/* -*- mode: C -*-  */
/*
   IGraph library.
   Copyright (C) 2005-2012  Gabor Csardi <csardi.gabor@gmail.com>
   334 Harvard street, Cambridge, MA 02139 USA

   This program is free software; you can redistribute it and/or modify
   it under the terms of the GNU General Public License as published by
   the Free Software Foundation; either version 2 of the License, or
   (at your option) any later version.

   This program is distributed in the hope that it will be useful,
   but WITHOUT ANY WARRANTY; without even the implied warranty of
   MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
   GNU General Public License for more details.

   You should have received a copy of the GNU General Public License
   along with this program; if not, write to the Free Software
   Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA
   02110-1301 USA

*/

#include "igraph_random.h"

#include "igraph_nongraph.h"
#include "igraph_error.h"
#include "igraph_types.h"
#include "igraph_vector.h"
#include "igraph_memory.h"

#include "core/math.h"

#include "config.h"
#include <math.h>
#include <string.h>

/**
 * \section about_rngs
 *
 * <section id="about-random-numbers-in-igraph">
 * <title>About random numbers in igraph, use cases</title>
 *
 * <para>
 * Some algorithms in igraph, e.g. the generation of random graphs,
 * require random number generators (RNGs). Prior to version 0.6
 * igraph did not have a sophisticated way to deal with random number
 * generators at the C level, but this has changed. From version 0.6
 * different and multiple random number generators are supported.
 * </para>
 * </section>
 *
 */

/**
 * \section rng_use_cases
 *
 * <section id="random-use-cases"><title>Use cases</title>
 *
 * <section id="random-normal-use"><title>Normal (default) use</title>
 * <para>
 * If the user does not use any of the RNG functions explicitly, but calls
 * some of the randomized igraph functions, then a default RNG is set
 * up the first time an igraph function needs random numbers. The
 * seed of this RNG is the output of the <code>time(0)</code> function
 * call, using the <code>time</code> function from the standard C
 * library. This ensures that igraph creates a different random graph,
 * each time the C program is called.
 * </para>
 *
 * <para>
 * The created default generator is stored internally and can be
 * queried with the \ref igraph_rng_default() function.
 * </para>
 * </section>
 *
 * <section id="random-reproducible-simulations"><title>Reproducible simulations</title>
 * <para>
 * If reproducible results are needed, then the user should set the
 * seed of the default random number generator explicitly, using the
 * \ref igraph_rng_seed() function on the default generator, \ref
 * igraph_rng_default(). When setting the seed to the same number,
 * igraph generates exactly the same random graph (or series of random
 * graphs).
 * </para>
 * </section>
 *
 * <section id="random-changing-default-generator"><title>Changing the default generator</title>
 * <para>
 * By default igraph uses the \ref igraph_rng_default() random number
 * generator. This can be changed any time by calling \ref
 * igraph_rng_set_default(), with an already initialized random number
 * generator. Note that the old (replaced) generator is not
 * destroyed, so no memory is deallocated.
 * </para>
 * </section>
 *
 * <section id="random-using-multiple-generators"><title>Using multiple generators</title>
 * <para>
 * igraph also provides functions to set up multiple random number
 * generators, using the \ref igraph_rng_init() function, and then
 * generating random numbers from them, e.g. with \ref igraph_rng_get_integer()
 * and/or \ref igraph_rng_get_unif() calls.
 * </para>
 *
 * <para>
 * Note that initializing a new random number generator is
 * independent of the generator that the igraph functions themselves
 * use. If you want to replace that, then please use \ref
 * igraph_rng_set_default().
 * </para>
 * </section>
 *
 * <section id="random-example"><title>Example</title>
 * <para>
 * \example examples/simple/random_seed.c
 * </para>
 * </section>
 *
 * </section>
 */

/* ------------------------------------ */

typedef struct {
    int i, j;
    long int x[31];
} igraph_i_rng_glibc2_state_t;

static unsigned long int igraph_i_rng_glibc2_get(int *i, int *j, int n, long int *x) {
    unsigned long int k;

    x[*i] += x[*j];
    k = (x[*i] >> 1) & 0x7FFFFFFF;

    (*i)++;
    if (*i == n) {
        *i = 0;
    }

    (*j)++ ;
    if (*j == n) {
        *j = 0;
    }

    return k;
}

static unsigned long int igraph_rng_glibc2_get(void *vstate) {
    igraph_i_rng_glibc2_state_t *state =
        (igraph_i_rng_glibc2_state_t*) vstate;
    return igraph_i_rng_glibc2_get(&state->i, &state->j, 31, state->x);
}

static igraph_real_t igraph_rng_glibc2_get_real(void *state) {
    return igraph_rng_glibc2_get(state) / 2147483648.0;
}

/* this function is independent of the bit size */

static void igraph_i_rng_glibc2_init(long int *x, int n,
                                     unsigned long int s) {
    int i;

    if (s == 0) {
        s = 1;
    }

    x[0] = (long) s;
    for (i = 1 ; i < n ; i++) {
        const long int h = s / 127773;
        const long int t = 16807 * ((long) s - h * 127773) - h * 2836;
        if (t < 0) {
            s = (unsigned long) t + 2147483647 ;
        } else {
            s = (unsigned long) t ;
        }

        x[i] = (long int) s ;
    }
}

static int igraph_rng_glibc2_seed(void *vstate, unsigned long int seed) {
    igraph_i_rng_glibc2_state_t *state =
        (igraph_i_rng_glibc2_state_t*) vstate;
    int i;

    igraph_i_rng_glibc2_init(state->x, 31, seed);

    state->i = 3;
    state->j = 0;

    for (i = 0; i < 10 * 31; i++) {
        igraph_rng_glibc2_get(state);
    }

    return IGRAPH_SUCCESS;
}

static int igraph_rng_glibc2_init(void **state) {
    igraph_i_rng_glibc2_state_t *st;

    st = IGRAPH_CALLOC(1, igraph_i_rng_glibc2_state_t);
    if (!st) {
        IGRAPH_ERROR("Cannot initialize RNG", IGRAPH_ENOMEM);
    }
    (*state) = st;

    igraph_rng_glibc2_seed(st, 0);

    return IGRAPH_SUCCESS;
}

static void igraph_rng_glibc2_destroy(void *vstate) {
    igraph_i_rng_glibc2_state_t *state =
        (igraph_i_rng_glibc2_state_t*) vstate;
    IGRAPH_FREE(state);
}

/**
 * \var igraph_rngtype_glibc2
 * \brief The random number generator introduced in GNU libc 2.
 *
 * This is a linear feedback shift register generator with a 128-byte
 * buffer. This generator was the default prior to igraph version 0.6,
 * at least on systems relying on GNU libc.
 *
 * This generator was ported from the GNU Scientific Library. It is a
 * reimplementation and does not call the system glibc generator.
 */

const igraph_rng_type_t igraph_rngtype_glibc2 = {
    /* name= */      "LIBC",
    /* min=  */      0,
    /* max=  */      0x7fffffffUL,
    /* init= */      igraph_rng_glibc2_init,
    /* destroy= */   igraph_rng_glibc2_destroy,
    /* seed= */      igraph_rng_glibc2_seed,
    /* get= */       igraph_rng_glibc2_get,
    /* get_real= */  igraph_rng_glibc2_get_real,
    /* get_norm= */  0,
    /* get_geom= */  0,
    /* get_binom= */ 0,
    /* get_exp= */   0,
    /* get_gamma= */ 0
};

/* ------------------------------------ */

typedef struct {
    unsigned long int x;
} igraph_i_rng_rand_state_t;

static unsigned long int igraph_rng_rand_get(void *vstate) {
    igraph_i_rng_rand_state_t *state = vstate;
    state->x = (1103515245 * state->x + 12345) & 0x7fffffffUL;
    return state->x;
}

static igraph_real_t igraph_rng_rand_get_real(void *vstate) {
    return igraph_rng_rand_get (vstate) / 2147483648.0 ;
}

static int igraph_rng_rand_seed(void *vstate, unsigned long int seed) {
    igraph_i_rng_rand_state_t *state = vstate;
    state->x = seed;
    return IGRAPH_SUCCESS;
}

static int igraph_rng_rand_init(void **state) {
    igraph_i_rng_rand_state_t *st;

    st = IGRAPH_CALLOC(1, igraph_i_rng_rand_state_t);
    if (!st) {
        IGRAPH_ERROR("Cannot initialize RNG", IGRAPH_ENOMEM);
    }
    (*state) = st;

    igraph_rng_rand_seed(st, 0);

    return IGRAPH_SUCCESS;
}

static void igraph_rng_rand_destroy(void *vstate) {
    igraph_i_rng_rand_state_t *state =
        (igraph_i_rng_rand_state_t*) vstate;
    IGRAPH_FREE(state);
}

