def runSimpleSMO():
dataArray,labelArray = svm.loadDataSet('testSet.txt')
b,alphas = svm.smoSimple(dataArray,labelArray,0.6,0.001,40)
print b
print alphas[alphas>0]
print shape(alphas[alphas>0])[1] #得到支持向量的个数
for i in range(100): #获得支持向量的坐标数据，和类别
if alphas[i]>0.0:
print dataArray[i],labelArray[i]
def test_2():
dataArr, labelArr = svmMLiA.loadDataSet('testSet.txt')
b, alphas = svmMLiA.smoSimple(dataArr, labelArr, 0.6, 0.001, 40)
print "b"
print b
print "alphas"
print alphas
print ">0"
print alphas[alphas > 0]
labelMat = mat(labelArr).transpose()
m, n = shape(datMat)
for i in range(m):
kernelEval = kernelTrans(sVs, datMat[i, :], kTup)
predict = kernelEval.T * multiply(labelSV, alphas[svInd]) + b
if sign(predict) != sign(labelArr[i]): errorCount += 1
print("the test error rate is: %f" % (float(errorCount) / m))
if __name__ == "__main__":
import svmMLiA
dataArr, labelArr = svmMLiA.loadDataSet('testSetSVM.txt')
print("dataArr:\n", dataArr)
print("labenlArr:\n", labelArr)
b, alphas = svmMLiA.smoSimple(dataArr, labelArr, 0.6, 0.001, 40)
print("\nb:\n", b, "\nalphas:\n",
alphas[alphas > 0]) #alphas[alphas>0], only can be used in numpy
print("\nshape of alphas>0:\n", shape(alphas[alphas > 0]))
for i in range(100):
if alphas[i] > 0.0:
print(dataArr[i], labelArr[i])
# b,alphas = svmMLiA.smoPK(dataArr,labelArr,0.6,0.001,40)
# print("\nb2:\n", b, "\nalphas2:\n", alphas[alphas > 0]) # alphas[alphas>0], only can be used in numpy
#
# ws=svmMLiA.calcWs(alphas,dataArr,labelArr)
# print("\nws:\n",ws)
# dataMat=mat(dataArr)
# print("datMat[i]: ",dataMat[2]*mat(ws)+b)
from numpy import *
import matplotlib.pyplot as plt
from matplotlib.patches import Circle
import svmMLiA
xcord0 = [] # 类别0的x坐标
ycord0 = [] # 类别0的y坐标
xcord1 = [] # 类别1的x坐标
ycord1 = [] # 类别1的y坐标
markers = []
colors = []
dataArr, labelArr = svmMLiA.loadDataSet('testSet.txt')
C = 0.6
b, alphas = svmMLiA.smoSimple(dataArr, labelArr, C, 0.001, 40)
b = float(b)
# 支持向量的坐标
xSupportVectors = []
ySupportVectors = []
alphaSupportVectors = [] # 支持向量对应的非零alpha
labelSupportVectors = []
featureDatasSupportVectors = [] # 支持向量的原始数据
for i in range(0, alphas.__len__()):
if alphas[i] > 0:
print i, ":", alphas[i]
alphaSupportVectors.append(float(alphas[i]))
labelSupportVectors.append(int(labelArr[i]))
xSupportVectors.append(dataArr[i][0])
ySupportVectors.append(dataArr[i][1])
featureDatasSupportVectors.append(dataArr[i])
#!/usr/bin/env python
# -*- coding: utf-8 -*-
# author:yiluzhang
import svmMLiA
if __name__ == '__main__':
# 简化版smo测试
data_arr, label_arr = svmMLiA.loadDataSet('testSet.txt')
# print(data_arr[:5])
# print(label_arr[:5])
w_t, b, alpha = svmMLiA.smoSimple(data_arr, label_arr, 0.6, 0.001, 40)
svmMLiA.smo_simple_plt(data_arr, w_t, b)
print(b)
import svmMLiA as SVM
dataArr, labelArr = SVM.loadDataSet('data/testSet.txt')
b, alphas = SVM.smoSimple(dataArr, labelArr, 0.6, 0.001, 40)
"""
output:
iteration number: 29
