# Title: Project 1 - Social Network Attribute Prediction
# Author: Rory Healy
# Date created - 18th April 2019

def friendship_closeness():
    '''Assigns a "friendship-closeness" value, from 0 to 1, based on the number 
    of degrees of seperation between people. The further the seperation, the 
    closer this value is to 0. Takes a dictionary of sets of friends
    "bestie_dict" and returns a dictionary of sets of friends with their
    "friendship-closeness" value.'''
    
def prioritise_friendships():
    '''Make the predictions prioritise those from the people with the highest 
    "friendship-closeness" value. Takes a user, stored as the string
    "unknown_user", and returns a list of sets of other people in order of 
    highest to lowest closeness (e.g. degree-one friends are in the first set,
    degree-two people are in the second set, etc.).'''

def friendly_prediction(unknown_user, features, bestie_dict, feat_dict):
    '''Takes a user, stored as the string "unknown_user", features of the user 
    that are used to predict attributes, stored as a set "features", a
    dictionary of sets of friends "bestie_dict", and a dictionary containing 
    the known attributes for each user "feat_dict". Returns a dictionary of
    features with a predicted list of values for each feature.'''
    
    # For the first given example, the predicted favourite author comes from
    # Kim, while the predicted university comes from both Sandy and Alex. As
    # Sandy and Alex are both degree-two friends, preference can be given to
    # neither without further information. This is a main limitation for the
    # 'friendship-closeness' value being used to make a prediction, as the
    # unknown user may be closer to one degree-two friends than another. I
    # know that this isn't necessarily relevent to creating this program, just
    # something that I was thinking about while fixing up the other parts of
    # this project.