# Path: app/random/biased_pseudo_random.py
# class BiasedRandomDistribution(object):
#     """
#     A pseudo random number generator that can be biased to promote a pseudo random distribution
#     of outbound calls, even if users have favors for choosing some particular dialog partners.
#     """
#     def __init__(self, cardinal):
#         """
#         :param cardinal: cardinality of the initial distribution vector as integer value with
#                          evenly distributed chance of 1/cardinal.
#                          or a list of integers representing the initial distribution vector.
#                          Example: [1, 1, 1, 1] initialisation vector results in random numbers
#                                   between 1 and 4 with initial chance of 1/4 each.
#                                   [1, 1, 2, 1]initialisation vector results in random numbers
#                                   between 1 and 4 where 1,2 and 4 have the initial chance of 1/5
#                                   and 3 has the initial chance of 2/5.
#         """
#         from array import array
#         if type(cardinal) == list:
#             self.distribution_vector = array('I', cardinal)
#             self.base = 0
#             for value in cardinal:
#                 self.base += value
#             self.cardinal = len(cardinal)
#         elif type(cardinal) == int:
#             self.distribution_vector = array('I')
#             for item in range(0, cardinal):
#                 self.distribution_vector.append(1)
#             self.base = cardinal
#             self.cardinal = cardinal
#         else:
#             raise TypeError
# 
#     def __iter__(self):
#         return iter(self.distribution_vector)
# 
#     def __getitem__(self, item):
#         return self.distribution_vector[item]
# 
#     def __str__(self):
#         return str(list(self.distribution_vector))
# 
#     def __len__(self):
#         return len(self.distribution_vector)
# 
#     def _get_biased_random_number_(self):
#         """
#         :return: biased pseudo random integer between 1 and initial cardinality.
#         """
#         from random import randint
#         basis = self._get_base_()
#         random = randint(0, basis - 1)
#         counter = 0
#         temp_value = 0
#         for number in self:
#             temp_value += number
#             if temp_value >= random:
#                 return counter
#             counter += 1
# 
#     def _get_base_(self):
#         """
#         Recalculates the base of the current distribution vector.
#         :return: int
#         """
#         self.base = 0
#         for number in self:
#             self.base += number
#         return self.base
# 
#     def add_sample(self, index):
#         """
#         Alters the distribution vector.
#         :param index: integer value between 0 and cardinality - 1
#         """
#         if index > self.cardinal - 1 or index < 0:
#             raise IndexError
#         loop_index = 0
#         for _ in self:
#             if loop_index != index:
#                 self.distribution_vector[loop_index] += 1
#             loop_index += 1
#         self._get_base_()
# 
#     def get_random_value(self):
#         """
#         :return: biased pseudo random integer
#         """
#         return self._get_biased_random_number_()
# 
# Path: app/models.py
# class Respondent(db.Model):
# class Campaign(db.Model):
# class Audience(db.Model):
# class RandomBias(db.Model):
# class PossibleAbuses(db.Model):
# class BlockedClients(db.Model):
# class CallStatistics(db.Model):
# class Role(db.Model, RoleMixin):
# class User(db.Model, UserMixin):
#     def __repr__(self):


from .biased_pseudo_random import BiasedRandomDistribution
from app.models import RandomBias, Audience, db
from operator import attrgetter
class BiasedRandomValue(object):

    def __init__(self):
        self.list_of_vectors = []
        audiences = sorted(Audience().query.all(), key=attrgetter('id'))
        for audience in audiences:
            vector = []
            biases = sorted(RandomBias().query.filter_by(id_audience=audience.id).all(), key=attrgetter('vector_index'))
            if len(biases) == 0:
                for _ in audience.respondents:
                    vector.append(1)
            else:
                for bias in biases:
                    vector.append(bias.distribution_value)
            self.list_of_vectors.append(BiasedRandomDistribution(vector))

    def __iter__(self):
        return iter(self.list_of_vectors)

    def __getitem__(self, item):
        return self.list_of_vectors[item - 1]

    def get_random_value(self, audience_id):
        return self[audience_id].get_random_value()

    def add_sample(self, audience_id, respondent_id):
        index = RandomBias().query.filter_by(id_audience=audience_id, id_respondent=respondent_id).first().vector_index
        self[audience_id].add_sample(index)
        biases = sorted(RandomBias().query.filter_by(id_audience=audience_id).all(), key=attrgetter('vector_index'))
        if len(biases) == 0:

