obernardocosta

class Vector(dict):
    def __init__(self, a, b):
        import uuid
        self._load(a, b)
        self.id = uuid.uuid4()
        self.__name__ = 'Vector:'
    def _build(self, a, b):
        return Vector(a, b)
    
    def _load(self, a, b):

obernardocosta

acumulator = [0, 0]

for _ in range(10**6):

  mh = MontyHall()
  mh.set_random_guess()
  mh.open_goat_door()
  mh.switch_door()  # comment to don't swich
  result = mh.validade_guess_door()
  if result: acumulator[0] += 1

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"""author: `Bernardo Costa <bernardoantunescosta at gmail.com>`"""

import random

class MontyHall:
  """Monty Hall Simulator Class."""

  def __init__(self):
    self.doors = list('123')
    self.reward_door = None

obernardocosta

Boto3 Hard Mock

Start Mock

boto3_resource = boto3.resource

def boto3_mock_resource(service_name, region_name):
  return boto3_resource(
                        service_name=service_name,
 region_name=region_name,

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"""Playing with primes."""


def get_square_root(n):
  """Return square root of n."""
  return n ** (1.0 / 2)


def is_divisor(n, d):
  """Return True if n divides d, False otherwise."""

obernardocosta

import pandas as pd


# create sample dataset
columns = ['ID', 'Beer', 'Diaper', 'Gum', 'Soda', 'Snack']
dataset = [[1, 1, 1, 1, 1, 0],
           [2, 1, 1, 0, 0, 0],
           [3, 1, 1, 1, 0, 1],
           [4, 1, 1, 0, 1, 1],
           [5, 0, 1, 0, 1, 0],

obernardocosta

# Running Apriori 

if 'ID' in df.columns: del df['ID'] # ID is not relevant to apriori 

apriori_runner = Apriori(df, threshold=0.4, transform_bol=True)
apriori_df = apriori_runner.run(use_colnames=True)
print(apriori_df)

#output
#    support        itemsets  length

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from mlxtend.frequent_patterns import apriori

class Apriori:
    """Apriori Class. Its has Apriori steps."""
    threshold = 0.5
    df = None

    def __init__(self, df, threshold=None, transform_bol=False):
        """Apriori Constructor. 

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Create virtual environment

python3 -m venv v-env

Active virtual environment

source v-env/bin/active

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# 1. open cloud shell
# 2. download the the resource lab named resources-echo-web.tar.gz
# 3. upload it in the cloud shell
# 4. extract the file
tar -xvzf resources-echo-web.tar.gz

# 5. build the image as v1 tag
docker build -t gcr.io/<PROJECT_ID>/echo-app:v1 .

# 6. check if the image was Built