def test_reaction(self):
#TODO: give specific net and specific values
an = AdaptiveNetwork(self.sample)
for _ in xrange(self.N):
pack = [random.randint(0, 10) for _ in range(4)]
out = np.array([w * ru.value_for(pack)
for ru, w in self.sample]).sum()
self.assertEqual(an.reaction(pack), out)
def setUp(self):
self.N = 100
self.sample = [(ReactiveUnit([1, 2, 3, 4]), 0.5),
(ReactiveUnit([1, 1, 1, 1]), 0.8),
(ReactiveUnit([2, 2, 2, 2]), 0.2),
(ReactiveUnit([3, 3, 3, 3]), 1)]
self.an = AdaptiveNetwork(self.sample)
def test_forgetting(self): # TODO set proper values
an = AdaptiveNetwork(self.sample)
an.forgetting()
for ru, w in an.units:
for ruw, ww in self.sample:
if ruw == ru:
self.assertTrue(w < ww or ww == 0)
def test_incrase_sample(self):
for ru, _ in self.sample:
an = AdaptiveNetwork(self.sample)
an.increase_sample(ru.central_value)
wwan = (an.units[an._index_of(ru)])[1]
for wewru, weww in self.sample:
print weww, (an.units[an._index_of(wewru)])[1]
self.assertTrue(weww <= (an.units[an._index_of(wewru)])[1])
if ru == wewru:
self.assertTrue(weww < wwan or weww == wwan == 1)
def test_remove_low_units(self):
an = AdaptiveNetwork(self.sample)
an.remove_low_units(0.9)
self.assertTrue(len(an.units) == 1)
def test__update_units_doubles(self):
an = AdaptiveNetwork(self.sample)
an._update_units(lambda u, w: (u, w * 2))
self.assertTrue(
len(an.units) == len(self.sample) and len(an.units) > 0)
self.assertEqual([(u, w * 2) for u, w in self.sample], an.units)
def test__update_units_to_None(self):
an = AdaptiveNetwork(self.sample)
an._update_units(lambda u, w: None)
self.assertEqual(an.units, [])
def test__index_of(self):
self.assertEqual(-1, self.an._index_of(ReactiveUnit([6, 6, 6])))
self.assertEqual(1, self.an._index_of(ReactiveUnit([1, 1, 1, 1])))
self.assertEqual(-1,
AdaptiveNetwork()._index_of(ReactiveUnit([23, 4])))
