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calu函数python calu函数c语言( 二 )

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def selectAlpha_j(svm, alpha_i, error_i):
svm.errorCache[alpha_i] = [1, error_i] # mark as valid(has been optimized)
candidateAlphaList = nonzero(svm.errorCache[:, 0].A)[0] # mat.A return array
maxStep = 0; alpha_j = 0; error_j = 0
# find the alpha with max iterative step
if len(candidateAlphaList)1:
for alpha_k in candidateAlphaList:
if alpha_k == alpha_i:
continue
error_k = calcError(svm, alpha_k)
if abs(error_k - error_i)maxStep:
maxStep = abs(error_k - error_i)
alpha_j = alpha_k
error_j = error_k
# if came in this loop first time, we select alpha j randomly
else:
alpha_j = alpha_i
while alpha_j == alpha_i:
alpha_j = int(random.uniform(0, svm.numSamples))
error_j = calcError(svm, alpha_j)
return alpha_j, error_j
# the inner loop for optimizing alpha i and alpha j
def innerLoop(svm, alpha_i):
error_i = calcError(svm, alpha_i)
### check and pick up the alpha who violates the KKT condition
## satisfy KKT condition
# 1) yi*f(i) = 1 and alpha == 0 (outside the boundary)
# 2) yi*f(i) == 1 and 0alpha C (on the boundary)
# 3) yi*f(i) = 1 and alpha == C (between the boundary)
## violate KKT condition
# because y[i]*E_i = y[i]*f(i) - y[i]^2 = y[i]*f(i) - 1, so
# 1) if y[i]*E_i0, so yi*f(i)1, if alphaC, violate!(alpha = C will be correct)
# 2) if y[i]*E_i0, so yi*f(i)1, if alpha0, violate!(alpha = 0 will be correct)
# 3) if y[i]*E_i = 0, so yi*f(i) = 1, it is on the boundary, needless optimized
if (svm.train_y[alpha_i] * error_i-svm.toler) and (svm.alphas[alpha_i]svm.C) or\
(svm.train_y[alpha_i] * error_isvm.toler) and (svm.alphas[alpha_i]0):
# step 1: select alpha j
alpha_j, error_j = selectAlpha_j(svm, alpha_i, error_i)
alpha_i_old = svm.alphas[alpha_i].copy()
alpha_j_old = svm.alphas[alpha_j].copy()
# step 2: calculate the boundary L and H for alpha j
if svm.train_y[alpha_i] != svm.train_y[alpha_j]:
L = max(0, svm.alphas[alpha_j] - svm.alphas[alpha_i])
H = min(svm.C, svm.C + svm.alphas[alpha_j] - svm.alphas[alpha_i])
else:
L = max(0, svm.alphas[alpha_j] + svm.alphas[alpha_i] - svm.C)
H = min(svm.C, svm.alphas[alpha_j] + svm.alphas[alpha_i])
if L == H:
return 0
# step 3: calculate eta (the similarity of sample i and j)
eta = 2.0 * svm.kernelMat[alpha_i, alpha_j] - svm.kernelMat[alpha_i, alpha_i] \
- svm.kernelMat[alpha_j, alpha_j]
if eta = 0:
return 0
# step 4: update alpha j
svm.alphas[alpha_j] -= svm.train_y[alpha_j] * (error_i - error_j) / eta
# step 5: clip alpha j
if svm.alphas[alpha_j]H:
svm.alphas[alpha_j] = H
if svm.alphas[alpha_j]L:
svm.alphas[alpha_j] = L
# step 6: if alpha j not moving enough, just return
if abs(alpha_j_old - svm.alphas[alpha_j])0.00001:
updateError(svm, alpha_j)
return 0
# step 7: update alpha i after optimizing aipha j
svm.alphas[alpha_i] += svm.train_y[alpha_i] * svm.train_y[alpha_j] \
* (alpha_j_old - svm.alphas[alpha_j])
# step 8: update threshold b
b1 = svm.b - error_i - svm.train_y[alpha_i] * (svm.alphas[alpha_i] - alpha_i_old) \
* svm.kernelMat[alpha_i, alpha_i] \
- svm.train_y[alpha_j] * (svm.alphas[alpha_j] - alpha_j_old) \
* svm.kernelMat[alpha_i, alpha_j]
b2 = svm.b - error_j - svm.train_y[alpha_i] * (svm.alphas[alpha_i] - alpha_i_old) \
* svm.kernelMat[alpha_i, alpha_j] \
- svm.train_y[alpha_j] * (svm.alphas[alpha_j] - alpha_j_old) \
* svm.kernelMat[alpha_j, alpha_j]
if (0svm.alphas[alpha_i]) and (svm.alphas[alpha_i]svm.C):
svm.b = b1
elif (0svm.alphas[alpha_j]) and (svm.alphas[alpha_j]svm.C):
svm.b = b2
else:
svm.b = (b1 + b2) / 2.0
# step 9: update error cache for alpha i, j after optimize alpha i, j and b
updateError(svm, alpha_j)

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