def test_distribution(self):
# TEST - correct distribution formulation raises no errors
ok = True
e0 = world.Event("box", [0, 1, 2], 0.5)
e1 = world.Event("box", [3], 0.3)
e2 = world.Event("box", [4, 5], 0.2)
try:
distr = world.Distribution([e0, e1, e2])
except:
ok = False
self.assertEqual(ok, True)
# TEST - distribution with prob <1 raises error
try:
distr = world.Distribution([e0, e2])
except:
ok = False
self.assertEqual(ok, False)
# TEST - distribution with mixed objs raises error
e3 = world.Event("plant", [0, 2], 0.5)
try:
distr = world.Distribution([e0, e3])
except:
ok = False
self.assertEqual(ok, False)
def test(self):
e0 = world.Event("box", [0, 1], 0.8)
e1 = world.Event("plant", [2], 0.75)
e2 = world.Event("robot", [0, 3], 0.05)
a = world.Arrangement([e0, e1, e2])
# TEST - arrangement probability calculations are correct
self.assertTrue(util.approx(a.prob(), 0.8 * 0.75 * 0.05))
def test(self):
g = world.Graph(6)
g.finalize()
e0 = world.Event("box", [0], 0.5)
e1 = world.Event("box", [1], 0.3)
e2 = world.Event("box", [2], 0.2)
d0 = world.Distribution([e0, e1, e2])
e3 = world.Event("plant", [3], 0.5)
e4 = world.Event("plant", [4], 0.5)
d1 = world.Distribution([e3, e4])
w = world.World(g, [d0, d1])
w.finalize()
a = world.ArrangementSpace(w)
# TEST - arrangement space returns the correct event probabilities
self.assertEqual(a.prob_obj("box", 0), 0.5)
self.assertEqual(a.prob_obj("box", 1), 0.3)
self.assertEqual(a.prob_obj("box", 2), 0.2)
self.assertEqual(a.prob_obj("box", 3), 0.0)
self.assertEqual(a.prob_obj("plant", 3), 0.5)
self.assertEqual(a.pot_objs_at(0), ["box"])
self.assertEqual(a.pot_objs_at(3), ["plant"])
self.assertEqual(a.pot_objs_at(5), [])
# TEST - observations update the arrangement space accordingly
a.observe("box", 0, False)
self.assertEqual(a.prob_obj("box", 0), 0)
self.assertEqual(a.prob_obj("box", 1), 0.6)
self.assertEqual(a.prob_obj("box", 2), 0.4)
self.assertEqual(a.prob_obj("box", 3), 0.0)
self.assertEqual(a.pot_objs_at(0), [])
a.observe("plant", 3, True)
self.assertEqual(a.prob_obj("plant", 3), 1.0)
self.assertEqual(a.prob_obj("plant", 4), 0.0)
self.assertEqual(a.pot_objs_at(3), ["plant"])
self.assertEqual(a.pot_objs_at(4), [])
a.observe("box", 1, False)
self.assertEqual(a.prob_obj("box", 1), 0.0)
self.assertEqual(a.prob_obj("box", 2), 1.0)
self.assertEqual(a.pot_objs_at(1), [])
self.assertEqual(a.pot_objs_at(2), ["box"])
def test_placement(self):
g = world.Graph(4)
g.finalize()
e0 = world.Event("box", [0], 0.6)
e1 = world.Event("box", [1], 0.3)
e2 = world.Event("box", [2], 0.1)
distr = world.Distribution([e0, e1, e2])
# TEST - objs are distributed with approx. the expected probabilities
placements = [0] * len(g.nodes)
n = 1000
for i in range(n):
locs = distr.place().locs
placements[locs[0]] += 1
self.assertTrue(util.approx(placements[0] / n, e0.prob, 0.1))
self.assertTrue(util.approx(placements[1] / n, e1.prob, 0.1))
self.assertTrue(util.approx(placements[2] / n, e2.prob, 0.1))
self.assertTrue(placements[3] == 0)
def test(self):
g = world.Graph(6)
g.finalize()
e0 = world.Event("box", [0, 1, 2], 0.5)
e1 = world.Event("box", [3], 0.3)
e2 = world.Event("box", [4, 5], 0.2)
d0 = world.Distribution([e0, e1, e2])
e3 = world.Event("plant", [2, 4], 0.5)
e4 = world.Event("plant", [1, 3], 0.5)
