python中svm方法实现

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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:
lineArr = line.split(',')
print(line)
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))

ws =
[[ 0.65307162]
[-0.17196128]]
b =
[[-2.89901748]]