j not moving enough
iteration number: 30
iter: 30 i:17, pairs changed 1
j not moving enough
iteration number: 0
j not moving enough
iteration number: 1
b: matrix([[-3.8486163]])
alphas[alphas>0] = matrix([[0.09313378, 0.27456007, 0.04445935, 0.3232345 ]])
"""
# To see which points of our dataset are support vectors
for i in range(100):
if alphas[i] > 0.0:
print(dataArr[i], labelArr[i])
"""
output:
[4.658191, 3.507396] -1.0
[3.457096, -0.082216] -1.0
[2.893743, -1.643468] -1.0
[6.080573, 0.418886] 1.0
# -*- coding: utf-8 -*-
from numpy import *
import svmMLiA
dm, ls = svmMLiA.loadDataSet('testSet.txt')
b, alphas = svmMLiA.smoSimple(dm, ls, 0.6, 0.001, 40) # 简版smo算法
alphas[alphas > 0]
shape(alphas[alphas > 0])
svMat = []
for i in range(100):
if alphas[i] > 0.0:
svMat.append(dm[i])
print dm[i], ls[i] # 支持向量
svmMLiA.plot(dm, ls, svMat)
import svmMLiA
dm, ls = svmMLiA.loadDataSet('testSet.txt')
b, alphas = svmMLiA.smoP(dm, ls, 0.6, 0.001, 40)
ws = svmMLiA.calcWs(alphas, dm, ls)
mat(dm)[0] * mat(ws) + b # 对第0个数据分类
ls[0]
import svmMLiA
dm, ls = svmMLiA.loadDataSet('testSet.txt')
b, alphas = svmMLiA.smoP(dm, ls, 0.6, 0.001, 40)
svMat = []
for i in range(100):
if alphas[i] > 0.0:
svMat.append(dm[i])
import svmMLiA
from numpy import *
import matplotlib.pyplot as plt
dataArr, labelArr = svmMLiA.loadDataSet('testSet.txt')
# print(labelArr) #[-1.0, -1.0, 1.0, -1.0, 1.0, 1.0, 1.0,...]
b, alphas = svmMLiA.smoSimple(dataArr, labelArr, 0.6, 0.001,
40) #数据集，类别标签，常数C，容错率，退出前最大的循环次数
print('martix b:', b)
am, an = shape(alphas)
print('am:', am, ',an:', an)
print('martix alphas:', alphas[alphas > 0])
print('支持向量的个数：', shape(alphas[alphas > 0]))
for i in range(100):
if alphas[i] > 0.0: print('这些点是支持向量：', dataArr[i], labelArr[i])
#output:
# martix b: [[-3.79474649]]
# am: 100 ,an: 1
# martix alphas: [[ 0.11482438 0.1309357 0.11348042 0.08434201 0.27489848]]
# 支持向量的个数： (1, 5)
# 这些点是支持向量： [4.658191, 3.507396] -1.0
# 这些点是支持向量： [3.457096, -0.082216] -1.0
# 这些点是支持向量： [2.893743, -1.643468] -1.0
# 这些点是支持向量： [5.286862, -2.358286] 1.0
# 这些点是支持向量： [6.080573, 0.418886] 1.0
ws = svmMLiA.calcWs(alphas, dataArr, labelArr)
x0 = []
y0 = []
x1 = []
y1 = []
import svmMLiA
dataMat, labelMat = svmMLiA.loadDataSet(
"E:\Desktop\machinelearninginaction\Ch06/testSet.txt")
print dataMat
print labelMat
b, alphas = svmMLiA.smoSimple(dataMat, labelMat, 0.6, 0.001, 40)
print b
print alphas[alphas > 0]
for i in range(100):
if alphas[i] > 0.0: print dataMat[i], labelMat[i]
def test_smo_simple():
dataArr, labelArr = svmMLiA.loadDataSet('testSet.txt')
b, alphas = svmMLiA.smoSimple(dataArr, labelArr, 0.6, 0.001, 40)
print(b)
print(alphas[alphas > 0])
def test_3():
dataArr, labelArr = svmMLiA.loadDataSet('testSet.txt')
b, alphas = svmMLiA.smoSimple(dataArr, labelArr, 0.6, 0.001, 40)
print shape(alphas[alphas > 0])
for i in range(100):
if alphas[i] > 0.0: print dataArr[i], labelArr[i]
