def unique1(s):
"""Return True is there are no duplicate elements in sequence s."""
for j in range(len(s)):
for k in range(j+1, len(s)):
if s[j] == s[k]:
return False
return True
def unique2(s):
"""Return True is there are no duplicate elements in sequence s."""
temp = sorted(s)
for j in range(1, len(s)):
if s[j-1] == s[j]:
return False
return True
if __name__ == '__main__':
n = 20
elapsed_time = []
for j in range(4):
s = [randint(0, 10000) for i in range(n)]
watch = StopWatch()
unique2(s)
elapsed_time += [watch.elapsed()]
print(n, elapsed_time[j],
elapsed_time[j]/elapsed_time[j-1] if j > 0 else None)
n *= 2
def run():
sw = StopWatch()
a = np.random.rand(n, 2) * s_range + s_min
b = np.random.rand(m, 2) * s_range + s_min
xa = a[:, 0]
ya = a[:, 1]
xb = b[:, 0]
yb = b[:, 1]
xlim = np.asarray(
[np.min([xa.min(), xb.min()]),
np.max([xa.max(), xb.max()])])
ylim = np.asarray(
[np.min([ya.min(), yb.min()]),
np.max([ya.max(), yb.max()])])
exp = (xlim[1] - xlim[0]) * x_exp
xlim += [-exp, exp]
exp = (ylim[1] - ylim[0]) * y_exp
ylim += [-exp, exp]
plt.xlim(xlim)
plt.ylim(ylim)
plt.plot(xa,
ya,
a_style,
alpha=set_opacity,
zorder=a_order,
markersize=a_size)
plt.plot(xb,
yb,
b_style,
alpha=set_opacity,
zorder=b_order,
markersize=b_size)
total_nv = 0
total_kd = 0
total_bkd = 0
total_obkd = 0
print "Initializing naive module..."
sw.start()
nf = NaiveFinder(a)
bt_nv = sw.elapsed()
total_nv += bt_nv
sw.reset()
print "Initializing K-D Tree module..."
sw.start()
kdf = KDFinder(a)
bt_kd = sw.elapsed()
total_kd += bt_kd
sw.lap()
print "Initializing Bucketed K-D Tree module..."
sw.start()
bkdf = BKDFinder(a)
bt_bkd = sw.elapsed()
total_bkd += bt_bkd
sw.lap()
print "Initializing Optimized Bucketed K-D Tree module..."
sw.start()
obkdf = BKDFinder(a)
bt_obkd = sw.elapsed()
total_obkd += bt_obkd
sw.lap()
for i in range(m):
print i
p1 = b[i, :]
sw.start()
found = nf.find_closest_m(p1, K)
total_nv += sw.elapsed()
def check_mismatch(h_f, finder):
# If there's a mismatch with ground-truth values, save K-D search steps for debugging
if not (np.asarray(found)[:, 1] == np.asarray(h_f)[:, 1]).all():
print "Mismatch", np.asarray(found)[:, 1], np.asarray(h_f)[:,
1]
for element in found:
p2 = element[0]
plt.plot([p1[0], p2[0]], [p1[1], p2[1]],
color=ground_truth_col,
zorder=2,
linewidth=2)
finder.setup_plot(xlim, ylim, True)
finder.find_closest_m(p1, 5)
sw.start()
finder.find_closest_m(p1, 5)
for element in h_f:
p2 = element[0]
plt.plot([p1[0], p2[0]], [p1[1], p2[1]],
color=test_col,
zorder=3,
linewidth=1.5)
print "Done"
plt.show()
sw.start()
kdfound = kdf.find_closest_m(p1, K)
total_kd += sw.elapsed()
check_mismatch(kdfound, kdf)
sw.start()
bkdfound = bkdf.find_closest_m(p1, K)
total_bkd += sw.elapsed()
check_mismatch(bkdfound, bkdf)
sw.start()
obkdfound = obkdf.find_closest_m(p1, K)
total_obkd += sw.elapsed()
check_mismatch(obkdfound, obkdf)
found = nf.find_closest_m(p1, 5)
for element in found:
p2 = element[0]
h1 = plt.plot([p1[0], p2[0]], [p1[1], p2[1]],
color=ground_truth_col,
zorder=2,
linewidth=2)
kdf.setup_plot(xlim, ylim, save_steps)
kdfound = kdf.find_closest_m(p1, 5)
for element in kdfound:
p2 = element[0]
h2 = plt.plot([p1[0], p2[0]], [p1[1], p2[1]],
color=test_col,
zorder=3,
linewidth=1.5)
if zoom_in:
points = np.asarray(kdfound)[:, 0]
xs = np.asarray([p[0] for p in points])
ys = np.asarray([p[1] for p in points])
xlim = np.asarray([xs.min(), xs.max()])
ylim = np.asarray([ys.min(), ys.max()])
exp = (xlim[1] - xlim[0]) * x_exp
xlim += [-exp, exp]
exp = (ylim[1] - ylim[0]) * y_exp
ylim += [-exp, exp]
for ax in plt.gcf().axes:
ax.set_xlim(xlim)
ax.set_ylim(ylim)
if full_screen:
mng = plt.get_current_fig_manager()
print mng.full_screen_toggle()
print ''
print 'Doing', m, 'queries in', n, 'records for', K, 'closest'
print ''
print 'Method\t\t\tTotal Time\t\t\tBuild Time\t\t\tMean per-query'
print 'Naive\t\t\t', total_nv, '\t\t', bt_nv, '\t\t', (total_nv -
bt_nv) / m
print 'KD Tree\t\t\t', total_kd, '\t\t', bt_kd, '\t\t', (total_kd -
bt_kd) / m
print 'BKD Tree\t\t', total_bkd, '\t\t', bt_bkd, '\t\t', (total_bkd -
bt_bkd) / m
print 'OBKD Tree\t\t', total_obkd, '\t\t', bt_obkd, '\t\t', (total_obkd -
bt_obkd) / m
plt.show()
