Add `@(require_results)` to `core:math/rand`

core/math/rand/distributions.odin

@@ -7,6 +7,7 @@ float32_uniform :: float32_range
 
 // Triangular Distribution
 // See: http://wikipedia.org/wiki/Triangular_distribution
+@(require_results)
 float64_triangular :: proc(lo, hi: f64, mode: Maybe(f64), r: ^Rand = nil) -> f64 {
 	if hi-lo == 0 {
 		return lo
@@ -24,6 +25,7 @@ float64_triangular :: proc(lo, hi: f64, mode: Maybe(f64), r: ^Rand = nil) -> f64
 }
 // Triangular Distribution
 // See: http://wikipedia.org/wiki/Triangular_distribution
+@(require_results)
 float32_triangular :: proc(lo, hi: f32, mode: Maybe(f32), r: ^Rand = nil) -> f32 {
 	if hi-lo == 0 {
 		return lo
@@ -41,20 +43,24 @@ float32_triangular :: proc(lo, hi: f32, mode: Maybe(f32), r: ^Rand = nil) -> f32
 
 
 // Normal/Gaussian Distribution
+@(require_results)
 float64_normal :: proc(mean, stddev: f64, r: ^Rand = nil) -> f64 {
 	return norm_float64(r) * stddev + mean
 }
 // Normal/Gaussian Distribution
+@(require_results)
 float32_normal :: proc(mean, stddev: f32, r: ^Rand = nil) -> f32 {
 	return f32(float64_normal(f64(mean), f64(stddev), r))
 }
 
 
 // Log Normal Distribution
+@(require_results)
 float64_log_normal :: proc(mean, stddev: f64, r: ^Rand = nil) -> f64 {
 	return math.exp(float64_normal(mean, stddev, r))
 }
 // Log Normal Distribution
+@(require_results)
 float32_log_normal :: proc(mean, stddev: f32, r: ^Rand = nil) -> f32 {
 	return f32(float64_log_normal(f64(mean), f64(stddev), r))
 }
@@ -65,6 +71,7 @@ float32_log_normal :: proc(mean, stddev: f32, r: ^Rand = nil) -> f32 {
 // Return values range from
 //     0 to positive infinity if lambda >  0
 //     negative infinity to 0 if lambda <= 0
+@(require_results)
 float64_exponential :: proc(lambda: f64, r: ^Rand = nil) -> f64 {
 	return - math.ln(1 - float64(r)) / lambda
 }
@@ -73,6 +80,7 @@ float64_exponential :: proc(lambda: f64, r: ^Rand = nil) -> f64 {
 // Return values range from
 //     0 to positive infinity if lambda >  0
 //     negative infinity to 0 if lambda <= 0
+@(require_results)
 float32_exponential :: proc(lambda: f32, r: ^Rand = nil) -> f32 {
 	return f32(float64_exponential(f64(lambda), r))
 }
@@ -87,6 +95,7 @@ float32_exponential :: proc(lambda: f32, r: ^Rand = nil) -> f32 {
 //              math.gamma(alpha) * math.pow(beta, alpha)
 //
 // mean is alpha*beta, variance is math.pow(alpha*beta, 2)
+@(require_results)
 float64_gamma :: proc(alpha, beta: f64, r: ^Rand = nil) -> f64 {
 	if alpha <= 0 || beta <= 0 {
 		panic(#procedure + ": alpha and beta must be > 0.0")
@@ -152,6 +161,7 @@ float64_gamma :: proc(alpha, beta: f64, r: ^Rand = nil) -> f64 {
 //              math.gamma(alpha) * math.pow(beta, alpha)
 //
 // mean is alpha*beta, variance is math.pow(alpha*beta, 2)
+@(require_results)
 float32_gamma :: proc(alpha, beta: f32, r: ^Rand = nil) -> f32 {
 	return f32(float64_gamma(f64(alpha), f64(beta), r))
 }
@@ -162,6 +172,7 @@ float32_gamma :: proc(alpha, beta: f32, r: ^Rand = nil) -> f32 {
 // Required: alpha > 0 and beta > 0
 //
 // Return values range between 0 and 1
+@(require_results)
 float64_beta :: proc(alpha, beta: f64, r: ^Rand = nil) -> f64 {
 	if alpha <= 0 || beta <= 0 {
 		panic(#procedure + ": alpha and beta must be > 0.0")
@@ -178,6 +189,7 @@ float64_beta :: proc(alpha, beta: f64, r: ^Rand = nil) -> f64 {
 // Required: alpha > 0 and beta > 0
 //
 // Return values range between 0 and 1
+@(require_results)
 float32_beta :: proc(alpha, beta: f32, r: ^Rand = nil) -> f32 {
 	return f32(float64_beta(f64(alpha), f64(beta), r))
 }
@@ -185,22 +197,26 @@ float32_beta :: proc(alpha, beta: f32, r: ^Rand = nil) -> f32 {
 
