# Authors: Liuqing Chen, Feng Shi,
# and Isaac Engel (13th September 2017)
# Last updated: 2nd December 2017
# ####################################################
from main import Tetris
import utils
import timeit
from copy import deepcopy
# Example target shape
# target = [[1, 0, 1, 1], [1, 1, 1, 1], [0, 1, 1, 1], [1, 1, 0, 0]] # NOTE: in your test, you may not use this example.
# Uncomment the following line to generate a random target shape
target = utils.generate_target(
width=100, height=100,
density=0.6) # NOTE: it is recommended to keep density below 0.8
solution = Tetris(deepcopy(target))
valid, missing, excess, error_pieces = utils.check_solution(
target, solution) # checks if the solution is valid
if not valid:
print("The solution is not valid!")
else: # if the solution is valid, test time performance and accuracy
# TIME PERFORMANCE
# There will be three different values of the parameter 'target' with increasing complexity in real test.
# ####################################################
import improved
#from UsingLimits import Tetris
#from Anusha import Tetris
from improved import Tetris
import utils
import timeit
#from ComparisonTest import CompareTarget
# Example target shape
#target = utils.generate_target(20,20,0.7) # NOTE: in your test, you may not use this example.
#print(target)
# Uncomment the following line to generate a random target shape
target = utils.generate_target(1000,1000,0.7) # NOTE: it is recommended to keep density below 0.8
#target = [[0, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0], [0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 0, 0, 1, 1, 0, 1, 1, 1, 0], [0, 1, 0, 1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1], [1, 1, 1, 0, 1, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0], [0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1], [1, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1], [0, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0, 0, 1, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 1, 1, 0], [0, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0], [1, 0, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 0, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1], [1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 1, 0, 1, 0, 0, 1], [0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 1, 0, 1, 1, 1, 1], [0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0], [1, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1], [0, 0, 0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1], [1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1]]
#target = [[1, 1, 1, 1, 1, 1, 1, 1, 0, 0], [1, 1, 1, 0, 0, 1, 1, 1, 1, 1], [0, 1, 1, 1, 1, 1, 1, 1, 1, 0], [1, 1, 1, 1, 0, 1, 1, 0, 1, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 1, 1, 1, 1, 1, 0, 1, 1, 1], [0, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 1, 1, 1, 1, 1, 0, 1, 1, 1], [0, 1, 1, 1, 0, 1, 0, 1, 1, 1], [1, 1, 1, 0, 0, 1, 1, 1, 0, 1]]
#print(target)
solution = Tetris(target)
#print(solution)
valid, missing, excess, error_pieces = utils.check_solution(target, solution) # checks if the solution is valid
#CompareTarget(target)
if not valid:
print("The solution is not valid!")
[0, 1, 1, 1, 0, 0],
[1, 1, 1, 1, 1, 0]
]
perfect_solution = [
[(0, 0), (0, 0), (8, 1), (0, 0), (0, 0), (0, 0)],
[(0, 0), (0, 0), (8, 1), (0, 0), (13, 2), (0, 0)],
[(0, 0), (8, 1), (8, 1), (13, 2), (13, 2), (13, 2)],
[(0, 0), (13, 3), (18, 4), (18, 4), (0, 0), (0, 0)],
[(13, 3), (13, 3), (13, 3), (18, 4), (18, 4), (0, 0)]
]
"""
# NOTE: This example is used for the mock solution from 'main.py' only.
# Uncomment the following line to generate a random target shape
target, perfect_solution = utils.generate_target(width=100, height=100, density=0.7, forbidden_pieces=the_forbidden_pieces) # NOTE: it is recommended to keep density below 0.8
solution = Tetris(deepcopy(target))
#solution = perfect_solution
valid, missing, excess, error_pieces = utils.check_solution(target, solution, the_forbidden_pieces) # checks if the solution is valid
if not valid or len(error_pieces)!=0:
if len(error_pieces) != 0:
print('WARNING: {} pieces have a wrong shapeID. They are labelled in image of the solution, and their PieceID are: {}.'
