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How to use num_active method in autotest

Best Python code snippet using autotest_python

solution.py

Source:solution.py

...53						row.append('.')54				dimension.append(row)55			updated_state.append(dimension)56		return updated_state57	def count_num_active(self,state):58		num_active = 059		num_dimensions = len(state)60		num_rows = len(state[0])61		for i in range(0,num_dimensions):62			for j in range(0,num_rows):63				for k in range(0,num_rows):64					if(state[i][j][k]=='#'):65						num_active = num_active + 166		return num_active67class Cube_4D():68	def __init__(self,state=[[[]]]):69		# state is 4D list: 1st D = hyper (w direction), 2nd D = dimension (z direction), 3rd D = row (x direction), 4th D = col (y direction)70		self.initial_state = state71	# add inactive borders to a state in hyper, dimension, row, and column.72	def add_border(self,state):73		num_hyper = len(state)74		num_dimensions = len(state[0])75		num_row = len(state[0][0])76		period_row = list('.'*(num_row + 2))77		period_hyper = []78		for j in range(0,num_dimensions+2):79			period_dimension = []80			for i in range(0,num_row + 2):81				period_dimension.append(period_row)82			period_hyper.append(period_dimension)83		new_state = [period_hyper]84		for h in range(0,num_hyper):85			hyper = [period_dimension]86			for i in range(0,num_dimensions):87				dimension = [period_row]88				for j in range(0,num_row):89					row = ['.']90					for k in range(0,num_row):91						row.append(state[h][i][j][k])92					row.append('.')93					dimension.append(row)94				dimension.append(period_row)95				hyper.append(dimension)96			hyper.append(period_dimension)97			new_state.append(hyper)98		new_state.append(period_hyper)99		return new_state100	def count_neighbors(self,state,indices):101		hyp, dim, row, col = indices[0],indices[1],indices[2],indices[3]102		num_active = 0103		for h in range(hyp-1,hyp+2):104			for i in range(dim-1,dim+2):105				for j in range(row-1,row+2):106					for k in range(col-1,col+2):107						if(state[h][i][j][k] == '#' and not (h == hyp and i == dim and j == row and k == col)):108							num_active = num_active + 1109		return num_active 110	def propagate(self,state):111		new_state = self.add_border(state) # makes it easier to check neighbors112		updated_state = []113		num_hyper = len(new_state)114		num_dimensions = len(new_state[0])115		num_rows = len(new_state[0][0])116		for h in range(1,num_hyper-1):117			hyper = []118			for i in range(1,num_dimensions-1):119				dimension = []120				for j in range(1,num_rows-1):121					row = []122					for k in range(1,num_rows-1):123						num_active = self.count_neighbors(new_state,[h,i,j,k])124						if(num_active == 3):125							row.append('#')126						elif(new_state[h][i][j][k]=='#' and num_active == 2):127							row.append('#')128						else:129							row.append('.')130					dimension.append(row)131				hyper.append(dimension)132			updated_state.append(hyper)133		return updated_state134	def count_num_active(self,state):135		num_active = 0136		num_hyper = len(state)137		num_dimensions = len(state[0])138		num_rows = len(state[0][0])139		for h in range(0,num_hyper):140			for i in range(0,num_dimensions):141				for j in range(0,num_rows):142					for k in range(0,num_rows):143						if(state[h][i][j][k]=='#'):144							num_active = num_active + 1145		return num_active146	def __repr__(self):147		string = ""148		for h in range(0,len(self.initial_state)):149			for i in range(0,len(self.initial_state[0])):150				for j in range(0,len(self.initial_state[0][0])):151					string = string + "".join(self.initial_state[h][i][j]) + "\n"152				string = string + "\n"153			string = string + "\n"154		string = string + "\nEND"155		return string156if __name__ == "__main__":157	with open(INPUT_FILE_NAME) as input_file:158		input_list = input_file.readlines()159		# create initial state that is 1 dimensional160		part1_cube = Cube_3D()161		for line in input_list:162			part1_cube.initial_state[0].append(list(line.rstrip()))163		for cycle in range(0,NUM_CYCLES):164			# add border165			new_state = part1_cube.add_border(part1_cube.initial_state)166			part1_cube.initial_state = None167			# propagate state168			part1_cube.initial_state = part1_cube.propagate(new_state)169		170		#count active states171		num_active = part1_cube.count_num_active(part1_cube.initial_state)172		print("(part 1): the number of active states after "+str(NUM_CYCLES)+" cycles for a 3D cube is "+str(num_active))	173		part2_cube = Cube_4D()174		for line in input_list:175			part2_cube.initial_state[0][0].append(list(line.rstrip()))	176		for cycle in range(0,NUM_CYCLES):177			# add border178			new_state = part2_cube.add_border(part2_cube.initial_state)179			part2_cube.initial_state = [[[]]]180			# propagate state181			part2_cube.initial_state = part2_cube.propagate(new_state)182		183		#count active states184		num_active = part2_cube.count_num_active(part2_cube.initial_state)...

