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-rw-r--r--numpy/random/src/distributions/distributions.c316
-rw-r--r--numpy/random/src/distributions/distributions.h215
-rw-r--r--numpy/random/src/distributions/random_hypergeometric.c8
-rw-r--r--numpy/random/src/distributions/random_mvhg_count.c131
-rw-r--r--numpy/random/src/distributions/random_mvhg_marginals.c138
5 files changed, 364 insertions, 444 deletions
diff --git a/numpy/random/src/distributions/distributions.c b/numpy/random/src/distributions/distributions.c
index 45f062c06..0b46dc6d8 100644
--- a/numpy/random/src/distributions/distributions.c
+++ b/numpy/random/src/distributions/distributions.c
@@ -1,4 +1,4 @@
-#include "distributions.h"
+#include "numpy/random/distributions.h"
#include "ziggurat_constants.h"
#include "logfactorial.h"
@@ -6,92 +6,42 @@
#include <intrin.h>
#endif
-/* Random generators for external use */
-float random_float(bitgen_t *bitgen_state) { return next_float(bitgen_state); }
-
-double random_double(bitgen_t *bitgen_state) {
- return next_double(bitgen_state);
+/* Inline generators for internal use */
+static NPY_INLINE uint32_t next_uint32(bitgen_t *bitgen_state) {
+ return bitgen_state->next_uint32(bitgen_state->state);
}
-
-static NPY_INLINE double next_standard_exponential(bitgen_t *bitgen_state) {
- return -log(1.0 - next_double(bitgen_state));
+static NPY_INLINE uint64_t next_uint64(bitgen_t *bitgen_state) {
+ return bitgen_state->next_uint64(bitgen_state->state);
}
-double random_standard_exponential(bitgen_t *bitgen_state) {
- return next_standard_exponential(bitgen_state);
+static NPY_INLINE float next_float(bitgen_t *bitgen_state) {
+ return (next_uint32(bitgen_state) >> 9) * (1.0f / 8388608.0f);
}
-void random_standard_exponential_fill(bitgen_t *bitgen_state, npy_intp cnt,
- double *out) {
- npy_intp i;
- for (i = 0; i < cnt; i++) {
- out[i] = next_standard_exponential(bitgen_state);
- }
+/* Random generators for external use */
+float random_standard_uniform_f(bitgen_t *bitgen_state) {
+ return next_float(bitgen_state);
}
-float random_standard_exponential_f(bitgen_t *bitgen_state) {
- return -logf(1.0f - next_float(bitgen_state));
+double random_standard_uniform(bitgen_t *bitgen_state) {
+ return next_double(bitgen_state);
}
-void random_double_fill(bitgen_t *bitgen_state, npy_intp cnt, double *out) {
+void random_standard_uniform_fill(bitgen_t *bitgen_state, npy_intp cnt, double *out) {
npy_intp i;
for (i = 0; i < cnt; i++) {
out[i] = next_double(bitgen_state);
}
}
-#if 0
-double random_gauss(bitgen_t *bitgen_state) {
- if (bitgen_state->has_gauss) {
- const double temp = bitgen_state->gauss;
- bitgen_state->has_gauss = false;
- bitgen_state->gauss = 0.0;
- return temp;
- } else {
- double f, x1, x2, r2;
-
- do {
- x1 = 2.0 * next_double(bitgen_state) - 1.0;
- x2 = 2.0 * next_double(bitgen_state) - 1.0;
- r2 = x1 * x1 + x2 * x2;
- } while (r2 >= 1.0 || r2 == 0.0);
-
- /* Polar method, a more efficient version of the Box-Muller approach. */
- f = sqrt(-2.0 * log(r2) / r2);
- /* Keep for next call */
- bitgen_state->gauss = f * x1;
- bitgen_state->has_gauss = true;
- return f * x2;
- }
-}
-float random_gauss_f(bitgen_t *bitgen_state) {
- if (bitgen_state->has_gauss_f) {
- const float temp = bitgen_state->gauss_f;
- bitgen_state->has_gauss_f = false;
- bitgen_state->gauss_f = 0.0f;
- return temp;
- } else {
- float f, x1, x2, r2;
-
- do {
- x1 = 2.0f * next_float(bitgen_state) - 1.0f;
- x2 = 2.0f * next_float(bitgen_state) - 1.0f;
- r2 = x1 * x1 + x2 * x2;
- } while (r2 >= 1.0 || r2 == 0.0);
-
- /* Polar method, a more efficient version of the Box-Muller approach. */
- f = sqrtf(-2.0f * logf(r2) / r2);
- /* Keep for next call */
- bitgen_state->gauss_f = f * x1;
- bitgen_state->has_gauss_f = true;
- return f * x2;
+void random_standard_uniform_fill_f(bitgen_t *bitgen_state, npy_intp cnt, float *out) {
+ npy_intp i;
+ for (i = 0; i < cnt; i++) {
+ out[i] = next_float(bitgen_state);
}
}
-#endif
-
-static NPY_INLINE double standard_exponential_zig(bitgen_t *bitgen_state);
-static double standard_exponential_zig_unlikely(bitgen_t *bitgen_state,
+static double standard_exponential_unlikely(bitgen_t *bitgen_state,
uint8_t idx, double x) {
if (idx == 0) {
/* Switch to 1.0 - U to avoid log(0.0), see GH 13361 */
