Python random.Random() Examples
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Example #1
Source File: imagenet.py From vergeml with MIT License | 7 votes |
def _makenet(x, num_layers, dropout, random_seed): from keras.layers import Dense, Dropout dropout_seeder = random.Random(random_seed) for i in range(num_layers - 1): # add intermediate layers if dropout: x = Dropout(dropout, seed=dropout_seeder.randint(0, 10000))(x) x = Dense(1024, activation="relu", name='dense_layer_{}'.format(i))(x) if dropout: # add the final dropout layer x = Dropout(dropout, seed=dropout_seeder.randint(0, 10000))(x) return x
Example #2
Source File: hmm.py From razzy-spinner with GNU General Public License v3.0 | 6 votes |
def demo_bw(): # demo Baum Welch by generating some sequences and then performing # unsupervised training on them print() print("Baum-Welch demo for market example") print() model, states, symbols = _market_hmm_example() # generate some random sequences training = [] import random rng = random.Random() rng.seed(0) for i in range(10): item = model.random_sample(rng, 5) training.append([(i[0], None) for i in item]) # train on those examples, starting with the model that generated them trainer = HiddenMarkovModelTrainer(states, symbols) hmm = trainer.train_unsupervised(training, model=model, max_iterations=1000)
Example #3
Source File: adventure.py From Dumb-Cogs with MIT License | 6 votes |
def __init__(self, seed=None): Data.__init__(self) self.output = '' self.yesno_callback = False self.yesno_casual = False # whether to insist they answer self.clock1 = 30 # counts down from finding last treasure self.clock2 = 50 # counts down until cave closes self.is_closing = False # is the cave closing? self.panic = False # they tried to leave during closing? self.is_closed = False # is the cave closed? self.is_done = False # caller can check for "game over" self.could_fall_in_pit = False # could the player fall into a pit? self.random_generator = random.Random() if seed is not None: self.random_generator.seed(seed)
Example #4
Source File: neural_programmer.py From DOTA_models with Apache License 2.0 | 6 votes |
def __init__(self): global FLAGS self.FLAGS = FLAGS self.unk_token = "UNK" self.entry_match_token = "entry_match" self.column_match_token = "column_match" self.dummy_token = "dummy_token" self.tf_data_type = {} self.tf_data_type["double"] = tf.float64 self.tf_data_type["float"] = tf.float32 self.np_data_type = {} self.np_data_type["double"] = np.float64 self.np_data_type["float"] = np.float32 self.operations_set = ["count"] + [ "prev", "next", "first_rs", "last_rs", "group_by_max", "greater", "lesser", "geq", "leq", "max", "min", "word-match" ] + ["reset_select"] + ["print"] self.word_ids = {} self.reverse_word_ids = {} self.word_count = {} self.random = Random(FLAGS.python_seed)
Example #5
Source File: bb_filters.py From buildbot-infra with MIT License | 6 votes |
def seeded_range(seed, start, stop=None, step=1, extra=None): """ A filter to produce deterministic random numbers. Produce a random item from range(start, stop[, step]), use the value and optional ``extra`` value to set the seed for the random number generator. Basic usage:: ansible_fqdn|seeded_range(60) "hello"|seeded_range(1, 10, extra="world") """ hashed_seed = new_hash('sha1') hashed_seed.update(seed) if extra is not None: hashed_seed.update(extra) hashed_seed = hashed_seed.digest() # We rely on randrange's interpretation of parameters return Random(hashed_seed).randrange(start, stop, step)
Example #6
Source File: test_prize.py From donation-tracker with Apache License 2.0 | 6 votes |
def setUp(self): self.rand = random.Random(None) self.event = randgen.generate_event(self.rand) self.event.save() self.run = randgen.generate_run(self.rand, event=self.event) self.run.order = 1 self.run.save() self.prize = randgen.generate_prize( self.rand, event=self.event, start_run=self.run, end_run=self.run, random_draw=True, ) self.prize.key_code = True self.prize.save() models.PrizeKey.objects.bulk_create( randgen.generate_prize_key(self.rand, prize=self.prize) for _ in range(100) ) self.prize_keys = self.prize.prizekey_set.all()
