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