Python(線性不可分SVM)
阿新 • • 發佈：2018-12-13
for t in range(maxgen):
i1 = v1(a,y_train,gram)
i2 = v2(i1,n)
old_a1,old_a2 = a[i1],a[i2]
old_y1,old_y2 = y_train[i1],y_train[i2]
if old_y1 == old_y2:
L = max(0,old_a2+old_a1-C)
H = min(C,old_a2+old_a1)
else:
L = max(0,old_a2-old_a1)
H = min(C,old_a2-old_a1+C)
E1 = np.dot((a*y_train).T,gram[:,i1])+b - y_train[i1]
E2 = np.dot((a*y_train).T,gram[:,i2])+b - y_train[i2]
g = gram[i1,i1]+gram[i2,i2]-2*gram[i1,i2]
tmp_a2 = old_a2 + y_train[i2]*(E1-E2)/g
if tmp_a2>H:
a[i2] = H
if tmp_a2 >=L and tmp_a2<=H:
a[i2] = tmp_a2
if tmp_a2<L:
a[i2] = L
deta_a2 = a[i2]-old_a2
a[i1] = old_a1-old_y1*old_y2*deta_a2
deta_a1 = a[i1]-old_a1
dw = (y_train[i1]*deta_a1,y_train[i2]*deta_a2)
b1 = -E1-y_train[i1]*gram[i1,i1]*deta_a1-y_train[i2]*gram[i1,i2]*deta_a2
b2 = -E2-y_train[i1]*gram[i1,i2]*deta_a1-y_train[i2]*gram[i2,i2]*deta_a2
db = (b1+b2)/2
p = p+dw[0]*gram[i1].T+dw[1]*gram[i2].T+db