/**
 * \var igraph_rngtype_rand
 * \brief The old BSD rand/srand random number generator.
 *
 * The sequence is
 *     <code>x_{n+1} = (a x_n + c) mod m</code>
 * with <code>a = 1103515245</code>, <code>c = 12345</code> and
 * <code>m = 2^31 = 2147483648</code>.
 * The seed specifies the initial value, <code>x_1</code>.
 *
 * </para><para>
 * The theoretical value of <code>x_{10001}</code> is 1910041713.
 *
 * </para><para>
 * The period of this generator is 2^31.
 *
 * </para><para>
 * This generator is not very good—the low bits of successive
 * numbers are correlated.
 *
 * </para><para>
 * This generator was ported from the GNU Scientific Library.
 */

const igraph_rng_type_t igraph_rngtype_rand = {
    /* name= */      "RAND",
    /* min=  */      0,
    /* max=  */      0x7fffffffUL,
    /* init= */      igraph_rng_rand_init,
    /* destroy= */   igraph_rng_rand_destroy,
    /* seed= */      igraph_rng_rand_seed,
    /* get= */       igraph_rng_rand_get,
    /* get_real= */  igraph_rng_rand_get_real,
    /* get_norm= */  0,
    /* get_geom= */  0,
    /* get_binom= */ 0,
    /* get_exp= */   0,
    /* get_gamma= */ 0
};

/* ------------------------------------ */

#define N 624   /* Period parameters */
#define M 397

/* most significant w-r bits */
static const unsigned long UPPER_MASK = 0x80000000UL;

/* least significant r bits */
static const unsigned long LOWER_MASK = 0x7fffffffUL;

typedef struct {
    unsigned long mt[N];
    int mti;
} igraph_i_rng_mt19937_state_t;

static unsigned long int igraph_rng_mt19937_get(void *vstate) {
    igraph_i_rng_mt19937_state_t *state = vstate;

    unsigned long k ;
    unsigned long int *const mt = state->mt;

#define MAGIC(y) (((y)&0x1) ? 0x9908b0dfUL : 0)

    if (state->mti >= N) {
        /* generate N words at one time */
        int kk;

        for (kk = 0; kk < N - M; kk++) {
            unsigned long y = (mt[kk] & UPPER_MASK) | (mt[kk + 1] & LOWER_MASK);
            mt[kk] = mt[kk + M] ^ (y >> 1) ^ MAGIC(y);
        }
        for (; kk < N - 1; kk++) {
            unsigned long y = (mt[kk] & UPPER_MASK) | (mt[kk + 1] & LOWER_MASK);
            mt[kk] = mt[kk + (M - N)] ^ (y >> 1) ^ MAGIC(y);
        }

        {
            unsigned long y = (mt[N - 1] & UPPER_MASK) | (mt[0] & LOWER_MASK);
            mt[N - 1] = mt[M - 1] ^ (y >> 1) ^ MAGIC(y);
        }

        state->mti = 0;
    }

#undef MAGIC

    /* Tempering */

    k = mt[state->mti];
    k ^= (k >> 11);
    k ^= (k << 7) & 0x9d2c5680UL;
    k ^= (k << 15) & 0xefc60000UL;
    k ^= (k >> 18);

    state->mti++;

    return k;
}

static igraph_real_t igraph_rng_mt19937_get_real(void *vstate) {
    return igraph_rng_mt19937_get (vstate) / 4294967296.0 ;
}

static int igraph_rng_mt19937_seed(void *vstate, unsigned long int seed) {
    igraph_i_rng_mt19937_state_t *state = vstate;
    int i;

    memset(state, 0, sizeof(igraph_i_rng_mt19937_state_t));

    if (seed == 0) {
        seed = 4357;   /* the default seed is 4357 */
    }
    state->mt[0] = seed & 0xffffffffUL;

    for (i = 1; i < N; i++) {
        /* See Knuth's "Art of Computer Programming" Vol. 2, 3rd
           Ed. p.106 for multiplier. */
        state->mt[i] =
            (1812433253UL * (state->mt[i - 1] ^ (state->mt[i - 1] >> 30)) +
             (unsigned long) i);
        state->mt[i] &= 0xffffffffUL;
    }

    state->mti = i;
    return IGRAPH_SUCCESS;
}

static int igraph_rng_mt19937_init(void **state) {
    igraph_i_rng_mt19937_state_t *st;

    st = IGRAPH_CALLOC(1, igraph_i_rng_mt19937_state_t);
    if (!st) {
        IGRAPH_ERROR("Cannot initialize RNG", IGRAPH_ENOMEM);
    }
    (*state) = st;

    igraph_rng_mt19937_seed(st, 0);

    return IGRAPH_SUCCESS;
}

static void igraph_rng_mt19937_destroy(void *vstate) {
    igraph_i_rng_mt19937_state_t *state =
        (igraph_i_rng_mt19937_state_t*) vstate;
    IGRAPH_FREE(state);
}

/**
 * \var igraph_rngtype_mt19937
 * \brief The MT19937 random number generator.
 *
 * The MT19937 generator of Makoto Matsumoto and Takuji Nishimura is a
 * variant of the twisted generalized feedback shift-register
 * algorithm, and is known as the “Mersenne Twister” generator. It has
 * a Mersenne prime period of 2^19937 - 1 (about 10^6000) and is
 * equi-distributed in 623 dimensions. It has passed the diehard
 * statistical tests. It uses 624 words of state per generator and is
 * comparable in speed to the other generators. The original generator
 * used a default seed of 4357 and choosing \c s equal to zero in
 * \c gsl_rng_set reproduces this. Later versions switched to 5489 as the
 * default seed, you can choose this explicitly via \ref igraph_rng_seed()
 * instead if you require it.
 *
 * </para><para>
 * For more information see,
 * Makoto Matsumoto and Takuji Nishimura, “Mersenne Twister: A
 * 623-dimensionally equidistributed uniform pseudorandom number
 * generator”. ACM Transactions on Modeling and Computer Simulation,
 * Vol. 8, No. 1 (Jan. 1998), Pages 3–30
 *
 * </para><para>
 * The generator \c igraph_rngtype_mt19937 uses the second revision of the
 * seeding procedure published by the two authors above in 2002. The
 * original seeding procedures could cause spurious artifacts for some
 * seed values.
 *
 * </para><para>
 * This generator was ported from the GNU Scientific Library.
 */

const igraph_rng_type_t igraph_rngtype_mt19937 = {
    /* name= */      "MT19937",
    /* min=  */      0,
    /* max=  */      0xffffffffUL,
    /* init= */      igraph_rng_mt19937_init,
    /* destroy= */   igraph_rng_mt19937_destroy,
    /* seed= */      igraph_rng_mt19937_seed,
    /* get= */       igraph_rng_mt19937_get,
    /* get_real= */  igraph_rng_mt19937_get_real,
    /* get_norm= */  0,
    /* get_geom= */  0,
    /* get_binom= */ 0,
    /* get_exp= */   0,
    /* get_gamma= */ 0
};

#undef N
#undef M

/* ------------------------------------ */

igraph_i_rng_mt19937_state_t igraph_i_rng_default_state;

#define addr(a) (&a)

/**
 * \var igraph_i_rng_default
 * The default igraph random number generator
 *
 * This generator is used by all builtin igraph functions that need to
 * generate random numbers; e.g. all random graph generators.
 *
 * You can use \ref igraph_i_rng_default with \ref igraph_rng_seed()
 * to set its seed.
 *
 * You can change the default generator using the \ref
 * igraph_rng_set_default() function.
 */

IGRAPH_THREAD_LOCAL igraph_rng_t igraph_i_rng_default = {
    addr(igraph_rngtype_mt19937),
    addr(igraph_i_rng_default_state),
    /* def= */ 1
};

