def randomized_select(self, select_range, priority_fn, lowestPriority=False):
solution = list()
total_profit = 0
bag = Bag(self.P, self.M, self.constraints)
options = list()
for item in self.items:
options.append((item, priority_fn(item)))
options.sort(key=lambda x: -x[1])
if lowestPriority:
options = list(reversed(options))
options = [x[0] for x in options]
while len(options) > 0 and not bag.full():
items = options[:min(select_range, len(options))]
item = items[int(random() * len(items))]
options.remove(item)
print(" progress: {0:.2f} %".format(max(bag._weight / bag.P, bag._cost/bag.M) * 100), end="\r")
if bag.has_enough_weight_for(item.weight) and bag.has_enough_money_for(item.cost):
bag.take(item)
solution.append(item)
total_profit += item.profit
new_options = list()
for x in options:
if bag.can_take(x.classNumber):
new_options.append(x)
options = new_options
return (total_profit, solution)
def randomized_select(self, select_range, priority_fn, lowestPriority=False):
solution = list()
total_profit = 0
bag = Bag(self.P, self.M, self.constraints)
options = list()
for item in self.items:
options.append((item, priority_fn(item)))
options.sort(key=lambda x: -x[1])
if lowestPriority:
options = list(reversed(options))
options = [x[0] for x in options]
while len(options) > 0 and not bag.full():
items = options[:min(select_range, len(options))]
item = items[int(random() * len(items))]
options.remove(item)
print(" progress: {0:.2f} %".format(max(bag._weight / bag.P, bag._cost/bag.M) * 100), end="\r")
if bag.has_enough_weight_for(item.weight) and bag.has_enough_money_for(item.cost):
bag.take(item)
solution.append(item)
total_profit += item.profit
new_options = list()
for x in options:
if bag.can_take(x.classNumber):
new_options.append(x)
options = new_options
return (total_profit, solution)
def greedy_on_fn(self, priority_fn, lowestPriority=False):
solution = list()
total_profit = 0
bag = Bag(self.P, self.M, self.constraints)
queue = get_priority_queue(self.items, priority_fn, lowestPriority)
while not queue.isEmpty() and not bag.full():
item = queue.pop()
#print(" remaining items: {0:.2f}%".format(queue.count / self.N * 100), end="\r")
print(" progress: {0:.2f} %".format(max(bag._weight / bag.P, bag._cost/bag.M) * 100), end="\r")
if bag.has_enough_weight_for(item.weight) and bag.has_enough_money_for(item.cost):
if bag.can_take(item.classNumber):
solution.append(item)
bag.take(item)
total_profit += item.profit
return (total_profit, solution)
def greedy_on_fn(self, priority_fn, lowestPriority=False):
solution = list()
total_profit = 0
bag = Bag(self.P, self.M, self.constraints)
queue = get_priority_queue(self.items, priority_fn, lowestPriority)
while not queue.isEmpty() and not bag.full():
item = queue.pop()
#print(" remaining items: {0:.2f}%".format(queue.count / self.N * 100), end="\r")
print(" progress: {0:.2f} %".format(max(bag._weight / bag.P, bag._cost/bag.M) * 100), end="\r")
if bag.has_enough_weight_for(item.weight) and bag.has_enough_money_for(item.cost):
if bag.can_take(item.classNumber):
solution.append(item)
bag.take(item)
total_profit += item.profit
return (total_profit, solution)
def deliver_one(instructions,
meta_request,
db_connection_info=MongoConnectionInfo()):
connected_bag = Bag(db_connection_info)
package = connected_bag.take(meta_request)
package_content = io.BytesIO(package)
return gz_unbox(unbox_instructions=instructions, mem=package_content)
def run_algorithm(self, num_solution_before_stop=100000, time_out=1000000):
"""
This is where we implement our logic and algorithms
Useful Parameters:
self.P -- max weight we can carry
self.M -- max purchasing power in dollars
self.N -- total number of avaliable items
self.C -- total number of constraints
self.items -- all items avaliable for choice
self.constraints -- a Constraint class with constraints
"""
# STEP: Create a hashmap from class number to its items
item_map = dict()
for item in self.items:
if item.classNumber not in item_map:
item_map[item.classNumber] = set()
item_map[item.classNumber].add(item)
# STEP: Calculate the total weight, cost, value, and profit of each class
def get_class_stats(items):
total_weight = 0
total_cost = 0
total_value = 0