.format(len(error_pieces), error_pieces))
print("Displaying solution...")
utils.visual_perfect(perfect_solution, solution, the_forbidden_pieces)
print("WARNING: The solution is not valid, no score will be given!")
else: # if the solution is valid, test time performance and accuracy
# TIME PERFORMANCE
#debugGrid(lambda x : x.stateid)
############################
#emptying the grid
solution = []
for y, row in enumerate(ogrid):
solution.append([])
for sq in row:
if sq.state == 0:
solution[y].append((0, 0))
elif sq.state == -1:
solution[y].append((0, 0))
else:
solution[y].append((sq.state, sq.stateid))
return solution
#utils.visualisation(target,solution)
width = 20
height = 25
dencity = 0.8
target, a, TheSolution = utils.generate_target(width, height, dencity)
solution2 = Tetris(target, a)
valid, missing, excess, error_pieces, use_diff = utils.check_solution(
target, solution2, a)
total_blocks = sum([sum(row) for row in target])
percent = (missing + excess) / total_blocks
print(1 - percent)
print(a)
16: 0,
17: 0,
18: 1,
19: 0
}
perfect_solution = [[(0, 0), (0, 0), (8, 1), (0, 0), (0, 0)],
[(0, 0), (0, 0), (8, 1), (1, 2), (1, 2)],
[(0, 0), (8, 1), (8, 1), (1, 2), (1, 2)],
[(0, 0), (13, 3), (18, 4), (18, 4), (0, 0)],
[(13, 3), (13, 3), (13, 3), (18, 4), (18, 4)]]
# NOTE: This example is used for the mock solution from 'main.py' only.
# Uncomment the following line to generate a random target shape
target, limit_tetris, perfect_solution = utils.generate_target(
width=20, height=20,
density=0.8) # NOTE: it is recommended to keep density below 0.8
solution = Tetris(deepcopy(target), deepcopy(limit_tetris))
valid, missing, excess, error_pieces, use_diff = utils.check_solution(
target, solution, limit_tetris) # checks if the solution is valid
if not valid or len(error_pieces) != 0:
if len(error_pieces) != 0:
print(
'WARNING: {} pieces have a wrong shapeID. They are labelled in image of the solution, and their PieceID are: {}.'
.format(len(error_pieces), error_pieces))
print("Displaying solution...")
utils.visual_perfect(perfect_solution, solution)
print("WARNING: The solution is not valid, no score will be given!")
#generate_shape(shape_id)
# Co ordingate are done backwards y,x
#
#
#
from copy import deepcopy # copy 'target' to avoid modifying it
import utils # it might be helpful to use 'utils.py'
from operator import add
import timeit
########################################
#Varible Bin!!!!