 // Pareto distribution, `alpha` is the shape parameter.
 // https://wikipedia.org/wiki/Pareto_distribution
+@(require_results)
 float64_pareto :: proc(alpha: f64, r: ^Rand = nil) -> f64 {
 	return math.pow(1 - float64(r), -1.0 / alpha)
 }
 // Pareto distribution, `alpha` is the shape parameter.
 // https://wikipedia.org/wiki/Pareto_distribution
+@(require_results)
 float32_pareto :: proc(alpha, beta: f32, r: ^Rand = nil) -> f32 {
 	return f32(float64_pareto(f64(alpha), r))
 }
 
 
 // Weibull distribution, `alpha` is the scale parameter, `beta` is the shape parameter.
+@(require_results)
 float64_weibull :: proc(alpha, beta: f64, r: ^Rand = nil) -> f64 {
 	u := 1 - float64(r)
 	return alpha * math.pow(-math.ln(u), 1.0/beta)
 }
 // Weibull distribution, `alpha` is the scale parameter, `beta` is the shape parameter.
+@(require_results)
 float32_weibull :: proc(alpha, beta: f32, r: ^Rand = nil) -> f32 {
 	return f32(float64_weibull(f64(alpha), f64(beta), r))
 }
@@ -210,6 +226,7 @@ float32_weibull :: proc(alpha, beta: f32, r: ^Rand = nil) -> f32 {
 // `mean_angle` is the in mean angle between 0 and 2pi radians
 // `kappa` is the concentration parameter which must be >= 0
 // When `kappa` is zero, the Distribution is a uniform Distribution over the range 0 to 2pi
+@(require_results)
 float64_von_mises :: proc(mean_angle, kappa: f64, r: ^Rand = nil) -> f64 {
 	// Fisher, N.I., "Statistical Analysis of Circular Data", Cambridge University Press, 1993.
 
@@ -245,6 +262,7 @@ float64_von_mises :: proc(mean_angle, kappa: f64, r: ^Rand = nil) -> f64 {
 // `mean_angle` is the in mean angle between 0 and 2pi radians
 // `kappa` is the concentration parameter which must be >= 0
 // When `kappa` is zero, the Distribution is a uniform Distribution over the range 0 to 2pi
+@(require_results)
 float32_von_mises :: proc(mean_angle, kappa: f32, r: ^Rand = nil) -> f32 {
 	return f32(float64_von_mises(f64(mean_angle), f64(kappa), r))
 }
@@ -252,6 +270,7 @@ float32_von_mises :: proc(mean_angle, kappa: f32, r: ^Rand = nil) -> f32 {
 
 // Cauchy-Lorentz Distribution
 // `x_0` is the location, `gamma` is the scale where `gamma` > 0
+@(require_results)
 float64_cauchy_lorentz :: proc(x_0, gamma: f64, r: ^Rand = nil) -> f64 {
 	assert(gamma > 0)
 
@@ -261,6 +280,7 @@ float64_cauchy_lorentz :: proc(x_0, gamma: f64, r: ^Rand = nil) -> f64 {
 }
 // Cauchy-Lorentz Distribution
 // `x_0` is the location, `gamma` is the scale where `gamma` > 0
+@(require_results)
 float32_cauchy_lorentz :: proc(x_0, gamma: f32, r: ^Rand = nil) -> f32 {
 	return f32(float64_cauchy_lorentz(f64(x_0), f64(gamma), r))
 }
@@ -268,12 +288,14 @@ float32_cauchy_lorentz :: proc(x_0, gamma: f32, r: ^Rand = nil) -> f32 {
 
 // Log Cauchy-Lorentz Distribution
 // `x_0` is the location, `gamma` is the scale where `gamma` > 0
+@(require_results)
 float64_log_cauchy_lorentz :: proc(x_0, gamma: f64, r: ^Rand = nil) -> f64 {
 	assert(gamma > 0)
 	return math.exp(math.tan(math.PI * (float64(r) - 0.5))*gamma + x_0)
 }
 // Log Cauchy-Lorentz Distribution
 // `x_0` is the location, `gamma` is the scale where `gamma` > 0
+@(require_results)
 float32_log_cauchy_lorentz :: proc(x_0, gamma: f32, r: ^Rand = nil) -> f32 {
 	return f32(float64_log_cauchy_lorentz(f64(x_0), f64(gamma), r))
 }
@@ -281,6 +303,7 @@ float32_log_cauchy_lorentz :: proc(x_0, gamma: f32, r: ^Rand = nil) -> f32 {
 