@@ -101,11 +51,11 @@ static double standard_exponential_zig_unlikely(bitgen_t *bitgen_state,
exp(-x)) {
return x;
} else {
- return standard_exponential_zig(bitgen_state);
+ return random_standard_exponential(bitgen_state);
}
}
-static NPY_INLINE double standard_exponential_zig(bitgen_t *bitgen_state) {
+double random_standard_exponential(bitgen_t *bitgen_state) {
uint64_t ri;
uint8_t idx;
double x;
@@ -117,24 +67,18 @@ static NPY_INLINE double standard_exponential_zig(bitgen_t *bitgen_state) {
if (ri < ke_double[idx]) {
return x; /* 98.9% of the time we return here 1st try */
}
- return standard_exponential_zig_unlikely(bitgen_state, idx, x);
+ return standard_exponential_unlikely(bitgen_state, idx, x);
}
-double random_standard_exponential_zig(bitgen_t *bitgen_state) {
- return standard_exponential_zig(bitgen_state);
-}
-
-void random_standard_exponential_zig_fill(bitgen_t *bitgen_state, npy_intp cnt,
- double *out) {
+void random_standard_exponential_fill(bitgen_t * bitgen_state, npy_intp cnt, double * out)
+{
npy_intp i;
for (i = 0; i < cnt; i++) {
- out[i] = standard_exponential_zig(bitgen_state);
+ out[i] = random_standard_exponential(bitgen_state);
}
}
-static NPY_INLINE float standard_exponential_zig_f(bitgen_t *bitgen_state);
-
-static float standard_exponential_zig_unlikely_f(bitgen_t *bitgen_state,
+static float standard_exponential_unlikely_f(bitgen_t *bitgen_state,
uint8_t idx, float x) {
if (idx == 0) {
/* Switch to 1.0 - U to avoid log(0.0), see GH 13361 */
@@ -144,11 +88,11 @@ static float standard_exponential_zig_unlikely_f(bitgen_t *bitgen_state,
expf(-x)) {
return x;
} else {
- return standard_exponential_zig_f(bitgen_state);
+ return random_standard_exponential_f(bitgen_state);
}
}
-static NPY_INLINE float standard_exponential_zig_f(bitgen_t *bitgen_state) {
+float random_standard_exponential_f(bitgen_t *bitgen_state) {
uint32_t ri;
uint8_t idx;
float x;
@@ -160,14 +104,35 @@ static NPY_INLINE float standard_exponential_zig_f(bitgen_t *bitgen_state) {
if (ri < ke_float[idx]) {
return x; /* 98.9% of the time we return here 1st try */
}
- return standard_exponential_zig_unlikely_f(bitgen_state, idx, x);
+ return standard_exponential_unlikely_f(bitgen_state, idx, x);
}
-float random_standard_exponential_zig_f(bitgen_t *bitgen_state) {
- return standard_exponential_zig_f(bitgen_state);
+void random_standard_exponential_fill_f(bitgen_t * bitgen_state, npy_intp cnt, float * out)
+{
+ npy_intp i;
+ for (i = 0; i < cnt; i++) {
+ out[i] = random_standard_exponential_f(bitgen_state);
+ }
}
-static NPY_INLINE double next_gauss_zig(bitgen_t *bitgen_state) {
+void random_standard_exponential_inv_fill(bitgen_t * bitgen_state, npy_intp cnt, double * out)
+{
+ npy_intp i;
+ for (i = 0; i < cnt; i++) {
+ out[i] = -log(1.0 - next_double(bitgen_state));
+ }
+}
+
+void random_standard_exponential_inv_fill_f(bitgen_t * bitgen_state, npy_intp cnt, float * out)
+{
+ npy_intp i;
+ for (i = 0; i < cnt; i++) {
+ out[i] = -log(1.0 - next_float(bitgen_state));
+ }
+}
+
+
+double random_standard_normal(bitgen_t *bitgen_state) {
uint64_t r;
int sign;
uint64_t rabs;
@@ -202,18 +167,14 @@ static NPY_INLINE double next_gauss_zig(bitgen_t *bitgen_state) {
}
}
-double random_gauss_zig(bitgen_t *bitgen_state) {
- return next_gauss_zig(bitgen_state);
-}
-
-void random_gauss_zig_fill(bitgen_t *bitgen_state, npy_intp cnt, double *out) {
+void random_standard_normal_fill(bitgen_t *bitgen_state, npy_intp cnt, double *out) {
npy_intp i;
for (i = 0; i < cnt; i++) {
- out[i] = next_gauss_zig(bitgen_state);
+ out[i] = random_standard_normal(bitgen_state);
}
}
-float random_gauss_zig_f(bitgen_t *bitgen_state) {
+float random_standard_normal_f(bitgen_t *bitgen_state) {
uint32_t r;
int sign;
uint32_t rabs;
@@ -247,113 +208,26 @@ float random_gauss_zig_f(bitgen_t *bitgen_state) {
}
}
-/*
-static NPY_INLINE double standard_gamma(bitgen_t *bitgen_state, double shape) {
- double b, c;
- double U, V, X, Y;
-
- if (shape == 1.0) {
- return random_standard_exponential(bitgen_state);
- } else if (shape < 1.0) {
- for (;;) {
- U = next_double(bitgen_state);
- V = random_standard_exponential(bitgen_state);
- if (U <= 1.0 - shape) {
- X = pow(U, 1. / shape);
- if (X <= V) {
- return X;
- }
- } else {
- Y = -log((1 - U) / shape);
- X = pow(1.0 - shape + shape * Y, 1. / shape);
- if (X <= (V + Y)) {
- return X;
- }
- }
