mirror of
https://github.com/Ed94/Odin.git
synced 2026-08-06 15:48:51 +00:00
Update core:math/rand to use context.random_generator and remove rand.Rand
This commit is contained in:
gingerBill
@@ -8,12 +8,12 @@ float32_uniform :: float32_range
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// Triangular Distribution
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// See: http://wikipedia.org/wiki/Triangular_distribution
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@(require_results)
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float64_triangular :: proc(lo, hi: f64, mode: Maybe(f64), r: ^Rand = nil) -> f64 {
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float64_triangular :: proc(lo, hi: f64, mode: Maybe(f64)) -> f64 {
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if hi-lo == 0 {
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return lo
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}
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lo, hi := lo, hi
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u := float64(r)
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u := float64()
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c := f64(0.5) if mode == nil else clamp((mode.?-lo) / (hi-lo), 0, 1)
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if u > c {
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u = 1-u
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@@ -26,12 +26,12 @@ float64_triangular :: proc(lo, hi: f64, mode: Maybe(f64), r: ^Rand = nil) -> f64
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// Triangular Distribution
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// See: http://wikipedia.org/wiki/Triangular_distribution
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@(require_results)
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float32_triangular :: proc(lo, hi: f32, mode: Maybe(f32), r: ^Rand = nil) -> f32 {
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float32_triangular :: proc(lo, hi: f32, mode: Maybe(f32)) -> f32 {
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if hi-lo == 0 {
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return lo
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}
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lo, hi := lo, hi
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u := float32(r)
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u := float32()
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c := f32(0.5) if mode == nil else clamp((mode.?-lo) / (hi-lo), 0, 1)
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if u > c {
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u = 1-u
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@@ -44,25 +44,25 @@ float32_triangular :: proc(lo, hi: f32, mode: Maybe(f32), r: ^Rand = nil) -> f32
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// Normal/Gaussian Distribution
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@(require_results)
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float64_normal :: proc(mean, stddev: f64, r: ^Rand = nil) -> f64 {
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return norm_float64(r) * stddev + mean
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float64_normal :: proc(mean, stddev: f64) -> f64 {
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return norm_float64() * stddev + mean
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}
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// Normal/Gaussian Distribution
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@(require_results)
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float32_normal :: proc(mean, stddev: f32, r: ^Rand = nil) -> f32 {
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return f32(float64_normal(f64(mean), f64(stddev), r))
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float32_normal :: proc(mean, stddev: f32) -> f32 {
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return f32(float64_normal(f64(mean), f64(stddev)))
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}
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// Log Normal Distribution
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@(require_results)
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float64_log_normal :: proc(mean, stddev: f64, r: ^Rand = nil) -> f64 {
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return math.exp(float64_normal(mean, stddev, r))
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float64_log_normal :: proc(mean, stddev: f64) -> f64 {
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return math.exp(float64_normal(mean, stddev))
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}
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// Log Normal Distribution
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@(require_results)
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float32_log_normal :: proc(mean, stddev: f32, r: ^Rand = nil) -> f32 {
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return f32(float64_log_normal(f64(mean), f64(stddev), r))
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float32_log_normal :: proc(mean, stddev: f32) -> f32 {
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return f32(float64_log_normal(f64(mean), f64(stddev)))
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}
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@@ -72,8 +72,8 @@ float32_log_normal :: proc(mean, stddev: f32, r: ^Rand = nil) -> f32 {
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// 0 to positive infinity if lambda > 0
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// negative infinity to 0 if lambda <= 0
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@(require_results)
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float64_exponential :: proc(lambda: f64, r: ^Rand = nil) -> f64 {
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return - math.ln(1 - float64(r)) / lambda
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float64_exponential :: proc(lambda: f64) -> f64 {
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return - math.ln(1 - float64()) / lambda
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}
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// Exponential Distribution
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// `lambda` is 1.0/(desired mean). It should be non-zero.
