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Merge branch 'master' of https://github.com/jiangzhonglian/MachineLearning; branch 'master' of https://github.com/apachecn/MachineLearning

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jiangzhonglian
2017-04-03 00:23:28 +08:00
parent 19b8689d2e aa6792502d
commit ea72fdad2f
1 changed files with 99 additions and 37 deletions
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136
src/python/06.SVM/svmMLiA.py
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@@ -75,7 +75,9 @@ def smoSimple(dataMatIn, classLabels, C, toler, maxIter):
Args:
dataMatIn: 数据集
classLabels: 类别标签
C: 常数C
C: 松弛变量，允许有些数据点可以处于分隔面的错误一侧。
控制最大化间隔和保证大部分的函数间隔小于1.0这两个目标的权重。
可以通过调节该参数达到不同的结果。
toler: 容错率
maxIter: 退出前最大的循环次数
@@ -131,6 +133,16 @@ def smoSimple(dataMatIn, classLabels, C, toler, maxIter):
def kernelTrans(X, A, kTup): # calc the kernel or transform data to a higher dimensional space
"""
核转换函数
Args:
X:
A:
kTup: 核函数的信息
Returns:
"""
m, n = shape(X)
K = mat(zeros((m, 1)))
if kTup[0] == 'lin':
@@ -151,14 +163,23 @@ class optStruct:
建立的数据结构来保存所有的重要值
"""
def __init__(self, dataMatIn, classLabels, C, toler, kTup): # Initialize the structure with the parameters
"""
Args:
dataMatIn:
classLabels:
C:
toler:
kTup: 包含核函数信息的元组
"""
self.X = dataMatIn
self.labelMat = classLabels
self.C = C
self.tol = toler
self.m = shape(dataMatIn)[0]
self.m = shape(dataMatIn)[0] # 数据的行数
self.alphas = mat(zeros((self.m, 1)))
self.b = 0
self.eCache = mat(zeros((self.m, 2))) # 第一列给出的是eCache是否有效的标志位，第二列给出的是实际的E值。
self.eCache = mat(zeros((self.m, 2))) # 误差缓存，第一列给出的是eCache是否有效的标志位，第二列给出的是实际的E值。
self.K = mat(zeros((self.m, self.m)))
for i in range(self.m):
self.K[:, i] = kernelTrans(self.X, self.X[i, :], kTup)
@@ -182,6 +203,7 @@ def calcEk(oS, k):
def selectJ(i, oS, Ei): # this is the second choice -heurstic, and calcs Ej
"""
内循环的启发式方法。
选择第二个(内循环)alpha的alpha值
这里的目标是选择合适的第二个alpha值以保证每次优化中采用最大步长。
该函数的误差与第一个alpha值Ei和下标i有关。
@@ -204,8 +226,9 @@ def selectJ(i, oS, Ei): # this is the second choice -heurstic, and calcs Ej
Ek = calcEk(oS, k)
deltaE = abs(Ei - Ek)
if (deltaE > maxDeltaE):
maxK = k;
maxDeltaE = deltaE;
# 选择具有最大步长的j
maxK = k
maxDeltaE = deltaE
Ej = Ek
return maxK, Ej
else: # 如果是第一次循环，则随机选择一个alpha值
@@ -230,30 +253,42 @@ def updateEk(oS, k): # after any alpha has changed update the new value in the
def innerL(i, oS):
"""
内循环代码
Args:
i:
oS:
Returns:
"""
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) # this has been changed from selectJrand
alphaIold = oS.alphas[i].copy();
alphaJold = oS.alphas[j].copy();
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
if L == H:
print("L==H")
return 0
eta = 2.0 * oS.K[i, j] - oS.K[i, i] - oS.K[j, j] # changed for kernel
if eta >= 0: print("eta>=0")
return 0
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) # added this for the Ecache
if (abs(oS.alphas[j] - alphaJold) < 0.00001): print("j not moving enough")
return 0
updateEk(oS, j) # 更新误差缓存
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]) # update i by the same amount as j
updateEk(oS, i) # added this for the Ecache #the update is in the oppostie direction
updateEk(oS, i) # 更新误差缓存 #the update is in the oppostie direction
b1 = oS.b - Ei - oS.labelMat[i] * (oS.alphas[i] - alphaIold) * oS.K[i, i] - oS.labelMat[j] * (
oS.alphas[j] - alphaJold) * oS.K[i, j]
b2 = oS.b - Ej - oS.labelMat[i] * (oS.alphas[i] - alphaIold) * oS.K[i, j] - oS.labelMat[j] * (
@@ -269,19 +304,32 @@ def innerL(i, oS):
return 0
def smoP(dataMatIn, classLabels, C, toler, maxIter, kTup=('lin', 0)): # full Platt SMO
def smoP(dataMatIn, classLabels, C, toler, maxIter, kTup=('lin', 0)):
"""
完整SMO算法外循环，与smoSimple有些类似，但这里的循环退出条件更多一些
Args:
dataMatIn:
classLabels:
C:
toler:
maxIter:
kTup:
Returns:
"""
oS = optStruct(mat(dataMatIn), mat(classLabels).transpose(), C, toler, kTup)
iter = 0
entireSet = True;
entireSet = True
alphaPairsChanged = 0
while (iter < maxIter) and ((alphaPairsChanged > 0) or (entireSet)):
alphaPairsChanged = 0
