A simple kNN implementation

data.csv

knn.py

import csv
import math
from collections import defaultdict

class point:
    """Representation of a labeled point in N-dimensional space"""
    vector = []
    label = None

    def __init__(self,vector,label):
        """A point is constructed from the data and a label"""
        self.vector = list(map(float,vector))
        self.label = int(label)

    @classmethod
    def fromlist(self,l):
        """Construct a point from a list, label being the last element"""
        return self(l[:-1],l[-1])

def euclid(a,b):
    """Euclidian distance in N dimensions"""
    s = 0
    for i in zip(a,b):
        s += (i[0]-i[1])**2
    return math.sqrt(s)

def vote(measure):
    """Find the most common element in a list"""
    count = defaultdict(int)
    majority = 0
    for m in majority:
        count[m] += 1
        if count[m] > majority:
            majority = m
    return majority

def knn(data,point,k,distance_f=euclid):
    """K-nearest-neighbor classifier"""
    distances = []
    for d in data:
        p = distance_f(d.vector,point)
        distances.append((p,d.label))
    distances.sort() #sort by distance, being the first element
    nearest = distances[:k]
    return vote([n[1] for n in nearest]) #only look at the labels

if __name__=="__main__":
    data = []
    with open('data.csv') as csvfile:
        reader = csv.reader(csvfile)
        for row in reader:
            d = point.fromlist(row)
            data.append(d)
    """Classify a new point"""
    p = [3,0]
    l = knn(data,p,3)
    print(l)