# Gauss test.py

(Difference between revisions)
 Revision as of 21:41, 22 September 2015Werner (Talk | contribs)← Previous diff Current revisionWerner (Talk | contribs) Line 1: Line 1: - 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_test.py, a direct-sampling algorithm for two independent Gaussian random numbers. This algorithm is used to illustrate the concept of "sample transformation" __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 + random.uniform(-delta, delta), a + random.uniform(-delta, delta)] - min_dist = min((b - c) ** 2 + (b - c) ** 2 for c in L if c != a) - box_cond = min(b, b) < sigma or max(b, b) > 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 42: Line 20: [x, y] = gauss_test(1.0) [x, y] = gauss_test(1.0) print x, y print x, y + =Version= + See history for version information. + + [[Category:Python]] [[Category:Honnef_2015]] [[Category:MOOC_SMAC]]

## Current revision

This page presents the program gauss_test.py, a direct-sampling algorithm for two independent Gaussian random numbers. This algorithm is used to illustrate the concept of "sample transformation"

# Program

```import random, math

def gauss_test(sigma):
phi = random.uniform(0.0, 2.0 * math.pi)
Upsilon = random.uniform(0.0, 1.0)
Psi = - math.log(Upsilon)
r = sigma * math.sqrt(2.0 * Psi)
x = r * math.cos(phi)
y = r * math.sin(phi)
return [x, y]

nsamples = 50
for sample in range(nsamples):
[x, y] = gauss_test(1.0)
print x, y
```

# Version

See history for version information.