Python中如何支持向量机？相信很多没有经验的人对此束手无策，为此本文总结了问题出现的原因和解决方法，通过这篇文章希望你能解决这个问题。
1、云计算，典型应用OpenStack。2、WEB前端开发，众多大型网站均为Python开发。3.人工智能应用，基于大数据分析和深度学习而发展出来的人工智能本质上已经无法离开python。4、系统运维工程项目，自动化运维的标配就是python+Django/flask。5、金融理财分析，量化交易，金融分析。6、大数据分析。
运行环境
Pyhton3
numpy(科学计算包)
matplotlib(画图所需，不画图可不必)
计算过程
st=>start: 开始
e=>end: 结束
op1=>operation: 读入数据
op2=>operation: 格式化数据
cond=>condition: 是否达到迭代次数
op3=>operation: 寻找超平面分割最小间隔
ccond=>conditon: 数据是否改变
op4=>operation: 输出结果
st->op1->op2->cond
cond(yes)->op4->e
cond(no)->op3
啊，这markdown flow好难用，我决定就画到这吧=。=
输入样例
/* testSet.txt */ 3.542485 1.977398 -1 3.018896 2.556416 -1 7.551510 -1.580030 1 2.114999 -0.004466 -1 8.127113 1.274372 1 7.108772 -0.986906 1 8.610639 2.046708 1 2.326297 0.265213 -1 3.634009 1.730537 -1 0.341367 -0.894998 -1 3.125951 0.293251 -1 2.123252 -0.783563 -1 0.887835 -2.797792 -1 7.139979 -2.329896 1 1.696414 -1.212496 -1 8.117032 0.623493 1 8.497162 -0.266649 1 4.658191 3.507396 -1 8.197181 1.545132 1 1.208047 0.213100 -1 1.928486 -0.321870 -1 2.175808 -0.014527 -1 7.886608 0.461755 1 3.223038 -0.552392 -1 3.628502 2.190585 -1 7.407860 -0.121961 1 7.286357 0.251077 1 2.301095 -0.533988 -1 -0.232542 -0.547690 -1 3.457096 -0.082216 -1 3.023938 -0.057392 -1 8.015003 0.885325 1 8.991748 0.923154 1 7.916831 -1.781735 1 7.616862 -0.217958 1 2.450939 0.744967 -1 7.270337 -2.507834 1 1.749721 -0.961902 -1 1.803111 -0.176349 -1 8.804461 3.044301 1 1.231257 -0.568573 -1 2.074915 1.410550 -1 -0.743036 -1.736103 -1 3.536555 3.964960 -1 8.410143 0.025606 1 7.382988 -0.478764 1 6.960661 -0.245353 1 8.234460 0.701868 1 8.168618 -0.903835 1 1.534187 -0.622492 -1 9.229518 2.066088 1 7.886242 0.191813 1 2.893743 -1.643468 -1 1.870457 -1.040420 -1 5.286862 -2.358286 1 6.080573 0.418886 1 2.544314 1.714165 -1 6.016004 -3.753712 1 0.926310 -0.564359 -1 0.870296 -0.109952 -1 2.369345 1.375695 -1 1.363782 -0.254082 -1 7.279460 -0.189572 1 1.896005 0.515080 -1 8.102154 -0.603875 1 2.529893 0.662657 -1 1.963874 -0.365233 -1 8.132048 0.785914 1 8.245938 0.372366 1 6.543888 0.433164 1 -0.236713 -5.766721 -1 8.112593 0.295839 1 9.803425 1.495167 1 1.497407 -0.552916 -1 1.336267 -1.632889 -1 9.205805 -0.586480 1 1.966279 -1.840439 -1 8.398012 1.584918 1 7.239953 -1.764292 1 7.556201 0.241185 1 9.015509 0.345019 1 8.266085 -0.230977 1 8.545620 2.788799 1 9.295969 1.346332 1 2.404234 0.570278 -1 2.037772 0.021919 -1 1.727631 -0.453143 -1 1.979395 -0.050773 -1 8.092288 -1.372433 1 1.667645 0.239204 -1 9.854303 1.365116 1 7.921057 -1.327587 1 8.500757 1.492372 1 1.339746 -0.291183 -1 3.107511 0.758367 -1 2.609525 0.902979 -1 3.263585 1.367898 -1 2.912122 -0.202359 -1 1.731786 0.589096 -1 2.387003 1.573131 -1
