IA 24.08.17

from random import random, randint
import math

def wineprice(rating, age):
    peak_age = rating-50
    price = rating/2
    if (age > peak_age):
        price = price*(5-(age-peak_age))
    else:
        price = price*(5*((age+1)/peak_age))
    if price < 0:
        price = 0
    return price

def winedataset():
    raws = []
    for i in range(10):
        rating = random()*50+50
        age = random()*50
        price = wineprice(rating, age)
        price *= (random()*0.4+0.8)
        raws.append({'input':(rating,age),
                    'result':price})
    return raws

def euclidean(v1,v2):
    d = 0.0
    for i in range(len(v1)):
        d += (v1[i]-v2[i])**2
    return math.sqrt(d)

def getdistances(data, vec1):
    distancelist = []

    for i in range(len(data)):
        vec2 = data[i]['input']
        distancelist.append((euclidean(vec1, vec2),i))

    distancelist.sort()

    return distancelist

def knnestimate(data, vec1, k = 5):
    # Get sorted distances
    dlist = getdistances(data,vec1)
    avg = 0.0

    # Take the average of the top k results
    for i in range(k):
        idx = dlist[i][1]
        avg += data[idx]['result']

    avg = avg / k

    return avg

#print(wineprice(95,3))
data = winedataset()
#print(knnestimate(data,(95.0,3.0)))
for i in data:
    print("Preco\t|\tClassificacao\t| Idade")
    print('{:.2f}\t|\t{:.2f}\t        | {:.2f}'.format(i['result'],i['input'][0],i['input'][1]))
    print(i['input'][0])
#dist = euclidean(data[0]['input'], data[1]['input'])
#print(dist)