def testBurges(self):
" testing Burges empirical risk bound "
res = numpy.array(
map(lambda x, y=self.nPts, z=self.eps2: Risk.BurgesRiskBound(
x[0], y, x[1], z),
self.dList))
res = numpy.array([
Risk.BurgesRiskBound(x[0], self.nPts, x[1], self.eps2)
for x in self.dList
])
target = numpy.array([.7445, .8157, .6698, .7649, .7506, .7658, .7896])
maxDev = max(abs(res - target))
assert maxDev < self.tol, 'maxDev too high'
def testCherkassky(self):
" testing Cherkassky empirical risk bound "
res = numpy.array([
Risk.CherkasskyRiskBound(x[0], self.nPts, x[1], self.eps)
for x in self.dList
])
target = numpy.array([.6654, .7450, .5378, .6329, .6010, .6175, .6501])
maxDev = max(abs(res - target))
assert maxDev < self.tol, 'maxDev too high'
def testChristiani(self):
" testing Christiani empirical risk bound "
res = numpy.array([
Risk.CristianiRiskBound(x[0], self.nPts, x[1], self.eps)
for x in self.dList
])
target = numpy.array(
[1.5617, 1.7438, 1.4797, 1.7394, 1.7235, 1.7653, 1.8235])
maxDev = max(abs(res - target))
assert maxDev < self.tol, 'maxDev too high'
def test_log2(self): self.assertEqual(Risk.log2(1), 0) self.assertEqual(Risk.log2(2), 1) self.assertEqual(Risk.log2(4), 2) self.assertEqual(Risk.log2(0.5), -1) self.assertEqual(Risk.log2(0.25), -2) self.assertEqual(Risk.log2(0.125), -3)
def test_log2(self):
self.assertEqual(Risk.log2(1), 0)
self.assertEqual(Risk.log2(2), 1)
self.assertEqual(Risk.log2(4), 2)
self.assertEqual(Risk.log2(0.5), -1)
self.assertEqual(Risk.log2(0.25), -2)
self.assertEqual(Risk.log2(0.125), -3)