mirror of
https://github.com/odin-lang/Odin.git
synced 2026-04-19 13:00:28 +00:00
Add gen := context.random_generator parameters to rand calls
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)) -> f64 {
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float64_triangular :: proc(lo, hi: f64, mode: Maybe(f64), gen := context.random_generator) -> 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()
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u := float64(gen)
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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)) -> 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)) -> f32 {
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float32_triangular :: proc(lo, hi: f32, mode: Maybe(f32), gen := context.random_generator) -> 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()
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u := float32(gen)
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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)) -> f32 {
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// Normal/Gaussian Distribution
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@(require_results)
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float64_normal :: proc(mean, stddev: f64) -> f64 {
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return norm_float64() * stddev + mean
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float64_normal :: proc(mean, stddev: f64, gen := context.random_generator) -> f64 {
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return norm_float64(gen) * 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) -> f32 {
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return f32(float64_normal(f64(mean), f64(stddev)))
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float32_normal :: proc(mean, stddev: f32, gen := context.random_generator) -> f32 {
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return f32(float64_normal(f64(mean), f64(stddev), gen))
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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) -> f64 {
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return math.exp(float64_normal(mean, stddev))
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float64_log_normal :: proc(mean, stddev: f64, gen := context.random_generator) -> f64 {
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return math.exp(float64_normal(mean, stddev, gen))
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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) -> f32 {
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return f32(float64_log_normal(f64(mean), f64(stddev)))
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float32_log_normal :: proc(mean, stddev: f32, gen := context.random_generator) -> f32 {
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return f32(float64_log_normal(f64(mean), f64(stddev), gen))
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}
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@@ -72,8 +72,8 @@ float32_log_normal :: proc(mean, stddev: f32) -> 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) -> f64 {
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return - math.ln(1 - float64()) / lambda
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float64_exponential :: proc(lambda: f64, gen := context.random_generator) -> f64 {
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return - math.ln(1 - float64(gen)) / 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) -> 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) -> f32 {
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return f32(float64_exponential(f64(lambda)))
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float32_exponential :: proc(lambda: f32, gen := context.random_generator) -> f32 {
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return f32(float64_exponential(f64(lambda), gen))
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}
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@@ -96,7 +96,7 @@ float32_exponential :: proc(lambda: f32) -> 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) -> f64 {
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float64_gamma :: proc(alpha, beta: f64, gen := context.random_generator) -> 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) -> 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()
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u1 := float64(gen)
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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()
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u2 := 1 - float64(gen)
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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) -> 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()) * beta
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return -math.ln(1 - float64(gen)) * 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()
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u := float64(gen)
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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) -> 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()
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u1 := float64(gen)
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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) -> 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) -> f32 {
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return f32(float64_gamma(f64(alpha), f64(beta)))
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float32_gamma :: proc(alpha, beta: f32, gen := context.random_generator) -> f32 {
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return f32(float64_gamma(f64(alpha), f64(beta), gen))
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}
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@@ -173,14 +173,14 @@ float32_gamma :: proc(alpha, beta: f32) -> 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) -> f64 {
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float64_beta :: proc(alpha, beta: f64, gen := context.random_generator) -> 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)
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y := float64_gamma(alpha, 1.0, gen)
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if y != 0 {
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return y / (y + float64_gamma(beta, 1.0))
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return y / (y + float64_gamma(beta, 1.0, gen))