total_profit = 0
for item in items:
total_weight += item.weight
total_cost += item.cost
total_value += item.value
total_profit += item.profit
return (total_weight, total_cost, total_value, total_profit)
class_stats = dict() # Format: key: class -> value: (weight, cost, value, profit)
for classNumber in item_map.keys():
class_stats[classNumber] = get_class_stats(item_map[classNumber])
# STEP: Create a BAG instance
bag = Bag(self.P, self.M, self.constraints)
# STEP: PriorityQueues of class's values
fn_extract_profit_per_weight_ratio = lambda x: x.profit_per_weight_ratio()
def fn_extractclass_ratio(x):
weight, _, _, profit = class_stats[x]
if weight == 0:
ratio = float("inf")
else:
ratio = profit / weight
return ratio
class_queue = PriorityQueue(lowest_priority=False) # based on class's item profit_per_weight_ratio
for classNumber in item_map.keys():
class_queue.push(classNumber, fn_extractclass_ratio(classNumber))
def add_to_queue(items, fn_extract_priority, queue):
for item in items:
priority_value = fn_extract_priority(item)
queue.push(item, -priority_value)
return queue
def get_queue_of_items(items, fn_extract_priority):
queue = PriorityQueue(lowest_priority=False)
return add_to_queue(items, fn_extract_priority, queue)
# STEP: pick from the bag with highest ratio
solutions_found = dict()
num_solution_found = 0
iteration = 0
class_not_used_due_to_conflict = Queue()
add_back_conflicts = True
while num_solution_found <= num_solution_before_stop and iteration <= time_out:
while not class_queue.isEmpty() and iteration <= time_out:
iteration += 1
if iteration % (time_out / 1000) == 0:
print("iteration {0} -- rate: {1:.2f} %".format(iteration, iteration / time_out * 100), end="\r")
if not class_not_used_due_to_conflict.isEmpty():
class_to_use = class_not_used_due_to_conflict.pop()
add_back_conflicts = not add_back_conflicts
else:
class_to_use = class_queue.pop()
add_back_conflicts = not add_back_conflicts
if bag.can_take(class_to_use):
items_queue = get_queue_of_items(item_map[class_to_use], \
fn_extract_profit_per_weight_ratio)
item = items_queue.pop()
while bag.take(item):
if not items_queue.isEmpty():
item = items_queue.pop()
else:
break
num_solution_found += 1
solutions_found[bag.score()] = bag.items()
print("solution {0} found".format(num_solution_found))
else:
class_not_used_due_to_conflict.push(class_to_use)
if num_solution_found >= num_solution_before_stop:
break
# print("iteration {0}".format(iteration))
iteration += 1
if add_back_conflicts:
add_to_queue(class_not_used_due_to_conflict.list, fn_extractclass_ratio, class_queue)
if num_solution_found >= num_solution_before_stop:
break
# STEP: return the best combination found
bestSolution = []
bestProfit = 0
for profit, soln in solutions_found.items():
if profit > bestProfit:
bestProfit = profit
bestSolution = soln
return bestSolution
def run_algorithm(self, num_solution_before_stop=100000, time_out=1000000):
"""
This is where we implement our logic and algorithms
Useful Parameters:
self.P -- max weight we can carry
self.M -- max purchasing power in dollars
self.N -- total number of avaliable items
self.C -- total number of constraints
self.items -- all items avaliable for choice
self.constraints -- a Constraint class with constraints
"""
# STEP: Create a hashmap from class number to its items
item_map = dict()
for item in self.items:
if item.classNumber not in item_map:
item_map[item.classNumber] = set()
item_map[item.classNumber].add(item)
# STEP: Calculate the total weight, cost, value, and profit of each class
def get_class_stats(items):
total_weight = 0
total_cost = 0
total_value = 0
total_profit = 0
for item in items:
total_weight += item.weight
total_cost += item.cost
total_value += item.value
total_profit += item.profit
return (total_weight, total_cost, total_value, total_profit)
class_stats = dict() # Format: key: class -> value: (weight, cost, value, profit)
for classNumber in item_map.keys():
class_stats[classNumber] = get_class_stats(item_map[classNumber])
# STEP: Create a BAG instance
bag = Bag(self.P, self.M, self.constraints)
# STEP: PriorityQueues of class's values
fn_extract_profit_per_weight_ratio = lambda x: x.profit_per_weight_ratio()