代码实现
# -*- coding:utf-8 -*-
#!python3
__author__ = 'Wsine'
from numpy import *
import matplotlib.pyplot as plt
import operator
import time
def loadDataSet(fileName):
dataMat = []
labelMat = []
with open(fileName) as fr:
for line in fr.readlines():
lineArr = line.strip().split('\t')
dataMat.append([float(lineArr[0]), float(lineArr[1])])
labelMat.append(float(lineArr[2]))
return dataMat, labelMat
def selectJrand(i, m):
j = i
while (j == i):
j = int(random.uniform(0, m))
return j
def clipAlpha(aj, H, L):
if aj > H:
aj = H
if L > aj:
aj = L
return aj
class optStruct:
def __init__(self, dataMatIn, classLabels, C, toler):
self.X = dataMatIn
self.labelMat = classLabels
self.C = C
self.tol = toler
self.m = shape(dataMatIn)[0]
self.alphas = mat(zeros((self.m, 1)))
self.b = 0
self.eCache = mat(zeros((self.m, 2)))
def calcEk(oS, k):
fXk = float(multiply(oS.alphas, oS.labelMat).T * (oS.X * oS.X[k, :].T)) + oS.b
Ek = fXk - float(oS.labelMat[k])
return Ek
def selectJ(i, oS, Ei):
maxK = -1
maxDeltaE = 0
Ej = 0
oS.eCache[i] = [1, Ei]
validEcacheList = nonzero(oS.eCache[:, 0].A)[0]
if (len(validEcacheList)) > 1:
for k in validEcacheList:
if k == i:
continue
Ek = calcEk(oS, k)
deltaE = abs(Ei - Ek)
if (deltaE > maxDeltaE):
maxK = k
maxDeltaE = deltaE
Ej = Ek
return maxK, Ej
else:
j = selectJrand(i, oS.m)
Ej = calcEk(oS, j)
return j, Ej
def updateEk(oS, k):
Ek = calcEk(oS, k)
oS.eCache[k] = [1, Ek]
def innerL(i, oS):
Ei = calcEk(oS, i)
if ((oS.labelMat[i] * Ei < -oS.tol) and (oS.alphas[i] < oS.C)) or ((oS.labelMat[i] * Ei > oS.tol) and (oS.alphas[i] > 0)):
j, Ej = selectJ(i, oS, Ei)
alphaIold = oS.alphas[i].copy()
alphaJold = oS.alphas[j].copy()
if (oS.labelMat[i] != oS.labelMat[j]):
L = max(0, oS.alphas[j] - oS.alphas[i])
H = min(oS.C, oS.C + oS.alphas[j] - oS.alphas[i])
else:
L = max(0, oS.alphas[j] + oS.alphas[i] - oS.C)
H = min(oS.C, oS.alphas[j] + oS.alphas[i])
if (L == H):
# print("L == H")
return 0
eta = 2.0 * oS.X[i, :] * oS.X[j, :].T - oS.X[i, :] * oS.X[i, :].T - oS.X[j, :] * oS.X[j, :].T
if eta >= 0:
# print("eta >= 0")
return 0
oS.alphas[j] -= oS.labelMat[j] * (Ei - Ej) / eta
oS.alphas[j] = clipAlpha(oS.alphas[j], H, L)
updateEk(oS, j)
if (abs(oS.alphas[j] - alphaJold) < 0.00001):
# print("j not moving enough")
return 0
oS.alphas[i] += oS.labelMat[j] * oS.labelMat[i] * (alphaJold - oS.alphas[j])
updateEk(oS, i)
b1 = oS.b - Ei - oS.labelMat[i] * (oS.alphas[i] - alphaIold) * oS.X[i, :] * oS.X[i, :].T - oS.labelMat[j] * (oS.alphas[j] - alphaJold) * oS.X[i, :] * oS.X[j, :].T
