SVM 支持向量机

阅读更多

SVM 支持向量机(support vector machine)


https://blog.csdn.net/v_july_v/article/details/7624837



原理：就是找到一个超平面，将数据进行分类，


超平面
这是平面中的直线、空间中的平面之推广（n大于3才被称为“超”平面）

如果空间是3维的，那么它的超平面是二维平面，而如果空间是二维的，则其超平面是一维线。

sklearn提供了三种基于svm的分类方法：
sklearn.svm.NuSVC()
sklearn.svm.LinearSVC()
sklearn.svm.SVC()

# coding=utf-8

import pandas as pd
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn import datasets
from sklearn import tree
import numpy as np
from sklearn.externals.six import StringIO
import pydot

from sklearn import svm
from sklearn import datasets
from sklearn.model_selection import train_test_split as ts


def drawPoint(x,y,color,marker):
    plt.scatter(x, y, color=color, marker=marker)  #


#中心为0，四边为1
train = [[0, 0.0, 1], [0, 0.2, 1], [0, 0.5, 1], [0, 0.7, 1], [0, 0.9, 1],
         [0, 1.0, 1], [0, 1.2, 1], [0, 1.5, 1], [0, 1.7, 1], [0, 1.9, 1],
         [0, 2.0, 1], [0, 2.3, 1], [0, 2.5, 1], [0, 2.8, 1], [0, 3.0, 1],
         [0, 3.1, 1], [0, 3.5, 1], [0, 3.6, 1], [0, 3.7, 1], [0, 3.8, 1],
         # [0, 4.1, 1], [0, 4.3, 1], [0, 4.7, 1], [0, 4.8, 1], [0, 4.9, 1],

         [4, 0.0, 1], [4, 0.2, 1], [4, 0.5, 1], [4, 0.7, 1], [4, 0.9, 1],
         [4, 1.0, 1], [4, 1.2, 1], [4, 1.5, 1], [4, 1.7, 1], [4, 1.9, 1],
         [4, 2.0, 1], [4, 2.3, 1], [4, 2.5, 1], [4, 2.8, 1], [4, 3.0, 1],
         [4, 3.1, 1], [4, 3.5, 1], [4, 3.6, 1], [4, 3.7, 1], [4, 3.8, 1],
         # [4, 4.1, 1], [4, 4.3, 1], [4, 4.7, 1], [4, 4.8, 1], [4, 4.9, 1],

         [0.0, 4, 1], [0.2, 4, 1], [0.5, 4, 1], [0.7, 4, 1], [0.9, 4, 1],
         [1.0, 4, 1], [1.2, 4, 1], [1.5, 4, 1], [1.7, 4, 1], [1.9, 4, 1],
         [2.0, 4, 1], [2.3, 4, 1], [2.5, 4, 1], [2.8, 4, 1], [3.0, 4, 1],
         [3.1, 4, 1], [3.5, 4, 1], [3.6, 4, 1], [3.7, 4, 1], [3.8, 4, 1],
         # [4.1, 4, 1], [4.3, 4, 1], [4.7, 4, 1], [4.8, 4, 1], [4.9, 4, 1],

         [0.0, 0, 1], [0.2, 0, 1], [0.5, 0, 1], [0.7, 0, 1], [0.9, 0, 1],
         [1.0, 0, 1], [1.2, 0, 1], [1.5, 0, 1], [1.7, 0, 1], [1.9, 0, 1],
         [2.0, 0, 1], [2.3, 0, 1], [2.5, 0, 1], [2.8, 0, 1], [3.0, 0, 1],
         [3.1, 0, 1], [3.5, 0, 1], [3.6, 0, 1], [3.7, 0, 1], [3.8, 0, 1],
         # [4.1, 0, 1], [4.3, 0, 1], [4.7, 0, 1], [4.8, 0, 1], [4.9, 0, 1],

         [2, 1.0, 0], [2, 1.2, 0], [2, 1.5, 0], [2, 1.7, 0], [2, 1.9, 0],
         [2, 2.1, 0], [2, 2.3, 0], [2, 2.5, 0], [2, 2.7, 0], [2, 2.9, 0],
         [2.5, 1.0, 0], [2.5, 1.2, 0], [2.5, 1.5, 0], [2.5, 1.7, 0], [2.5, 1.9, 0],
         [2.5, 2.1, 0], [2.5, 2.3, 0], [2.5, 2.5, 0], [2.5, 2.7, 0], [2.5, 2.9, 0],

         [3, 1.0, 0], [3, 1.2, 0], [3, 1.5, 0], [3, 1.7, 0], [3, 1.9, 0],
         [3, 2.0, 0], [3, 2.2, 0], [3, 2.5, 0], [3, 2.7, 0], [3, 2.9, 0],
         ]

trainData = np.array(train)
# print(trainData)

# trainX = trainData[:, 0]
# trainY = trainData[:, 1]
# trainZ = trainData[:, 2]

trainX = trainData[:, 0:2]
trainY = trainData[:, 2]

# print(trainX)
# print(trainY)

#调用SVC()
clf = svm.SVC()

clf.fit(trainX,trainY)

predict_y = clf.predict([[2.4,2.4],[2.3,1.3],[0.2,0.5],[3.7,3.5],[7,7]])
print(predict_y)


#画图
drawX1 = np.linspace(0,4,100)
drawY1 = np.linspace(0,4,100)

drawX2,drawY2 =  np.meshgrid(drawX1,drawY1)

drawX3 = np.ravel(drawX2)
drawY3 = np.ravel(drawY2)

draw4 = np.c_[drawX3,drawY3]

predict_drawZ1 = clf.predict(draw4)
print(predict_drawZ1)


drawX3_0 = drawX3[np.where(predict_drawZ1==0)]
drawY3_0 = drawY3[np.where(predict_drawZ1==0)]

drawX3_1 = drawX3[np.where(predict_drawZ1==1)]
drawY3_1 = drawY3[np.where(predict_drawZ1==1)]

drawPoint(drawX3_0, drawY3_0, 'r', '+')
drawPoint(drawX3_1, drawY3_1, 'g', '+')

#样本点
trainX2 = trainX[:,0]
trainY2 = trainX[:,1]
trainZ2 = trainData[:, 2]


trainX2_0 = trainX2[np.where(trainZ2==0)]
trainY2_0 = trainY2[np.where(trainZ2==0)]

trainX2_1 = trainX2[np.where(trainZ2==1)]
trainY2_1 = trainY2[np.where(trainZ2==1)]

drawPoint(trainX2_0, trainY2_0, 'b', 'o')
drawPoint(trainX2_1, trainY2_1, 'm', 'v')


plt.show()

• 
• 大小: 19.1 KB

• 查看图片附件