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|
import math
import rand
import rand.dist
const (
// The sample size to be used
count = 2000
// Accepted error is within 5% of the actual values.
error = 0.05
// The seeds used (for reproducible testing)
seeds = [[u32(0xffff24), 0xabcd], [u32(0x141024), 0x42851],
[u32(0x1452), 0x90cd],
]
)
fn test_bernoulli() {
ps := [0.0, 0.1, 1.0 / 3.0, 0.5, 0.8, 17.0 / 18.0, 1.0]
for seed in seeds {
rand.seed(seed)
for p in ps {
mut successes := 0
for _ in 0 .. count {
if dist.bernoulli(p) {
successes++
}
}
assert math.abs(f64(successes) / count - p) < error
}
}
}
fn test_binomial() {
ns := [100, 200, 1000]
ps := [0.0, 0.5, 0.95, 1.0]
for seed in seeds {
rand.seed(seed)
for n in ns {
for p in ps {
np := n * p
npq := np * (1 - p)
mut sum := 0
mut var := 0.0
for _ in 0 .. count {
x := dist.binomial(n, p)
sum += x
dist := (x - np)
var += dist * dist
}
assert math.abs(f64(sum / count) - np) / n < error
assert math.abs(f64(var / count) - npq) / n < error
}
}
}
}
fn test_normal_pair() {
mus := [0, 10, 100, -40]
sigmas := [1, 2, 40, 5]
total := 2 * count
for seed in seeds {
rand.seed(seed)
for mu in mus {
for sigma in sigmas {
mut sum := 0.0
mut var := 0.0
for _ in 0 .. count {
x, y := dist.normal_pair(mu: mu, sigma: sigma)
sum += x + y
dist_x := x - mu
dist_y := y - mu
var += dist_x * dist_x
var += dist_y * dist_y
}
variance := sigma * sigma
assert math.abs(f64(sum / total) - mu) / sigma < 1
assert math.abs(f64(var / total) - variance) / variance < 2 * error
}
}
}
}
fn test_normal() {
mus := [0, 10, 100, -40, 20]
sigmas := [1, 2, 5]
for seed in seeds {
rand.seed(seed)
for mu in mus {
for sigma in sigmas {
mut sum := 0.0
mut var := 0.0
for _ in 0 .. count {
x := dist.normal(mu: mu, sigma: sigma)
sum += x
dist := x - mu
var += dist * dist
}
variance := sigma * sigma
assert math.abs(f64(sum / count) - mu) / sigma < 1
assert math.abs(f64(var / count) - variance) / variance < 2 * error
}
}
}
}
fn test_exponential() {
lambdas := [1.0, 10, 1 / 20.0, 1 / 10000.0, 1 / 524.0, 200]
for seed in seeds {
rand.seed(seed)
for lambda in lambdas {
mu := 1 / lambda
variance := mu * mu
mut sum := 0.0
mut var := 0.0
for _ in 0 .. count {
x := dist.exponential(lambda)
sum += x
dist := x - mu
var += dist * dist
}
assert math.abs((f64(sum / count) - mu) / mu) < error
assert math.abs((f64(var / count) - variance) / variance) < 2 * error
}
}
}
|