Example #7
Source File: test_admin.py From donation-tracker with Apache License 2.0 | 6 votes |
def setUp(self): self.rand = random.Random(None) self.superuser = User.objects.create_superuser( 'superuser', 'super@example.com', 'password', ) self.processor = User.objects.create(username='processor', is_staff=True) self.processor.user_permissions.add( Permission.objects.get(name='Can change donor'), Permission.objects.get(name='Can change donation'), ) self.head_processor = User.objects.create( username='head_processor', is_staff=True ) self.head_processor.user_permissions.add( Permission.objects.get(name='Can change donor'), Permission.objects.get(name='Can change donation'), Permission.objects.get(name='Can send donations to the reader'), ) self.event = randgen.build_random_event(self.rand) self.session = self.client.session self.session['admin-event'] = self.event.id self.session.save()
Example #8
Source File: test_prize.py From donation-tracker with Apache License 2.0 | 6 votes |
def setUp(self): self.rand = random.Random(None) self.event = randgen.generate_event(self.rand, start_time=today_noon) self.event.save() randgen.generate_runs(self.rand, self.event, 1, scheduled=True) self.write_in_prize = randgen.generate_prizes(self.rand, self.event, 1)[0] self.write_in_donor = randgen.generate_donors(self.rand, 1)[0] models.PrizeWinner.objects.create( prize=self.write_in_prize, winner=self.write_in_donor, acceptcount=1 ) self.donation_prize = randgen.generate_prizes(self.rand, self.event, 1)[0] self.donation_donor = randgen.generate_donors(self.rand, 1)[0] models.Donation.objects.create( event=self.event, donor=self.donation_donor, transactionstate='COMPLETED', amount=5, ) models.PrizeWinner.objects.create( prize=self.donation_prize, winner=self.donation_donor, acceptcount=1 )
Example #9
Source File: run_squad.py From Extending-Google-BERT-as-Question-and-Answering-model-and-Chatbot with Apache License 2.0 | 6 votes |
def PredictAnswer(ContextSummary, question, tokenizer, estimator): data = {'data': [{'title': 'Random', 'paragraphs': [{'context': ContextSummary, 'qas': [{'answers': [], 'question': question, 'id': '56be4db0acb8001400a502ec'}]}]}], 'version': '1.1'} FLAGS.interact = True eval_examples = read_squad_examples(data = data, is_training=False) eval_features, all_results = testing_model(eval_examples, tokenizer, estimator) output_prediction_file = os.path.join(FLAGS.output_dir, "predictions.json") output_nbest_file = os.path.join(FLAGS.output_dir, "nbest_predictions.json") output_null_log_odds_file = os.path.join(FLAGS.output_dir, "null_odds.json") Prediction = write_predictions(eval_examples, eval_features, all_results, FLAGS.n_best_size, FLAGS.max_answer_length, FLAGS.do_lower_case, output_prediction_file, output_nbest_file, output_null_log_odds_file) return list(Prediction.values())[0]
Example #10
Source File: test_prizemail.py From donation-tracker with Apache License 2.0 | 6 votes |
def setUp(self): self.rand = random.Random(None) self.numDonors = 20 self.numPrizes = 40 self.event = randgen.build_random_event( self.rand, num_runs=20, num_prizes=self.numPrizes, num_donors=self.numDonors ) for prize in self.rand.sample( list(self.event.prize_set.all()), self.numPrizes // 10 ): prize.key_code = True prize.save() randgen.generate_prize_key(self.rand, prize=prize).save() self.templateEmail = post_office.models.EmailTemplate.objects.create( name='testing_prize_shipping_notification', description='', subject='A Test', content=self.testTemplateContent, ) self.sender = 'nobody@nowhere.com'
Example #11
Source File: rng.py From iGAN with MIT License | 5 votes |
def set_seed(n): global seed, py_rng, np_rng, t_rng print('set seed = %d' % n) seed = n py_rng = Random(seed) np_rng = RandomState(seed) t_rng = RandomStreams(seed)
Example #12
Source File: evaluation.py From tsinfer with GNU General Public License v3.0 | 5 votes |