#undef addr

/**
 * \function igraph_rng_set_default
 * \brief Set the default igraph random number generator.
 *
 * \param rng The random number generator to use as default from now
 *    on. Calling \ref igraph_rng_destroy() on it, while it is still
 *    being used as the default will result in crashes and/or
 *    unpredictable results.
 *
 * Time complexity: O(1).
 */

void igraph_rng_set_default(igraph_rng_t *rng) {
    igraph_i_rng_default = (*rng);
}


/* ------------------------------------ */

/**
 * \function igraph_rng_default
 * \brief Query the default random number generator.
 *
 * \return A pointer to the default random number generator.
 *
 * \sa igraph_rng_set_default()
 */

igraph_rng_t *igraph_rng_default() {
    return &igraph_i_rng_default;
}

/* ------------------------------------ */

static double igraph_norm_rand(igraph_rng_t *rng);
static double igraph_rgeom(igraph_rng_t *rng, double p);
static double igraph_rbinom(igraph_rng_t *rng, double nin, double pp);
static double igraph_rexp(igraph_rng_t *rng, double rate);
static double igraph_rgamma(igraph_rng_t *rng, double shape, double scale);

/**
 * \function igraph_rng_init
 * \brief Initialize a random number generator.
 *
 * This function allocates memory for a random number generator, with
 * the given type, and sets its seed to the default.
 *
 * \param rng Pointer to an uninitialized RNG.
 * \param type The type of the RNG, like \ref igraph_rngtype_glibc2,
 * \ref igraph_rngtype_mt19937 or \ref igraph_rngtype_rand.
 * \return Error code.
 *
 * Time complexity: depends on the type of the generator, but usually
 * it should be O(1).
 */

int igraph_rng_init(igraph_rng_t *rng, const igraph_rng_type_t *type) {
    rng->type = type;
    IGRAPH_CHECK(rng->type->init(&rng->state));
    return IGRAPH_SUCCESS;
}

/**
 * \function igraph_rng_destroy
 * \brief Deallocate memory associated with a random number generator.
 *
 * \param rng The RNG to destroy. Do not destroy an RNG that is used
 *    as the default igraph RNG.
 *
 * Time complexity: O(1).
 */

void igraph_rng_destroy(igraph_rng_t *rng) {
    rng->type->destroy(rng->state);
}

/**
 * \function igraph_rng_seed
 * \brief Set the seed of a random number generator.
 *
 * \param rng The RNG.
 * \param seed The new seed.
 * \return Error code.
 *
 * Time complexity: usually O(1), but may depend on the type of the
 * RNG.
 */
int igraph_rng_seed(igraph_rng_t *rng, unsigned long int seed) {
    const igraph_rng_type_t *type = rng->type;
    rng->def = 0;
    IGRAPH_CHECK(type->seed(rng->state, seed));
    return IGRAPH_SUCCESS;
}

/**
 * \function igraph_rng_max
 * \brief Query the maximum possible integer for a random number generator.
 *
 * \param rng The RNG.
 * \return The largest possible integer that can be generated by
 *         calling \ref igraph_rng_get_integer() on the RNG.
 *
 * Time complexity: O(1).
 */

unsigned long int igraph_rng_max(igraph_rng_t *rng) {
    const igraph_rng_type_t *type = rng->type;
    return type->max;
}

/**
 * \function igraph_rng_min
 * \brief Query the minimum possible integer for a random number generator.
 *
 * <emphasis>This function will be removed in a future version. Assume zero
 * as the retun value.</emphasis>
 *
 * \param rng The RNG.
 * \return The smallest possible integer that can be generated by
 *         calling \ref igraph_rng_get_integer() on the RNG.
 *
 * Time complexity: O(1).
 *
 * \deprecated 0.9.3
 */

unsigned long int igraph_rng_min(igraph_rng_t *rng) {
    const igraph_rng_type_t *type = rng->type;
    IGRAPH_WARNING("igraph_rng_min() is deprecated; assume 0 as the return value.");
    return type->min;
}

/**
 * \function igraph_rng_name
 * \brief Query the type of a random number generator.
 *
 * \param rng The RNG.
 * \return The name of the type of the generator. Do not deallocate or
 *         change the returned string pointer.
 *
 * Time complexity: O(1).
 */

const char *igraph_rng_name(igraph_rng_t *rng) {
    const igraph_rng_type_t *type = rng->type;
    return type->name;
}

/**
 * \function igraph_rng_get_integer
 * \brief Generate an integer random number from an interval.
 *
 * \param rng Pointer to the RNG to use for the generation. Use \ref
 *        igraph_rng_default() here to use the default igraph RNG.
 * \param l Lower limit, inclusive, it can be negative as well.
 * \param h Upper limit, inclusive, it can be negative as well, but it
 *        should be at least <code>l</code>.
 * \return The generated random integer.
 *
 * Time complexity: depends on the generator, but should be usually
 * O(1).
 */

long int igraph_rng_get_integer(igraph_rng_t *rng,
                                long int l, long int h) {
    const igraph_rng_type_t *type = rng->type;
    /* We require the random integer to be in the range [l, h]. We do so by
     * first casting (truncate toward zero) to the range [0, h - l] and then add
     * l to arrive at the range [l, h]. That is, we calculate
     * (long)( r * (h - l + 1) ) + l
     * instead of
     * (long)( r * (h - l + 1) + l),
     * please note the difference in the parentheses.
     *
     * In the latter formulation, if l is negative, this would incorrectly lead
     * to the range [l + 1, h] instead of the desired [l, h] because negative
     * numbers are truncated towards zero when cast. For example, if l = -5, any
     * real in the range (-5, -4] would get cast to -4, not to -5.
     */
    if (type->get_real) {
        return (long int)(type->get_real(rng->state) * (h - l + 1)) + l;
    } else if (type->get) {
        unsigned long int max = type->max;
        return (long int)(type->get(rng->state) / ((double)max + 1) * (h - l + 1)) + l;
    }
    IGRAPH_FATAL("Internal random generator error");
}

/**
 * \function igraph_rng_get_normal
 * \brief Normally distributed random numbers.
 *
 * \param rng Pointer to the RNG to use. Use \ref igraph_rng_default()
 *        here to use the default igraph RNG.
 * \param m The mean.
 * \param s Standard deviation.
 * \return The generated normally distributed random number.
 *
 * Time complexity: depends on the type of the RNG.
 */

igraph_real_t igraph_rng_get_normal(igraph_rng_t *rng,
                                    igraph_real_t m, igraph_real_t s) {
    const igraph_rng_type_t *type = rng->type;
    if (type->get_norm) {
        return type->get_norm(rng->state) * s + m;
    } else {
        return igraph_norm_rand(rng) * s + m;
    }
}

/**
 * \function igraph_rng_get_unif
 * \brief Generate real, uniform random numbers from an interval.
 *
 * \param rng Pointer to the RNG to use. Use \ref igraph_rng_default()
 *        here to use the default igraph RNG.
 * \param l The lower bound, it can be negative.
 * \param h The upper bound, it can be negative, but it has to be
 *        larger than the lower bound.
 * \return The generated uniformly distributed random number.
 *
 * Time complexity: depends on the type of the RNG.
 */

igraph_real_t igraph_rng_get_unif(igraph_rng_t *rng,
                                  igraph_real_t l, igraph_real_t h) {
    const igraph_rng_type_t *type = rng->type;
    if (type->get_real) {
        return type->get_real(rng->state) * (h - l) + l;
    } else if (type->get) {
        unsigned long int max = type->max;
        return type->get(rng->state) / ((double)max + 1) * (double)(h - l) + l;
    }
    IGRAPH_FATAL("Internal random generator error");
}

/**
 * \function igraph_rng_get_unif01
 * \brief Generate real, uniform random number from the unit interval.
 *
 * \param rng Pointer to the RNG to use. Use \ref igraph_rng_default()
 *        here to use the default igraph RNG.
 * \return The generated uniformly distributed random number.
 *
 * Time complexity: depends on the type of the RNG.
 */

igraph_real_t igraph_rng_get_unif01(igraph_rng_t *rng) {
    const igraph_rng_type_t *type = rng->type;
    if (type->get_real) {
        return type->get_real(rng->state);
    } else if (type->get) {
        unsigned long int max = type->max;
        return type->get(rng->state) / ((double)max + 1);
    }
    IGRAPH_FATAL("Internal random generator error");
}

