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#!/usr/bin/env python
#-*- coding:utf-8 -*-
from distances import *
Contains all classifiers
__author__ = 'Filipe Wanderley Lima (fwl), Juliana Medeiros de Lucena (jml) and Tiago Ferreira Lima (tfl2)'
def knn(distance_metric, solution, training_examples, e, adaptative=False):
k-NN algorithm
@type calc_distance_method: methodname
@param calc_distance_method: The function to be used to calculate the distance
@type k: int
@param k: The quantity of neighbors
@type training_examples: dict
@param training_examples: The training set
@type e: list
@param e: The element to be classified
@rtype: str
@returns: The predicted class for the element
k = solution[2]
# Calculating the distances
distances = list()
for c, l in training_examples.items():
for i in l:
if adaptative:
d = adaptative_distance(e, i, distance_metric, training_examples, solution) # the order of 'e' and 'i' matters
d = distance_metric(i, e, solution)
distances.append((d, c))
distances = sorted(distances)
# Getting k near classes
near_classes = dict()
for ki in range(k):
(d, i_class) = distances[ki]
if near_classes.has_key(i_class):
if near_classes[i_class][0] > d:
near_classes[i_class] = (d, near_classes[i_class][1] + 1)
near_classes[i_class] = (near_classes[i_class][0], near_classes[i_class][1] + 1)
near_classes[i_class] = (d, 1)
# Few instances for k number
except IndexError:
near_classes_ordered = list()
for c, t in near_classes.items():
near_classes_ordered.append((t[1], t[0], c))
return near_classes_ordered[0][2]
def knn_euclidian(solution, training_examples, e, adaptative=False):
return knn(euclidian_distance, solution, training_examples, e, adaptative)
def knn_manhattan(k, training_examples, e, adaptative=False):
return knn(manhattan_distance, k, training_examples, e, adaptative)
def knn_adaptative(k, training_examples, e, adaptative=False):
return knn(adaptative_distance, k, training_examples, e, adaptative)