СНЗ - Лаб 4 - Задача 3 - Табела на слични корисници

from __future__ import print_function
import json
from math import sqrt

oceniPoKorisnici={
    'Lisa Rose': {'Catch Me If You Can': 3.0 , 'Snakes on a Plane': 3.5, 'Superman Returns': 3.5, 'You, Me and Dupree': 2.5, 'The Night Listener': 3.0, 'Snitch': 3.0},
    'Gene Seymour': {'Lady in the Water': 3.0, 'Snakes on a Plane': 3.5, 'Just My Luck': 1.5,  'The Night Listener': 3.0,'You, Me and Dupree': 3.5},
    'Michael Phillips': {'Catch Me If You Can': 2.5, 'Lady in the Water': 2.5,'Superman Returns': 3.5, 'The Night Listener': 4.0, 'Snitch': 2.0},
    'Claudia Puig': {'Snakes on a Plane': 3.5, 'Just My Luck': 3.0,'The Night Listener': 4.5, 'Superman Returns': 4.0,'You, Me and Dupree': 2.5},
    'Mick LaSalle': {'Lady in the Water': 3.0, 'Snakes on a Plane': 4.0,'Just My Luck': 2.0, 'Superman Returns': 3.0, 'You, Me and Dupree': 2.0},
    'Jack Matthews': {'Catch Me If You Can': 4.5, 'Lady in the Water': 3.0, 'Snakes on a Plane': 4.0,'The Night Listener': 3.0, 'Superman Returns': 5.0, 'You, Me and Dupree': 3.5, 'Snitch': 4.5},
    'Toby': {'Snakes on a Plane':4.5, 'Snitch': 5.0},
    'Michelle Nichols': {'Just My Luck' : 1.0, 'The Night Listener': 4.5, 'You, Me and Dupree': 3.5, 'Catch Me If You Can': 2.5, 'Snakes on a Plane': 3.0},
    'Gary Coleman': {'Lady in the Water': 1.0, 'Catch Me If You Can': 1.5, 'Superman Returns': 1.5, 'You, Me and Dupree': 2.0},
    'Larry': {'Lady in the Water': 3.0, 'Just My Luck': 3.5, 'Snitch': 1.5, 'The Night Listener': 3.5}
    }

def sim_distance(oceni, person1, person2):
    # Se pravi lista na zaednicki predmeti (filmovi)

    filmovi1=set(oceni[person1].keys())
    filmovi2=set(oceni[person2].keys())
    zaednicki = filmovi1.intersection(filmovi2)
#     print(filmovi1)
#     print(filmovi2)
#     print(zaednicki)
#     for item in oceni[person1].keys():
#         if item in oceni[person2]:
#             zaednicki.add(item)
#     # ako nemaat zaednicki rejtinzi, vrati 0
    if len(zaednicki) == 0: return 0
#     # Soberi gi kvadratite na zaednickite razliki
    suma = 0.0
    for item in zaednicki:
        ocena1 = oceni[person1][item]
        ocena2 = oceni[person2][item]
        suma += (ocena1 - ocena2) ** 2
#         print(item, person1, ocena1, person2, ocena2)

    return 1 / (1 + sqrt(suma))
def sim_pearson(oceni, p1, p2):
    # Se kreira recnik vo koj ke se cuvaat predmetite (filmovi) koi se oceneti od dvajcata
    # Vo recnikot ni se vazni samo klucevite za da gi cuvame iminjata na filmovite koi se zaednicki, a vrednostite ne ni se vazni
    zaednicki = set()
    for item in oceni[p1]:
        if item in oceni[p2]:
            zaednicki.add(item)

    # Se presmetuva brojot na predmeti oceneti od dvajcata
    n = len(zaednicki)

    # Ako nemaat zaednicki predmeti vrati korelacija 0
    if n == 0: return 0.0, 0

    # Soberi gi zaednickite oceni (rejtinzi) za  sekoja licnost posebno
    sum1 = 0
    sum2 = 0

    # Soberi gi kvadratite od zaednickite oceni (rejtinzi) za  sekoja licnost posebno
    sum1Sq = 0
    sum2Sq = 0

    # Soberi gi proizvodite od ocenite na dvete licnosti
    pSum = 0
    for item in zaednicki:
        ocena1 = oceni[p1][item]
        ocena2 = oceni[p2][item]
        sum1 += ocena1
        sum1Sq += ocena1 ** 2
        sum2 += ocena2
        sum2Sq += ocena2 ** 2
        pSum += ocena1 * ocena2