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@@ -81,8 +81,8 @@ float64_exponential :: proc(lambda: f64, r: ^Rand = nil) -> f64 {
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// 0 to positive infinity if lambda > 0
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// negative infinity to 0 if lambda <= 0
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@(require_results)
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float32_exponential :: proc(lambda: f32, r: ^Rand = nil) -> f32 {
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return f32(float64_exponential(f64(lambda), r))
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float32_exponential :: proc(lambda: f32) -> f32 {
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return f32(float64_exponential(f64(lambda)))
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}
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@@ -96,7 +96,7 @@ float32_exponential :: proc(lambda: f32, r: ^Rand = nil) -> f32 {
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//
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// mean is alpha*beta, variance is math.pow(alpha*beta, 2)
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@(require_results)
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float64_gamma :: proc(alpha, beta: f64, r: ^Rand = nil) -> f64 {
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float64_gamma :: proc(alpha, beta: f64) -> f64 {
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if alpha <= 0 || beta <= 0 {
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panic(#procedure + ": alpha and beta must be > 0.0")
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}
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@@ -112,11 +112,11 @@ float64_gamma :: proc(alpha, beta: f64, r: ^Rand = nil) -> f64 {
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bbb := alpha - LOG4
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ccc := alpha + ainv
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for {
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u1 := float64(r)
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u1 := float64()
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if !(1e-7 < u1 && u1 < 0.9999999) {
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continue
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}
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u2 := 1 - float64(r)
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u2 := 1 - float64()
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v := math.ln(u1 / (1 - u1)) / ainv
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x := alpha * math.exp(v)
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z := u1 * u1 * u2
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@@ -127,12 +127,12 @@ float64_gamma :: proc(alpha, beta: f64, r: ^Rand = nil) -> f64 {
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}
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case alpha == 1:
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// float64_exponential(1/beta)
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return -math.ln(1 - float64(r)) * beta
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return -math.ln(1 - float64()) * beta
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case:
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// ALGORITHM GS of Statistical Computing - Kennedy & Gentle
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x: f64
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for {
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u := float64(r)
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u := float64()
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b := (math.e + alpha) / math.e
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p := b * u
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if p <= 1 {
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@@ -140,7 +140,7 @@ float64_gamma :: proc(alpha, beta: f64, r: ^Rand = nil) -> f64 {
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} else {
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x = -math.ln((b - p) / alpha)
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}
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u1 := float64(r)
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u1 := float64()
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if p > 1 {
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if u1 <= math.pow(x, alpha-1) {
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break
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@@ -162,8 +162,8 @@ float64_gamma :: proc(alpha, beta: f64, r: ^Rand = nil) -> f64 {
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//
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// mean is alpha*beta, variance is math.pow(alpha*beta, 2)
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@(require_results)
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float32_gamma :: proc(alpha, beta: f32, r: ^Rand = nil) -> f32 {
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return f32(float64_gamma(f64(alpha), f64(beta), r))
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float32_gamma :: proc(alpha, beta: f32) -> f32 {
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return f32(float64_gamma(f64(alpha), f64(beta)))
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}
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@@ -173,14 +173,14 @@ float32_gamma :: proc(alpha, beta: f32, r: ^Rand = nil) -> f32 {
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//
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// Return values range between 0 and 1
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@(require_results)
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float64_beta :: proc(alpha, beta: f64, r: ^Rand = nil) -> f64 {
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float64_beta :: proc(alpha, beta: f64) -> f64 {
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if alpha <= 0 || beta <= 0 {
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panic(#procedure + ": alpha and beta must be > 0.0")
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}
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// Knuth Vol 2 Ed 3 pg 134 "the beta distribution"
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y := float64_gamma(alpha, 1.0, r)
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y := float64_gamma(alpha, 1.0)
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if y != 0 {
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return y / (y + float64_gamma(beta, 1.0, r))
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return y / (y + float64_gamma(beta, 1.0))
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}
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return 0
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}
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@@ -190,35 +190,35 @@ float64_beta :: proc(alpha, beta: f64, r: ^Rand = nil) -> f64 {
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//
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// Return values range between 0 and 1
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@(require_results)
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float32_beta :: proc(alpha, beta: f32, r: ^Rand = nil) -> f32 {
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return f32(float64_beta(f64(alpha), f64(beta), r))
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float32_beta :: proc(alpha, beta: f32) -> f32 {
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return f32(float64_beta(f64(alpha), f64(beta)))
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}
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// Pareto distribution, `alpha` is the shape parameter.
