example.py

import numpy as np
import kd_tree as kd
import json

# generate 1000 random data points and building a tree from them
exampledictionary=dict(enumerate(np.random.rand(1000,4).tolist()))
exampletree=kd.build_tree(exampledictionary)
# finding the approximate nearest neighbor and its distance to a value
print(kd.distance(kd.find_approx_nearest(exampletree,[0.2,0.7,0.9,0.5]).value,
            [0.2,0.7,0.9,0.5]),
    kd.find_approx_nearest(exampletree,[0.2,0.7,0.9,0.5]).value)
# finding the exact nearest neighbor
print(kd.find_exact_nearest(exampletree,[0.2,0.7,0.9,0.5]))
# compare with a linear search
def linear_search(dict,value):
    lowestdist=float("inf")
    lowest={}
    for key, item in dict.items():
        if kd.distance(item,value)<lowestdist:
            lowestdist=kd.distance(item,value)
            lowest={key:item}
    return (lowestdist,lowest)
print(linear_search(exampledictionary,[0.2,0.7,0.9,0.5]))

# importing a dataset of paint colors and their position in the LAB colorspace
with open ("paintcolors.json") as json_file:
    paintcolors=json.load(json_file)
# creating a tree out of the paintcolors
painttree=kd.build_tree(paintcolors)
# finding the approximate and exact nearest color to [0,0,0]
print((kd.distance(kd.find_approx_nearest(painttree,[0,0,0]).value,[0,0,0]),
    kd.find_approx_nearest(painttree,[0,0,0]).name,
    kd.find_approx_nearest(painttree,[0,0,0]).value))
print(kd.find_exact_nearest(painttree,[0,0,0]))