data_split.py

Source:data_split.py

1import torch2import random3import hashlib4import json5def get_split(num_active, num_inactive, seed, dataset_name, shrink=False):6    active_idx = list(range(num_active))7    inactive_idx = list(range(num_active, num_active + num_inactive))8    random.seed(seed)9    random.shuffle(active_idx)10    random.shuffle(inactive_idx)11    if shrink == False:12        num_active_train = round(num_active * 0.8)13        num_inactive_train = round(num_inactive * 0.8)14        num_active_valid = round(num_active * 0.1)15        num_inactive_valid = round(num_inactive * 0.1)16        num_active_test = num_active - num_active_train - num_active_valid17        num_inactive_test = round(num_inactive * 0.1)18        filename = f'data_split/{dataset_name}_seed{seed}.pt'19    else:20        num_active_train = round(num_active * 0.8)21        num_inactive_train = 10000 if num_inactive >10000 else round(num_inactive*0.8)22        num_active_valid = round(num_active * 0.1)23        num_inactive_valid = round(num_inactive * 0.1)24        num_active_test = num_active - num_active_train - num_active_valid25        num_inactive_test = round(num_inactive * 0.1)26        filename = f'data_split/shrink_{dataset_name}_seed{seed}.pt'27    split_dict = {}28    split_dict['train'] = active_idx[:num_active_train]\29                          + inactive_idx[:num_inactive_train]30    split_dict['valid'] = active_idx[31                          num_active_train:num_active_train32                                           +num_active_valid] \33                          + inactive_idx[34                            num_inactive_train:num_inactive_train35                                               +num_inactive_valid]            36    split_dict['test'] = active_idx[37                         num_active_train + num_active_valid38                         : num_active_train39                           + num_active_valid40                           + num_active_test] \41                         + inactive_idx[42                           num_inactive_train + num_inactive_valid43                           : num_inactive_train44                             + num_inactive_valid45                             + num_inactive_test]46    # print(f'split_dict:{split_dict["test"]}')47    num_train = len(split_dict['train'])48    num_valid = len(split_dict['valid'])49    num_test = len(split_dict['test'])50    print(f'num_train:{num_train}, num_valid:{num_valid}, num_test:{num_test}')51    52    torch.save(split_dict, filename)53    data_md5 = hashlib.md5(json.dumps(split_dict, sort_keys=True).encode('utf-8')).hexdigest()54    print(f'data_md5_checksum:{data_md5}')55    print(f'file saved at {filename}')56    with open(f'{filename}.checksum', 'w+') as checksum_file:57        checksum_file.write(data_md5)58if __name__ == '__main__':59    dataset_info = {60        '435008':{'num_active':233, 'num_inactive':217923},#{'num_active':233, 'num_inactive':217925},61        '1798':{'num_active':187, 'num_inactive':61645},#{'num_active':187, 'num_inactive':61645},62        '435034': {'num_active':362, 'num_inactive':61393},#{'num_active':362, 'num_inactive':61394},63        '1843': {'num_active':172, 'num_inactive':301318},#{'num_active':172, 'num_inactive':301321},64        '2258': {'num_active':213, 'num_inactive':302189},#{'num_active':213, 'num_inactive':302192},65        '463087': {'num_active':703, 'num_inactive':100171},#{'num_active':703, 'num_inactive':100172},66        '488997': {'num_active':252, 'num_inactive':302051},#{'num_active':252, 'num_inactive':302054},67        '2689': {'num_active':172, 'num_inactive':319617},#{'num_active':172, 'num_inactive':319620},68        '485290': {'num_active':278, 'num_inactive':341026},#{'num_active':281, 'num_inactive':341084},69        '9999':{'num_active':37, 'num_inactive':226},70    }71    seed_list = [1,2,3]72    dataset_name_list = ['435008', '1798', '435034', '1843', '2258', '463087', '488997','2689', '485290', '9999']73    # dataset_name_list = ['1798']74    for dataset_name in dataset_name_list:75        for seed in seed_list:76            num_active = dataset_info[dataset_name]['num_active']77            num_inactive = dataset_info[dataset_name]['num_inactive']...