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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) -> 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) -> f32 {
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return f32(float64_beta(f64(alpha), f64(beta)))
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float32_beta :: proc(alpha, beta: f32, gen := context.random_generator) -> f32 {
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return f32(float64_beta(f64(alpha), f64(beta), gen))
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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) -> f64 {
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return math.pow(1 - float64(), -1.0 / alpha)
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float64_pareto :: proc(alpha: f64, gen := context.random_generator) -> f64 {
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return math.pow(1 - float64(gen), -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) -> f32 {
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return f32(float64_pareto(f64(alpha)))
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float32_pareto :: proc(alpha, beta: f32, gen := context.random_generator) -> f32 {
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return f32(float64_pareto(f64(alpha), gen))
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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) -> f64 {
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u := 1 - float64()
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float64_weibull :: proc(alpha, beta: f64, gen := context.random_generator) -> f64 {
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u := 1 - float64(gen)
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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) -> f32 {
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return f32(float64_weibull(f64(alpha), f64(beta)))
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float32_weibull :: proc(alpha, beta: f32, gen := context.random_generator) -> f32 {
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return f32(float64_weibull(f64(alpha), f64(beta), gen))
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}
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@@ -227,23 +227,23 @@ float32_weibull :: proc(alpha, beta: f32) -> 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) -> f64 {
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float64_von_mises :: proc(mean_angle, kappa: f64, gen := context.random_generator) -> 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()
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return math.TAU * float64(gen)
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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()
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u1 := float64(gen)
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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()
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u2 := float64(gen)
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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) -> 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()
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u3 := float64(gen)
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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) -> 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) -> f32 {
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return f32(float64_von_mises(f64(mean_angle), f64(kappa)))
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float32_von_mises :: proc(mean_angle, kappa: f32, gen := context.random_generator) -> f32 {
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return f32(float64_von_mises(f64(mean_angle), f64(kappa), gen))
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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) -> f64 {
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float64_cauchy_lorentz :: proc(x_0, gamma: f64, gen := context.random_generator) -> 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() - 0.5))*gamma + x_0
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return math.tan(math.PI * (float64(gen) - 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) -> f32 {
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return f32(float64_cauchy_lorentz(f64(x_0), f64(gamma)))
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float32_cauchy_lorentz :: proc(x_0, gamma: f32, gen := context.random_generator) -> f32 {
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return f32(float64_cauchy_lorentz(f64(x_0), f64(gamma), gen))
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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) -> f64 {
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float64_log_cauchy_lorentz :: proc(x_0, gamma: f64, gen := context.random_generator) -> f64 {
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assert(gamma > 0)
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return math.exp(math.tan(math.PI * (float64() - 0.5))*gamma + x_0)
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return math.exp(math.tan(math.PI * (float64(gen) - 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) -> f32 {
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return f32(float64_log_cauchy_lorentz(f64(x_0), f64(gamma)))
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float32_log_cauchy_lorentz :: proc(x_0, gamma: f32, gen := context.random_generator) -> f32 {
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return f32(float64_log_cauchy_lorentz(f64(x_0), f64(gamma), gen))
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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) -> f64 {
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float64_laplace :: proc(mean, b: f64, gen := context.random_generator) -> f64 {
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assert(b > 0)
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p := float64()-0.5
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p := float64(gen)-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) -> f32 {
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return f32(float64_laplace(f64(mean), f64(b)))
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float32_laplace :: proc(mean, b: f32, gen := context.random_generator) -> f32 {
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return f32(float64_laplace(f64(mean), f64(b), gen))
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}
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@@ -321,18 +321,18 @@ float32_laplace :: proc(mean, b: f32) -> 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) -> f64 {