- }
- } else {
- b = shape - 1. / 3.;
- c = 1. / sqrt(9 * b);
- for (;;) {
- do {
- X = random_gauss(bitgen_state);
- V = 1.0 + c * X;
- } while (V <= 0.0);
-
- V = V * V * V;
- U = next_double(bitgen_state);
- if (U < 1.0 - 0.0331 * (X * X) * (X * X))
- return (b * V);
- if (log(U) < 0.5 * X * X + b * (1. - V + log(V)))
- return (b * V);
- }
- }
-}
-
-static NPY_INLINE float standard_gamma_float(bitgen_t *bitgen_state, float
-shape) { float b, c; float U, V, X, Y;
-
- if (shape == 1.0f) {
- return random_standard_exponential_f(bitgen_state);
- } else if (shape < 1.0f) {
- for (;;) {
- U = next_float(bitgen_state);
- V = random_standard_exponential_f(bitgen_state);
- if (U <= 1.0f - shape) {
- X = powf(U, 1.0f / shape);
- if (X <= V) {
- return X;
- }
- } else {
- Y = -logf((1.0f - U) / shape);
- X = powf(1.0f - shape + shape * Y, 1.0f / shape);
- if (X <= (V + Y)) {
- return X;
- }
- }
- }
- } else {
- b = shape - 1.0f / 3.0f;
- c = 1.0f / sqrtf(9.0f * b);
- for (;;) {
- do {
- X = random_gauss_f(bitgen_state);
- V = 1.0f + c * X;
- } while (V <= 0.0f);
-
- V = V * V * V;
- U = next_float(bitgen_state);
- if (U < 1.0f - 0.0331f * (X * X) * (X * X))
- return (b * V);
- if (logf(U) < 0.5f * X * X + b * (1.0f - V + logf(V)))
- return (b * V);
- }
+void random_standard_normal_fill_f(bitgen_t *bitgen_state, npy_intp cnt, float *out) {
+ npy_intp i;
+ for (i = 0; i < cnt; i++) {
+ out[i] = random_standard_normal_f(bitgen_state);
}
}
-
-double random_standard_gamma(bitgen_t *bitgen_state, double shape) {
- return standard_gamma(bitgen_state, shape);
-}
-
-float random_standard_gamma_f(bitgen_t *bitgen_state, float shape) {
- return standard_gamma_float(bitgen_state, shape);
-}
-*/
-
-static NPY_INLINE double standard_gamma_zig(bitgen_t *bitgen_state,
+double random_standard_gamma(bitgen_t *bitgen_state,
double shape) {
double b, c;
double U, V, X, Y;
if (shape == 1.0) {
- return random_standard_exponential_zig(bitgen_state);
+ return random_standard_exponential(bitgen_state);
} else if (shape == 0.0) {
return 0.0;
} else if (shape < 1.0) {
for (;;) {
U = next_double(bitgen_state);
- V = random_standard_exponential_zig(bitgen_state);
+ V = random_standard_exponential(bitgen_state);
if (U <= 1.0 - shape) {
X = pow(U, 1. / shape);
if (X <= V) {
@@ -372,7 +246,7 @@ static NPY_INLINE double standard_gamma_zig(bitgen_t *bitgen_state,
c = 1. / sqrt(9 * b);
for (;;) {
do {
- X = random_gauss_zig(bitgen_state);
+ X = random_standard_normal(bitgen_state);
V = 1.0 + c * X;
} while (V <= 0.0);
@@ -387,19 +261,19 @@ static NPY_INLINE double standard_gamma_zig(bitgen_t *bitgen_state,
}
}
-static NPY_INLINE float standard_gamma_zig_f(bitgen_t *bitgen_state,
+float random_standard_gamma_f(bitgen_t *bitgen_state,
float shape) {
float b, c;
float U, V, X, Y;
if (shape == 1.0f) {
- return random_standard_exponential_zig_f(bitgen_state);
+ return random_standard_exponential_f(bitgen_state);
} else if (shape == 0.0) {
return 0.0;
} else if (shape < 1.0f) {
for (;;) {
U = next_float(bitgen_state);
- V = random_standard_exponential_zig_f(bitgen_state);
+ V = random_standard_exponential_f(bitgen_state);
if (U <= 1.0f - shape) {
X = powf(U, 1.0f / shape);
if (X <= V) {
@@ -418,7 +292,7 @@ static NPY_INLINE float standard_gamma_zig_f(bitgen_t *bitgen_state,
c = 1.0f / sqrtf(9.0f * b);
for (;;) {
do {
- X = random_gauss_zig_f(bitgen_state);
+ X = random_standard_normal_f(bitgen_state);
V = 1.0f + c * X;
} while (V <= 0.0f);
@@ -433,14 +307,6 @@ static NPY_INLINE float standard_gamma_zig_f(bitgen_t *bitgen_state,
}
}
-double random_standard_gamma_zig(bitgen_t *bitgen_state, double shape) {
- return standard_gamma_zig(bitgen_state, shape);
-}
-
-float random_standard_gamma_zig_f(bitgen_t *bitgen_state, float shape) {
- return standard_gamma_zig_f(bitgen_state, shape);
-}
-
int64_t random_positive_int64(bitgen_t *bitgen_state) {
return next_uint64(bitgen_state) >> 1;
}
@@ -470,10 +336,10 @@ uint64_t random_uint(bitgen_t *bitgen_state) {
* algorithm comes from SPECFUN by Shanjie Zhang and Jianming Jin and their
* book "Computation of Special Functions", 1996, John Wiley & Sons, Inc.
*
- * If loggam(k+1) is being used to compute log(k!) for an integer k, consider
+ * If random_loggam(k+1) is being used to compute log(k!) for an integer k, consider
* using logfactorial(k) instead.