b2 = oS.b - Ei - oS.labelMat[i] * (oS.alphas[i] - alphaIold) * oS.X[i, :] * oS.X[j, :].T - oS.labelMat[j] * (oS.alphas[j] - alphaJold) * oS.X[j, :] * oS.X[j, :].T
if (0 < oS.alphas[i]) and (oS.C > oS.alphas[i]):
oS.b = b1
elif (0 < oS.alphas[j]) and (oS.C > oS.alphas[j]):
oS.b = b2
else:
oS.b = (b1 + b2) / 2.0
return 1
else:
return 0
def smoP(dataMatIn, classLabels, C, toler, maxIter, kTup=('lin', 0)):
"""
输入：数据集, 类别标签, 常数C, 容错率, 最大循环次数
输出：目标b, 参数alphas
"""
oS = optStruct(mat(dataMatIn), mat(classLabels).transpose(), C, toler)
iterr = 0
entireSet = True
alphaPairsChanged = 0
while (iterr < maxIter) and ((alphaPairsChanged > 0) or (entireSet)):
alphaPairsChanged = 0
if entireSet:
for i in range(oS.m):
alphaPairsChanged += innerL(i, oS)
# print("fullSet, iter: %d i:%d, pairs changed %d" % (iterr, i, alphaPairsChanged))
iterr += 1
else:
nonBoundIs = nonzero((oS.alphas.A > 0) * (oS.alphas.A < C))[0]
for i in nonBoundIs:
alphaPairsChanged += innerL(i, oS)
# print("non-bound, iter: %d i:%d, pairs changed %d" % (iterr, i, alphaPairsChanged))
iterr += 1
if entireSet:
entireSet = False
elif (alphaPairsChanged == 0):
entireSet = True
# print("iteration number: %d" % iterr)
return oS.b, oS.alphas
def calcWs(alphas, dataArr, classLabels):
"""
输入：alphas, 数据集, 类别标签
输出：目标w
"""
X = mat(dataArr)
labelMat = mat(classLabels).transpose()
m, n = shape(X)
w = zeros((n, 1))
for i in range(m):
w += multiply(alphas[i] * labelMat[i], X[i, :].T)
return w
def plotFeature(dataMat, labelMat, weights, b):
dataArr = array(dataMat)
n = shape(dataArr)[0]
xcord1 = []; ycord1 = []
xcord2 = []; ycord2 = []
for i in range(n):
if int(labelMat[i]) == 1:
xcord1.append(dataArr[i, 0])
ycord1.append(dataArr[i, 1])
else:
xcord2.append(dataArr[i, 0])
ycord2.append(dataArr[i, 1])
fig = plt.figure()
ax = fig.add_subplot(111)
ax.scatter(xcord1, ycord1, s=30, c='red', marker='s')
ax.scatter(xcord2, ycord2, s=30, c='green')
x = arange(2, 7.0, 0.1)
y = (-b[0, 0] * x) - 10 / linalg.norm(weights)
ax.plot(x, y)
plt.xlabel('X1'); plt.ylabel('X2')
plt.show()
def main():
trainDataSet, trainLabel = loadDataSet('testSet.txt')
b, alphas = smoP(trainDataSet, trainLabel, 0.6, 0.0001, 40)
ws = calcWs(alphas, trainDataSet, trainLabel)
print("ws = \n", ws)
print("b = \n", b)
plotFeature(trainDataSet, trainLabel, ws, b)
if __name__ == '__main__':
start = time.clock()
main()
end = time.clock()
print('finish all in %s' % str(end - start))输出样例
ws =
[[ 0.65307162]
[-0.17196128]]
b =
[[-2.89901748]]
finish all in 2.5683854014099112
看完上述内容，你们掌握Python中如何支持向量机的方法了吗？如果还想学到更多技能或想了解更多相关内容，欢迎关注亿速云行业资讯频道，感谢各位的阅读！
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