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float64_gompertz :: proc(eta, b: f64, gen := context.random_generator) -> 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()
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p := float64(gen)
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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) -> f32 {
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return f32(float64_gompertz(f64(eta), f64(b)))
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float32_gompertz :: proc(eta, b: f32, gen := context.random_generator) -> f32 {
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return f32(float64_gompertz(f64(eta), f64(b), gen))
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}
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@@ -16,7 +16,7 @@ import "core:math"
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// https://www.jstatsoft.org/article/view/v005i08 [web page]
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//
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@(require_results)
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exp_float64 :: proc() -> f64 {
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exp_float64 :: proc(gen := context.random_generator) -> f64 {
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re :: 7.69711747013104972
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@(static, rodata)
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@@ -199,16 +199,16 @@ exp_float64 :: proc() -> f64 {
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}
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for {
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j := uint32()
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j := uint32(gen)
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i := j & 0xFF
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x := f64(j) * f64(we[i])
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if j < ke[i] {
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return x
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}
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if i == 0 {
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return re - math.ln(float64())
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return re - math.ln(float64(gen))
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}
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if fe[i]+f32(float64())*(fe[i-1]-fe[i]) < f32(math.exp(-x)) {
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if fe[i]+f32(float64(gen))*(fe[i-1]-fe[i]) < f32(math.exp(-x)) {
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return x
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}
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}
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@@ -18,7 +18,7 @@ import "core:math"
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// https://www.jstatsoft.org/article/view/v005i08 [web page]
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//
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@(require_results)
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norm_float64 :: proc() -> f64 {
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norm_float64 :: proc(gen := context.random_generator) -> f64 {
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rn :: 3.442619855899
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@(static, rodata)
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@@ -116,7 +116,7 @@ norm_float64 :: proc() -> f64 {
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}
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||||
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||||
for {
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||||
j := i32(uint32())
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||||
j := i32(uint32(gen))
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i := j & 0x7f
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x := f64(j) * f64(wn[i])
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if u32(abs(j)) < kn[i] {
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||||
@@ -126,15 +126,15 @@ norm_float64 :: proc() -> f64 {
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||||
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||||
if i == 0 {
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||||
for {
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||||
x = -math.ln(float64()) * (1.0/ rn)
|
||||
y := -math.ln(float64())
|
||||
x = -math.ln(float64(gen)) * (1.0/ rn)
|
||||
y := -math.ln(float64(gen))
|
||||
if y+y >= x*x {
|
||||
break
|
||||
}
|
||||
}
|
||||
return j > 0 ? rn + x : -rn - x
|
||||
}
|
||||
if fn[i]+f32(float64())*(fn[i-1]-fn[i]) < f32(math.exp(-0.5*x*x)) {
|
||||
if fn[i]+f32(float64(gen))*(fn[i-1]-fn[i]) < f32(math.exp(-0.5*x*x)) {
|
||||
return x
|
||||
}
|
||||
}
|
||||
|
||||
@@ -62,15 +62,15 @@ Possible Output:
|
||||
|
||||
10
|
||||
*/
|
||||
reset :: proc(seed: u64) {
|
||||
runtime.random_generator_reset_u64(context.random_generator, seed)
|
||||
reset :: proc(seed: u64, gen := context.random_generator) {
|
||||
runtime.random_generator_reset_u64(gen, seed)
|
||||
}
|
||||
|
||||
|
||||
@(private)
|
||||
_random_u64 :: proc() -> (res: u64) {
|
||||
ok := runtime.random_generator_read_ptr(context.random_generator, &res, size_of(res))
|
||||
assert(ok, "uninitialized context.random_generator")
|
||||
_random_u64 :: proc(gen := context.random_generator) -> (res: u64) {
|
||||
ok := runtime.random_generator_read_ptr(gen, &res, size_of(res))
|
||||
assert(ok, "uninitialized gen/context.random_generator")
|
||||
return
|
||||
}
|
||||
|
||||
@@ -95,7 +95,7 @@ Possible Output:
|
||||
|
||||
*/
|
||||
@(require_results)
|
||||
uint32 :: proc() -> (val: u32) { return u32(_random_u64()) }
|
||||
uint32 :: proc(gen := context.random_generator) -> (val: u32) { return u32(_random_u64(gen)) }
|
||||
|
||||
/*
|
||||
Generates a random 64 bit value using the provided random number generator. If no generator is provided the global random number generator will be used.
|
||||
@@ -118,7 +118,7 @@ Possible Output:
|
||||
|
||||
*/
|
||||
@(require_results)
|
||||
uint64 :: proc() -> (val: u64) { return _random_u64() }
|
||||
uint64 :: proc(gen := context.random_generator) -> (val: u64) { return _random_u64(gen) }
|
||||
|
||||
/*
|
||||
Generates a random 128 bit value using the provided random number generator. If no generator is provided the global random number generator will be used.