if entireSet: # go over all
if entireSet: # 在数据集上遍历所有可能的alpha
for i in range(oS.m):
alphaPairsChanged += innerL(i, oS)
print("fullSet, iter: %d i:%d, pairs changed %d" % (iter, i, alphaPairsChanged))
iter += 1
else: # go over non-bound (railed) alphas
else: # 遍历所有的非边界alpha值，也就是不在边界0或C上的值。
nonBoundIs = nonzero((oS.alphas.A > 0) * (oS.alphas.A < C))[0]
for i in nonBoundIs:
alphaPairsChanged += innerL(i, oS)
@@ -296,7 +344,17 @@ def smoP(dataMatIn, classLabels, C, toler, maxIter, kTup=('lin', 0)): # full Pl
def calcWs(alphas, dataArr, classLabels):
X = mat(dataArr);
"""
基于alpha计算w值
Args:
alphas:
dataArr:
classLabels:
Returns:
"""
X = mat(dataArr)
labelMat = mat(classLabels).transpose()
m, n = shape(X)
w = zeros((n, 1))
@@ -308,11 +366,11 @@ def calcWs(alphas, dataArr, classLabels):
def testRbf(k1=1.3):
dataArr, labelArr = loadDataSet('testSetRBF.txt')
b, alphas = smoP(dataArr, labelArr, 200, 0.0001, 10000, ('rbf', k1)) # C=200 important
datMat = mat(dataArr);
datMat = mat(dataArr)
labelMat = mat(labelArr).transpose()
svInd = nonzero(alphas.A > 0)[0]
sVs = datMat[svInd] # get matrix of only support vectors
labelSV = labelMat[svInd];
labelSV = labelMat[svInd]
print("there are %d Support Vectors" % shape(sVs)[0])
m, n = shape(datMat)
errorCount = 0
@@ -323,7 +381,7 @@ def testRbf(k1=1.3):
print("the training error rate is: %f" % (float(errorCount) / m))
dataArr, labelArr = loadDataSet('testSetRBF2.txt')
errorCount = 0
datMat = mat(dataArr);
datMat = mat(dataArr)
labelMat = mat(labelArr).transpose()
m, n = shape(datMat)
for i in range(m):
@@ -346,6 +404,7 @@ def img2vector(filename):
def loadImages(dirName):
from os import listdir
hwLabels = []
print(dirName)
trainingFileList = listdir(dirName) # load the training set
m = len(trainingFileList)
trainingMat = zeros((m, 1024))
@@ -364,11 +423,11 @@ def loadImages(dirName):
def testDigits(kTup=('rbf', 10)):
dataArr, labelArr = loadImages('trainingDigits')
b, alphas = smoP(dataArr, labelArr, 200, 0.0001, 10000, kTup)
datMat = mat(dataArr);
datMat = mat(dataArr)
labelMat = mat(labelArr).transpose()
svInd = nonzero(alphas.A > 0)[0]
sVs = datMat[svInd]
labelSV = labelMat[svInd];
labelSV = labelMat[svInd]
print("there are %d Support Vectors" % shape(sVs)[0])
m, n = shape(datMat)
errorCount = 0
@@ -379,7 +438,7 @@ def testDigits(kTup=('rbf', 10)):
print("the training error rate is: %f" % (float(errorCount) / m))
dataArr, labelArr = loadImages('testDigits')
errorCount = 0
datMat = mat(dataArr);
datMat = mat(dataArr)
labelMat = mat(labelArr).transpose()
m, n = shape(datMat)
for i in range(m):
@@ -424,8 +483,8 @@ def selectJK(i, oS, Ei): # this is the second choice -heurstic, and calcs Ej
Ek = calcEk(oS, k)
deltaE = abs(Ei - Ek)
if (deltaE > maxDeltaE):
maxK = k;
maxDeltaE = deltaE;
maxK = k
maxDeltaE = deltaE
Ej = Ek
return maxK, Ej
else: # in this case (first time around) we don't have any valid eCache values
@@ -444,24 +503,27 @@ def innerLK(i, oS):
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) # this has been changed from selectJrand
alphaIold = oS.alphas[i].copy();
alphaJold = oS.alphas[j].copy();
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
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
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) # added this for the Ecache
if (abs(oS.alphas[j] - alphaJold) < 0.00001): print("j not moving enough")
return 0
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]) # update i by the same amount as j
updateEk(oS, i) # added this for the Ecache #the update is in the oppostie direction
b1 = oS.b - Ei - oS.labelMat[i] * (oS.alphas[i] - alphaIold) * oS.X[i, :] * oS.X[i, :].T - oS.labelMat[j] * (
@@ -482,7 +544,7 @@ def innerLK(i, oS):
def smoPK(dataMatIn, classLabels, C, toler, maxIter): # full Platt SMO
oS = optStruct(mat(dataMatIn), mat(classLabels).transpose(), C, toler)
iter = 0
entireSet = True;
entireSet = True
alphaPairsChanged = 0
while (iter < maxIter) and ((alphaPairsChanged > 0) or (entireSet)):
alphaPairsChanged = 0