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// https://wikipedia.org/wiki/Pareto_distribution
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@(require_results)
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float64_pareto :: proc(alpha: f64, r: ^Rand = nil) -> f64 {
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return math.pow(1 - float64(r), -1.0 / alpha)
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float64_pareto :: proc(alpha: f64) -> f64 {
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return math.pow(1 - float64(), -1.0 / alpha)
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}
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// Pareto distribution, `alpha` is the shape parameter.
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// https://wikipedia.org/wiki/Pareto_distribution
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@(require_results)
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float32_pareto :: proc(alpha, beta: f32, r: ^Rand = nil) -> f32 {
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return f32(float64_pareto(f64(alpha), r))
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float32_pareto :: proc(alpha, beta: f32) -> f32 {
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return f32(float64_pareto(f64(alpha)))
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}
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// Weibull distribution, `alpha` is the scale parameter, `beta` is the shape parameter.
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@(require_results)
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float64_weibull :: proc(alpha, beta: f64, r: ^Rand = nil) -> f64 {
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u := 1 - float64(r)
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float64_weibull :: proc(alpha, beta: f64) -> f64 {
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u := 1 - float64()
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return alpha * math.pow(-math.ln(u), 1.0/beta)
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}
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// Weibull distribution, `alpha` is the scale parameter, `beta` is the shape parameter.
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@(require_results)
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float32_weibull :: proc(alpha, beta: f32, r: ^Rand = nil) -> f32 {
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return f32(float64_weibull(f64(alpha), f64(beta), r))
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float32_weibull :: proc(alpha, beta: f32) -> f32 {
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return f32(float64_weibull(f64(alpha), f64(beta)))
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}
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@@ -227,23 +227,23 @@ float32_weibull :: proc(alpha, beta: f32, r: ^Rand = nil) -> f32 {
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// `kappa` is the concentration parameter which must be >= 0
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// When `kappa` is zero, the Distribution is a uniform Distribution over the range 0 to 2pi
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@(require_results)
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float64_von_mises :: proc(mean_angle, kappa: f64, r: ^Rand = nil) -> f64 {
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float64_von_mises :: proc(mean_angle, kappa: f64) -> f64 {
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// Fisher, N.I., "Statistical Analysis of Circular Data", Cambridge University Press, 1993.
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mu := mean_angle
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if kappa <= 1e-6 {
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return math.TAU * float64(r)
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return math.TAU * float64()
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}
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s := 0.5 / kappa
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t := s + math.sqrt(1 + s*s)
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z: f64
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for {
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u1 := float64(r)
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u1 := float64()
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z = math.cos(math.TAU * 0.5 * u1)
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d := z / (t + z)
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u2 := float64(r)
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u2 := float64()
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if u2 < 1 - d*d || u2 <= (1-d)*math.exp(d) {
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break
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}
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@@ -251,7 +251,7 @@ float64_von_mises :: proc(mean_angle, kappa: f64, r: ^Rand = nil) -> f64 {
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q := 1.0 / t
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f := (q + z) / (1 + q*z)
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u3 := float64(r)
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u3 := float64()
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if u3 > 0.5 {
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return math.mod(mu + math.acos(f), math.TAU)
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} else {
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@@ -263,57 +263,57 @@ float64_von_mises :: proc(mean_angle, kappa: f64, r: ^Rand = nil) -> f64 {
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// `kappa` is the concentration parameter which must be >= 0
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// When `kappa` is zero, the Distribution is a uniform Distribution over the range 0 to 2pi
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@(require_results)
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float32_von_mises :: proc(mean_angle, kappa: f32, r: ^Rand = nil) -> f32 {
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return f32(float64_von_mises(f64(mean_angle), f64(kappa), r))
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float32_von_mises :: proc(mean_angle, kappa: f32) -> f32 {
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return f32(float64_von_mises(f64(mean_angle), f64(kappa)))
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}
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// Cauchy-Lorentz Distribution
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// `x_0` is the location, `gamma` is the scale where `gamma` > 0