|
||||
@@ -141,9 +141,9 @@ Possible Output:
|
||||
|
||||
*/
|
||||
@(require_results)
|
||||
uint128 :: proc() -> (val: u128) {
|
||||
a := u128(_random_u64())
|
||||
b := u128(_random_u64())
|
||||
uint128 :: proc(gen := context.random_generator) -> (val: u128) {
|
||||
a := u128(_random_u64(gen))
|
||||
b := u128(_random_u64(gen))
|
||||
return (a<<64) | b
|
||||
}
|
||||
|
||||
@@ -168,7 +168,7 @@ Possible Output:
|
||||
389
|
||||
|
||||
*/
|
||||
@(require_results) int31 :: proc() -> (val: i32) { return i32(uint32() << 1 >> 1) }
|
||||
@(require_results) int31 :: proc(gen := context.random_generator) -> (val: i32) { return i32(uint32(gen) << 1 >> 1) }
|
||||
|
||||
/*
|
||||
Generates a random 63 bit value using the provided random number generator. If no generator is provided the global random number generator will be used.
|
||||
@@ -191,7 +191,7 @@ Possible Output:
|
||||
389
|
||||
|
||||
*/
|
||||
@(require_results) int63 :: proc() -> (val: i64) { return i64(uint64() << 1 >> 1) }
|
||||
@(require_results) int63 :: proc(gen := context.random_generator) -> (val: i64) { return i64(uint64(gen) << 1 >> 1) }
|
||||
|
||||
/*
|
||||
Generates a random 127 bit value using the provided random number generator. If no generator is provided the global random number generator will be used.
|
||||
@@ -214,7 +214,7 @@ Possible Output:
|
||||
389
|
||||
|
||||
*/
|
||||
@(require_results) int127 :: proc() -> (val: i128) { return i128(uint128() << 1 >> 1) }
|
||||
@(require_results) int127 :: proc(gen := context.random_generator) -> (val: i128) { return i128(uint128(gen) << 1 >> 1) }
|
||||
|
||||
/*
|
||||
Generates a random 31 bit value in the range `[0, n)` using the provided random number generator. If no generator is provided the global random number generator will be used.
|
||||
@@ -242,17 +242,17 @@ Possible Output:
|
||||
|
||||
*/
|
||||
@(require_results)
|
||||
int31_max :: proc(n: i32) -> (val: i32) {
|
||||
int31_max :: proc(n: i32, gen := context.random_generator) -> (val: i32) {
|
||||
if n <= 0 {
|
||||
panic("Invalid argument to int31_max")
|
||||
}
|
||||
if n&(n-1) == 0 {
|
||||
return int31() & (n-1)
|
||||
return int31(gen) & (n-1)
|
||||
}
|
||||
max := i32((1<<31) - 1 - (1<<31)%u32(n))
|
||||
v := int31()
|
||||
v := int31(gen)
|
||||
for v > max {
|
||||
v = int31()
|
||||
v = int31(gen)
|
||||
}
|
||||
return v % n
|
||||
}
|
||||
@@ -283,17 +283,17 @@ Possible Output:
|
||||
|
||||
*/
|
||||
@(require_results)
|
||||
int63_max :: proc(n: i64) -> (val: i64) {
|
||||
int63_max :: proc(n: i64, gen := context.random_generator) -> (val: i64) {