def sim_true_and_inferred_ancestors(args): """ Run a simulation under args and return the samples, plus the true and the inferred ancestors """ MB = 10 ** 6 rng = random.Random(args.random_seed) np.random.seed(args.random_seed) sim_args = { "sample_size": args.sample_size, "length": args.length * MB, "recombination_rate": args.recombination_rate, "mutation_rate": args.mutation_rate, "Ne": args.Ne, "model": "smc_prime", "random_seed": rng.randint(1, 2 ** 30), } ts = msprime.simulate(**sim_args) sample_data = generate_samples(ts, args.error) inferred_anc = tsinfer.generate_ancestors(sample_data, engine=args.engine) true_anc = tsinfer.AncestorData(sample_data) tsinfer.build_simulated_ancestors(sample_data, true_anc, ts) true_anc.finalise() return sample_data, true_anc, inferred_anc
Example #13
Source File: modeling_test.py From Extending-Google-BERT-as-Question-and-Answering-model-and-Chatbot with Apache License 2.0 | 5 votes |
def ids_tensor(cls, shape, vocab_size, rng=None, name=None): """Creates a random int32 tensor of the shape within the vocab size.""" if rng is None: rng = random.Random() total_dims = 1 for dim in shape: total_dims *= dim values = [] for _ in range(total_dims): values.append(rng.randint(0, vocab_size - 1)) return tf.constant(value=values, dtype=tf.int32, shape=shape, name=name)
Example #14
Source File: run_squad.py From Extending-Google-BERT-as-Question-and-Answering-model-and-Chatbot with Apache License 2.0 | 5 votes |
def model_definition(bert_config, run_config): train_examples = None num_train_steps = None num_warmup_steps = None if FLAGS.do_train: train_examples = read_squad_examples( input_file=FLAGS.train_file, is_training=True) num_train_steps = int( len(train_examples) / FLAGS.train_batch_size * FLAGS.num_train_epochs) num_warmup_steps = int(num_train_steps * FLAGS.warmup_proportion) # Pre-shuffle the input to avoid having to make a very large shuffle # buffer in in the `input_fn`. rng = random.Random(12345) rng.shuffle(train_examples) model_fn = model_fn_builder( bert_config=bert_config, init_checkpoint=FLAGS.init_checkpoint, learning_rate=FLAGS.learning_rate, num_train_steps=num_train_steps, num_warmup_steps=num_warmup_steps, use_tpu=FLAGS.use_tpu, use_one_hot_embeddings=FLAGS.use_tpu) # If TPU is not available, this will fall back to normal Estimator on CPU # or GPU. estimator = tf.contrib.tpu.TPUEstimator( use_tpu=FLAGS.use_tpu, model_fn=model_fn, config=run_config, train_batch_size=FLAGS.train_batch_size, predict_batch_size=FLAGS.predict_batch_size) return model_fn, estimator
Example #15
Source File: test_prize.py From donation-tracker with Apache License 2.0 | 5 votes |
def setUp(self): self.rand = random.Random(None) self.event = randgen.generate_event(self.rand) self.event.save() self.runs = randgen.generate_runs(self.rand, self.event, 4, scheduled=True)
Example #16
Source File: create_pretraining_data.py From Extending-Google-BERT-as-Question-and-Answering-model-and-Chatbot with Apache License 2.0 | 5 votes |
def main(_): tf.logging.set_verbosity(tf.logging.INFO) tokenizer = tokenization.FullTokenizer( vocab_file=FLAGS.vocab_file, do_lower_case=FLAGS.do_lower_case) input_files = [] for input_pattern in FLAGS.input_file.split(","): input_files.extend(tf.gfile.Glob(input_pattern)) tf.logging.info("*** Reading from input files ***") for input_file in input_files: tf.logging.info(" %s", input_file) rng = random.Random(FLAGS.random_seed) instances = create_training_instances( input_files, tokenizer, FLAGS.max_seq_length, FLAGS.dupe_factor, FLAGS.short_seq_prob, FLAGS.masked_lm_prob, FLAGS.max_predictions_per_seq, rng) output_files = FLAGS.output_file.split(",") tf.logging.info("*** Writing to output files ***") for output_file in output_files: tf.logging.info(" %s", output_file) write_instance_to_example_files(instances, tokenizer, FLAGS.max_seq_length, FLAGS.max_predictions_per_seq, output_files)
Example #17
Source File: flask_httpauth.py From jbox with MIT License | 5 votes |