def fn_extractclass_ratio(x):
weight, _, _, profit = class_stats[x]
if weight == 0:
ratio = float("inf")
else:
ratio = profit / weight
return ratio
class_queue = PriorityQueue(lowest_priority=False) # based on class's item profit_per_weight_ratio
for classNumber in item_map.keys():
class_queue.push(classNumber, fn_extractclass_ratio(classNumber))
def add_to_queue(items, fn_extract_priority, queue):
for item in items:
priority_value = fn_extract_priority(item)
queue.push(item, -priority_value)
return queue
def get_queue_of_items(items, fn_extract_priority):
queue = PriorityQueue(lowest_priority=False)
return add_to_queue(items, fn_extract_priority, queue)
# STEP: pick from the bag with highest ratio
solutions_found = dict()
num_solution_found = 0
iteration = 0
class_not_used_due_to_conflict = Queue()
add_back_conflicts = True
while num_solution_found <= num_solution_before_stop and iteration <= time_out:
while not class_queue.isEmpty() and iteration <= time_out:
iteration += 1
if iteration % (time_out / 1000) == 0:
print("iteration {0} -- rate: {1:.2f} %".format(iteration, iteration / time_out * 100), end="\r")
if not class_not_used_due_to_conflict.isEmpty():
class_to_use = class_not_used_due_to_conflict.pop()
add_back_conflicts = not add_back_conflicts
else:
class_to_use = class_queue.pop()
add_back_conflicts = not add_back_conflicts
if bag.can_take(class_to_use):
items_queue = get_queue_of_items(item_map[class_to_use], \
fn_extract_profit_per_weight_ratio)
item = items_queue.pop()
while bag.take(item):
if not items_queue.isEmpty():
item = items_queue.pop()
else:
break
num_solution_found += 1
solutions_found[bag.score()] = bag.items()
print("solution {0} found".format(num_solution_found))
else:
class_not_used_due_to_conflict.push(class_to_use)
if num_solution_found >= num_solution_before_stop:
break
# print("iteration {0}".format(iteration))
iteration += 1
if add_back_conflicts:
add_to_queue(class_not_used_due_to_conflict.list, fn_extractclass_ratio, class_queue)
if num_solution_found >= num_solution_before_stop:
break
# STEP: return the best combination found
bestSolution = []
bestProfit = 0
for profit, soln in solutions_found.items():
if profit > bestProfit:
bestProfit = profit
bestSolution = soln
return bestSolution
class Gameplay:
def __init__(self) -> None:
self.matrix = Matrix()
self.bag = Bag()
self.piece: Piece
self.score = Score()
self.input: Input
self.cancel_input: Input
self.popups: List[Popup] = []
self.holder: Optional[Piece] = None
self.hold_lock = False
self.level = 1
self.countdown = 3
self.countdown_last = ctx.now - 1.0
self.game_over = False
self.game_over_sent = False
self.last_fall: float
self.fall_interval = 1.0
self.last_piece_movement_counter = 0
self.t_spin = False
self.clearing = False
self.clearing_rows: List[int] = []
self.clearing_last: float
self.garbage_adding = False
self.garbage_hole = 0
self.garbage_left = 0
self.garbage_last: float
self.send = False
self.cancel = False
def get_matrix(self) -> Matrix:
return self.matrix
def get_piece(self) -> Piece:
return self.piece
def get_bag(self) -> Bag:
return self.bag
def get_popups(self) -> List[Popup]:
return self.popups
def clear_popups(self) -> None:
self.popups = []
def set_device(self, device: Device) -> None:
self.input = Input(device)
self.cancel_input = Input(device)
def initialize(self) -> None:
self.input.bind({
"down": self.action_down,
"right": self.action_right,
"left": self.action_left,
"rotate_right": self.action_rotate_right,
"rotate_left": self.action_rotate_left,
"soft_fall": self.action_soft_fall,
"hard_fall": self.action_hard_fall,
"hold": self.action_hold,
})
self.cancel_input.bind({"cancel": self.action_cancel})
self.new_piece()
def action_cancel(self) -> None:
self.cancel = True
def action_down(self) -> None:
if self.piece.move(0, 1, self.matrix.collision):
self.reset_fall()
ctx.mixer.play("move")
def action_right(self) -> None:
if self.piece.move(1, 0, self.matrix.collision):
ctx.mixer.play("move")
if self.piece.touching_floor:
self.reset_fall()
def action_left(self) -> None:
if self.piece.move(-1, 0, self.matrix.collision):
ctx.mixer.play("move")
if self.piece.touching_floor:
self.reset_fall()
