Gauss 3d.py
From Werner KRAUTH
(Difference between revisions)
| Revision as of 21:41, 22 September 2015 Werner (Talk | contribs) ← Previous diff |
Current revision Werner (Talk | contribs) |
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| - | This page presents the program markov_disks_box.py, a Markov-chain algorithm for four disks in a square box of sides 1. | + | This page presents the program gauss_3d.py, a direct-sampling algorithm for three independent Gaussians. |
| __FORCETOC__ | __FORCETOC__ | ||
| Line 5: | Line 5: | ||
| =Program= | =Program= | ||
| - | |||
| - | import random | ||
| - | |||
| - | L = [[0.25, 0.25], [0.75, 0.25], [0.25, 0.75], [0.75, 0.75]] | ||
| - | sigma = 0.15 | ||
| - | sigma_sq = sigma ** 2 | ||
| - | delta = 0.1 | ||
| - | n_steps = 1000 | ||
| - | for steps in range(n_steps): | ||
| - | a = random.choice(L) | ||
| - | b = [a[0] + random.uniform(-delta, delta), a[1] + random.uniform(-delta, delta)] | ||
| - | min_dist = min((b[0] - c[0]) ** 2 + (b[1] - c[1]) ** 2 for c in L if c != a) | ||
| - | box_cond = min(b[0], b[1]) < sigma or max(b[0], b[1]) > 1.0 - sigma | ||
| - | if not (box_cond or min_dist < 4.0 * sigma ** 2): | ||
| - | a[:] = b | ||
| - | print L | ||
| - | |||
| - | =Version= | ||
| - | See history for version information. | ||
| - | |||
| - | [[Category:Python]] | ||
| - | |||
| import random, math | import random, math | ||
| Line 35: | Line 13: | ||
| random.gauss(0.0, 1.0)) | random.gauss(0.0, 1.0)) | ||
| print x, y, z | print x, y, z | ||
| + | =Version= | ||
| + | See history for version information. | ||
| + | |||
| + | [[Category:Python]] | ||
| + | [[Category:Honnef_2015]] | ||
| + | [[Category:MOOC_SMAC]] | ||
Current revision
This page presents the program gauss_3d.py, a direct-sampling algorithm for three independent Gaussians.
Contents |
[edit]
Description
[edit]
Program
import random, math
nsamples = 100
for sample in xrange(nsamples):
x, y, z = (random.gauss(0.0, 1.0),
random.gauss(0.0, 1.0),
random.gauss(0.0, 1.0))
print x, y, z
[edit]
Version
See history for version information.