def __init__(self, scheme=None, realm=None, use_ha1_pw=False): super(HTTPDigestAuth, self).__init__(scheme or 'Digest', realm) self.use_ha1_pw = use_ha1_pw self.random = SystemRandom() try: self.random.random() except NotImplementedError: # pragma: no cover self.random = Random() self.generate_nonce_callback = None self.verify_nonce_callback = None self.generate_opaque_callback = None self.verify_opaque_callback = None def _generate_random(): return md5(str(self.random.random()).encode('utf-8')).hexdigest() def default_generate_nonce(): session["auth_nonce"] = _generate_random() return session["auth_nonce"] def default_verify_nonce(nonce): return nonce == session.get("auth_nonce") def default_generate_opaque(): session["auth_opaque"] = _generate_random() return session["auth_opaque"] def default_verify_opaque(opaque): return opaque == session.get("auth_opaque") self.generate_nonce(default_generate_nonce) self.generate_opaque(default_generate_opaque) self.verify_nonce(default_verify_nonce) self.verify_opaque(default_verify_opaque)
Example #18
Source File: test_admin.py From donation-tracker with Apache License 2.0 | 5 votes |
def setUp(self): self.rand = random.Random(None) self.superuser = User.objects.create_superuser( 'superuser', 'super@example.com', 'password', ) self.event = randgen.build_random_event(self.rand) self.session = self.client.session self.session['admin-event'] = self.event.id self.session.save()
Example #19
Source File: test_prize.py From donation-tracker with Apache License 2.0 | 5 votes |
def setUp(self): self.rand = random.Random(None) self.event = randgen.generate_event(self.rand) self.event.save() self.runs = randgen.generate_runs(self.rand, self.event, 4, scheduled=True) self.event_prize = models.Prize.objects.create( name='Event Wide Prize', startrun=self.runs[0], endrun=self.runs[3] ) self.start_prize = models.Prize.objects.create( name='Start Prize', startrun=self.runs[0], endrun=self.runs[0] ) self.middle_prize = models.Prize.objects.create( name='Middle Prize', startrun=self.runs[1], endrun=self.runs[1] ) self.end_prize = models.Prize.objects.create( name='End Prize', startrun=self.runs[3], endrun=self.runs[3] ) self.start_span_prize = models.Prize.objects.create( name='Start Span Prize', startrun=self.runs[0], endrun=self.runs[1] ) self.middle_span_prize = models.Prize.objects.create( name='Middle Span Prize', startrun=self.runs[1], endrun=self.runs[2] ) self.end_span_prize = models.Prize.objects.create( name='End Span Prize', startrun=self.runs[2], endrun=self.runs[3] )
Example #20
Source File: test_prize.py From donation-tracker with Apache License 2.0 | 5 votes |
def setUpBeforeMigration(self, apps): Prize = apps.get_model('tracker', 'Prize') Event = apps.get_model('tracker', 'Event') SpeedRun = apps.get_model('tracker', 'SpeedRun') self.rand = random.Random(None) self.event = Event.objects.create( short='test', name='Test Event', datetime=today_noon, targetamount=100 ) self.run1 = SpeedRun.objects.create( event=self.event, name='Test Run 1', order=1, run_time='0:05:00' ) self.run2 = SpeedRun.objects.create( event=self.event, name='Test Run 2', order=2, run_time='0:05:00' ) self.run3 = SpeedRun.objects.create( event=self.event, name='Test Run 3', order=3, run_time='0:05:00' ) self.prize1 = Prize.objects.create( event=self.event, name='Test Prize 1', startrun=self.run1, endrun=self.run1 ) self.prize2 = Prize.objects.create( event=self.event, name='Test Prize 2', startrun=self.run2, endrun=self.run2 ) self.prize3 = Prize.objects.create( event=self.event, name='Test Prize 3', startrun=self.run3, endrun=self.run3 )
Example #21
Source File: test_prize.py From donation-tracker with Apache License 2.0 | 5 votes |
def setUp(self): self.rand = random.Random(None) self.event = randgen.build_random_event(self.rand) self.event.save()
Example #22
Source File: test_prize.py From donation-tracker with Apache License 2.0 | 5 votes |
def setUp(self): self.rand = random.Random(None) self.event = randgen.generate_event(self.rand) self.event.save() # checks that a prize with a single eligible winner keeps track of a # declined prize, and disallows that person from being drawn again
Example #23
Source File: test_prize.py From donation-tracker with Apache License 2.0 | 5 votes |
def setUp(self): self.eventStart = parse_date('2012-01-01 01:00:00Z') self.rand = random.Random() self.event = randgen.build_random_event(self.rand, start_time=self.eventStart) self.event.save()
Example #24