    # Presmetaj go koeficientot na korelacija
    num = pSum - (sum1 * sum2 / n)
    den = sqrt((sum1Sq - pow(sum1, 2) / n) * (sum2Sq - pow(sum2, 2) / n))
    if den == 0: return 0.0, 0
    r = num / den
    return r, n
def transformoceni(oceni):
    result = {}
    for person in oceni.keys():
        for item in oceni[person]:
            result.setdefault(item, {})
            # Zameni gi mestata na licnosta i predmetot
            result[item][person] = oceni[person][item]
    return result
def item_based(critics, person1, n=3):
    oceni_po_film = transformoceni(critics)
    similarity_per_item = {}
    for item in critics[person1].keys():
        similar_items = topMatches(oceni_po_film, item, n=None)
        my_rating = critics[person1][item]

        for similarity, item2 in similar_items:
            if item2 in critics[person1] or similarity <= 0:
#                 print('Slicnost', similarity, 'na', item,'so', item2)
                continue
            weight= similarity * my_rating
#             print('Slicnost', similarity, 'na', item,'so', item2, weight)
            similarity_per_item.setdefault(item2, [])
            similarity_per_item[item2].append(weight)
#         print(item, my_rating, list(similarity_per_item.items()))
    similarity_per_item_avg = []
    import numpy as np
    for item in similarity_per_item:
        print(item, similarity_per_item[item])
        avg_sim = np.mean(similarity_per_item[item])
        similarity_per_item_avg.append((avg_sim, item))
    similarity_per_item_avg.sort(reverse=True)
    return similarity_per_item_avg[:n]
def topMatches_inline(oceni, person, n=5, similarity=sim_pearson):
    scores = [(similarity(oceni, person, other), other)
              for other in oceni if other != person]
    # Se sortira listata vo rastecki redosled
    scores.sort()
    # Se prevrtuva za najslicnite (so najgolema vrednost) da bidat prvi
    scores.reverse()
    return scores[0:n]
def topMatches(oceni, person, n=5, similarity=sim_pearson):
    scores = []
    for person2 in oceni.keys():
        if person != person2:
            s = similarity(oceni, person, person2)
            scores.append((s, person2))
    # Se sortira listata vo rastecki redosled
    scores.sort()
    # Se prevrtuva za najslicnite (so najgolema vrednost) da bidat prvi
    scores.reverse()
    if n is None:
        return scores
    else:
        return scores[0:n]
def getRecommendations(oceni, person, similarity=sim_pearson, min_zaednicki=None):
    totals = {}
    simSums = {}
    for person2 in oceni.keys():
        # Za da ne se sporeduva so samiot sebe
        if person2 == person: continue
        filmovi1=set(oceni[person].keys())
        filmovi2=set(oceni[person2].keys())
        zaednicki = filmovi1.intersection(filmovi2)
        # ova e ako se bara minimum zaednicki filmovi
        # za da se zemat vo predvid ocenite na drugiot korisnik
        if min_zaednicki and len(zaednicki)<min_zaednicki:
            print('So korisnikot', person2, 'imame samo',len(zaednicki),'filmovi, pa go preskoknuvame')
            continue
        sim = similarity(oceni, person, person2)
#         print(person,person2,sim)
        # ne se zemaat vo predvid rezultati <= 0
        if sim <= 0: continue
        print(person,person2,sim)
        for item in oceni[person2].keys():
#             print(item, oceni[person].get(item, None), oceni[person2].get(item, None))
            # za tekovniot korisnik gi zemame samo filmovite sto gi nemame veke gledano
            if item not in oceni[person]: # or oceni[person][item] == 0:
                # similarity * Score   (Slicnost * Ocena)
                print(item, sim, oceni[person2][item], sim* oceni[person2][item])
                totals.setdefault(item, 0)
                totals[item] += oceni[person2][item] * sim

                # Sumuma na slicnosti
                simSums.setdefault(item, 0)
                simSums[item] += sim
        print()
#     return
    print()
    # Kreiranje na normalizirana lista so rejtinzi
    # rankings = [(total / simSums[item], item) for item, total in totals.items()]
    rankings = []
    for item, weighted_score in totals.items():
        sim_total = simSums[item]
        my_score = round(weighted_score / sim_total, 1)
        print(item, weighted_score, sim_total, my_score)
        rankings.append((my_score, item))

    # Sortiranje na listata vo rastecki redosled
    rankings.sort(reverse=True)
    # Prevrtuvanje na lista za najgolemite vrednosti da bidat napred
#     rankings.reverse()
    return rankings

def newDict(data):
    SlicniKorisnici = {}
    for i in data.keys():
        for j in data.keys():
            if i != j:
                if i not in SlicniKorisnici.keys():
                    SlicniKorisnici[i]={}
                p, n = sim_pearson(oceniPoKorisnici, i, j)
                d = sim_distance(oceniPoKorisnici, i, j)
                SlicniKorisnici[i][j] = (round(d,3), round(p,3),n)
    return SlicniKorisnici

if __name__ == "__main__":
    user1 = input()
    user2 = input()
    SlicniKorisnici = newDict(oceniPoKorisnici)
    print(SlicniKorisnici[user1][user2])