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How to use _get_data_from_file method in avocado
Best Python code snippet using avocado_python
dataset.py
Source:dataset.py 
1import numpy as np2import mixture_multivariateBernoulli.config as config3def _get_data_from_file(args):4 with np.load(args['data_file']) as parameters:5 all_outcomes = parameters['all_outcomes']6 prob_of_outcomes = parameters['prob_of_outcomes']7 data = parameters['train_data']8 data_probs = parameters['train_data_probs']9 if config.random_data:10 rnd_prm = np.random.permutation(len(data))11 data = data[rnd_prm]12 # if not(data_probs == None):13 # if (data_probs is not None):14 if len(data_probs.shape) > 0:15 data_probs = data_probs[rnd_prm]16 train_data = data[0:args['train_size']]17 valid_data = data[args['train_size']:(args['train_size'] + args['valid_size'])]18 # if not(data_probs == None):19 # if data_probs is not None:20 if len(data_probs.shape) > 0:21 train_data_probs = data_probs[0:args['train_size']]22 valid_data_probs = data_probs[args['train_size']:(args['train_size'] + args['valid_size'])]23 else:24 train_data_probs = None25 valid_data_probs = None26 if args['test_size'] == 'FULL_TEST':27 test_data = all_outcomes28 test_data_probs = prob_of_outcomes29 else:30 data = parameters['test_data']31 data_probs = parameters['test_data_probs']32 if config.random_data:33 rnd_prm = np.random.permutation(len(data))34 data = data[rnd_prm]35 # if not(data_probs == None):36 # if data_probs is not None:37 if len(data_probs.shape) > 0:38 data_probs = data_probs[rnd_prm]39 test_data = data[0:args['test_size']]40 # if not(data_probs == None):41 # if data_probs is not None:42 if len(data_probs.shape) > 0:43 test_data_probs = data_probs[0:args['test_size']]44 else:45 test_data_probs = None46 return {'train_data': train_data,47 'train_data_probs': train_data_probs,48 'valid_data': valid_data,49 'valid_data_probs': valid_data_probs,50 'test_data': test_data,51 'test_data_probs': test_data_probs}52def get_data(args):53 if args['data_name'] == 'grid':54 args['data_file'] = 'datasets/grid' + str(args['height']) + 'by' + str(args['width']) + '.npz'55 return _get_data_from_file(args)56 elif args['data_name'] == 'Boltzmann':57 args['data_file'] = 'datasets/Boltzman_' + str(args['n']) + '&' + str(args['m']) + '.npz'58 return _get_data_from_file(args)59 elif args['data_name'].startswith('mnist'):60 if args['digit'] == 'All':61 tr = args['train_size']62 va = args['valid_size']63 te = args['test_size']64 args['train_size'] = args['train_size'] // 1065 args['valid_size'] = args['valid_size'] // 1066 args['test_size'] = args['test_size'] // 1067 args['data_file'] = 'datasets/binary_mnist_' + str(0) + '.npz'68 res = _get_data_from_file(args)69 for d in range(1, 10):70 args['data_file'] = 'datasets/binary_mnist_' + str(d) + '.npz'71 tmp = _get_data_from_file(args)72 res['train_data'] = np.concatenate([res['train_data'], tmp['train_data']], axis=0)73 res['valid_data'] = np.concatenate([res['valid_data'], tmp['valid_data']], axis=0)74 res['test_data'] = np.concatenate([res['test_data'], tmp['test_data']], axis=0)75 np.random.shuffle(res['train_data'])76 np.random.shuffle(res['valid_data'])77 np.random.shuffle(res['test_data'])78 args['train_size'] = tr79 args['valid_size'] = va80 args['test_size'] = te81 return res82 else:83 raise Exception("ERROR: mnist should not be run for just one digit")84 # args['data_file'] = 'datasets/binary_mnist_'+ str(args['digit']) + '.npz'85 # return _get_data_from_file(args)86 elif args['data_name'].startswith('ocr'):87 tr = args['train_size']88 va = args['valid_size']89 te = args['test_size']90 args['train_size'] = tr // 2091 args['valid_size'] = va // 2092 args['test_size'] = te // 2093 args['data_file'] = 'datasets/ocr_' + str(config.ocr_characters[0]) + '.npz'94 res = _get_data_from_file(args)95 for d in range(1, 20):96 args['train_size'] = tr // 2097 args['valid_size'] = va // 2098 args['test_size'] = te // 2099 args['data_file'] = 'datasets/ocr_' + str(config.ocr_characters[d]) + '.npz'100 tmp = _get_data_from_file(args)101 res['train_data'] = np.concatenate([res['train_data'], tmp['train_data']], axis=0)102 res['valid_data'] = np.concatenate([res['valid_data'], tmp['valid_data']], axis=0)103 res['test_data'] = np.concatenate([res['test_data'], tmp['test_data']], axis=0)104 np.random.shuffle(res['train_data'])105 np.random.shuffle(res['valid_data'])106 np.random.shuffle(res['test_data'])107 #################### in baayad avaz beshe!!!108 args['train_size'] = tr109 args['valid_size'] = va110 args['test_size'] = te111 return res112 elif args['data_name'] == 'k_sparse':113 args['data_file'] = 'datasets/k_sparse_' + str(args['n']) + '_' + str(args['sparsity_degree']) + '.npz'114 return _get_data_from_file(args)115 elif args['data_name'] == 'rcv1':116 args['data_file'] = 'datasets/rcv1.npz'117 return _get_data_from_file(args)118 elif args['data_name'] == 'BayesNet':119 args['data_file'] = 'datasets/BayesNet_' + str(args['n']) + '_' + str(args['par_num']) + '.npz'120 return _get_data_from_file(args)121 else:...
data_getter.py
Source:data_getter.py
...5 TOWN_DATA = CURRENT_DIR + "/../../preprocessing_data/towns.txt"6 CSV_TRAIN_DATA = CURRENT_DIR + "/../../data/Test_rev1.csv"7 UNIQUE_JOB_DATA = CURRENT_DIR + "/../../preprocessing_data/job_roles_unique.txt"8 @staticmethod9 def _get_data_from_file(filename, lowercase=True):10 with open(filename, 'r') as f:11 read_data = []12 for row in f.readlines():13 row_data = row.lower() if lowercase else row14 read_data.append(row_data.replace("\n", ""))15 return read_data16 @classmethod17 def get_towns(cls, lowercase=True):18 """19 get list of towns above 1000020 :return:list of towns21 """22 return DataGetter._get_data_from_file(cls.TOWN_DATA, lowercase)23 @classmethod24 def get_stop_word_inc_cities(cls, lowercase=True):25 """26 get list of towns above 10000 pop + stop words27 :return:list of towns28 """29 return DataGetter._get_data_from_file(cls.STOP_WORDS_WITH_CITIES, lowercase)30 @classmethod31 def get_unique_job_roles(cls, lowercase=True):32 """33 Get list of unique jobs as specifed by british achieve34 """...
cpc6128.py
Source:cpc6128.py
...4from memory import Memory, RomChunk, UpperRomChunk5from instruction import Cpu6import cpcio7import screen8def _get_data_from_file(fname):9 with open(fname, 'rb') as fin:10 data = fin.read()11 return data12def _build_memory(screen):13 """ Initialize CPC464 memory map """14 memory = Memory(screen)15 memory.add_chunk(0x0000, RomChunk(_get_data_from_file('6128L.rom')))16 memory.add_chunk(0xC000, UpperRomChunk(0, _get_data_from_file('6128U-basic.rom')))17 memory.apply_ram_map(0)18 memory.dump_map()19 return memory20def _build_cpu(memory_map):21 return Cpu(memory_map)22class Cpc6128(object):23 def __init__(self):24 self.screen = scr = screen.Screen()25 self.memory = _build_memory(self.screen)26 self.cpu = _build_cpu(self.memory)27 self.gatearray = cpcio.GateArray(self.cpu, self.memory, scr)28 self.ppi8255 = cpcio.PPI8255(self.cpu)29 self.printer = cpcio.PrinterPort(self.cpu)30 self.crtc = cpcio.Crtc(self.cpu, scr)...
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