d1 = world.Distribution([e3, e4])
e5 = world.Event("robot", [1], 1.0)
d2 = world.Distribution([e5])
w = world.World(g, [d0, d1, d2])
w.finalize()
# TEST - world returns the correct probabilities of object occurrence
self.assertEqual(w.objs, ["box", "plant", "robot"])
self.assertEqual(w.prob_obj("box", 0), 0.5)
self.assertEqual(w.prob_obj("box", 5), 0.2)
self.assertEqual(w.prob_obj("plant", 2), 0.5)
self.assertEqual(w.prob_obj("plant", 0), 0.0)
self.assertEqual(w.prob_obj("robot", 0), 0.0)
self.assertEqual(w.prob_obj("robot", 1), 1.0)
# TEST - world returns the correct potential objects to find at a loc
self.assertEqual(w.pot_objs_at(0), ["box"])
self.assertEqual(w.pot_objs_at(2), ["box", "plant"])
self.assertEqual(w.pot_objs_at(1), ["box", "plant", "robot"])
# TEST - populated world contains the specified objects
box_count = 0
plant_count = 0
robot_count = 0
w.populate()
for loc in w.graph.nodes:
objs = w.objs_at(loc)
for obj in objs:
if obj == "box":
self.assertTrue(loc < 6)
box_count += 1
elif obj == "plant":
self.assertTrue(1 <= loc <= 4)
plant_count += 1
elif obj == "robot":
self.assertTrue(loc == 1)
robot_count += 1
else:
self.assertTrue(False)
self.assertTrue(box_count >= 1)
self.assertTrue(plant_count == 2)
self.assertTrue(robot_count == 1)
def parse_world(fname):
"""Parses a scavenger hunt world from a datfile.
Parameters
----------
fname : str
source file name
Returns
-------
world.World
finalized scavenger hunt world
list of str
scavenger hunt
str
start location name
"""
src = open(fname, "r")
sec = None
start_loc = None
conns = [] # 3-tuples (from, to, cost)
nodes = {} # User-specified loc names -> integer IDs
node_count = 0
distrs = []
hunt = []
for line in src.readlines():
# Blank lines and comments
line = line.strip()
if len(line) == 0 or line[0] == '#':
continue
# Section header line
if line[0] == '[':
sec = line[1:line.find(']')]
continue
# Map section
if sec == "map":
args = line.split()
assert len(args) == 3
n_from, n_to = args[0], args[1]
# Parse for starting location
if '*' in n_from:
n_from = n_from.replace('*', '')
if start_loc is None:
start_loc = n_from
elif '*' in n_to:
n_to = n_to.replace('*', '')
if start_loc is None:
start_loc = n_to
cost = float(args[2])
if n_from not in nodes:
nodes[n_from] = node_count
node_count += 1
if n_to not in nodes:
nodes[n_to] = node_count
node_count += 1
conns.append((n_from, n_to, cost))
# Distribution section
elif sec == "distr":
args = line.split()
assert len(args) > 2
obj = args[0]
events = []
ind = 1
if obj not in hunt:
hunt.append(obj)
while ind < len(args):
locs = []
prob_ind = ind
while args[prob_ind] in nodes:
locs.append(nodes[args[prob_ind]])
prob_ind += 1
prob_arg = args[prob_ind]
if '/' in prob_arg:
frac = prob_arg.split('/')
prob = float(frac[0]) / float(frac[1])
else:
prob = float(prob_arg)
events.append(world.Event(obj, locs, prob))
ind = prob_ind + 1
distrs.append(world.Distribution(events))
else:
assert False
src.close()
# Build graph
g = world.Graph(node_count)
for conn in conns:
id_from, id_to, cost = nodes[conn[0]], nodes[conn[1]], conn[2]
g.connect(id_from, id_to, cost)
g.name_ids[conn[0]] = id_from
g.name_ids[conn[1]] = id_to
g.finalize()
# Build world
w = world.World(g, distrs)
w.finalize()
return w, hunt, start_loc