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