class CompareAlgoNetworkBowdenDataSet(unittest.TestCase):
"""
Compares the algorithm and the network performance for the Bowden et al.
(2003) dataset.
Maximal number of searched items is 8.
"""
def setUp(self):
self.net = Network()
path = '../../ratdata/bowden/rat_items'
self.items = np.loadtxt(path, dtype=np.character)
self.nr_words = 10
self.net.max_visited = self.nr_words
def testEqual(self):
equal = 0
not_equal = []
for i, rat_item in enumerate(self.items):
# words
cues, target = rat_item[:3], rat_item[3]
# word ids
cue_ids = [self.net.voc[c] for c in cues]
target_id = self.net.voc[target]
# run the algorithm simulation
_, visited_alg = spread_activity(init_nodes=cue_ids,
target=target_id,
W=self.net.W,
max_visited=self.nr_words)
# ...and the network simulation
self.net.setup_problem(cues, target)
print '\n', i, cues, target, target_id
# WTA can fail if noise added to two equal numbers is not enough
# to pick a winner. If this happens, the simulation is repeated
ok = True
while ok:
try:
self.net.run()
ok = False
except BaseException:
print 'WTA failed, retrying the run!'
continue
l1, l2 = visited_alg, list(self.net.visited())
if target_id in set(l1) and target_id not in set(l2) or\
target_id not in set(l1) and target_id in set(l2):
not_equal.append(i)
else:
print 'ok', equal
equal += 1
print 'A:', l1
print 'N:', l2
print not_equal
class CompareAlgoNetworkBowdenDataSet(unittest.TestCase):
"""
Compares the algorithm and the network performance for the Bowden et al.
(2003) dataset.
Maximal number of searched items is 8.
"""
def setUp(self):
self.net = Network()
path = "../../ratdata/bowden/rat_items"
self.items = np.loadtxt(path, dtype=np.character)
self.nr_words = 10
self.net.max_visited = self.nr_words
def testEqual(self):
equal = 0
not_equal = []
for i, rat_item in enumerate(self.items):
# words
cues, target = rat_item[:3], rat_item[3]
# word ids
cue_ids = [self.net.voc[c] for c in cues]
target_id = self.net.voc[target]
# run the algorithm simulation
_, visited_alg = spread_activity(
init_nodes=cue_ids, target=target_id, W=self.net.W, max_visited=self.nr_words
)
# ...and the network simulation
self.net.setup_problem(cues, target)
print "\n", i, cues, target, target_id
# WTA can fail if noise added to two equal numbers is not enough
# to pick a winner. If this happens, the simulation is repeated
ok = True
while ok:
try:
self.net.run()
ok = False
except BaseException:
print "WTA failed, retrying the run!"
continue
l1, l2 = visited_alg, list(self.net.visited())
if target_id in set(l1) and target_id not in set(l2) or target_id not in set(l1) and target_id in set(l2):
not_equal.append(i)
else:
print "ok", equal
equal += 1
print "A:", l1
print "N:", l2
print not_equal
class TestActivityLevels(unittest.TestCase):
"""
This test-suite compares the performance of the search algorithm and the
neural network model:
a) testActivityEqualSingleCue
compares the activity of all nodes in
the algorithm and the network after presenting a single cue.
b) testActivityEqualThreeCues
compares the activity of all nodes in
the algorithm and the network after presenting all three cues.
c) testOrderEightWords
compares the order of the explored nodes, and the activity levels
up to a certain tolerance level
"""
def setUp(self):
self.net = Network()
def testActivityEqualSingleCue(self):
cue = "match"
target = "fire"
target_id = self.net.voc[target]
max_visited = 1
self.net.max_visited = max_visited
act_alg, _ = spread_activity(
init_nodes=[self.net.voc[cue]], target=target_id, W=self.net.W, max_visited=max_visited
)
self.net.setup_problem([cue], target)
self.net.run()
np.testing.assert_almost_equal(act_alg, self.net.a[self.net.t_max], decimal=3)
def testActivityEqualThreeCues(self):
cues = ["match", "game", "stick"]
target = "fire"
target_id = self.net.voc[target]
max_visited = 3
self.net.max_visited = max_visited
self.net.setup_problem(cues, target)
act_alg, _ = spread_activity(
init_nodes=self.net.cue_ids, target=target_id, W=self.net.W, max_visited=max_visited
)
self.net.run()
np.testing.assert_almost_equal(act_alg, self.net.a[self.net.t_max], decimal=2)
def testOrderEightWords(self):
# number of words along the search path
nr_words = 8
cues = ["cottage", "swiss", "cake"]
target = "cheese"
target_id = self.net.voc[target]
# get word ids
cue_ids = [self.net.voc[c] for c in cues]
act_alg, visited_alg = spread_activity(init_nodes=cue_ids, target=target_id, W=self.net.W, max_visited=nr_words)
self.net.setup_problem(cues, target)
self.net.max_visited = nr_words
self.net.run()
visited_net = self.net.visited()
np.testing.assert_equal(visited_alg, visited_net)
a1 = self.net.a[self.net.t_max, visited_net]
a2 = act_alg[visited_alg]
# tolerate diffrences in activities up to 0.1
np.testing.assert_allclose(a1, a2, rtol=1e-1)
class TestActivityLevels(unittest.TestCase):
"""
This test-suite compares the performance of the search algorithm and the
neural network model:
a) testActivityEqualSingleCue
compares the activity of all nodes in
the algorithm and the network after presenting a single cue.
b) testActivityEqualThreeCues
compares the activity of all nodes in
the algorithm and the network after presenting all three cues.
c) testOrderEightWords
compares the order of the explored nodes, and the activity levels
up to a certain tolerance level
"""
def setUp(self):
self.net = Network()
def testActivityEqualSingleCue(self):
cue = 'match'
target = 'fire'
target_id = self.net.voc[target]
max_visited = 1
self.net.max_visited = max_visited
act_alg, _ = spread_activity(init_nodes=[self.net.voc[cue]],
target=target_id,
W=self.net.W,
max_visited=max_visited)
self.net.setup_problem([cue], target)
self.net.run()
np.testing.assert_almost_equal(act_alg,
self.net.a[self.net.t_max],
decimal=3)
def testActivityEqualThreeCues(self):
cues = ['match', 'game', 'stick']
target = 'fire'
target_id = self.net.voc[target]
max_visited = 3
self.net.max_visited = max_visited
self.net.setup_problem(cues, target)
act_alg, _ = spread_activity(init_nodes=self.net.cue_ids,
target=target_id,
W=self.net.W,
max_visited=max_visited)
self.net.run()
np.testing.assert_almost_equal(act_alg,
self.net.a[self.net.t_max],
decimal=2)
def testOrderEightWords(self):
# number of words along the search path
nr_words = 8
cues = ['cottage', 'swiss', 'cake']
target = 'cheese'
target_id = self.net.voc[target]
# get word ids
cue_ids = [self.net.voc[c] for c in cues]
act_alg, visited_alg = spread_activity(init_nodes=cue_ids,
target=target_id,
W=self.net.W,
max_visited=nr_words)
self.net.setup_problem(cues, target)
self.net.max_visited = nr_words
self.net.run()
visited_net = self.net.visited()
np.testing.assert_equal(visited_alg, visited_net)
a1 = self.net.a[self.net.t_max, visited_net]
a2 = act_alg[visited_alg]
# tolerate diffrences in activities up to 0.1
np.testing.assert_allclose(a1, a2, rtol=1e-1)