new_data_split.py

Source:new_data_split.py

1import torch2import random3import hashlib4import json5def get_split(num_active, num_inactive, seed, dataset_name, shrink=False):6    active_idx = list(range(num_active))7    inactive_idx = list(range(num_active, num_active + num_inactive))8    random.seed(seed)9    random.shuffle(active_idx)10    random.shuffle(inactive_idx)11    num_active_train = round(num_active * 0.8)12    num_inactive_train = round(num_inactive * 0.8)13    num_active_valid = round(num_active * 0.1)14    num_inactive_valid = round(num_inactive * 0.1)15    num_active_test = num_active - num_active_train - num_active_valid16    num_inactive_test = round(num_inactive * 0.1)17    print(f'num_active_train:{num_active_train} num_active_valid:{num_active_valid } num_active_test:{num_active_test}')18    19    split_dict = {}20    split_dict['train'] = active_idx[:num_active_train]\21                          + inactive_idx[:num_inactive_train]22    split_dict['valid'] = active_idx[23                          num_active_train:num_active_train24                                           +num_active_valid] \25                          + inactive_idx[26                            num_inactive_train:num_inactive_train27                                               +num_inactive_valid]            28    split_dict['test'] = active_idx[29                         num_active_train + num_active_valid30                         : num_active_train31                           + num_active_valid32                           + num_active_test] \33                         + inactive_idx[34                           num_inactive_train + num_inactive_valid35                           : num_inactive_train36                             + num_inactive_valid37                             + num_inactive_test]38    if shrink == True:39        trim_number = 10000 if num_inactive >10000 else round(num_inactive*0.8)40        split_dict['train'] = split_dict['train'][:(trim_number+num_active)]41        filename = f'data_split/preserve_shrink_{dataset_name}_seed{seed}.pt'42    else:43        filename = f'data_split/preserve_{dataset_name}_seed{seed}.pt'44    # print(f'split_dict:{split_dict["test"]}')45    num_train = len(split_dict['train'])46    num_valid = len(split_dict['valid'])47    num_test = len(split_dict['test'])48    print(f'num_train:{num_train}, num_valid:{num_valid}, num_test:{num_test}')49    50    torch.save(split_dict, filename)51    data_md5 = hashlib.md5(json.dumps(split_dict, sort_keys=True).encode('utf-8')).hexdigest()52    print(f'data_md5_checksum:{data_md5}')53    print(f'file saved at {filename}')54    with open(f'{filename}.checksum', 'w+') as checksum_file:55        checksum_file.write(data_md5)56if __name__ == '__main__':57    dataset_info = {58        '435008':{'num_active':233, 'num_inactive':217923},#{'num_active':233, 'num_inactive':217925},59        '1798':{'num_active':187, 'num_inactive':61645},#{'num_active':187, 'num_inactive':61645},60        '435034': {'num_active':362, 'num_inactive':61393},#{'num_active':362, 'num_inactive':61394},61        '1843': {'num_active':172, 'num_inactive':301318},#{'num_active':172, 'num_inactive':301321},62        '2258': {'num_active':213, 'num_inactive':302189},#{'num_active':213, 'num_inactive':302192},63        '463087': {'num_active':703, 'num_inactive':100171},#{'num_active':703, 'num_inactive':100172},64        '488997': {'num_active':252, 'num_inactive':302051},#{'num_active':252, 'num_inactive':302054},65        '2689': {'num_active':172, 'num_inactive':319617},#{'num_active':172, 'num_inactive':319620},66        '485290': {'num_active':278, 'num_inactive':341026},#{'num_active':281, 'num_inactive':341084},67        '9999':{'num_active':37, 'num_inactive':226},68    }69    seed_list = [1,2,3]70    dataset_name_list = ['435008', '1798', '435034', '1843', '2258', '463087', '488997','2689', '485290', '9999']71    # dataset_name_list = ['1798']72    for dataset_name in dataset_name_list:73        for seed in seed_list:74            num_active = dataset_info[dataset_name]['num_active']75            num_inactive = dataset_info[dataset_name]['num_inactive']...

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