SVC_rbf(X_train,X_test,y_train,y_test)

def test_SVC_sigmoid(*data):
'''
测试 sigmoid 核的 SVC 的预测性能随 gamma、coef0 的影响.
'''
X_train,X_test,y_train,y_test=data
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=svm.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=svm.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() # 调用 test_SVC_sigmoid
test_SVC_sigmoid(X_train,X_test,y_train,y_test)

吴裕雄 python 机器学习——支持向量机SVM非线性分类SVC模型的更多相关文章

  1. 吴裕雄 python 机器学习——支持向量机线性分类LinearSVC模型

    import numpy as np import matplotlib.pyplot as plt from sklearn import datasets, linear_model,svm fr ...

  2. 吴裕雄 python 机器学习——支持向量机非线性回归SVR模型

    import numpy as np import matplotlib.pyplot as plt from sklearn import datasets, linear_model,svm fr ...

  3. 吴裕雄 python 机器学习——支持向量机线性回归SVR模型

    import numpy as np import matplotlib.pyplot as plt from sklearn import datasets, linear_model,svm fr ...

  4. 吴裕雄 python 机器学习——集成学习AdaBoost算法回归模型

    import numpy as np import matplotlib.pyplot as plt from sklearn import datasets,ensemble from sklear ...

  5. 吴裕雄 python 机器学习——多项式贝叶斯分类器MultinomialNB模型

    import numpy as np import matplotlib.pyplot as plt from sklearn import datasets,naive_bayes from skl ...

  6. 吴裕雄 python 机器学习——人工神经网络与原始感知机模型

    import numpy as np from matplotlib import pyplot as plt from mpl_toolkits.mplot3d import Axes3D from ...

  7. 吴裕雄 python 机器学习——数据预处理包裹式特征选取模型

    from sklearn.svm import LinearSVC from sklearn.datasets import load_iris from sklearn.feature_select ...

  8. 吴裕雄 python 机器学习——等度量映射Isomap降维模型

    # -*- coding: utf-8 -*- import numpy as np import matplotlib.pyplot as plt from sklearn import datas ...

  9. 吴裕雄 python 机器学习——多维缩放降维MDS模型

    # -*- coding: utf-8 -*- import numpy as np import matplotlib.pyplot as plt from sklearn import datas ...

随机推荐

  1. sonn_game网站开发01:写在最前面

    之前做的个人博客项目,日向博客现在已经进入后期完善阶段了.是时候开始打造一个新坑了. 然而改造个什么坑呢?构思了好几天,想了好多方案,都觉得没啥动手欲望.因为,我想做的是那种,自己能用得上,而且有一定 ...

  2. office openxml学习(一)

    以前用过,aspose.dll处理word ,excel,之后发现 npoi,使用了一段时间,总觉得是第三方,不明白底层的实现,直到最近发现了office openxml ,其实这个技术,很久以前就有 ...

  3. 删除SSMS中保存的帐户信息

    通常我们在对象资源管理器中连接服务器时,会发现在服务器名称下保存有之前的实例信息.随着连接增多,要找某个连接还得费劲.sql2012:此时可以删除C:\Users\Administrator\AppD ...

  4. Python Quick Start

    1.安装Python 官网下载python: https://www.python.org/ 有2.x 3.x版本, 注意,python3.0不向下兼容2.x版本,有很多包3.0不提供 下载完后直接点 ...

  5. linux使用LVM合并硬盘

    目的将两块空硬盘合并为"一块",挂载到指定目录下,达到在一个目录使用2块硬盘所有空间的效果.条件硬盘1 /dev/sdb 硬盘2 /dev/sdc方法创建pvpvcreate /d ...

  6. MQTT控制---pingreq

    心跳请求 客户端向服务端发送PINGREQ报文用于: 在没有任何其他控制报文从client发给server时,告诉server,client还活着 请求server发送 响应确认它还活着 使用网络以确 ...

  7. day-08文件的操作

    三种字符串 1.普通字符串:u‘以字符作为输出单位’ print(u'abc') # 用于显示 2.二进制字符串:b‘二进制字符串以字节作为输出单位’ print(b'abc') # 用于传输 3.原 ...

  8. mysql 慢日志分析

    mysql 调优首先需要找到那些有问题的SQL语句. 怎么找到这些语句呢? mysql 提供了很方便的功能. 1.慢日志 在my.cnf 文件中,增加如下配置 log-error            ...

  9. rospy 中service

    Server部分: #!/usr/bin/env python import sys import os import rospy #from beginner.srv import * from b ...

  10. BZOJ4475: [Jsoi2015]子集选取【找规律】【数学】

    Description Input 输入包含一行两个整数N和K,1<=N,K<=10^9 Output 一行一个整数,表示不同方案数目模1,000,000,007的值. Sample In ...