/**
 * \function igraph_rng_get_geom
 * \brief Generate geometrically distributed random numbers.
 *
 * \param rng Pointer to the RNG to use. Use \ref igraph_rng_default()
 *        here to use the default igraph RNG.
 * \param p The probability of success in each trial. Must be larger
 *        than zero and smaller or equal to 1.
 * \return The generated geometrically distributed random number.
 *
 * Time complexity: depends on the type of the RNG.
 */

igraph_real_t igraph_rng_get_geom(igraph_rng_t *rng, igraph_real_t p) {
    const igraph_rng_type_t *type = rng->type;
    if (type->get_geom) {
        return type->get_geom(rng->state, p);
    } else {
        return igraph_rgeom(rng, p);
    }
}

/**
 * \function igraph_rng_get_binom
 * \brief Generate binomially distributed random numbers.
 *
 * \param rng Pointer to the RNG to use. Use \ref igraph_rng_default()
 *        here to use the default igraph RNG.
 * \param n Number of observations.
 * \param p Probability of an event.
 * \return The generated binomially distributed random number.
 *
 * Time complexity: depends on the type of the RNG.
 */

igraph_real_t igraph_rng_get_binom(igraph_rng_t *rng, long int n,
                                   igraph_real_t p) {
    const igraph_rng_type_t *type = rng->type;
    if (type->get_binom) {
        return type->get_binom(rng->state, n, p);
    } else {
        return igraph_rbinom(rng, n, p);
    }
}

/**
 * \function igraph_rng_get_gamma
 * \brief Generate sample from a Gamma distribution.
 *
 * \param rng Pointer to the RNG to use. Use \ref igraph_rng_default()
 *        here to use the default igraph RNG.
 * \param shape Shape parameter.
 * \param scale Scale parameter.
 * \return The generated sample
 *
 * Time complexity: depends on RNG.
 */

igraph_real_t igraph_rng_get_gamma(igraph_rng_t *rng, igraph_real_t shape,
                                   igraph_real_t scale) {
    const igraph_rng_type_t *type = rng->type;
    if (type->get_gamma) {
        return type->get_gamma(rng->state, shape, scale);
    } else {
        return igraph_rgamma(rng, shape, scale);
    }
}

unsigned long int igraph_rng_get_int31(igraph_rng_t *rng) {
    const igraph_rng_type_t *type = rng->type;
    unsigned long int max = type->max;
    if (type->get && max == 0x7FFFFFFFUL) {
        return type->get(rng->state);
    } else if (type->get_real) {
        return (unsigned long int) (type->get_real(rng->state) * 0x7FFFFFFFUL);
    } else {
        return (unsigned long int) (igraph_rng_get_unif01(rng) * 0x7FFFFFFFUL);
    }
}

igraph_real_t igraph_rng_get_exp(igraph_rng_t *rng, igraph_real_t rate) {
    const igraph_rng_type_t *type = rng->type;
    if (type->get_exp) {
        return type->get_exp(rng->state, rate);
    } else {
        return igraph_rexp(rng, rate);
    }
}


/*
 * \ingroup internal
 *
 * This function appends the rest of the needed random number to the
 * result vector.
 */

static int igraph_i_random_sample_alga(igraph_vector_t *res,
                                       igraph_integer_t l, igraph_integer_t h,
                                       igraph_integer_t length) {
    igraph_real_t N = h - l + 1;
    igraph_real_t n = length;

    igraph_real_t top = N - n;
    igraph_real_t Nreal = N;
    igraph_real_t S = 0;
    igraph_real_t V, quot;

    l = l - 1;

    while (n >= 2) {
        V = RNG_UNIF01();
        S = 1;
        quot = top / Nreal;
        while (quot > V) {
            S += 1;
            top = -1.0 + top;
            Nreal = -1.0 + Nreal;
            quot = (quot * top) / Nreal;
        }
        l += S;
        igraph_vector_push_back(res, l);    /* allocated */
        Nreal = -1.0 + Nreal; n = -1 + n;
    }

    S = floor(round(Nreal) * RNG_UNIF01());
    l += S + 1;
    igraph_vector_push_back(res, l);  /* allocated */

    return IGRAPH_SUCCESS;
}

/**
 * \ingroup nongraph
 * \function igraph_random_sample
 * \brief Generates an increasing random sequence of integers.
 *
 * </para><para>
 * This function generates an increasing sequence of random integer
 * numbers from a given interval. The algorithm is taken literally
 * from (Vitter 1987). This method can be used for generating numbers from a
 * \em very large interval. It is primarily created for randomly
 * selecting some edges from the sometimes huge set of possible edges
 * in a large graph.
 * </para><para>
 * Note that the type of the lower and the upper limit is \c igraph_real_t,
 * not \c igraph_integer_t. This does not mean that you can pass fractional
 * numbers there; these values must still be integral, but we need the
 * longer range of \c igraph_real_t in several places in the library
 * (for instance, when generating Erdos-Renyi graphs).
 * \param res Pointer to an initialized vector. This will hold the
 *        result. It will be resized to the proper size.
 * \param l The lower limit of the generation interval (inclusive). This must
 *        be less than or equal to the upper limit, and it must be integral.
 *        Passing a fractional number here results in undefined behaviour.
 * \param h The upper limit of the generation interval (inclusive). This must
 *        be greater than or equal to the lower limit, and it must be integral.
 *        Passing a fractional number here results in undefined behaviour.
 * \param length The number of random integers to generate.
 * \return The error code \c IGRAPH_EINVAL is returned in each of the
 *         following cases: (1) The given lower limit is greater than the
 *         given upper limit, i.e. \c l &gt; \c h. (2) Assuming that
 *         \c l &lt; \c h and N is the sample size, the above error code is
 *         returned if N &gt; |\c h - \c l|, i.e. the sample size exceeds the
 *         size of the candidate pool.
 *
 * Time complexity: according to (Vitter 1987), the expected
 * running time is O(length).
 *
 * </para><para>
 * Reference:
 * \clist
 * \cli (Vitter 1987)
 *   J. S. Vitter. An efficient algorithm for sequential random sampling.
 *   \emb ACM Transactions on Mathematical Software, \eme 13(1):58--67, 1987.
 * \endclist
 *
 * \example examples/simple/igraph_random_sample.c
 */

int igraph_random_sample(igraph_vector_t *res, igraph_real_t l, igraph_real_t h,
                         igraph_integer_t length) {
    igraph_real_t N = h - l + 1;
    igraph_real_t n = length;
    int retval;

    igraph_real_t nreal = length;
    igraph_real_t ninv = (nreal != 0) ? 1.0 / nreal : 0.0;
    igraph_real_t Nreal = N;
    igraph_real_t Vprime;
    igraph_real_t qu1 = -n + 1 + N;
    igraph_real_t qu1real = -nreal + 1.0 + Nreal;
    igraph_real_t negalphainv = -13;
    igraph_real_t threshold = -negalphainv * n;
    igraph_real_t S;

    /* getting back some sense of sanity */
    if (l > h) {
        IGRAPH_ERROR("Lower limit is greater than upper limit", IGRAPH_EINVAL);
    }
    /* now we know that l <= h */
    if (length > N) {
        IGRAPH_ERROR("Sample size exceeds size of candidate pool", IGRAPH_EINVAL);
    }

    /* treat rare cases quickly */
    if (l == h) {
        IGRAPH_CHECK(igraph_vector_resize(res, 1));
        VECTOR(*res)[0] = l;
        return IGRAPH_SUCCESS;
    }
    if (length == 0) {
        igraph_vector_clear(res);
        return IGRAPH_SUCCESS;
    }
    if (length == N) {
        long int i = 0;
        IGRAPH_CHECK(igraph_vector_resize(res, length));
        for (i = 0; i < length; i++) {
            VECTOR(*res)[i] = l++;
        }
        return IGRAPH_SUCCESS;
    }

    igraph_vector_clear(res);
    IGRAPH_CHECK(igraph_vector_reserve(res, length));

    RNG_BEGIN();

