from src.good_center import find import time from sklearn.metrics.pairwise import euclidean_distances as distances import sklearn.datasets as dss from scipy.spatial.distance import euclidean sample_number, dimension = 10000, 2 blobs = dss.make_blobs(sample_number, dimension, cluster_std=50) blob = blobs[0] radius, desired_points = 50, 2000 start_time = time.time() result = find(blob, sample_number, dimension, radius, desired_points, 0.1, 0.05, 0.5, 2**-20) print result print len([p for p in blob if euclidean(p, result) <= radius]) print "run-time: %.2f seconds" % (time.time() - start_time)
from scipy.spatial.distance import euclidean sample_number, k, r = 2**12, 2, 4 center = 100 data_2d = round(normal(center, 50, (sample_number, 2))) domain_end = max(abs(np.min(data_2d)), np.max(data_2d)) start_time = time.time() domain, desired_amount_of_points = (domain_end, 1), 2000 approximation, failure, eps, delta, promise = 0.1, 0.1, 0.5, 2 ** -20, 100 radius = gr.find(data_2d, domain, desired_amount_of_points, failure, eps) print "the radius: %d" % radius middle_time = time.time() print "good-radius run-time: %.2f seconds" % (middle_time - start_time) center = gc.find(data_2d, sample_number, 2, radius, desired_amount_of_points, failure, approximation, eps, delta) print "the center: %s" % str(center) print "good-center run-time: %.2f seconds" % (time.time() - middle_time) ball = [p for p in data_2d if euclidean(p, center) <= radius] print "number of points in the resulting ball: %d" % len(ball) zipped_data = zip(*data_2d) plt.scatter(*zipped_data, c='b') zipped_ball = zip(*ball) plt.scatter(*zipped_ball, c='r') plt.show()
import matplotlib.pyplot as plt
import time
from numpy import round
from scipy.spatial.distance import euclidean
sample_number, k, r = 2**12, 2, 4
center = 100
data_2d = round(normal(center, 50, (sample_number, 2)))
domain_end = max(abs(np.min(data_2d)), np.max(data_2d))
start_time = time.time()
domain, desired_amount_of_points = (domain_end, 1), 2000
approximation, failure, eps, delta, promise = 0.1, 0.1, 0.5, 2**-20, 100
radius = gr.find(data_2d, domain, desired_amount_of_points, failure, eps)
print "the radius: %d" % radius
middle_time = time.time()
print "good-radius run-time: %.2f seconds" % (middle_time - start_time)
center = gc.find(data_2d, sample_number, 2, radius, desired_amount_of_points,
failure, approximation, eps, delta)
print "the center: %s" % str(center)
print "good-center run-time: %.2f seconds" % (time.time() - middle_time)
ball = [p for p in data_2d if euclidean(p, center) <= radius]
print "number of points in the resulting ball: %d" % len(ball)
zipped_data = zip(*data_2d)
plt.scatter(*zipped_data, c='b')
zipped_ball = zip(*ball)
plt.scatter(*zipped_ball, c='r')
plt.show()
