def report_state(self, data):
state = State.from_json(data)
if self.state_keeper.challenge_state(state):
print "Energy of current state:", self.state_evaluator.evaluate(state)
return 'Accepted'
else:
return 'Discarded'
def report_state(self, data):
state = State.from_json(data)
if self.state_keeper.challenge_state(state):
print "Energy of current state:", self.state_evaluator.evaluate(state)
return "Accepted"
else:
return "Discarded"
def run(self):
evaluator = TablePositionAgnosticClosnessEvaluator()
searcher = SingleThreadedSearcher(
ClosenessStepper(evaluator),
SquareStateEvaluator(evaluator),
PrintLogger()
)
while True:
response = requests.get('http://%s:%s/get_best_state' % (self.addr, self.port))
state = State.from_json(response.content)
state, _ = searcher.search(state, n=1000)
requests.post('http://%s:%s/report_state' % (self.addr, self.port), data=state.to_json())
def run(self):
evaluator = TablePositionAgnosticClosnessEvaluator()
searcher = SingleThreadedSearcher(ClosenessStepper(evaluator),
SquareStateEvaluator(evaluator),
PrintLogger())
while True:
response = requests.get('http://%s:%s/get_best_state' %
(self.addr, self.port))
state = State.from_json(response.content)
state, _ = searcher.search(state, n=1000)
requests.post('http://%s:%s/report_state' % (self.addr, self.port),
data=state.to_json())
def _to_state(excel_data):
"""
@type excel_data: ExcelData
"""
group_names = []
group_weights = []
group_indexes = []
# Check that placements still respect dimensions constraint
reset_placement = False
if excel_data.dimensions is not None:
for (dimensions_name, sizes), (placement_name, positions) in zip(
excel_data.dimensions, excel_data.placements):
if dimensions_name != placement_name:
reset_placement = True
break
for size, position in zip(sizes, positions):
if size != len(position):
reset_placement = True
if not reset_placement:
# Allocate room given placement
cnt = 0
for placement_name, positions in excel_data.placements:
group_names.append(placement_name)
group_indexes.append([cnt, cnt + len(positions)])
group_weights.append(1)
cnt += len(positions)
else:
# Allocate room given dimensions
cnt = 0
for dimension_name, sizes in excel_data.dimensions:
group_names.append(dimension_name)
group_indexes.append([cnt, cnt + len(sizes)])
group_weights.append(1)
cnt += len(sizes)
# Allocate room for groups
placement_names = set(group_names)
for group_name, group in excel_data.groups:
group_name, weight_str = re.match(r"\s*([^(]*)\s*(?:\((\d+)\))?",
group_name).groups()
if weight_str:
weight = int(weight_str)
else:
weight = 1
if group_name in placement_names:
continue
group_names.append(group_name.strip())
group_indexes.append([cnt, cnt + 1])
group_weights.append(weight)
cnt += 1
names = excel_data.names
matrix = numpy.zeros((len(names), cnt), dtype=int)
fixed = numpy.zeros((len(names), cnt), dtype=bool)
person_index_by_name = {name: i for i, name in enumerate(names)}
if not reset_placement:
# Fill in positions from placement
matrix_col = 0
for _, positions in excel_data.placements:
for position in positions:
for person, is_fixed in position:
matrix_row = person_index_by_name[person]
matrix[matrix_row, matrix_col] = 1
fixed[matrix_row, matrix_col] = is_fixed
matrix_col += 1
else:
# Fill in positions in name order
groups_by_group_name = {
group_name: group
for group_name, group in excel_data.groups
}
for (i, j), (dimension_name, sizes) in zip(group_indexes,
excel_data.dimensions):
# Traverse names in group with same name (or all persons if there is no such group)
persons = groups_by_group_name.get(dimension_name, names)
person_iter = iter(persons)
try:
for matrix_col, size in zip(range(i, j), sizes):
for _ in range(size):
person = next(person_iter)
matrix_row = person_index_by_name[person]
matrix[matrix_row, matrix_col] = 1
fixed[matrix_row, matrix_col] = False
next(person_iter)
msg = "Not enough places for all %d persons in '%s' to fit in the given tables." % (
len(persons), dimension_name)
raise Exception(msg)
except StopIteration:
pass
matrix_col = j
for group_name, group in excel_data.groups:
if group_name in placement_names:
continue
for name in group:
matrix_row = person_index_by_name[name]
matrix[matrix_row, matrix_col] = 1
matrix_col += 1
geometry = matrix.copy().transpose()
return State(names=names,
group_names=group_names,
group_indexes=group_indexes,
group_weights=group_weights,
seating=matrix,
fixed=fixed,
geometry=geometry)
def test_json(initial):
json = initial.to_json()
actual = State.from_json(json)
_assert_same_state(initial, actual)
def read_text(content):
"""
@type content: str
"""
group_names = []
group_weights = []
groups = []
group = []
position = []
for line in BytesIO(content):
if line.startswith('#'):
name, weight_str = re.match(r"#\s*([^(]*)\s*(?:\((\d+)\))?",
line.strip()).groups()
group_names.append(name.strip())
weight = int(weight_str) if weight_str else 1
group_weights.append(weight)
if position:
group.append(position)
position = []
if group:
groups.append(group)
group = []
elif line.strip() == '':
if position:
group.append(position)
position = []
else:
fixed = line.startswith('*')
if fixed:
line = line[1:]
position.append([line.strip(), fixed])
if position:
group.append(position)
if group:
groups.append(group)
matrix_col = 0
group_indexes = []
for group in groups:
group_indexes.append([matrix_col, matrix_col + len(group)])
matrix_col += len(group)
names = list({
name
for group in groups for position in group for (name, _) in position
})
names.sort()
matrix = numpy.zeros((len(names), group_indexes[-1][1]), dtype=int)
fixed = numpy.zeros((len(names), group_indexes[-1][1]), dtype=bool)
persons_by_name = {name: idx for idx, name in enumerate(names)}
matrix_col = 0
for group in groups:
for position in group:
for name, is_fixed in position:
matrix_rox = persons_by_name[name]
matrix[matrix_rox, matrix_col] = 1
fixed[matrix_rox, matrix_col] = is_fixed
matrix_col += 1
geometry = matrix.copy().transpose()
return State(names=names,
group_names=group_names,
group_indexes=group_indexes,
group_weights=group_weights,
seating=matrix,
fixed=fixed,
geometry=geometry)