def action_rotate_right(self) -> None:
if self.piece.rotate(self.matrix.collision, clockwise=True):
ctx.mixer.play("rotate")
if self.piece.touching_floor:
self.reset_fall()
def action_rotate_left(self) -> None:
if self.piece.rotate(self.matrix.collision, clockwise=False):
ctx.mixer.play("rotate")
if self.piece.touching_floor:
self.reset_fall()
def action_soft_fall(self) -> None:
rows = self.piece.fall(self.matrix.collision)
if rows > 0:
ctx.mixer.play("soft_fall")
self.reset_fall()
self.score.update_soft_drop(rows)
def action_hard_fall(self) -> None:
rows = self.piece.fall(self.matrix.collision)
self.lock_piece()
if rows > 0:
ctx.mixer.play("hard_fall")
self.score.update_hard_drop(rows)
def action_hold(self) -> None:
if self.hold_lock:
ctx.mixer.play("hold_fail")
return
self.hold_lock = True
if self.holder is not None:
self.holder, self.piece = self.piece, self.holder
self.reset_piece()
else:
self.holder = self.piece
self.new_piece()
self.send = True
ctx.mixer.play("hold")
def new_piece(self) -> None:
self.send = True
self.piece = self.bag.take()
self.piece.reset()
self.reset_fall()
if self.matrix.collision(self.piece):
self.game_over = True
if self.garbage_left > 0:
self.garbage_adding = True
self.garbage_last = ctx.now
def reset_piece(self) -> None:
self.piece.reset()
for rows in range(self.piece.shape.grid[0].height, 0, -1):
if self.piece.move(0, rows, self.matrix.collision):
break
self.reset_fall()
def reset_fall(self) -> None:
self.last_fall = ctx.now
def lock_piece(self) -> None:
self.hold_lock = False
self.t_spin = False
if self.matrix.collision(
self.piece) or not self.matrix.lock(self.piece):
self.game_over = True
else:
if TSpin.detect(self.matrix, self.piece):
self.t_spin = True
self.new_piece()
if self.game_over:
return
rows = self.matrix.get_full_rows()
if rows:
popup = self.score.update_clear(self.level, rows, self.t_spin)
ctx.mixer.play("erase" + str(len(rows)))
self.clear_rows(rows)
self.popups.append(popup)
else:
self.score.reset_combo()
def clear_rows(self, rows: List[int]) -> None:
self.clearing = True
self.clearing_rows = rows
self.clearing_last = ctx.now + 0.15
for row in rows:
self.matrix.erase_row(row)
def add_garbage(self, hole: int, count: int) -> None:
self.garbage_hole = hole
self.garbage_left = count
def update(self) -> None:
self.cancel_input.update()
if self.clearing:
if ctx.now - self.clearing_last > 0.02:
self.send = True
self.matrix.collapse_row(self.clearing_rows.pop(0))
self.clearing_last = ctx.now
ctx.mixer.play("line_fall")
if not self.clearing_rows:
self.clearing = False
self.reset_piece()
elif self.garbage_adding:
if ctx.now - self.garbage_last > 0.03:
self.send = True
self.matrix.add_garbage(self.garbage_hole)
self.garbage_last = ctx.now
ctx.mixer.play("garbage")
self.garbage_left -= 1
if self.garbage_left == 0:
self.garbage_adding = False
self.reset_piece()
if self.game_over:
if not self.game_over_sent:
self.game_over_sent = True
self.send = True
return
if self.countdown >= 0:
if ctx.now - self.countdown_last > 1.0:
self.send = True
self.countdown_last = ctx.now
ctx.mixer.play("countdown")
if self.countdown == 0:
self.popups.append(
Popup("GO!", size=6, color="green", duration=0.4))
ctx.mixer.play("go")
ctx.mixer.play_music("main_theme")
else:
self.popups.append(
Popup(str(self.countdown), size=6, duration=0.4))
self.countdown -= 1
return
self.input.update()
if self.piece.movement_counter != self.last_piece_movement_counter:
self.send = True
self.last_piece_movement_counter = self.piece.movement_counter
if ctx.now - self.last_fall > self.fall_interval:
self.send = True
if self.piece.move(0, 1, self.matrix.collision):
self.reset_fall()
else:
self.lock_piece()
def draw(self, x: int, y: int, draw_piece=True) -> None:
self.matrix.draw(x, y)
if draw_piece and self.piece:
self.matrix.get_ghost(self.piece).draw(x, y)
self.piece.draw(x, y)
self.score.draw(x, y - 70)
self.bag.draw(x + 340, y + 70)
if self.holder is not None:
holder_x = int(x - 65 - self.holder.shape.get_width(0) * 11.25)
self.holder.shape.draw(0, holder_x, y + 60, 22, 1.0)
else:
shape.SHAPE_HOLD_NONE.draw(0, x - 85, y + 60, 22, 1.0)
Text.draw("Hold", centerx=x - 75, top=y + 20)
Text.draw("Next", centerx=x + 370, top=y + 20)