def assert_kkt(SVM):
X,Y,Alphas,b,C,kernel=SVM
N=len(Alphas)
for i in xrange(N):
if Alphas[i] == 0.:
assert Y[i]*_svm_apply(SVM,X[i])+eps >= 1
elif Alphas[i] == C:
assert Y[i]*_svm_apply(SVM,X[i])-eps <= 1
else:
assert abs(Y[i]*_svm_apply(SVM,X[i])-1) <= eps
def assert_kkt(SVM):
X, Y, Alphas, b, C, kernel = SVM
N = len(Alphas)
for i in xrange(N):
if Alphas[i] == 0.:
assert Y[i] * _svm_apply(SVM, X[i]) + eps >= 1
elif Alphas[i] == C:
assert Y[i] * _svm_apply(SVM, X[i]) - eps <= 1
else:
assert abs(Y[i] * _svm_apply(SVM, X[i]) - 1) <= eps
def assert_more_than_50(SVM,X,Y):
N = len(X)
correct = 0
for i in xrange(N):
correct += (_svm_apply(SVM,X[i]) * Y[i] > 0)
assert correct > N/2
def assert_all_correctly_classified(SVM,X,Y):
N = len(X)
for i in xrange(N):
assert _svm_apply(SVM,X[i]) * Y[i] > 0
def assert_more_than_50(SVM, X, Y):
N = len(X)
correct = 0
for i in xrange(N):
correct += (_svm_apply(SVM, X[i]) * Y[i] > 0)
assert correct > N / 2
def assert_all_correctly_classified(SVM, X, Y):
N = len(X)
for i in xrange(N):
assert _svm_apply(SVM, X[i]) * Y[i] > 0