 // Laplace Distribution
 // `b` is the scale where `b` > 0
+@(require_results)
 float64_laplace :: proc(mean, b: f64, r: ^Rand = nil) -> f64 {
 	assert(b > 0)
 	p := float64(r)-0.5
@@ -288,6 +311,7 @@ float64_laplace :: proc(mean, b: f64, r: ^Rand = nil) -> f64 {
 }
 // Laplace Distribution
 // `b` is the scale where `b` > 0
+@(require_results)
 float32_laplace :: proc(mean, b: f32, r: ^Rand = nil) -> f32 {
 	return f32(float64_laplace(f64(mean), f64(b), r))
 }
@@ -296,6 +320,7 @@ float32_laplace :: proc(mean, b: f32, r: ^Rand = nil) -> f32 {
 // Gompertz Distribution
 // `eta` is the shape, `b` is the scale
 // Both `eta` and `b` must be > 0
+@(require_results)
 float64_gompertz :: proc(eta, b: f64, r: ^Rand = nil) -> f64 {
 	if eta <= 0 || b <= 0 {
 		panic(#procedure + ": eta and b must be > 0.0")
@@ -307,6 +332,7 @@ float64_gompertz :: proc(eta, b: f64, r: ^Rand = nil) -> f64 {
 // Gompertz Distribution
 // `eta` is the shape, `b` is the scale
 // Both `eta` and `b` must be > 0
+@(require_results)
 float32_gompertz :: proc(eta, b: f32, r: ^Rand = nil) -> f32 {
 	return f32(float64_gompertz(f64(eta), f64(b), r))
 }

core/math/rand/exp.odin

@@ -15,6 +15,7 @@ import "core:math"
 //    https://www.jstatsoft.org/index.php/jss/article/view/v005i08/ziggurat.pdf [pdf]
 //    https://www.jstatsoft.org/article/view/v005i08 [web page]
 //
+@(require_results)
 exp_float64 :: proc(r: ^Rand = nil) -> f64 {
 	re :: 7.69711747013104972
 

core/math/rand/normal.odin

@@ -17,6 +17,7 @@ import "core:math"
 //    https://www.jstatsoft.org/index.php/jss/article/view/v005i08/ziggurat.pdf [pdf]
 //    https://www.jstatsoft.org/article/view/v005i08 [web page]
 //
+@(require_results)
 norm_float64 :: proc(r: ^Rand = nil) -> f64 {
 	rn :: 3.442619855899
 

core/math/rand/rand.odin

@@ -16,6 +16,7 @@ set_global_seed :: proc(seed: u64) {
 	init(&global_rand, seed)
 }
 
+@(require_results)
 create :: proc(seed: u64) -> Rand {
 	r: Rand
 	init(&r, seed)
@@ -60,14 +61,17 @@ _random :: proc(r: ^Rand) -> u32 {
 	return (xor_shifted >> rot) | (xor_shifted << ((-rot) & 31))
 }
 
+@(require_results)
 uint32 :: proc(r: ^Rand = nil) -> u32 { return _random(r) }
 
+@(require_results)
 uint64 :: proc(r: ^Rand = nil) -> u64 {
 	a := u64(_random(r))
 	b := u64(_random(r))
 	return (a<<32) | b
 }
 
+@(require_results)
 uint128 :: proc(r: ^Rand = nil) -> u128 {
 	a := u128(_random(r))
 	b := u128(_random(r))
@@ -76,10 +80,11 @@ uint128 :: proc(r: ^Rand = nil) -> u128 {
 	return (a<<96) | (b<<64) | (c<<32) | d
 }
 
-int31  :: proc(r: ^Rand = nil) -> i32  { return i32(uint32(r) << 1 >> 1) }
-int63  :: proc(r: ^Rand = nil) -> i64  { return i64(uint64(r) << 1 >> 1) }
-int127 :: proc(r: ^Rand = nil) -> i128 { return i128(uint128(r) << 1 >> 1) }
+@(require_results) int31  :: proc(r: ^Rand = nil) -> i32  { return i32(uint32(r) << 1 >> 1) }
+@(require_results) int63  :: proc(r: ^Rand = nil) -> i64  { return i64(uint64(r) << 1 >> 1) }
+@(require_results) int127 :: proc(r: ^Rand = nil) -> i128 { return i128(uint128(r) << 1 >> 1) }
 
