# Path: recommender/connection_trainer.py
# NEUTRAL_PREDICTION = 0.5
# POSITIVE = True
# NEGATIVE = False
# def Sigmoid(z):
#   def __init__(self, rating, user, weight, num_ratings_ago):
#   def __init__(self, user, weight):
#   def GetRecommendations(self, item, user, user_rating):
#   def GetWeight(self, subscriber, publisher, positive):
#   def SetWeight(self,
#                 subscriber,
#                 publisher,
#                 positive,
#                 weight,
#                 shared_item=None,
#                 publisher_voted=False,
#                 reverted=False):
#   def GetSubscribers(self, publisher, positive=True):
#   def CanSubscribe(self, user):
#   def __init__(self,
#                connection_store,
#                learning_rate,
#                ignore_learning_rate,
#                default_contribution=0.1):
#   def GetDecayedWeight(self, initial_weight, num_items):
#   def DecayConnectionWeightToPublisher(self, publisher, num_items=1):
#   def RecommendationAdded(self, user, item, rating):
# class Rating(object):
# class Connection(object):
# class ConnectionStore(object):
# class Trainer(object):


import unittest
from recommender import connection_trainer
                publisher_voted=False):
    connections = (
        self.positive_connections if positive else self.negative_connections)
    connections.setdefault(subscriber, {})[publisher] = weight

  def GetRecommendations(self, item, user, user_rating):
    return [
        connection_trainer.Rating(
            rating=r.rating,
            user=r.user,
            weight=self.GetWeight(user, r.user, r.rating > 0),
            num_ratings_ago=1)
        for r in self.recommendations.get(item, [])
        if r.user != user
    ]

  def GetSubscribers(self, user, positive=True):
    connections_set = (
        self.positive_connections if positive else self.negative_connections)
    result = []
    for subscriber, connections in connections_set.iteritems():
      for publisher, weight in connections.iteritems():
        if publisher == user:
          result.append(connection_trainer.Connection(subscriber, weight))
    return result


class InMemoryTrainer(connection_trainer.Trainer):

  def __init__(self, learning_rate, ignore_learning_rate):

# Path: recommender/connection_trainer.py
# NEUTRAL_PREDICTION = 0.5
# POSITIVE = True
# NEGATIVE = False
# def Sigmoid(z):
#   def __init__(self, rating, user, weight, num_ratings_ago):
#   def __init__(self, user, weight):
#   def GetRecommendations(self, item, user, user_rating):
#   def GetWeight(self, subscriber, publisher, positive):
#   def SetWeight(self,
#                 subscriber,
#                 publisher,
#                 positive,
#                 weight,
#                 shared_item=None,
#                 publisher_voted=False,
#                 reverted=False):
#   def GetSubscribers(self, publisher, positive=True):
#   def CanSubscribe(self, user):
#   def __init__(self,
#                connection_store,
#                learning_rate,
#                ignore_learning_rate,
#                default_contribution=0.1):
#   def GetDecayedWeight(self, initial_weight, num_items):
#   def DecayConnectionWeightToPublisher(self, publisher, num_items=1):
#   def RecommendationAdded(self, user, item, rating):
# class Rating(object):
# class Connection(object):
# class ConnectionStore(object):
# class Trainer(object):


# Path: recommender/connection_trainer_test.py
import unittest
from recommender import connection_trainer

                publisher_voted=False):
    connections = (
        self.positive_connections if positive else self.negative_connections)
    connections.setdefault(subscriber, {})[publisher] = weight

  def GetRecommendations(self, item, user, user_rating):
    return [
        connection_trainer.Rating(
            rating=r.rating,
            user=r.user,
            weight=self.GetWeight(user, r.user, r.rating > 0),
            num_ratings_ago=1)
        for r in self.recommendations.get(item, [])
        if r.user != user
    ]

  def GetSubscribers(self, user, positive=True):
    connections_set = (
        self.positive_connections if positive else self.negative_connections)
    result = []
    for subscriber, connections in connections_set.iteritems():
      for publisher, weight in connections.iteritems():
        if publisher == user:
          result.append(connection_trainer.Connection(subscriber, weight))
    return result


class InMemoryTrainer(connection_trainer.Trainer):

  def __init__(self, learning_rate, ignore_learning_rate):

    connection_trainer.Trainer.__init__(self, InMemoryStore(), learning_rate,