def main():
"""
loop throught all config file and display result.
:return:
"""
logger = Logger('Main')
logger.info('begin operation on bloom filter')
# ---------------------------------------------------------------
# Get parameters from config file
list_visualisation = []
dic_response = create_bloom_filters(logger, METHOD)
list_visualisation.append(dic_response[Constants.DIS_INPUTS])
list_visualisation.append(dic_response[Constants.DIS_TESTS])
list_visualisation.append(dic_response[Constants.DIS_DOUBLE])
list_visualisation.append(dic_response[ARF_METHOD])
visualize_curve(list_visualisation, "dimension", " Rate false positive (%)", "DELTA "+str(DELTA) + " N and tests "+
str(SIZE_DATA)+" Dim "+str(DIM)+" DOMAIN "+str(DOMAIN))
def main():
"""
loop throught all config file and display result.
:return:
"""
logger = Logger('Main')
logger.info('begin operation on bloom filter')
# ---------------------------------------------------------------
# Get parameters from config file
list_visualisation = []
for config_file in os.listdir(PATH_CONFIG):
print(config_file)
list_visualisation.append(
run_test_on_bloom_filter(logger, PATH_CONFIG, config_file))
visualize_curve(list_visualisation, "ratio m/n",
" rate false positive (%)",
"Comparaison discretization method in Dimension 2")
def main():
"""
loop throught all config file and display result.
:return:
"""
logger = Logger('Main')
logger.info('begin operation on bloom filter')
# ---------------------------------------------------------------
# Get parameters from config file
list_visualisation = []
a = create_bloom_filters(logger)
list_visualisation.append(("dis_input", a[0], a[1][0]))
list_visualisation.append(("dis_test", a[0], a[1][1]))
list_visualisation.append(("dis_double", a[0], a[1][2]))
#list_visualisation.append(("dis_circle", a[0], a[1][3]))
list_visualisation.append(("arf", a[0], a[1][3]))
#list_visualisation.append(("dis_none", a[0], a[1][3]))
visualize_curve(
list_visualisation, "delta", " rate false positive (%)",
"Dimension: " + str(DIMENSION) + " size_set n: " + str(SIZE_DATA) +
" rate size_bloom/size_set n/m: " + str(RATE_M_N) + " domain: " +
str(DOMAIN) + " number_of tests: " + str(TESTS))
def __init__(self):
self.__logger = Logger().get_logger()
self.__access_token = str()
def __init__(self):
self.__logger = Logger().get_logger()
self.__repository = GameUserRepository()
self.__auth_business = AuthBusiness()
def __init__(self, path):
self.path = path
self.logger = Logger('Arf')
self.compile()
def __init__(self, dimension, file_path):
DataProvider.__init__(self, dimension)
self.file_path = file_path
self.logger = Logger('ReaderFromFile')
import pandas as pd
import numpy as np
from random import randint, uniform
from src.providerData.DataProvider import DataProvider
from src.structureData.Point import Point
from src.util.Logger import Logger
# -----------------------------------------------------------------------------------------
# Constant
DATA_FOLDER = "../../data"
# -----------------------------------------------------------------------------------------
# Code
logger = Logger("RandomDataGenerator")
class RandomDataGenerator(DataProvider):
"""
This class allow to provide data randomly.
Args :
:param dimension: int that represent dimention of the vector that will be in the data.
:param size_of_data_set: Size of the data set.
:param domain: int that represent domain ([0,<domain>]) for dimension of the vectors that will be in the data.
:param distribution: distribution of the random value:
- 0 : uniform
- x (10 > x >= 1): x normal laws superposed
:param point_list: DataFrame that contain the data points:
"""
def __init__(self,
def __init__(self):
self.__mongo_configuration = MongoConfiguration()
self.__database = self.__mongo_configuration.database
self.__game_user_collection = self.__database.game_user_collection
self.__logger = Logger().get_logger()
def __init__(self):
self.__logger = Logger().get_logger()
self.__mongo_configuration = MongoConfiguration()
self.__database = self.__mongo_configuration.database
self.__user_collection = self.__database.user_collection
self.__user_collection.create_index("login", unique=True)
def __init__(self):
self.__logger = Logger().get_logger()
# -------- Aim of the file
# This file provide a class Point that will be our data strutur for the data that will fit our Bloom Filter
# -------- Import
import abc
from src.util.Logger import Logger
# --------- Constant
# --------- Code
logger = Logger("DataProvider")
class DataProvider:
"""
This class is the interface for all data providers. The data provided will be use by the Bloom filter.
Args :
:param dimension: int that represent dimention of the vector that will be in the data.
:param size_of_data_set: Size of the data set.
"""
__metaclass__ = abc.ABCMeta
def __init__(self, dimension):
self.dimension = dimension
self.point_list = []
@abc.abstractmethod
def get_points(self):
def __init__(self):
self.__user_bussiness = UserBusiness()
self.__logger = Logger().get_logger()
def __init__(self):
self.__user_repository = UserRepository()
self.__access_token_business = AccessTokenBusiness()
self.__logger = Logger().get_logger()