    Vprime = exp(log(RNG_UNIF01()) * ninv);
    l = l - 1;

    while (n > 1 && threshold < N) {
        igraph_real_t X, U;
        igraph_real_t limit, t;
        igraph_real_t negSreal, y1, y2, top, bottom;
        igraph_real_t nmin1inv = 1.0 / (-1.0 + nreal);
        while (1) {
            while (1) {
                X = Nreal * (-Vprime + 1.0);
                S = floor(X);
                /* if (S==0) { S=1; } */
                if (S < qu1) {
                    break;
                }
                Vprime = exp(log(RNG_UNIF01()) * ninv);
            }
            U = RNG_UNIF01();
            negSreal = -S;

            y1 = exp(log(U * Nreal / qu1real) * nmin1inv);
            Vprime = y1 * (-X / Nreal + 1.0) * (qu1real / (negSreal + qu1real));
            if (Vprime <= 1.0) {
                break;
            }

            y2 = 1.0;
            top = -1.0 + Nreal;
            if (-1 + n > S) {
                bottom = -nreal + Nreal;
                limit = -S + N;
            } else {
                bottom = -1.0 + negSreal + Nreal;
                limit = qu1;
            }
            for (t = -1 + N; t >= limit; t--) {
                y2 = (y2 * top) / bottom;
                top = -1.0 + top;
                bottom = -1.0 + bottom;
            }
            if (Nreal / (-X + Nreal) >= y1 * exp(log(y2)*nmin1inv)) {
                Vprime = exp(log(RNG_UNIF01()) * nmin1inv);
                break;
            }
            Vprime = exp(log(RNG_UNIF01()) * ninv);
        }

        l += S + 1;
        igraph_vector_push_back(res, l);    /* allocated */
        N = -S + (-1 + N);   Nreal = negSreal + (-1.0 + Nreal);
        n = -1 + n;   nreal = -1.0 + nreal; ninv = nmin1inv;
        qu1 = -S + qu1; qu1real = negSreal + qu1real;
        threshold = threshold + negalphainv;
    }

    if (n > 1) {
        retval = igraph_i_random_sample_alga(res, (igraph_integer_t) l + 1,
                                             (igraph_integer_t) h,
                                             (igraph_integer_t) n);
    } else {
        retval = 0;
        S = floor(N * Vprime);
        l += S + 1;
        igraph_vector_push_back(res, l);    /* allocated */
    }

    RNG_END();

    return retval;
}

/*
 *  Mathlib : A C Library of Special Functions
 *  Copyright (C) 1998 Ross Ihaka
 *  Copyright (C) 2000 The R Development Core Team
 *  based on AS 111 (C) 1977 Royal Statistical Society
 *  and   on AS 241 (C) 1988 Royal Statistical Society
 *
 *  This program is free software; you can redistribute it and/or modify
 *  it under the terms of the GNU General Public License as published by
 *  the Free Software Foundation; either version 2 of the License, or
 *  (at your option) any later version.
 *
 *  This program is distributed in the hope that it will be useful,
 *  but WITHOUT ANY WARRANTY; without even the implied warranty of
 *  MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
 *  GNU General Public License for more details.
 *
 *  You should have received a copy of the GNU General Public License
 *  along with this program; if not, write to the Free Software
 *  Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA  02111-1307 USA.
 *
 *  SYNOPSIS
 *
 *  double qnorm5(double p, double mu, double sigma,
 *            int lower_tail, int log_p)
 *            {qnorm (..) is synonymous and preferred inside R}
 *
 *  DESCRIPTION
 *
 *  Compute the quantile function for the normal distribution.
 *
 *  For small to moderate probabilities, algorithm referenced
 *  below is used to obtain an initial approximation which is
 *  polished with a final Newton step.
 *
 *  For very large arguments, an algorithm of Wichura is used.
 *
 *  REFERENCE
 *
 *  Beasley, J. D. and S. G. Springer (1977).
 *  Algorithm AS 111: The percentage points of the normal distribution,
 *  Applied Statistics, 26, 118-121.
 *
 *      Wichura, M.J. (1988).
 *      Algorithm AS 241: The Percentage Points of the Normal Distribution.
 *      Applied Statistics, 37, 477-484.
 */

/*
 *  Mathlib : A C Library of Special Functions
 *  Copyright (C) 1998-2004  The R Development Core Team
 *
 *  This program is free software; you can redistribute it and/or modify
 *  it under the terms of the GNU General Public License as published by
 *  the Free Software Foundation; either version 2 of the License, or
 *  (at your option) any later version.
 *
 *  This program is distributed in the hope that it will be useful,
 *  but WITHOUT ANY WARRANTY; without even the implied warranty of
 *  MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
 *  GNU General Public License for more details.
 *
 *  You should have received a copy of the GNU General Public License
 *  along with this program; if not, write to the Free Software
 *  Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA  02111-1307  USA
 *
 */

/* The ISNAN macro is used in some of the code borrowed from R below. */
#define ISNAN isnan

/* Indicates that we use systems which support NaN values. */
#define IEEE_754 1

/* Private header file for use during compilation of Mathlib */
#ifndef MATHLIB_PRIVATE_H
#define MATHLIB_PRIVATE_H

#define ML_POSINF IGRAPH_INFINITY
#define ML_NEGINF -IGRAPH_INFINITY
#define ML_NAN    IGRAPH_NAN

#define ML_ERROR(x) /* nothing */
#define ML_UNDERFLOW    (DBL_MIN * DBL_MIN)
#define ML_VALID(x) (!ISNAN(x))

#define ME_NONE     0
/*  no error */
#define ME_DOMAIN   1
/*  argument out of domain */
#define ME_RANGE    2
/*  value out of range */
#define ME_NOCONV   4
/*  process did not converge */
#define ME_PRECISION    8
/*  does not have "full" precision */
#define ME_UNDERFLOW    16
/*  and underflow occurred (important for IEEE)*/

#define ML_ERR_return_NAN { ML_ERROR(ME_DOMAIN); return ML_NAN; }

/* Wilcoxon Rank Sum Distribution */

#define WILCOX_MAX 50

/* Wilcoxon Signed Rank Distribution */

#define SIGNRANK_MAX 50

/* Formerly private part of Mathlib.h */

/* always remap internal functions */
#define bd0             Rf_bd0
#define chebyshev_eval  Rf_chebyshev_eval
#define chebyshev_init  Rf_chebyshev_init
#define i1mach          Rf_i1mach
#define gammalims       Rf_gammalims
#define lfastchoose     Rf_lfastchoose
#define lgammacor       Rf_lgammacor
#define stirlerr        Rf_stirlerr

/* Chebyshev Series */

int chebyshev_init(double*, int, double);
double  chebyshev_eval(double, const double *, const int);

/* Gamma and Related Functions */

void    gammalims(double*, double*);
double  lgammacor(double); /* log(gamma) correction */
double  stirlerr(double);  /* Stirling expansion "error" */

double  lfastchoose(double, double);

double  bd0(double, double);

/* Consider adding these two to the API (Rmath.h): */
double  dbinom_raw(double, double, double, double, int);
double  dpois_raw (double, double, int);
double  pnchisq_raw(double, double, double, double, double, int);

int i1mach(int);

/* From toms708.c */
void bratio(double a, double b, double x, double y,
            double *w, double *w1, int *ierr);


#endif /* MATHLIB_PRIVATE_H */


/* Utilities for `dpq' handling (density/probability/quantile) */

/* give_log in "d";  log_p in "p" & "q" : */
#define give_log log_p
/* "DEFAULT" */
/* --------- */
#define R_D__0  (log_p ? ML_NEGINF : 0.)        /* 0 */
#define R_D__1  (log_p ? 0. : 1.)           /* 1 */
#define R_DT_0  (lower_tail ? R_D__0 : R_D__1)      /* 0 */
#define R_DT_1  (lower_tail ? R_D__1 : R_D__0)      /* 1 */

#define R_D_Lval(p) (lower_tail ? (p) : (1 - (p)))  /*  p  */
#define R_D_Cval(p) (lower_tail ? (1 - (p)) : (p))  /*  1 - p */