width = 1000
height = 1000
dencity = 0.7
target, testParts, TheSolution = utils.generate_target(width, height, dencity)
orderlist = "?" # ok so now this may be usefull
######################
#Running varibles
shapesPlaced = 1
#####################################
#test
testgrid = [[0, 1, 1, 0, 1, 0, 1, 0, 0, 1], [0, 1, 1, 0, 1, 0, 1, 1, 1, 1],
[0, 1, 0, 1, 1, 1, 1, 1, 1, 1], [0, 1, 1, 1, 1, 0, 1, 1, 1, 1],
[0, 1, 1, 0, 1, 1, 1, 0, 1, 0], [0, 0, 1, 1, 0, 1, 1, 1, 0, 0],
[1, 1, 1, 1, 1, 1, 0, 1, 1, 0], [1, 1, 0, 1, 1, 0, 1, 1, 0, 1],
[1, 1, 1, 0, 1, 1, 1, 0, 1, 1], [1, 0, 0, 1, 1, 1, 1, 0, 1, 0]]
#testParts = {1: 4, 2: 0, 3: 1, 4: 1, 5: 1, 6: 0, 7: 1, 8: 1, 9: 1, 10: 0, 11: 0, 12: 2, 13: 0, 14: 1, 15: 0, 16: 1, 17: 1, 18: 1, 19: 1}
actions_dict[a.split()[1]] = int(a.split()[0])
num_classes = len(actions_dict)
# train
trainer = Trainer(num_stages, num_layers, num_f_maps, features_dim,
num_classes)
no_change = 1
if args.action == "train":
batch_gen = BatchGenerator(num_classes, actions_dict, segmentation_path,
features_path, sample_rate)
batch_gen.read_data(vid_list_file)
weights = batch_gen.set_class_weights()
trainer.ce(weight=weights)
while (no_change):
trainer.train(model_dir,
batch_gen,
num_epochs=num_epochs,
batch_size=bz,
learning_rate=lr,
device=device)
trainer.predict(model_dir, temp_results_dir, features_path,
vid_list_file, num_epochs, actions_dict, device,
sample_rate)
utils.generate_target(segmentation_path, temp_results_dir,
vid_list_file)
if args.action == "predict":
trainer.predict(model_dir, results_dir, features_path, vid_list_file_tst,
num_epochs, actions_dict, device, sample_rate)
#limit_tetris = {1: 1, 2: 0, 3: 0, 4: 0, 5: 0, 6: 0, 7: 0, 8: 1, 9: 0, 10: 0, 11: 0, 12: 0, 13: 1, 14: 0, 15: 0, 16: 0, 17: 0, 18: 1, 19: 0}
#
#perfect_solution = [
# [(0, 0), (0, 0), (8, 1), (0, 0), (0, 0)],
# [(0, 0), (0, 0), (8, 1), (1, 2), (1, 2)],
# [(0, 0), (8, 1), (8, 1), (1, 2), (1, 2)],
# [(0, 0), (13, 3), (18, 4), (18, 4), (0, 0)],
# [(13, 3), (13, 3), (13, 3), (18, 4), (18, 4)]
# ]
# NOTE: This example is used for the mock solution from 'main.py' only.
# Uncomment the following line to generate a random target shape
#target, limit_tetris, perfect_solution = utils.generate_target(width=8, height=8, density=0.5) # NOTE: it is recommended to keep density below 0.8
target, limit_tetris, perfect_solution = utils.generate_target(
cols, rows, density)
solution = Tetris(deepcopy(target), deepcopy(limit_tetris))
valid, missing, excess, error_pieces, use_diff = utils.check_solution(
target, solution, limit_tetris) # checks if the solution is valid
if not valid or len(error_pieces) != 0:
if len(error_pieces) != 0:
print(
'WARNING: {} pieces have a wrong shapeID. They are labelled in image of the solution, and their PieceID are: {}.'
.format(len(error_pieces), error_pieces))
print("Displaying solution...")
utils.visual_perfect(perfect_solution, solution)
print("WARNING: The solution is not valid, no score will be given!")
else: # if the solution is valid, test time performance and accuracy
else: # if the solution is valid, test time performance and accuracy
# TIME PERFORMANCE
# There will be three different values of the parameter 'target' with increasing complexity in real test.
# ACCURACY
if len(error_pieces) != 0:
raise TypeError('Wrong shape')
total_blocks = sum([sum(row) for row in target])
total_error = (100 * missing / total_blocks) + (100 * excess /
total_blocks)
print('total error: {}'.format(total_error))
return total_error
e_main = 0
for n in range(0, 100):
#sample = Partial_samples(10, 10, 1, 'Three')
#sample.fill_square()
#T = sample.T.tolist()
T = utils.generate_target(width=50, height=50, density=0.6)
e_main += test(T, main.Tetris)
print('----------- For solvable samples --------------')
print('avg e_main: {}'.format(e_main / 100))