Source File: test_prize.py From donation-tracker with Apache License 2.0 | 5 votes |
def setUp(self): self.rand = random.Random(9239234) self.event = randgen.generate_event(self.rand) self.event.save()
Example #25
Source File: test_prize.py From donation-tracker with Apache License 2.0 | 5 votes |
def setUp(self): self.eventStart = parse_date('2014-01-01 16:00:00Z') self.rand = random.Random(516273) self.event = randgen.build_random_event( self.rand, start_time=self.eventStart, num_donors=100, num_runs=50 ) self.runsList = list(models.SpeedRun.objects.filter(event=self.event)) self.donorList = list(models.Donor.objects.all())
Example #26
Source File: test_event.py From donation-tracker with Apache License 2.0 | 5 votes |
def setUp(self): self.super_user = User.objects.create_superuser( 'admin', 'admin@example.com', 'password' ) timezone = pytz.timezone(settings.TIME_ZONE) self.event = models.Event.objects.create( targetamount=5, datetime=today_noon, timezone=timezone, name='test event', short='test', ) self.rand = random.Random(None) self.client.force_login(self.super_user)
Example #27
Source File: test_prizemail.py From donation-tracker with Apache License 2.0 | 5 votes |
def setUp(self): self.rand = random.Random(None) self.numDonors = 20 self.numPrizes = 30 self.event = randgen.build_random_event( self.rand, num_runs=20, num_prizes=self.numPrizes, num_donors=self.numDonors ) self.templateEmail = post_office.models.EmailTemplate.objects.create( name='testing_prize_accept_notification', description='', subject='A Test', content=self.testTemplateContent, ) self.sender = 'nobody@nowhere.com'
Example #28
Source File: test_prizemail.py From donation-tracker with Apache License 2.0 | 5 votes |
def setUp(self): self.rand = random.Random(None) self.numDonors = 10 self.numPrizes = 40 self.event = randgen.build_random_event( self.rand, num_runs=20, num_prizes=self.numPrizes, num_donors=self.numDonors ) self.templateEmail = post_office.models.EmailTemplate.objects.create( name='testing_prize_submission_response', description='', subject='A Test', content=self.testTemplateContent, )
Example #29
Source File: util.py From donation-tracker with Apache License 2.0 | 5 votes |
def setUp(self): self.rand = random.Random() self.factory = RequestFactory() self.locked_event = models.Event.objects.create( datetime=long_ago_noon, targetamount=5, short='locked', name='Locked Event' ) self.event = models.Event.objects.create( datetime=today_noon, targetamount=5, short='event', name='Test Event' ) self.anonymous_user = AnonymousUser() self.user = User.objects.create(username='test') self.add_user = User.objects.create(username='add') self.locked_user = User.objects.create(username='locked') self.locked_user.user_permissions.add( Permission.objects.get(name='Can edit locked events') ) if self.model_name: self.add_user.user_permissions.add( Permission.objects.get(name='Can add %s' % self.model_name), Permission.objects.get(name='Can change %s' % self.model_name), ) self.locked_user.user_permissions.add( Permission.objects.get(name='Can add %s' % self.model_name), Permission.objects.get(name='Can change %s' % self.model_name), ) self.super_user = User.objects.create(username='super', is_superuser=True) self.maxDiff = None
Example #30
Source File: prizeutil.py From donation-tracker with Apache License 2.0 | 5 votes |
def draw_keys(prize, seed=None, rand=None): try: rand = rand or random.Random(seed) except TypeError: return False, {'error': 'Seed parameter was unhashable'} eligible = prize.eligible_donors() if not eligible: return False, {'error': 'Prize: ' + prize.name + ' has no eligible donors.'} unclaimed_keys = ( PrizeKey.objects.select_for_update() .filter(prize=prize, prize_winner_id=None) .order_by() ) if unclaimed_keys.count() >= len(eligible): winners = eligible else: winners = rand.sample(eligible, unclaimed_keys.count()) for key, d in zip(unclaimed_keys, winners): key.prize_winner = PrizeWinner.objects.create( prize=prize, winner_id=d['donor'], pendingcount=0, acceptcount=1, emailsent=True, acceptemailsentcount=1, shippingstate='SHIPPED', ) key.save() return True, {'winners': [w['donor'] for w in winners]}