*/
-double loggam(double x) {
+double random_loggam(double x) {
double x0, x2, xp, gl, gl0;
RAND_INT_TYPE k, n;
@@ -513,12 +379,12 @@ double random_normal(bitgen_t *bitgen_state, double loc, double scale) {
}
*/
-double random_normal_zig(bitgen_t *bitgen_state, double loc, double scale) {
- return loc + scale * random_gauss_zig(bitgen_state);
+double random_normal(bitgen_t *bitgen_state, double loc, double scale) {
+ return loc + scale * random_standard_normal(bitgen_state);
}
double random_exponential(bitgen_t *bitgen_state, double scale) {
- return scale * standard_exponential_zig(bitgen_state);
+ return scale * random_standard_exponential(bitgen_state);
}
double random_uniform(bitgen_t *bitgen_state, double lower, double range) {
@@ -526,11 +392,11 @@ double random_uniform(bitgen_t *bitgen_state, double lower, double range) {
}
double random_gamma(bitgen_t *bitgen_state, double shape, double scale) {
- return scale * random_standard_gamma_zig(bitgen_state, shape);
+ return scale * random_standard_gamma(bitgen_state, shape);
}
-float random_gamma_float(bitgen_t *bitgen_state, float shape, float scale) {
- return scale * random_standard_gamma_zig_f(bitgen_state, shape);
+float random_gamma_f(bitgen_t *bitgen_state, float shape, float scale) {
+ return scale * random_standard_gamma_f(bitgen_state, shape);
}
double random_beta(bitgen_t *bitgen_state, double a, double b) {
@@ -562,14 +428,14 @@ double random_beta(bitgen_t *bitgen_state, double a, double b) {
}
}
} else {
- Ga = random_standard_gamma_zig(bitgen_state, a);
- Gb = random_standard_gamma_zig(bitgen_state, b);
+ Ga = random_standard_gamma(bitgen_state, a);
+ Gb = random_standard_gamma(bitgen_state, b);
return Ga / (Ga + Gb);
}
}
double random_chisquare(bitgen_t *bitgen_state, double df) {
- return 2.0 * random_standard_gamma_zig(bitgen_state, df / 2.0);
+ return 2.0 * random_standard_gamma(bitgen_state, df / 2.0);
}
double random_f(bitgen_t *bitgen_state, double dfnum, double dfden) {
@@ -578,22 +444,22 @@ double random_f(bitgen_t *bitgen_state, double dfnum, double dfden) {
}
double random_standard_cauchy(bitgen_t *bitgen_state) {
- return random_gauss_zig(bitgen_state) / random_gauss_zig(bitgen_state);
+ return random_standard_normal(bitgen_state) / random_standard_normal(bitgen_state);
}
double random_pareto(bitgen_t *bitgen_state, double a) {
- return exp(standard_exponential_zig(bitgen_state) / a) - 1;
+ return exp(random_standard_exponential(bitgen_state) / a) - 1;
}
double random_weibull(bitgen_t *bitgen_state, double a) {
if (a == 0.0) {
return 0.0;
}
- return pow(standard_exponential_zig(bitgen_state), 1. / a);
+ return pow(random_standard_exponential(bitgen_state), 1. / a);
}
double random_power(bitgen_t *bitgen_state, double a) {
- return pow(1 - exp(-standard_exponential_zig(bitgen_state)), 1. / a);
+ return pow(1 - exp(-random_standard_exponential(bitgen_state)), 1. / a);
}
double random_laplace(bitgen_t *bitgen_state, double loc, double scale) {
@@ -634,7 +500,7 @@ double random_logistic(bitgen_t *bitgen_state, double loc, double scale) {
}
double random_lognormal(bitgen_t *bitgen_state, double mean, double sigma) {
- return exp(random_normal_zig(bitgen_state, mean, sigma));
+ return exp(random_normal(bitgen_state, mean, sigma));
}
double random_rayleigh(bitgen_t *bitgen_state, double mode) {
@@ -644,8 +510,8 @@ double random_rayleigh(bitgen_t *bitgen_state, double mode) {
double random_standard_t(bitgen_t *bitgen_state, double df) {
double num, denom;
- num = random_gauss_zig(bitgen_state);
- denom = random_standard_gamma_zig(bitgen_state, df / 2);
+ num = random_standard_normal(bitgen_state);
+ denom = random_standard_gamma(bitgen_state, df / 2);
return sqrt(df / 2) * num / sqrt(denom);
}
@@ -699,7 +565,7 @@ static RAND_INT_TYPE random_poisson_ptrs(bitgen_t *bitgen_state, double lam) {
/* log(V) == log(0.0) ok here */
/* if U==0.0 so that us==0.0, log is ok since always returns */
if ((log(V) + log(invalpha) - log(a / (us * us) + b)) <=
- (-lam + k * loglam - loggam(k + 1))) {
+ (-lam + k * loglam - random_loggam(k + 1))) {
return k;
}
}
@@ -934,7 +800,7 @@ double random_noncentral_chisquare(bitgen_t *bitgen_state, double df,
}
if (1 < df) {
const double Chi2 = random_chisquare(bitgen_state, df - 1);
- const double n = random_gauss_zig(bitgen_state) + sqrt(nonc);
+ const double n = random_standard_normal(bitgen_state) + sqrt(nonc);
return Chi2 + n * n;
} else {
const RAND_INT_TYPE i = random_poisson(bitgen_state, nonc / 2.0);
@@ -953,7 +819,7 @@ double random_wald(bitgen_t *bitgen_state, double mean, double scale) {
double mu_2l;
mu_2l = mean / (2 * scale);
- Y = random_gauss_zig(bitgen_state);
+ Y = random_standard_normal(bitgen_state);
Y = mean * Y * Y;
X = mean + mu_2l * (Y - sqrt(4 * scale * Y + Y * Y));
U = next_double(bitgen_state);
@@ -1092,8 +958,8 @@ RAND_INT_TYPE random_zipf(bitgen_t *bitgen_state, double a) {
while (1) {
double T, U, V, X;
- U = 1.0 - random_double(bitgen_state);
- V = random_double(bitgen_state);
+ U = 1.0 - next_double(bitgen_state);
+ V = next_double(bitgen_state);
X = floor(pow(U, -1.0 / am1));
/*
* The real result may be above what can be represented in a signed
diff --git a/numpy/random/src/distributions/distributions.h b/numpy/random/src/distributions/distributions.h
deleted file mode 100644
index b778968d7..000000000
--- a/numpy/random/src/distributions/distributions.h
+++ /dev/null
@@ -1,215 +0,0 @@
-#ifndef _RANDOMDGEN__DISTRIBUTIONS_H_
-#define _RANDOMDGEN__DISTRIBUTIONS_H_
-
-#pragma once
-#include <stddef.h>
-#include <stdbool.h>
-#include <stdint.h>
-
-#include "Python.h"
-#include "numpy/npy_common.h"
-#include "numpy/npy_math.h"
-#include "src/bitgen.h"
-
-/*
- * RAND_INT_TYPE is used to share integer generators with RandomState which
- * used long in place of int64_t. If changing a distribution that uses
- * RAND_INT_TYPE, then the original unmodified copy must be retained for
- * use in RandomState by copying to the legacy distributions source file.
