def test_bound_at():
points, labels, C, alphas, bias = three_point_problem()
svm = smo.SVM(points, labels, C, alphas, bias)
assert svm.bound_at(0)
assert not svm.bound_at(1)
assert not svm.bound_at(2)
def test_kkt_fails():
points, labels, C, alphas, bias = three_point_problem()
svm = smo.SVM(points, labels, C, alphas, bias)
assert svm.kkt_fails(0)
assert svm.kkt_fails(1)
assert svm.kkt_fails(2)
assert svm.some_kkt_fails()
def test_select_indices():
points, labels, C, alphas, bias = three_point_problem()
svm = smo.SVM(points, labels, C, alphas, bias)
assert not svm.eligible_indices
assert not svm.eligible_indices_nonbound
i1, i2 = svm.select_indices()
assert len(svm.eligible_indices) == 2
assert len(svm.eligible_indices_nonbound) == 1
assert i1 == 1 # nonbound
assert i2 == 0 # biggest E discrepancy w/ i1
def test_dual_form_evaluate():
points, labels, C, alphas, bias = three_point_problem()
w = normalize([3, 1])
svm = smo.SVM(points, labels, C, alphas, bias)
assert svm.evaluate(points[0]) < 0
assert svm.evaluate(points[1]) < 0
assert svm.evaluate(points[2]) > 0
assert abs(svm.evaluate(points[0]) - (-1.2649110640673518)) < 1e-8
assert abs(svm.evaluate(points[1]) - (-0.3162277660168379)) < 1e-8
assert abs(svm.evaluate(points[2]) - 0.3162277660168379) < 1e-8
assert dot_product(w, points[0]) + bias == svm.evaluate(points[0])
assert dot_product(w, points[1]) + bias == svm.evaluate(points[1])
assert dot_product(w, points[2]) + bias == svm.evaluate(points[2])
def test_constructor():
w = normalize((random.random(), random.random()))
points, labels = zip(*sample_data(w))
smo.SVM(points, labels)