##Introducion
python-KNN is a simple implementation of K nearest neighbors algorithm in Python.
Algorithm used kd-tree as basic data structure.
Download the latest python-KNN source code, unzip it. Or you can just clone this repo to your own PC.
Import this module from python-KNN import * (make sure the path of python-KNN has already appended into the sys.path). Or you can just store it in current folder of you program, and then import it.
The script examples.py gives a simple code of how to use kdtree and knn.
###Usage of kdtree.py kdtree.py is a python implementation of the 'kd tree' algorithm.
>>> import kdtree
# the point list
>>> point = [(2,3),(5,4),(9,6),(4,7),(8,1),(7,2)]
# create a kdtree using point as data
>>> root = kdtree.create(point,dimensions=2)
# visualize
>>> print("point list")
>>> print(point1)
point list
[(2, 3), (4, 7), (5, 4), (7, 2), (8, 1), (9, 6)]
>>> print("visualize the kd-tree: ")
>>> kdtree.visualize(root)
visualize the kd-tree:
(7, 2)
(5, 4) (9, 6)
(2, 3) (4, 7) (8, 1)
# search for k nearest neighbors
>>> ans = root.search_knn(point=(7,3),k=2,dist=None)
>>> print (ans)
[(<KDNode - (7, 2)>, 1.0), (<KDNode - (8, 1)>, 5.0)]
>>> ans = root.search_knn(point=(7,3),k=3,dist=None)
>>> print (ans)
[(<KDNode - (7, 2)>, 1.0), (<KDNode - (8, 1)>, 5.0), (<KDNode - (9, 6)>, 13.0)]
###Usage of knn.py
Based on kdtree algorithm, using python implemented a k-nearest neighbor algorithm.
>>> data = [((3,5),1),((2, 3), 1), ((5, 4), 1), ((9, 6), 0), ((4, 7), 1), ((8, 1), 0), ((7, 2), 1), ((8, 8), 0)]
>>> m = KNN(data,dimensions=2)
>>> print "Samples:",m.train_data
Samples: {(5, 4): 1, (8, 1): 0, (4, 7): 1, (8, 8): 0, (2, 3): 1, (9, 6): 0, (7, 2): 1, (3, 5): 1}
>>> print "\nLabel prb:",m.class_prb
Label prb: {0: 0.4, 1: 0.6}
>>> print "\n\nvisualize the kd-tree: "
>>> m.visualize_kdtree()
visualize the kd-tree:
(7, 2)
(3, 5) (9, 6)
(5, 4) (4, 7) (8, 1) (8, 8)
(2, 3)
# Manhattan distance
>>> f = lambda a, b: sum( abs(a[axis]-b[axis]) for axis in range(len(a)))
# Using Euclidean distance
>>>print "the label of point",(9,9),"is",m.classify(point=(9,9),k=3,dist=None)
the label of point (9, 9) is 0
# Using Manhattan distance
>>>print "the label of point",(2,8),"is",m.classify(point=(2,8),k=3,dist=f)
the label of point (2, 8) is 1