- */
-#ifdef NP_RANDOM_LEGACY
-#define RAND_INT_TYPE long
-#define RAND_INT_MAX LONG_MAX
-#else
-#define RAND_INT_TYPE int64_t
-#define RAND_INT_MAX INT64_MAX
-#endif
-
-#ifdef DLL_EXPORT
-#define DECLDIR __declspec(dllexport)
-#else
-#define DECLDIR extern
-#endif
-
-#ifndef MIN
-#define MIN(x, y) (((x) < (y)) ? x : y)
-#define MAX(x, y) (((x) > (y)) ? x : y)
-#endif
-
-#ifndef M_PI
-#define M_PI 3.14159265358979323846264338328
-#endif
-
-typedef struct s_binomial_t {
- int has_binomial; /* !=0: following parameters initialized for binomial */
- double psave;
- RAND_INT_TYPE nsave;
- double r;
- double q;
- double fm;
- RAND_INT_TYPE m;
- double p1;
- double xm;
- double xl;
- double xr;
- double c;
- double laml;
- double lamr;
- double p2;
- double p3;
- double p4;
-} binomial_t;
-
-/* Inline generators for internal use */
-static NPY_INLINE uint32_t next_uint32(bitgen_t *bitgen_state) {
- return bitgen_state->next_uint32(bitgen_state->state);
-}
-
-static NPY_INLINE uint64_t next_uint64(bitgen_t *bitgen_state) {
- return bitgen_state->next_uint64(bitgen_state->state);
-}
-
-static NPY_INLINE float next_float(bitgen_t *bitgen_state) {
- return (next_uint32(bitgen_state) >> 9) * (1.0f / 8388608.0f);
-}
-
-static NPY_INLINE double next_double(bitgen_t *bitgen_state) {
- return bitgen_state->next_double(bitgen_state->state);
-}
-
-DECLDIR double loggam(double x);
-
-DECLDIR float random_float(bitgen_t *bitgen_state);
-DECLDIR double random_double(bitgen_t *bitgen_state);
-DECLDIR void random_double_fill(bitgen_t *bitgen_state, npy_intp cnt, double *out);
-
-DECLDIR int64_t random_positive_int64(bitgen_t *bitgen_state);
-DECLDIR int32_t random_positive_int32(bitgen_t *bitgen_state);
-DECLDIR int64_t random_positive_int(bitgen_t *bitgen_state);
-DECLDIR uint64_t random_uint(bitgen_t *bitgen_state);
-
-DECLDIR double random_standard_exponential(bitgen_t *bitgen_state);
-DECLDIR void random_standard_exponential_fill(bitgen_t *bitgen_state, npy_intp cnt,
- double *out);
-DECLDIR float random_standard_exponential_f(bitgen_t *bitgen_state);
-DECLDIR double random_standard_exponential_zig(bitgen_t *bitgen_state);
-DECLDIR void random_standard_exponential_zig_fill(bitgen_t *bitgen_state,
- npy_intp cnt, double *out);
-DECLDIR float random_standard_exponential_zig_f(bitgen_t *bitgen_state);
-
-/*
-DECLDIR double random_gauss(bitgen_t *bitgen_state);
-DECLDIR float random_gauss_f(bitgen_t *bitgen_state);
-*/
-DECLDIR double random_gauss_zig(bitgen_t *bitgen_state);
-DECLDIR float random_gauss_zig_f(bitgen_t *bitgen_state);
-DECLDIR void random_gauss_zig_fill(bitgen_t *bitgen_state, npy_intp cnt,
- double *out);
-
-/*
-DECLDIR double random_standard_gamma(bitgen_t *bitgen_state, double shape);
-DECLDIR float random_standard_gamma_f(bitgen_t *bitgen_state, float shape);
-*/
-DECLDIR double random_standard_gamma_zig(bitgen_t *bitgen_state, double shape);
-DECLDIR float random_standard_gamma_zig_f(bitgen_t *bitgen_state, float shape);
-
-/*
-DECLDIR double random_normal(bitgen_t *bitgen_state, double loc, double scale);
-*/
-DECLDIR double random_normal_zig(bitgen_t *bitgen_state, double loc, double scale);
-
-DECLDIR double random_gamma(bitgen_t *bitgen_state, double shape, double scale);
-DECLDIR float random_gamma_float(bitgen_t *bitgen_state, float shape, float scale);
-
-DECLDIR double random_exponential(bitgen_t *bitgen_state, double scale);
-DECLDIR double random_uniform(bitgen_t *bitgen_state, double lower, double range);
-DECLDIR double random_beta(bitgen_t *bitgen_state, double a, double b);
-DECLDIR double random_chisquare(bitgen_t *bitgen_state, double df);
-DECLDIR double random_f(bitgen_t *bitgen_state, double dfnum, double dfden);
-DECLDIR double random_standard_cauchy(bitgen_t *bitgen_state);
-DECLDIR double random_pareto(bitgen_t *bitgen_state, double a);
-DECLDIR double random_weibull(bitgen_t *bitgen_state, double a);
-DECLDIR double random_power(bitgen_t *bitgen_state, double a);
-DECLDIR double random_laplace(bitgen_t *bitgen_state, double loc, double scale);
-DECLDIR double random_gumbel(bitgen_t *bitgen_state, double loc, double scale);