#define R_D_val(x)  (log_p  ? log(x) : (x))     /*  x  in pF(x,..) */
#define R_D_qIv(p)  (log_p  ? exp(p) : (p))     /*  p  in qF(p,..) */
#define R_D_exp(x)  (log_p  ?  (x)   : exp(x))  /* exp(x) */
#define R_D_log(p)  (log_p  ?  (p)   : log(p))  /* log(p) */
#define R_D_Clog(p) (log_p  ? log1p(-(p)) : (1 - (p)))/* [log](1-p) */

/* log(1-exp(x)):  R_D_LExp(x) == (log1p(- R_D_qIv(x))) but even more stable:*/
#define R_D_LExp(x)     (log_p ? R_Log1_Exp(x) : log1p(-x))

/*till 1.8.x:
 * #define R_DT_val(x)  R_D_val(R_D_Lval(x))
 * #define R_DT_Cval(x) R_D_val(R_D_Cval(x)) */
#define R_DT_val(x) (lower_tail ? R_D_val(x)  : R_D_Clog(x))
#define R_DT_Cval(x)    (lower_tail ? R_D_Clog(x) : R_D_val(x))

/*#define R_DT_qIv(p)   R_D_Lval(R_D_qIv(p))         *  p  in qF ! */
#define R_DT_qIv(p) (log_p ? (lower_tail ? exp(p) : - expm1(p)) \
                     : R_D_Lval(p))

/*#define R_DT_CIv(p)   R_D_Cval(R_D_qIv(p))         *  1 - p in qF */
#define R_DT_CIv(p) (log_p ? (lower_tail ? -expm1(p) : exp(p)) \
                     : R_D_Cval(p))

#define R_DT_exp(x) R_D_exp(R_D_Lval(x))        /* exp(x) */
#define R_DT_Cexp(x)    R_D_exp(R_D_Cval(x))        /* exp(1 - x) */

#define R_DT_log(p) (lower_tail? R_D_log(p) : R_D_LExp(p))/* log(p) in qF */
#define R_DT_Clog(p)    (lower_tail? R_D_LExp(p): R_D_log(p))/* log(1-p) in qF*/
#define R_DT_Log(p) (lower_tail? (p) : R_Log1_Exp(p))
/* ==   R_DT_log when we already "know" log_p == TRUE :*/

#define R_Q_P01_check(p)            \
    if ((log_p  && p > 0) ||            \
        (!log_p && (p < 0 || p > 1)) )      \
        ML_ERR_return_NAN

/* additions for density functions (C.Loader) */
#define R_D_fexp(f,x)     (give_log ? -0.5*log(f)+(x) : exp(x)/sqrt(f))
#define R_D_forceint(x)   floor((x) + 0.5)
#define R_D_nonint(x)     (fabs((x) - floor((x)+0.5)) > 1e-7)
/* [neg]ative or [non int]eger : */
#define R_D_negInonint(x) (x < 0. || R_D_nonint(x))

#define R_D_nonint_check(x)                 \
    if(R_D_nonint(x)) {                  \
        MATHLIB_WARNING("non-integer x = %f", x);   \
        return R_D__0;                  \
    }

double igraph_qnorm5(double p, double mu, double sigma, int lower_tail, int log_p) {
    double p_, q, r, val;

#ifdef IEEE_754
    if (ISNAN(p) || ISNAN(mu) || ISNAN(sigma)) {
        return p + mu + sigma;
    }
#endif
    if (p == R_DT_0) {
        return ML_NEGINF;
    }
    if (p == R_DT_1) {
        return ML_POSINF;
    }
    R_Q_P01_check(p);

    if (sigma  < 0) {
        ML_ERR_return_NAN;
    }
    if (sigma == 0) {
        return mu;
    }

    p_ = R_DT_qIv(p);/* real lower_tail prob. p */
    q = p_ - 0.5;

    /*-- use AS 241 --- */
    /* double ppnd16_(double *p, long *ifault)*/
    /*      ALGORITHM AS241  APPL. STATIST. (1988) VOL. 37, NO. 3

            Produces the normal deviate Z corresponding to a given lower
            tail area of P; Z is accurate to about 1 part in 10**16.

            (original fortran code used PARAMETER(..) for the coefficients
             and provided hash codes for checking them...)
    */
    if (fabs(q) <= .425) {/* 0.075 <= p <= 0.925 */
        r = .180625 - q * q;
        val =
            q * (((((((r * 2509.0809287301226727 +
                       33430.575583588128105) * r + 67265.770927008700853) * r +
                     45921.953931549871457) * r + 13731.693765509461125) * r +
                   1971.5909503065514427) * r + 133.14166789178437745) * r +
                 3.387132872796366608)
            / (((((((r * 5226.495278852854561 +
                     28729.085735721942674) * r + 39307.89580009271061) * r +
                   21213.794301586595867) * r + 5394.1960214247511077) * r +
                 687.1870074920579083) * r + 42.313330701600911252) * r + 1.);
    } else { /* closer than 0.075 from {0,1} boundary */

        /* r = min(p, 1-p) < 0.075 */
        if (q > 0) {
            r = R_DT_CIv(p);    /* 1-p */
        } else {
            r = p_;    /* = R_DT_Iv(p) ^=  p */
        }

        r = sqrt(- ((log_p &&
                     ((lower_tail && q <= 0) || (!lower_tail && q > 0))) ?
                    p : /* else */ log(r)));
        /* r = sqrt(-log(r))  <==>  min(p, 1-p) = exp( - r^2 ) */

        if (r <= 5.) { /* <==> min(p,1-p) >= exp(-25) ~= 1.3888e-11 */
            r += -1.6;
            val = (((((((r * 7.7454501427834140764e-4 +
                         .0227238449892691845833) * r + .24178072517745061177) *
                       r + 1.27045825245236838258) * r +
                      3.64784832476320460504) * r + 5.7694972214606914055) *
                    r + 4.6303378461565452959) * r +
                   1.42343711074968357734)
                  / (((((((r *
                           1.05075007164441684324e-9 + 5.475938084995344946e-4) *
                          r + .0151986665636164571966) * r +
                         .14810397642748007459) * r + .68976733498510000455) *
                       r + 1.6763848301838038494) * r +
                      2.05319162663775882187) * r + 1.);
        } else { /* very close to  0 or 1 */
            r += -5.;
            val = (((((((r * 2.01033439929228813265e-7 +
                         2.71155556874348757815e-5) * r +
                        .0012426609473880784386) * r + .026532189526576123093) *
                      r + .29656057182850489123) * r +
                     1.7848265399172913358) * r + 5.4637849111641143699) *
                   r + 6.6579046435011037772)
                  / (((((((r *
                           2.04426310338993978564e-15 + 1.4215117583164458887e-7) *
                          r + 1.8463183175100546818e-5) * r +
                         7.868691311456132591e-4) * r + .0148753612908506148525)
                       * r + .13692988092273580531) * r +
                      .59983220655588793769) * r + 1.);
        }

        if (q < 0.0) {
            val = -val;
        }
        /* return (q >= 0.)? r : -r ;*/
    }
    return mu + sigma * val;
}

static double fsign(double x, double y) {
#ifdef IEEE_754
    if (ISNAN(x) || ISNAN(y)) {
        return x + y;
    }
#endif
    return ((y >= 0) ? fabs(x) : -fabs(x));
}

static int imax2(int x, int y) {
    return (x < y) ? y : x;
}

static int imin2(int x, int y) {
    return (x < y) ? x : y;
}

static double igraph_norm_rand(igraph_rng_t *rng) {

    double u1;

#define BIG 134217728 /* 2^27 */
    /* unif_rand() alone is not of high enough precision */
    u1 = igraph_rng_get_unif01(rng);
    u1 = (int)(BIG * u1) + igraph_rng_get_unif01(rng);
    return igraph_qnorm5(u1 / BIG, 0.0, 1.0, 1, 0);
}