|
||||
if n <= 0 {
|
||||
panic("Invalid argument to int63_max")
|
||||
}
|
||||
if n&(n-1) == 0 {
|
||||
return int63() & (n-1)
|
||||
return int63(gen) & (n-1)
|
||||
}
|
||||
max := i64((1<<63) - 1 - (1<<63)%u64(n))
|
||||
v := int63()
|
||||
v := int63(gen)
|
||||
for v > max {
|
||||
v = int63()
|
||||
v = int63(gen)
|
||||
}
|
||||
return v % n
|
||||
}
|
||||
@@ -324,17 +324,17 @@ Possible Output:
|
||||
|
||||
*/
|
||||
@(require_results)
|
||||
int127_max :: proc(n: i128) -> (val: i128) {
|
||||
int127_max :: proc(n: i128, gen := context.random_generator) -> (val: i128) {
|
||||
if n <= 0 {
|
||||
panic("Invalid argument to int127_max")
|
||||
}
|
||||
if n&(n-1) == 0 {
|
||||
return int127() & (n-1)
|
||||
return int127(gen) & (n-1)
|
||||
}
|
||||
max := i128((1<<127) - 1 - (1<<127)%u128(n))
|
||||
v := int127()
|
||||
v := int127(gen)
|
||||
for v > max {
|
||||
v = int127()
|
||||
v = int127(gen)
|
||||
}
|
||||
return v % n
|
||||
}
|
||||
@@ -365,14 +365,14 @@ Possible Output:
|
||||
|
||||
*/
|
||||
@(require_results)
|
||||
int_max :: proc(n: int) -> (val: int) {
|
||||
int_max :: proc(n: int, gen := context.random_generator) -> (val: int) {
|
||||
if n <= 0 {
|
||||
panic("Invalid argument to int_max")
|
||||
}
|
||||
when size_of(int) == 4 {
|
||||
return int(int31_max(i32(n)))
|
||||
return int(int31_max(i32(n), gen))
|
||||
} else {
|
||||
return int(int63_max(i64(n)))
|
||||
return int(int63_max(i64(n), gen))
|
||||
}
|
||||
}
|
||||
|
||||
@@ -396,7 +396,7 @@ Possible Output:
|
||||
0.511
|
||||
|
||||
*/
|
||||
@(require_results) float64 :: proc() -> (val: f64) { return f64(int63_max(1<<53)) / (1 << 53) }
|
||||
@(require_results) float64 :: proc(gen := context.random_generator) -> (val: f64) { return f64(int63_max(1<<53, gen)) / (1 << 53) }
|
||||
|
||||
/*
|
||||
Generates a random single floating point value in the range `[0, 1)` using the provided random number generator. If no generator is provided the global random number generator will be used.
|
||||
@@ -418,7 +418,7 @@ Possible Output:
|
||||
0.511
|
||||
|
||||
*/
|
||||
@(require_results) float32 :: proc() -> (val: f32) { return f32(int31_max(1<<24)) / (1 << 24) }
|
||||
@(require_results) float32 :: proc(gen := context.random_generator) -> (val: f32) { return f32(int31_max(1<<24, gen)) / (1 << 24) }
|
||||
|
||||
/*
|
||||
Generates a random double floating point value in the range `[low, high)` using the provided random number generator. If no generator is provided the global random number generator will be used.