-DECLDIR double random_logistic(bitgen_t *bitgen_state, double loc, double scale);
-DECLDIR double random_lognormal(bitgen_t *bitgen_state, double mean, double sigma);
-DECLDIR double random_rayleigh(bitgen_t *bitgen_state, double mode);
-DECLDIR double random_standard_t(bitgen_t *bitgen_state, double df);
-DECLDIR double random_noncentral_chisquare(bitgen_t *bitgen_state, double df,
- double nonc);
-DECLDIR double random_noncentral_f(bitgen_t *bitgen_state, double dfnum,
- double dfden, double nonc);
-DECLDIR double random_wald(bitgen_t *bitgen_state, double mean, double scale);
-DECLDIR double random_vonmises(bitgen_t *bitgen_state, double mu, double kappa);
-DECLDIR double random_triangular(bitgen_t *bitgen_state, double left, double mode,
- double right);
-
-DECLDIR RAND_INT_TYPE random_poisson(bitgen_t *bitgen_state, double lam);
-DECLDIR RAND_INT_TYPE random_negative_binomial(bitgen_t *bitgen_state, double n,
- double p);
-
-DECLDIR RAND_INT_TYPE random_binomial_btpe(bitgen_t *bitgen_state,
- RAND_INT_TYPE n,
- double p,
- binomial_t *binomial);
-DECLDIR RAND_INT_TYPE random_binomial_inversion(bitgen_t *bitgen_state,
- RAND_INT_TYPE n,
- double p,
- binomial_t *binomial);
-DECLDIR int64_t random_binomial(bitgen_t *bitgen_state, double p,
- int64_t n, binomial_t *binomial);
-
-DECLDIR RAND_INT_TYPE random_logseries(bitgen_t *bitgen_state, double p);
-DECLDIR RAND_INT_TYPE random_geometric_search(bitgen_t *bitgen_state, double p);
-DECLDIR RAND_INT_TYPE random_geometric_inversion(bitgen_t *bitgen_state, double p);
-DECLDIR RAND_INT_TYPE random_geometric(bitgen_t *bitgen_state, double p);
-DECLDIR RAND_INT_TYPE random_zipf(bitgen_t *bitgen_state, double a);
-DECLDIR int64_t random_hypergeometric(bitgen_t *bitgen_state,
- int64_t good, int64_t bad, int64_t sample);
-
-DECLDIR uint64_t random_interval(bitgen_t *bitgen_state, uint64_t max);
-
-/* Generate random uint64 numbers in closed interval [off, off + rng]. */
-DECLDIR uint64_t random_bounded_uint64(bitgen_t *bitgen_state, uint64_t off,
- uint64_t rng, uint64_t mask,
- bool use_masked);
-
-/* Generate random uint32 numbers in closed interval [off, off + rng]. */
-DECLDIR uint32_t random_buffered_bounded_uint32(bitgen_t *bitgen_state,
- uint32_t off, uint32_t rng,
- uint32_t mask, bool use_masked,
- int *bcnt, uint32_t *buf);
-DECLDIR uint16_t random_buffered_bounded_uint16(bitgen_t *bitgen_state,
- uint16_t off, uint16_t rng,
- uint16_t mask, bool use_masked,
- int *bcnt, uint32_t *buf);
-DECLDIR uint8_t random_buffered_bounded_uint8(bitgen_t *bitgen_state, uint8_t off,
- uint8_t rng, uint8_t mask,
- bool use_masked, int *bcnt,
- uint32_t *buf);
-DECLDIR npy_bool random_buffered_bounded_bool(bitgen_t *bitgen_state, npy_bool off,
- npy_bool rng, npy_bool mask,
- bool use_masked, int *bcnt,
- uint32_t *buf);
-
-DECLDIR void random_bounded_uint64_fill(bitgen_t *bitgen_state, uint64_t off,
- uint64_t rng, npy_intp cnt,
- bool use_masked, uint64_t *out);
-DECLDIR void random_bounded_uint32_fill(bitgen_t *bitgen_state, uint32_t off,
- uint32_t rng, npy_intp cnt,
- bool use_masked, uint32_t *out);
-DECLDIR void random_bounded_uint16_fill(bitgen_t *bitgen_state, uint16_t off,
- uint16_t rng, npy_intp cnt,
- bool use_masked, uint16_t *out);
-DECLDIR void random_bounded_uint8_fill(bitgen_t *bitgen_state, uint8_t off,
- uint8_t rng, npy_intp cnt,
- bool use_masked, uint8_t *out);
-DECLDIR void random_bounded_bool_fill(bitgen_t *bitgen_state, npy_bool off,
- npy_bool rng, npy_intp cnt,
- bool use_masked, npy_bool *out);
-
-DECLDIR void random_multinomial(bitgen_t *bitgen_state, RAND_INT_TYPE n, RAND_INT_TYPE *mnix,
- double *pix, npy_intp d, binomial_t *binomial);
-
-#endif
diff --git a/numpy/random/src/distributions/random_hypergeometric.c b/numpy/random/src/distributions/random_hypergeometric.c
index 59a3a4b9b..0da49bd62 100644
--- a/numpy/random/src/distributions/random_hypergeometric.c
+++ b/numpy/random/src/distributions/random_hypergeometric.c
@@ -1,6 +1,6 @@
-#include <stdint.h>
-#include "distributions.h"
+#include "numpy/random/distributions.h"
#include "logfactorial.h"
+#include <stdint.h>
/*
* Generate a sample from the hypergeometric distribution.
