Python random.Random方法代碼示例

# 需要導入模塊: import random [as 別名]
# 或者: from random import Random [as 別名]
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 

# 需要導入模塊: import random [as 別名]
# 或者: from random import Random [as 別名]
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) 

# 需要導入模塊: import random [as 別名]
# 或者: from random import Random [as 別名]
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) 

# 需要導入模塊: import random [as 別名]
# 或者: from random import Random [as 別名]
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) 

# 需要導入模塊: import random [as 別名]
# 或者: from random import Random [as 別名]
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' 

# 需要導入模塊: import random [as 別名]
# 或者: from random import Random [as 別名]
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() 

# 需要導入模塊: import random [as 別名]
# 或者: from random import Random [as 別名]
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() 

# 需要導入模塊: import random [as 別名]
# 或者: from random import Random [as 別名]
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) 

# 需要導入模塊: import random [as 別名]
# 或者: from random import Random [as 別名]
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] 

# 需要導入模塊: import random [as 別名]
# 或者: from random import Random [as 別名]
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) 

# 需要導入模塊: import random [as 別名]
# 或者: from random import Random [as 別名]
def read_samples(self, split, index, n=1):
        items = self.files[split][index:index+n]
        items = [(self.read_file(filename), meta) for filename, meta in items]
        
        res = []
        for item, meta in items:
            rng = random.Random(str(self.random_seed) + meta['filename'])
            res.append(Sample(item, None, meta.copy(), rng)) 

        return res 

# 需要導入模塊: import random [as 別名]
# 或者: from random import Random [as 別名]
def read_samples(self, split, index, n=1):
        items = self.data[split][index: index+n]
        return [Sample(item, item+5, {'meta': item}, random.Random(self.random_seed + item))
                for item in items] 

# 需要導入模塊: import random [as 別名]
# 或者: from random import Random [as 別名]
def read_samples(self, split, index, n=1):
        items = self.files[split][index:index+n]


        items = [(Path(filename).read_text(), meta) for filename, meta in items]

        res = []
        for item, meta in items:
            rng = random.Random(str(self.random_seed) + meta['filename'])
            res.append(Sample(item, None, meta.copy(), rng))

        return res 

# 需要導入模塊: import random [as 別名]
# 或者: from random import Random [as 別名]
def read_samples(self, split, index, n=1):
        items = self.files[split][index:index+n]
        items = [(self.read_file(filename), meta) for filename, meta in items]

        res = []
        for item, meta in items:
            rng = random.Random(str(self.random_seed) + meta['filename'])
            res.append(Sample(item, None, meta.copy(), rng))

        return res 

# 需要導入模塊: import random [as 別名]
# 或者: from random import Random [as 別名]
def read_samples(self, split, index, n=1):
        items = self.files[split][index:index+n]
        items = [(Path(filename).read_text(), meta) for filename, meta in items]

        res = []
        for item, meta in items:
            rng = random.Random(str(self.random_seed) + meta['filename'])
            res.append(Sample(item, None, meta.copy(), rng))

        return res