+@(require_results)
 int31_max :: proc(n: i32, r: ^Rand = nil) -> i32 {
 	if n <= 0 {
 		panic("Invalid argument to int31_max")
@@ -95,6 +100,7 @@ int31_max :: proc(n: i32, r: ^Rand = nil) -> i32 {
 	return v % n
 }
 
+@(require_results)
 int63_max :: proc(n: i64, r: ^Rand = nil) -> i64 {
 	if n <= 0 {
 		panic("Invalid argument to int63_max")
@@ -110,6 +116,7 @@ int63_max :: proc(n: i64, r: ^Rand = nil) -> i64 {
 	return v % n
 }
 
+@(require_results)
 int127_max :: proc(n: i128, r: ^Rand = nil) -> i128 {
 	if n <= 0 {
 		panic("Invalid argument to int127_max")
@@ -125,6 +132,7 @@ int127_max :: proc(n: i128, r: ^Rand = nil) -> i128 {
 	return v % n
 }
 
+@(require_results)
 int_max :: proc(n: int, r: ^Rand = nil) -> int {
 	if n <= 0 {
 		panic("Invalid argument to int_max")
@@ -137,13 +145,14 @@ int_max :: proc(n: int, r: ^Rand = nil) -> int {
 }
 
 // Uniform random distribution [0, 1)
-float64 :: proc(r: ^Rand = nil) -> f64 { return f64(int63_max(1<<53, r)) / (1 << 53) }
+@(require_results) float64 :: proc(r: ^Rand = nil) -> f64 { return f64(int63_max(1<<53, r)) / (1 << 53) }
 // Uniform random distribution [0, 1)
-float32 :: proc(r: ^Rand = nil) -> f32 { return f32(float64(r)) }
+@(require_results) float32 :: proc(r: ^Rand = nil) -> f32 { return f32(float64(r)) }
 
-float64_range :: proc(lo, hi: f64, r: ^Rand = nil) -> f64 { return (hi-lo)*float64(r) + lo }
-float32_range :: proc(lo, hi: f32, r: ^Rand = nil) -> f32 { return (hi-lo)*float32(r) + lo }
+@(require_results) float64_range :: proc(lo, hi: f64, r: ^Rand = nil) -> f64 { return (hi-lo)*float64(r) + lo }
+@(require_results) float32_range :: proc(lo, hi: f32, r: ^Rand = nil) -> f32 { return (hi-lo)*float32(r) + lo }
 
+@(require_results)
 read :: proc(p: []byte, r: ^Rand = nil) -> (n: int) {
 	pos := i8(0)
 	val := i64(0)
@@ -160,6 +169,7 @@ read :: proc(p: []byte, r: ^Rand = nil) -> (n: int) {
 }
 
 // perm returns a slice of n ints in a pseudo-random permutation of integers in the range [0, n)
+@(require_results)
 perm :: proc(n: int, r: ^Rand = nil, allocator := context.allocator) -> []int {
 	m := make([]int, n, allocator)
 	for i := 0; i < n; i += 1 {
@@ -184,6 +194,7 @@ shuffle :: proc(array: $T/[]$E, r: ^Rand = nil) {
 }
 
 // Returns a random element from the given slice
+@(require_results)
 choice :: proc(array: $T/[]$E, r: ^Rand = nil) -> (res: E) {
 	n := i64(len(array))
 	if n < 1 {

core/math/rand/system_darwin.odin

@@ -2,6 +2,7 @@ package rand
 
 import "core:sys/darwin"
 
+@(require_results)
 _system_random :: proc() -> u32 {
 	for {
 		value: u32

core/math/rand/system_linux.odin

@@ -2,6 +2,7 @@ package rand
 
 import "core:sys/unix"
 
+@(require_results)
 _system_random :: proc() -> u32 {
 	for {
 		value: u32

core/math/rand/system_windows.odin

@@ -2,6 +2,7 @@ package rand
 
 import win32 "core:sys/windows"
 
+@(require_results)
 _system_random :: proc() -> u32 {
 	value: u32
 	status := win32.BCryptGenRandom(nil, ([^]u8)(&value), 4, win32.BCRYPT_USE_SYSTEM_PREFERRED_RNG)