@@ -188,8 +188,8 @@ static int64_t hypergeometric_hrua(bitgen_t *bitgen_state,
while (1) {
double U, V, X, T;
double gp;
- U = random_double(bitgen_state);
- V = random_double(bitgen_state); // "U star" in Stadlober (1989)
+ U = next_double(bitgen_state);
+ V = next_double(bitgen_state); // "U star" in Stadlober (1989)
X = a + h*(V - 0.5) / U;
// fast rejection:
diff --git a/numpy/random/src/distributions/random_mvhg_count.c b/numpy/random/src/distributions/random_mvhg_count.c
new file mode 100644
index 000000000..7cbed1f9e
--- /dev/null
+++ b/numpy/random/src/distributions/random_mvhg_count.c
@@ -0,0 +1,131 @@
+#include <stdint.h>
+#include <stdlib.h>
+#include <stdbool.h>
+
+#include "numpy/random/distributions.h"
+
+/*
+ * random_multivariate_hypergeometric_count
+ *
+ * Draw variates from the multivariate hypergeometric distribution--
+ * the "count" algorithm.
+ *
+ * Parameters
+ * ----------
+ * bitgen_t *bitgen_state
+ * Pointer to a `bitgen_t` instance.
+ * int64_t total
+ * The sum of the values in the array `colors`. (This is redundant
+ * information, but we know the caller has already computed it, so
+ * we might as well use it.)
+ * size_t num_colors
+ * The length of the `colors` array.
+ * int64_t *colors
+ * The array of colors (i.e. the number of each type in the collection
+ * from which the random variate is drawn).
+ * int64_t nsample
+ * The number of objects drawn without replacement for each variate.
+ * `nsample` must not exceed sum(colors). This condition is not checked;
+ * it is assumed that the caller has already validated the value.
+ * size_t num_variates
+ * The number of variates to be produced and put in the array
+ * pointed to by `variates`. One variate is a vector of length
+ * `num_colors`, so the array pointed to by `variates` must have length
+ * `num_variates * num_colors`.
+ * int64_t *variates
+ * The array that will hold the result. It must have length
+ * `num_variates * num_colors`.
+ * The array is not initialized in the function; it is expected that the
+ * array has been initialized with zeros when the function is called.
+ *
+ * Notes
+ * -----
+ * The "count" algorithm for drawing one variate is roughly equivalent to the
+ * following numpy code:
+ *
+ * choices = np.repeat(np.arange(len(colors)), colors)
+ * selection = np.random.choice(choices, nsample, replace=False)
+ * variate = np.bincount(selection, minlength=len(colors))
+ *
+ * This function uses a temporary array with length sum(colors).
+ *
+ * Assumptions on the arguments (not checked in the function):
+ * * colors[k] >= 0 for k in range(num_colors)
+ * * total = sum(colors)
+ * * 0 <= nsample <= total
+ * * the product total * sizeof(size_t) does not exceed SIZE_MAX
+ * * the product num_variates * num_colors does not overflow
+ */
+
+int random_multivariate_hypergeometric_count(bitgen_t *bitgen_state,
+ int64_t total,
+ size_t num_colors, int64_t *colors,
+ int64_t nsample,
+ size_t num_variates, int64_t *variates)
+{
+ size_t *choices;
+ bool more_than_half;
+
+ if ((total == 0) || (nsample == 0) || (num_variates == 0)) {
+ // Nothing to do.
+ return 0;
+ }
+
+ choices = malloc(total * (sizeof *choices));
+ if (choices == NULL) {
+ return -1;
+ }
+
+ /*
+ * If colors contains, for example, [3 2 5], then choices
+ * will contain [0 0 0 1 1 2 2 2 2 2].
+ */
+ for (size_t i = 0, k = 0; i < num_colors; ++i) {
+ for (int64_t j = 0; j < colors[i]; ++j) {
+ choices[k] = i;
+ ++k;
+ }
+ }
+
+ more_than_half = nsample > (total / 2);
+ if (more_than_half) {
+ nsample = total - nsample;
+ }
+
+ for (size_t i = 0; i < num_variates * num_colors; i += num_colors) {
+ /*
+ * Fisher-Yates shuffle, but only loop through the first
+ * `nsample` entries of `choices`. After the loop,
+ * choices[:nsample] contains a random sample from the
+ * the full array.
+ */
+ for (size_t j = 0; j < (size_t) nsample; ++j) {
+ size_t tmp, k;
+ // Note: nsample is not greater than total, so there is no danger
+ // of integer underflow in `(size_t) total - j - 1`.
+ k = j + (size_t) random_interval(bitgen_state,
+ (size_t) total - j - 1);
+ tmp = choices[k];
+ choices[k] = choices[j];
+ choices[j] = tmp;
+ }
+ /*
+ * Count the number of occurrences of each value in choices[:nsample].
+ * The result, stored in sample[i:i+num_colors], is the sample from
+ * the multivariate hypergeometric distribution.