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@(require_results)
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float64_cauchy_lorentz :: proc(x_0, gamma: f64, r: ^Rand = nil) -> f64 {
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float64_cauchy_lorentz :: proc(x_0, gamma: f64) -> f64 {
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assert(gamma > 0)
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// Calculated from the inverse CDF
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return math.tan(math.PI * (float64(r) - 0.5))*gamma + x_0
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return math.tan(math.PI * (float64() - 0.5))*gamma + x_0
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}
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// Cauchy-Lorentz Distribution
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// `x_0` is the location, `gamma` is the scale where `gamma` > 0
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@(require_results)
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float32_cauchy_lorentz :: proc(x_0, gamma: f32, r: ^Rand = nil) -> f32 {
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return f32(float64_cauchy_lorentz(f64(x_0), f64(gamma), r))
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float32_cauchy_lorentz :: proc(x_0, gamma: f32) -> f32 {
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return f32(float64_cauchy_lorentz(f64(x_0), f64(gamma)))
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}
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// Log Cauchy-Lorentz Distribution
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// `x_0` is the location, `gamma` is the scale where `gamma` > 0
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@(require_results)
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float64_log_cauchy_lorentz :: proc(x_0, gamma: f64, r: ^Rand = nil) -> f64 {
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float64_log_cauchy_lorentz :: proc(x_0, gamma: f64) -> f64 {
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assert(gamma > 0)
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return math.exp(math.tan(math.PI * (float64(r) - 0.5))*gamma + x_0)
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return math.exp(math.tan(math.PI * (float64() - 0.5))*gamma + x_0)
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}
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// Log Cauchy-Lorentz Distribution
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// `x_0` is the location, `gamma` is the scale where `gamma` > 0
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@(require_results)
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float32_log_cauchy_lorentz :: proc(x_0, gamma: f32, r: ^Rand = nil) -> f32 {
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return f32(float64_log_cauchy_lorentz(f64(x_0), f64(gamma), r))
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float32_log_cauchy_lorentz :: proc(x_0, gamma: f32) -> f32 {
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return f32(float64_log_cauchy_lorentz(f64(x_0), f64(gamma)))
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}
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// Laplace Distribution
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// `b` is the scale where `b` > 0
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@(require_results)
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float64_laplace :: proc(mean, b: f64, r: ^Rand = nil) -> f64 {
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float64_laplace :: proc(mean, b: f64) -> f64 {
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assert(b > 0)
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p := float64(r)-0.5
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p := float64()-0.5
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return -math.sign(p)*math.ln(1 - 2*abs(p))*b + mean
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}
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// Laplace Distribution
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// `b` is the scale where `b` > 0
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@(require_results)
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float32_laplace :: proc(mean, b: f32, r: ^Rand = nil) -> f32 {
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return f32(float64_laplace(f64(mean), f64(b), r))
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float32_laplace :: proc(mean, b: f32) -> f32 {
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return f32(float64_laplace(f64(mean), f64(b)))
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}
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@@ -321,18 +321,18 @@ float32_laplace :: proc(mean, b: f32, r: ^Rand = nil) -> f32 {
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// `eta` is the shape, `b` is the scale
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// Both `eta` and `b` must be > 0
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@(require_results)
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float64_gompertz :: proc(eta, b: f64, r: ^Rand = nil) -> f64 {
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float64_gompertz :: proc(eta, b: f64) -> f64 {
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if eta <= 0 || b <= 0 {
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panic(#procedure + ": eta and b must be > 0.0")
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}
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p := float64(r)
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p := float64()
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return math.ln(1 - math.ln(1 - p)/eta)/b
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}
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// Gompertz Distribution
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// `eta` is the shape, `b` is the scale
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// Both `eta` and `b` must be > 0
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@(require_results)
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float32_gompertz :: proc(eta, b: f32, r: ^Rand = nil) -> f32 {
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return f32(float64_gompertz(f64(eta), f64(b), r))
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float32_gompertz :: proc(eta, b: f32) -> f32 {
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return f32(float64_gompertz(f64(eta), f64(b)))
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}
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