|
||||
@@ -446,9 +446,9 @@ Possible Output:
|
||||
673.130
|
||||
|
||||
*/
|
||||
@(require_results) float64_range :: proc(low, high: f64) -> (val: f64) {
|
||||
@(require_results) float64_range :: proc(low, high: f64, gen := context.random_generator) -> (val: f64) {
|
||||
assert(low <= high, "low must be lower than or equal to high")
|
||||
val = (high-low)*float64() + low
|
||||
val = (high-low)*float64(gen) + low
|
||||
if val >= high {
|
||||
val = max(low, high * (1 - math.F64_EPSILON))
|
||||
}
|
||||
@@ -481,9 +481,9 @@ Possible Output:
|
||||
673.130
|
||||
|
||||
*/
|
||||
@(require_results) float32_range :: proc(low, high: f32) -> (val: f32) {
|
||||
@(require_results) float32_range :: proc(low, high: f32, gen := context.random_generator) -> (val: f32) {
|
||||
assert(low <= high, "low must be lower than or equal to high")
|
||||
val = (high-low)*float32() + low
|
||||
val = (high-low)*float32(gen) + low
|
||||
if val >= high {
|
||||
val = max(low, high * (1 - math.F32_EPSILON))
|
||||
}
|
||||
@@ -518,12 +518,12 @@ Possible Output:
|
||||
|
||||
*/
|
||||
@(require_results)
|
||||
read :: proc(p: []byte) -> (n: int) {
|
||||
read :: proc(p: []byte, gen := context.random_generator) -> (n: int) {
|
||||
pos := i8(0)
|
||||
val := i64(0)
|
||||
for n = 0; n < len(p); n += 1 {
|
||||
if pos == 0 {
|
||||
val = int63()
|
||||
val = int63(gen)
|
||||
pos = 7
|
||||
}
|
||||
p[n] = byte(val)
|
||||
@@ -566,10 +566,10 @@ Possible Output:
|
||||
|
||||
*/
|
||||
@(require_results)
|
||||
perm :: proc(n: int, allocator := context.allocator) -> (res: []int, err: mem.Allocator_Error) #optional_allocator_error {
|
||||
perm :: proc(n: int, allocator := context.allocator, gen := context.random_generator) -> (res: []int, err: mem.Allocator_Error) #optional_allocator_error {
|
||||
m := make([]int, n, allocator) or_return
|
||||
for i := 0; i < n; i += 1 {
|
||||
j := int_max(i+1)
|
||||
j := int_max(i+1, gen)
|
||||
m[i] = m[j]
|
||||
m[j] = i
|
||||
}
|
||||
@@ -599,14 +599,14 @@ Possible Output:
|
||||
[2, 4, 3, 1]
|
||||
|
||||
*/
|
||||
shuffle :: proc(array: $T/[]$E) {
|
||||
shuffle :: proc(array: $T/[]$E, gen := context.random_generator) {
|
||||
n := i64(len(array))
|
||||
if n < 2 {
|
||||
return
|
||||
}
|
||||
|
||||
for i := i64(n - 1); i > 0; i -= 1 {
|
||||
j := int63_max(i + 1)
|
||||
j := int63_max(i + 1, gen)
|
||||
array[i], array[j] = array[j], array[i]
|
||||
}
|
||||
}
|
||||
@@ -641,17 +641,17 @@ Possible Output:
|
||||
|
||||
*/
|
||||
@(require_results)
|
||||
choice :: proc(array: $T/[]$E) -> (res: E) {
|
||||
choice :: proc(array: $T/[]$E, gen := context.random_generator) -> (res: E) {
|
||||
n := i64(len(array))
|
||||
if n < 1 {
|
||||
return E{}
|
||||
}
|
||||
return array[int63_max(n)]
|
||||
return array[int63_max(n, gen)]
|
||||
}
|
||||
|
||||
|
||||
@(require_results)
|
||||
choice_enum :: proc($T: typeid) -> T
|
||||
choice_enum :: proc($T: typeid, gen := context.random_generator) -> T
|
||||
where
|
||||
intrinsics.type_is_enum(T),
|
||||
size_of(T) <= 8,
|
||||
@@ -659,11 +659,11 @@ choice_enum :: proc($T: typeid) -> T
|
||||
{
|
||||
when intrinsics.type_is_unsigned(intrinsics.type_core_type(T)) &&
|
||||
u64(max(T)) > u64(max(i64)) {
|
||||
i := uint64() % u64(len(T))
|
||||
i := uint64(gen) % u64(len(T))
|
||||
i += u64(min(T))
|
||||
return T(i)
|
||||
} else {
|
||||
i := int63_max(i64(len(T)))
|
||||
i := int63_max(i64(len(T)), gen)
|
||||
i += i64(min(T))
|
||||
return T(i)
|
||||
}
|
||||
|
||||
Reference in New Issue
Block a user