+ */
+ for (size_t j = 0; j < (size_t) nsample; ++j) {
+ variates[i + choices[j]] += 1;
+ }
+
+ if (more_than_half) {
+ for (size_t k = 0; k < num_colors; ++k) {
+ variates[i + k] = colors[k] - variates[i + k];
+ }
+ }
+ }
+
+ free(choices);
+
+ return 0;
+}
diff --git a/numpy/random/src/distributions/random_mvhg_marginals.c b/numpy/random/src/distributions/random_mvhg_marginals.c
new file mode 100644
index 000000000..809d129de
--- /dev/null
+++ b/numpy/random/src/distributions/random_mvhg_marginals.c
@@ -0,0 +1,138 @@
+#include <stdint.h>
+#include <stddef.h>
+#include <stdbool.h>
+#include <math.h>
+
+#include "numpy/random/distributions.h"
+#include "logfactorial.h"
+
+
+/*
+ * random_multivariate_hypergeometric_marginals
+ *
+ * Draw samples from the multivariate hypergeometric distribution--
+ * the "marginals" algorithm.
+ *
+ * This version generates the sample by iteratively calling
+ * hypergeometric() (the univariate hypergeometric distribution).
+ *
+ * Parameters
+ * ----------
+ * bitgen_t *bitgen_state
+ * Pointer to a `bitgen_t` instance.
+ * int64_t total
+ * The sum of the values in the array `colors`. (This is redundant
+ * information, but we know the caller has already computed it, so
+ * we might as well use it.)
+ * size_t num_colors
+ * The length of the `colors` array. The functions assumes
+ * num_colors > 0.
+ * int64_t *colors
+ * The array of colors (i.e. the number of each type in the collection
+ * from which the random variate is drawn).
+ * int64_t nsample
+ * The number of objects drawn without replacement for each variate.
+ * `nsample` must not exceed sum(colors). This condition is not checked;
+ * it is assumed that the caller has already validated the value.
+ * size_t num_variates
+ * The number of variates to be produced and put in the array
+ * pointed to by `variates`. One variate is a vector of length
+ * `num_colors`, so the array pointed to by `variates` must have length
+ * `num_variates * num_colors`.
+ * int64_t *variates
+ * The array that will hold the result. It must have length
+ * `num_variates * num_colors`.
+ * The array is not initialized in the function; it is expected that the
+ * array has been initialized with zeros when the function is called.
+ *
+ * Notes
+ * -----
+ * Here's an example that demonstrates the idea of this algorithm.
+ *
+ * Suppose the urn contains red, green, blue and yellow marbles.
+ * Let nred be the number of red marbles, and define the quantities for
+ * the other colors similarly. The total number of marbles is
+ *
+ * total = nred + ngreen + nblue + nyellow.
+ *
+ * To generate a sample using rk_hypergeometric:
+ *
+ * red_sample = hypergeometric(ngood=nred, nbad=total - nred,
+ * nsample=nsample)
+ *
+ * This gives us the number of red marbles in the sample. The number of
+ * marbles in the sample that are *not* red is nsample - red_sample.
+ * To figure out the distribution of those marbles, we again use
+ * rk_hypergeometric:
+ *
+ * green_sample = hypergeometric(ngood=ngreen,
+ * nbad=total - nred - ngreen,
+ * nsample=nsample - red_sample)
+ *
+ * Similarly,
+ *
+ * blue_sample = hypergeometric(
+ * ngood=nblue,
+ * nbad=total - nred - ngreen - nblue,
+ * nsample=nsample - red_sample - green_sample)
+ *
+ * Finally,
+ *
+ * yellow_sample = total - (red_sample + green_sample + blue_sample).
+ *
+ * The above sequence of steps is implemented as a loop for an arbitrary
+ * number of colors in the innermost loop in the code below. `remaining`
+ * is the value passed to `nbad`; it is `total - colors[0]` in the first
+ * call to random_hypergeometric(), and then decreases by `colors[j]` in
+ * each iteration. `num_to_sample` is the `nsample` argument. It
+ * starts at this function's `nsample` input, and is decreased by the
+ * result of the call to random_hypergeometric() in each iteration.
+ *
+ * Assumptions on the arguments (not checked in the function):
+ * * colors[k] >= 0 for k in range(num_colors)
+ * * total = sum(colors)
+ * * 0 <= nsample <= total
+ * * the product num_variates * num_colors does not overflow
+ */
+
+void random_multivariate_hypergeometric_marginals(bitgen_t *bitgen_state,
+ int64_t total,
+ size_t num_colors, int64_t *colors,
+ int64_t nsample,
+ size_t num_variates, int64_t *variates)
+{
+ bool more_than_half;
+
+ if ((total == 0) || (nsample == 0) || (num_variates == 0)) {
+ // Nothing to do.
+ return;
+ }
+
+ more_than_half = nsample > (total / 2);
+ if (more_than_half) {
+ nsample = total - nsample;
+ }
+
+ for (size_t i = 0; i < num_variates * num_colors; i += num_colors) {
+ int64_t num_to_sample = nsample;
+ int64_t remaining = total;
+ for (size_t j = 0; (num_to_sample > 0) && (j + 1 < num_colors); ++j) {
+ int64_t r;
+ remaining -= colors[j];
+ r = random_hypergeometric(bitgen_state,
+ colors[j], remaining, num_to_sample);
+ variates[i + j] = r;
+ num_to_sample -= r;
+ }
+
+ if (num_to_sample > 0) {
+ variates[i + num_colors - 1] = num_to_sample;
+ }
+
+ if (more_than_half) {
+ for (size_t k = 0; k < num_colors; ++k) {
+ variates[i + k] = colors[k] - variates[i + k];
+ }
+ }
+ }
+}
generated by cgit v1.2.3 (git 2.25.1) at 2024-08-06 02:29:14 +0000