# Path: app/random/biased_pseudo_random.py
# class BiasedRandomDistribution(object):
#     """
#     A pseudo random number generator that can be biased to promote a pseudo random distribution
#     of outbound calls, even if users have favors for choosing some particular dialog partners.
#     """
#     def __init__(self, cardinal):
#         """
#         :param cardinal: cardinality of the initial distribution vector as integer value with
#                          evenly distributed chance of 1/cardinal.
#                          or a list of integers representing the initial distribution vector.
#                          Example: [1, 1, 1, 1] initialisation vector results in random numbers
#                                   between 1 and 4 with initial chance of 1/4 each.
#                                   [1, 1, 2, 1]initialisation vector results in random numbers
#                                   between 1 and 4 where 1,2 and 4 have the initial chance of 1/5
#                                   and 3 has the initial chance of 2/5.
#         """
#         from array import array
#         if type(cardinal) == list:
#             self.distribution_vector = array('I', cardinal)
#             self.base = 0
#             for value in cardinal:
#                 self.base += value
#             self.cardinal = len(cardinal)
#         elif type(cardinal) == int:
#             self.distribution_vector = array('I')
#             for item in range(0, cardinal):
#                 self.distribution_vector.append(1)
#             self.base = cardinal
#             self.cardinal = cardinal
#         else:
#             raise TypeError
# 
#     def __iter__(self):
#         return iter(self.distribution_vector)
# 
#     def __getitem__(self, item):
#         return self.distribution_vector[item]
# 
#     def __str__(self):
#         return str(list(self.distribution_vector))
# 
#     def __len__(self):
#         return len(self.distribution_vector)
# 
#     def _get_biased_random_number_(self):
#         """
#         :return: biased pseudo random integer between 1 and initial cardinality.
#         """
#         from random import randint
#         basis = self._get_base_()
#         random = randint(0, basis - 1)
#         counter = 0
#         temp_value = 0
#         for number in self:
#             temp_value += number
#             if temp_value >= random:
#                 return counter
#             counter += 1
# 
#     def _get_base_(self):
#         """
#         Recalculates the base of the current distribution vector.
#         :return: int
#         """
#         self.base = 0
#         for number in self:
#             self.base += number
#         return self.base
# 
#     def add_sample(self, index):
#         """
#         Alters the distribution vector.
#         :param index: integer value between 0 and cardinality - 1
#         """
#         if index > self.cardinal - 1 or index < 0:
#             raise IndexError
#         loop_index = 0
#         for _ in self:
#             if loop_index != index:
#                 self.distribution_vector[loop_index] += 1
#             loop_index += 1
#         self._get_base_()
# 
#     def get_random_value(self):
#         """
#         :return: biased pseudo random integer
#         """
#         return self._get_biased_random_number_()
# 
# Path: app/models.py
# class Respondent(db.Model):
# class Campaign(db.Model):
# class Audience(db.Model):
# class RandomBias(db.Model):
# class PossibleAbuses(db.Model):
# class BlockedClients(db.Model):
# class CallStatistics(db.Model):
# class Role(db.Model, RoleMixin):
# class User(db.Model, UserMixin):
#     def __repr__(self):


# Path: app/random/bias.py
from .biased_pseudo_random import BiasedRandomDistribution
from app.models import RandomBias, Audience, db
from operator import attrgetter

class BiasedRandomValue(object):

    def __init__(self):
        self.list_of_vectors = []
        audiences = sorted(Audience().query.all(), key=attrgetter('id'))
        for audience in audiences:
            vector = []
            biases = sorted(RandomBias().query.filter_by(id_audience=audience.id).all(), key=attrgetter('vector_index'))
            if len(biases) == 0:
                for _ in audience.respondents:
                    vector.append(1)
            else:
                for bias in biases:
                    vector.append(bias.distribution_value)
            self.list_of_vectors.append(BiasedRandomDistribution(vector))

    def __iter__(self):
        return iter(self.list_of_vectors)

    def __getitem__(self, item):
        return self.list_of_vectors[item - 1]

    def get_random_value(self, audience_id):
        return self[audience_id].get_random_value()

    def add_sample(self, audience_id, respondent_id):
        index = RandomBias().query.filter_by(id_audience=audience_id, id_respondent=respondent_id).first().vector_index
        self[audience_id].add_sample(index)
        biases = sorted(RandomBias().query.filter_by(id_audience=audience_id).all(), key=attrgetter('vector_index'))
        if len(biases) == 0:

            audience = Audience().query.filter_by(id=audience_id).first()