/*
 *  Mathlib : A C Library of Special Functions
 *  Copyright (C) 1998 Ross Ihaka
 *  Copyright (C) 2000-2002 the R Development Core Team
 *
 *  This program is free software; you can redistribute it and/or modify
 *  it under the terms of the GNU General Public License as published by
 *  the Free Software Foundation; either version 2 of the License, or
 *  (at your option) any later version.
 *
 *  This program is distributed in the hope that it will be useful,
 *  but WITHOUT ANY WARRANTY; without even the implied warranty of
 *  MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
 *  GNU General Public License for more details.
 *
 *  You should have received a copy of the GNU General Public License
 *  along with this program; if not, write to the Free Software
 *  Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA  02111-1307 USA.
 *
 *  SYNOPSIS
 *
 *    #include <Rmath.h>
 *    double exp_rand(void);
 *
 *  DESCRIPTION
 *
 *    Random variates from the standard exponential distribution.
 *
 *  REFERENCE
 *
 *    Ahrens, J.H. and Dieter, U. (1972).
 *    Computer methods for sampling from the exponential and
 *    normal distributions.
 *    Comm. ACM, 15, 873-882.
 */

double igraph_exp_rand(igraph_rng_t *rng) {
    /* q[k-1] = sum(log(2)^k / k!)  k=1,..,n, */
    /* The highest n (here 8) is determined by q[n-1] = 1.0 */
    /* within standard precision */
    const double q[] = {
        0.6931471805599453,
        0.9333736875190459,
        0.9888777961838675,
        0.9984959252914960,
        0.9998292811061389,
        0.9999833164100727,
        0.9999985691438767,
        0.9999998906925558,
        0.9999999924734159,
        0.9999999995283275,
        0.9999999999728814,
        0.9999999999985598,
        0.9999999999999289,
        0.9999999999999968,
        0.9999999999999999,
        1.0000000000000000
    };
    double a, u, ustar, umin;
    int i;

    a = 0.;
    /* precaution if u = 0 is ever returned */
    u = igraph_rng_get_unif01(rng);
    while (u <= 0.0 || u >= 1.0) {
        u = igraph_rng_get_unif01(rng);
    }
    for (;;) {
        u += u;
        if (u > 1.0) {
            break;
        }
        a += q[0];
    }
    u -= 1.;

    if (u <= q[0]) {
        return a + u;
    }

    i = 0;
    ustar = igraph_rng_get_unif01(rng);
    umin = ustar;
    do {
        ustar = igraph_rng_get_unif01(rng);
        if (ustar < umin) {
            umin = ustar;
        }
        i++;
    } while (u > q[i]);
    return a + umin * q[0];
}

/*
 *  Mathlib : A C Library of Special Functions
 *  Copyright (C) 1998 Ross Ihaka
 *  Copyright (C) 2000-2001 The R Development Core Team
 *
 *  This program is free software; you can redistribute it and/or modify
 *  it under the terms of the GNU General Public License as published by
 *  the Free Software Foundation; either version 2 of the License, or
 *  (at your option) any later version.
 *
 *  This program is distributed in the hope that it will be useful,
 *  but WITHOUT ANY WARRANTY; without even the implied warranty of
 *  MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
 *  GNU General Public License for more details.
 *
 *  You should have received a copy of the GNU General Public License
 *  along with this program; if not, write to the Free Software
 *  Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA  02111-1307 USA.
 *
 *  SYNOPSIS
 *
 *    #include <Rmath.h>
 *    double rpois(double lambda)
 *
 *  DESCRIPTION
 *
 *    Random variates from the Poisson distribution.
 *
 *  REFERENCE
 *
 *    Ahrens, J.H. and Dieter, U. (1982).
 *    Computer generation of Poisson deviates
 *    from modified normal distributions.
 *    ACM Trans. Math. Software 8, 163-179.
 */

#define a0  -0.5
#define a1   0.3333333
#define a2  -0.2500068
#define a3   0.2000118
#define a4  -0.1661269
#define a5   0.1421878
#define a6  -0.1384794
#define a7   0.1250060

#define one_7   0.1428571428571428571
#define one_12  0.0833333333333333333
#define one_24  0.0416666666666666667

#define repeat for(;;)

#define FALSE 0
#define TRUE  1
#define M_1_SQRT_2PI    0.398942280401432677939946059934     /* 1/sqrt(2pi) */

static double igraph_rpois(igraph_rng_t *rng, double mu) {
    /* Factorial Table (0:9)! */
    const double fact[10] = {
        1., 1., 2., 6., 24., 120., 720., 5040., 40320., 362880.
    };

    /* These are static --- persistent between calls for same mu : */
    static IGRAPH_THREAD_LOCAL int l, m;

    static IGRAPH_THREAD_LOCAL double b1, b2, c, c0, c1, c2, c3;
    static IGRAPH_THREAD_LOCAL double pp[36], p0, p, q, s, d, omega;
    static IGRAPH_THREAD_LOCAL double big_l;/* integer "w/o overflow" */
    static IGRAPH_THREAD_LOCAL double muprev = 0., muprev2 = 0.;/*, muold    = 0.*/

    /* Local Vars  [initialize some for -Wall]: */
    double del, difmuk = 0., E = 0., fk = 0., fx, fy, g, px, py, t, u = 0., v, x;
    double pois = -1.;
    int k, kflag, big_mu, new_big_mu = FALSE;

    if (!igraph_finite(mu)) {
        ML_ERR_return_NAN;
    }

    if (mu <= 0.) {
        return 0.;
    }

    big_mu = mu >= 10.;
    if (big_mu) {
        new_big_mu = FALSE;
    }

    if (!(big_mu && mu == muprev)) {/* maybe compute new persistent par.s */

        if (big_mu) {
            new_big_mu = TRUE;
            /* Case A. (recalculation of s,d,l  because mu has changed):
             * The Poisson probabilities pk exceed the discrete normal
             * probabilities fk whenever k >= m(mu).
             */
            muprev = mu;
            s = sqrt(mu);
            d = 6. * mu * mu;
            big_l = floor(mu - 1.1484);
            /* = an upper bound to m(mu) for all mu >= 10.*/
        } else { /* Small mu ( < 10) -- not using normal approx. */

            /* Case B. (start new table and calculate p0 if necessary) */

            /*muprev = 0.;-* such that next time, mu != muprev ..*/
            if (mu != muprev) {
                muprev = mu;
                m = imax2(1, (int) mu);
                l = 0; /* pp[] is already ok up to pp[l] */
                q = p0 = p = exp(-mu);
            }

            repeat {
                /* Step U. uniform sample for inversion method */
                u = igraph_rng_get_unif01(rng);
                if (u <= p0) {
                    return 0.;
                }

                /* Step T. table comparison until the end pp[l] of the
                   pp-table of cumulative Poisson probabilities
                   (0.458 > ~= pp[9](= 0.45792971447) for mu=10 ) */
                if (l != 0) {
                    for (k = (u <= 0.458) ? 1 : imin2(l, m);  k <= l; k++)
                        if (u <= pp[k]) {
                            return (double)k;
                        }
                    if (l == 35) { /* u > pp[35] */
                        continue;
                    }
                }
                /* Step C. creation of new Poisson
                   probabilities p[l..] and their cumulatives q =: pp[k] */
                l++;
                for (k = l; k <= 35; k++) {
                    p *= mu / k;
                    q += p;
                    pp[k] = q;
                    if (u <= q) {
                        l = k;
                        return (double)k;
                    }
                }
                l = 35;
            } /* end(repeat) */
        }/* mu < 10 */

    } /* end {initialize persistent vars} */

    /* Only if mu >= 10 : ----------------------- */

    /* Step N. normal sample */
    g = mu + s * igraph_norm_rand(rng);/* norm_rand() ~ N(0,1), standard normal */

    if (g >= 0.) {
        pois = floor(g);
        /* Step I. immediate acceptance if pois is large enough */
        if (pois >= big_l) {
            return pois;
        }
        /* Step S. squeeze acceptance */
        fk = pois;
        difmuk = mu - fk;
        u = igraph_rng_get_unif01(rng); /* ~ U(0,1) - sample */
        if (d * u >= difmuk * difmuk * difmuk) {
            return pois;
        }
    }

