from src.good_radius import *
from numpy.random import normal
import numpy as np
import time
from numpy import round
from __non_private_cluster__ import find_cluster
sample_number = 2**12
center = 100
data_2d = round(normal(center, 50, (sample_number, 2)))
domain_end = max(abs(np.min(data_2d)), np.max(data_2d))
domain, goal_number = (domain_end, 1), 500
failure, eps, delta = 0.1, 0.5, 2**-20
r, c = find_cluster(data_2d, goal_number)
print "non-private radius : %d " % r
start_time = time.time()
result = find(data_2d, domain, goal_number, failure, eps)
print "run-time: %.2f seconds" % (time.time() - start_time)
print "good radius : %d " % result
points_in_resulting_ball = len(
[i for i in data_2d if np.linalg.norm(i - (center, center)) <= result])
print "number of points in the resulting ball : %d" % points_in_resulting_ball
from src.good_radius import find from numpy.random import normal import numpy as np import time from numpy import round from __non_private_cluster__ import find_cluster sample_number = 2**11 center = 100 data_2d = round(normal(center, 50, (sample_number, 2))) domain_end = max(abs(np.min(data_2d)), np.max(data_2d)) domain, goal_number = (domain_end, 1), 1000 failure, eps, delta = 0.1, 0.5, 2**-20 r, c = find_cluster(data_2d, goal_number) print "non-private radius : %d " % r start_time = time.time() result = find(data_2d, domain, goal_number, failure, eps) print "run-time: %.2f seconds" % (time.time() - start_time) print "good radius : %d " % result points_in_resulting_ball = len([i for i in data_2d if np.linalg.norm(i - (center, center)) <= result]) print "number of points in the resulting ball : %d" % points_in_resulting_ball start_time = time.time() result = find(data_2d, domain, goal_number, failure, eps, False) print "run-time: %.2f seconds" % (time.time() - start_time) print "good radius : %d " % result points_in_resulting_ball = len([i for i in data_2d if np.linalg.norm(i - (center, center)) <= result]) print "number of points in the resulting ball : %d" % points_in_resulting_ball
sample_number = 5000
dimension = 20
blobs = dss.make_blobs(sample_number, dimension, cluster_std=50)
blob = np.round(blobs[0], 2)
approximation, failure, eps, delta = 0.1, 0.1, 0.5, 2**-10
domain_end = max(abs(np.min(blob)), np.max(blob))
domain = (domain_end, 0.01)
desired_amount_of_points = 1000
failure_bound = bound(sample_number, dimension, eps, delta, approximation)
print "The probability of failure is somewhere between %s\n" % str(failure_bound)
radius, center = find_cluster(blob, desired_amount_of_points)
print "Test-radius: %d" % radius
for i in xrange(8):
middle_time = time.time()
try:
radius, center = cluster.find(blob, dimension, domain, desired_amount_of_points,
approximation, failure, eps, delta, shrink=False, use_histograms=False)
ball = [p for p in blob if euclidean(p, center) <= radius]
print "Good-radius: %d" % radius
# print "Good-center: %s" % str(center)
print "Desired number of points in resulting ball: %d" % desired_amount_of_points
print "Number of points in the resulting ball: %d" % len(ball)
except ValueError:
ball = []
print '_|_'
# return here after good_radius_concave is complete
from src.good_radius import find
from src.good_radius_concave import find as find2
import numpy as np
import time
from __non_private_cluster__ import find_cluster
import sklearn.datasets as dss
sample_number = 3000
dimension, domain = 3, (0, 100)
blobs = dss.make_blobs(sample_number, dimension, cluster_std=70)
blob = blobs[0]
desired_amount_of_points, approximation, failure, eps, delta, promise = 1000, 0.1, 0.1, 0.5, 2**-10, 70
r, c = find_cluster(blob, desired_amount_of_points)
print r
start_time = time.time()
result = find(blob, domain, desired_amount_of_points, failure, eps)
print "run-time: %.2f seconds" % (time.time() - start_time)
print "good radius : %d " % result
points_in_resulting_ball = len(
[i for i in blob if np.linalg.norm(i - (c, c)) < result])
print "number of points in the resulting ball : %d" % points_in_resulting_ball
start_time = time.time()
result = find2(blob, domain, desired_amount_of_points, failure, eps, delta,
promise)
print "run-time: %.2f seconds" % (time.time() - start_time)
print "good radius : %d " % result
fig = plt.figure() # ax = fig.add_subplot(111, projection='3d') ax = fig.add_subplot(211, aspect='equal') zipped_data = zip(*blob) ax.scatter(*zipped_data, c='g') zipped_ball = zip(*ball) ax.scatter(*zipped_ball, c='r') print "Good-radius: %d" % radius print "Good-center: %s" % str(center) print "Desired number of points in resulting ball: %d" % desired_amount_of_points print "Number of points in the resulting ball: %d" % len(ball) print "Run-time: %.2f seconds" % (end_time - start_time) start_time = time.time() test_radius, test_center = find_cluster(blob, desired_amount_of_points) end_time = time.time() ball = [p for p in blob if euclidean(p, test_center) <= test_radius] zipped_data = zip(*blob) ax2 = fig.add_subplot(212, aspect='equal') ax2.scatter(*zipped_data, c='b') zipped_ball = zip(*ball) ax2.scatter(*zipped_ball, c='r') print "Test-radius: %d" % test_radius print "Test-center: %s" % str(test_center) print "Desired number of points in resulting ball: %d" % desired_amount_of_points print "Number of points in the resulting ball: %d" % len(ball) print "Run-time: %.2f seconds" % (end_time - start_time) plt.show()
from src.good_radius import find from src.good_radius_concave import find as find2 import numpy as np import time from __non_private_cluster__ import find_cluster import sklearn.datasets as dss sample_number = 3000 dimension, domain = 3, (0, 100) blobs = dss.make_blobs(sample_number, dimension, cluster_std=70) blob = blobs[0] desired_amount_of_points, approximation, failure, eps, delta, promise = 1000, 0.1, 0.1, 0.5, 2**-10, 70 r, c = find_cluster(blob, desired_amount_of_points) print r start_time = time.time() result = find(blob, domain, desired_amount_of_points, failure, eps) print "run-time: %.2f seconds" % (time.time() - start_time) print "good radius : %d " % result points_in_resulting_ball = len([i for i in blob if np.linalg.norm(i - (c, c)) < result]) print "number of points in the resulting ball : %d" % points_in_resulting_ball start_time = time.time() result = find2(blob, domain, desired_amount_of_points, failure, eps, delta, promise) print "run-time: %.2f seconds" % (time.time() - start_time) print "good radius : %d " % result points_in_resulting_ball = len([i for i in blob if np.linalg.norm(i - (c, c)) < result]) print "number of points in the resulting ball : %d" % points_in_resulting_ball