PYTHON HEAD FIRST, FIRST STEPS SESSION4

vamos a tratar el siguiente archivo, recomendaciones de críticos de películas,

critics = {
    'Lisa Rose': {
        'Lady in the Water': 2.5,
        'Snakes on a Plane': 3.5,
        'Just My Luck': 3.0,
        'Superman Returns': 3.5,
        'You, Me and Dupree': 2.5,
        'The Night Listener': 3.0,
    },
    'Gene Seymour': {
        'Lady in the Water': 3.0,
        'Snakes on a Plane': 3.5,
        'Just My Luck': 1.5,
        'Superman Returns': 5.0,
        'The Night Listener': 3.0,
        'You, Me and Dupree': 3.5,
    },
    'Michael Phillips': {
        'Lady in the Water': 2.5,
        'Snakes on a Plane': 3.0,
        'Superman Returns': 3.5,
        'The Night Listener': 4.0,
    },
    '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,
        'The Night Listener': 3.0,
        'You, Me and Dupree': 2.0,
    },
    'Jack Matthews': {
        '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,
    },
    'Toby': {'Snakes on a Plane': 4.5, 'You, Me and Dupree': 1.0,
             'Superman Returns': 4.0},
}


aplicando la distancia entre puntos cartesianos

def sim_distance(prefs, p1, p2):
    '''
    Returns a distance-based similarity score for person1 and person2.
    '''

    # Get the list of shared_items
    si = {}
    for item in prefs[p1]:
        if item in prefs[p2]:
            si[item] = 1
    # If they have no ratings in common, return 0
    if len(si) == 0:
        return 0
    # Add up the squares of all the differences
    sum_of_squares = sum([pow(prefs[p1][item] - prefs[p2][item], 2) for item in
                         prefs[p1] if item in prefs[p2]])
    return 1 / (1 + sqrt(sum_of_squares))

 Returns the Pearson correlation coefficient for p1 and p2.

    '''

    Returns the Pearson correlation coefficient for p1 and p2.

    '''

    # Get the list of mutually rated items

    si = {}

    for item in prefs[p1]:

        if item in prefs[p2]:

            si[item] = 1

    # If they are no ratings in common, return 0

    if len(si) == 0:

        return 0

    # Sum calculations

    n = len(si)

    # Sums of all the preferences

    sum1 = sum([prefs[p1][it] for it in si])

    sum2 = sum([prefs[p2][it] for it in si])

    # Sums of the squares

    sum1Sq = sum([pow(prefs[p1][it], 2) for it in si])

    sum2Sq = sum([pow(prefs[p2][it], 2) for it in si])

    # Sum of the products

    pSum = sum([prefs[p1][it] * prefs[p2][it] for it in si])

    # Calculate r (Pearson score)

    num = pSum - sum1 * sum2 / n

    den = sqrt((sum1Sq - pow(sum1, 2) / n) * (sum2Sq - pow(sum2, 2) / n))

    if den == 0:

        return 0

    r = num / den

    return r