    /* Step P. preparations for steps Q and H.
       (recalculations of parameters if necessary) */

    if (new_big_mu || mu != muprev2) {
        /* Careful! muprev2 is not always == muprev
        because one might have exited in step I or S
        */
        muprev2 = mu;
        omega = M_1_SQRT_2PI / s;
        /* The quantities b1, b2, c3, c2, c1, c0 are for the Hermite
         * approximations to the discrete normal probabilities fk. */

        b1 = one_24 / mu;
        b2 = 0.3 * b1 * b1;
        c3 = one_7 * b1 * b2;
        c2 = b2 - 15. * c3;
        c1 = b1 - 6. * b2 + 45. * c3;
        c0 = 1. - b1 + 3. * b2 - 15. * c3;
        c = 0.1069 / mu; /* guarantees majorization by the 'hat'-function. */
    }

    if (g >= 0.) {
        /* 'Subroutine' F is called (kflag=0 for correct return) */
        kflag = 0;
        goto Step_F;
    }


    repeat {
        /* Step E. Exponential Sample */

        E = igraph_exp_rand(rng);/* ~ Exp(1) (standard exponential) */

        /*  sample t from the laplace 'hat'
            (if t <= -0.6744 then pk < fk for all mu >= 10.) */
        u = 2 * igraph_rng_get_unif01(rng) - 1.;
        t = 1.8 + fsign(E, u);
        if (t > -0.6744) {
            pois = floor(mu + s * t);
            fk = pois;
            difmuk = mu - fk;

            /* 'subroutine' F is called (kflag=1 for correct return) */
            kflag = 1;

Step_F: /* 'subroutine' F : calculation of px,py,fx,fy. */

            if (pois < 10) { /* use factorials from table fact[] */
                px = -mu;
                py = pow(mu, pois) / fact[(int)pois];
            } else {
                /* Case pois >= 10 uses polynomial approximation
                   a0-a7 for accuracy when advisable */
                del = one_12 / fk;
                del = del * (1. - 4.8 * del * del);
                v = difmuk / fk;
                if (fabs(v) <= 0.25)
                    px = fk * v * v * (((((((a7 * v + a6) * v + a5) * v + a4) *
                                          v + a3) * v + a2) * v + a1) * v + a0)
                    - del;
                else { /* |v| > 1/4 */
                    px = fk * log(1. + v) - difmuk - del;
                }
                py = M_1_SQRT_2PI / sqrt(fk);
            }
            x = (0.5 - difmuk) / s;
            x *= x;/* x^2 */
            fx = -0.5 * x;
            fy = omega * (((c3 * x + c2) * x + c1) * x + c0);
            if (kflag > 0) {
                /* Step H. Hat acceptance (E is repeated on rejection) */
                if (c * fabs(u) <= py * exp(px + E) - fy * exp(fx + E)) {
                    break;
                }
            } else
                /* Step Q. Quotient acceptance (rare case) */
                if (fy - u * fy <= py * exp(px - fx)) {
                    break;
                }
        }/* t > -.67.. */
    }
    return pois;
}

#undef a1
#undef a2
#undef a3
#undef a4
#undef a5
#undef a6
#undef a7

static double igraph_rgeom(igraph_rng_t *rng, double p) {
    if (igraph_is_nan(p) || p <= 0 || p > 1) {
        ML_ERR_return_NAN;
    }

    return igraph_rpois(rng, igraph_exp_rand(rng) * ((1 - p) / p));
}

/* This is from nmath/rbinom.c */

#define repeat for(;;)

static double igraph_rbinom(igraph_rng_t *rng, double nin, double pp) {
    /* FIXME: These should become THREAD_specific globals : */

    static IGRAPH_THREAD_LOCAL double c, fm, npq, p1, p2, p3, p4, qn;
    static IGRAPH_THREAD_LOCAL double xl, xll, xlr, xm, xr;

    static IGRAPH_THREAD_LOCAL double psave = -1.0;
    static IGRAPH_THREAD_LOCAL int nsave = -1;
    static IGRAPH_THREAD_LOCAL int m;

    double f, f1, f2, u, v, w, w2, x, x1, x2, z, z2;
    double p, q, np, g, r, al, alv, amaxp, ffm, ynorm;
    int i, ix, k, n;

    if (!igraph_finite(nin)) {
        ML_ERR_return_NAN;
    }
    n = floor(nin + 0.5);
    if (n != nin) {
        ML_ERR_return_NAN;
    }

    if (!igraph_finite(pp) ||
        /* n=0, p=0, p=1 are not errors <TSL>*/
        n < 0 || pp < 0. || pp > 1.) {
        ML_ERR_return_NAN;
    }

    if (n == 0 || pp == 0.) {
        return 0;
    }
    if (pp == 1.) {
        return n;
    }

    p = fmin(pp, 1. - pp);
    q = 1. - p;
    np = n * p;
    r = p / q;
    g = r * (n + 1);

    /* Setup, perform only when parameters change [using static (globals): */

    /* FIXING: Want this thread safe
       -- use as little (thread globals) as possible
    */
    if (pp != psave || n != nsave) {
        psave = pp;
        nsave = n;
        if (np < 30.0) {
            /* inverse cdf logic for mean less than 30 */
            qn = pow(q, (double) n);
            goto L_np_small;
        } else {
            ffm = np + p;
            m = ffm;
            fm = m;
            npq = np * q;
            p1 = (int)(2.195 * sqrt(npq) - 4.6 * q) + 0.5;
            xm = fm + 0.5;
            xl = xm - p1;
            xr = xm + p1;
            c = 0.134 + 20.5 / (15.3 + fm);
            al = (ffm - xl) / (ffm - xl * p);
            xll = al * (1.0 + 0.5 * al);
            al = (xr - ffm) / (xr * q);
            xlr = al * (1.0 + 0.5 * al);
            p2 = p1 * (1.0 + c + c);
            p3 = p2 + c / xll;
            p4 = p3 + c / xlr;
        }
    } else if (n == nsave) {
        if (np < 30.0) {
            goto L_np_small;
        }
    }

    /*-------------------------- np = n*p >= 30 : ------------------- */
    repeat {
        u = igraph_rng_get_unif01(rng) * p4;
        v = igraph_rng_get_unif01(rng);
        /* triangular region */
        if (u <= p1) {
            ix = xm - p1 * v + u;
            goto finis;
        }
        /* parallelogram region */
        if (u <= p2) {
            x = xl + (u - p1) / c;
            v = v * c + 1.0 - fabs(xm - x) / p1;
            if (v > 1.0 || v <= 0.) {
                continue;
            }
            ix = x;
        } else {
            if (u > p3) { /* right tail */
                ix = xr - log(v) / xlr;
                if (ix > n) {
                    continue;
                }
                v = v * (u - p3) * xlr;
            } else {/* left tail */
                ix = xl + log(v) / xll;
                if (ix < 0) {
                    continue;
                }
                v = v * (u - p2) * xll;
            }
        }
        /* determine appropriate way to perform accept/reject test */
        k = abs(ix - m);
        if (k <= 20 || k >= npq / 2 - 1) {
            /* explicit evaluation */
            f = 1.0;
            if (m < ix) {
                for (i = m + 1; i <= ix; i++) {
                    f *= (g / i - r);
                }
            } else if (m != ix) {
                for (i = ix + 1; i <= m; i++) {
                    f /= (g / i - r);
                }
            }
            if (v <= f) {
                goto finis;
            }
        } else {
            /* squeezing using upper and lower bounds on log(f(x)) */
            amaxp = (k / npq) * ((k * (k / 3. + 0.625) + 0.1666666666666) / npq + 0.5);
            ynorm = -k * k / (2.0 * npq);
            alv = log(v);
            if (alv < ynorm - amaxp) {
                goto finis;
            }
            if (alv <= ynorm + amaxp) {
                /* Stirling's formula to machine accuracy */
                /* for the final acceptance/rejection test */
                x1 = ix + 1;
                f1 = fm + 1.0;
                z = n + 1 - fm;
                w = n - ix + 1.0;
                z2 = z * z;
                x2 = x1 * x1;
                f2 = f1 * f1;
                w2 = w * w;
                if (alv <= xm * log(f1 / x1) + (n - m + 0.5) * log(z / w) + (ix - m) * log(w * p / (x1 * q)) + (13860.0 - (462.0 - (132.0 - (99.0 - 140.0 / f2) / f2) / f2) / f2) / f1 / 166320.0 + (13860.0 - (462.0 - (132.0 - (99.0 - 140.0 / z2) / z2) / z2) / z2) / z / 166320.0 + (13860.0 - (462.0 - (132.0 - (99.0 - 140.0 / x2) / x2) / x2) / x2) / x1 / 166320.0 + (13860.0 - (462.0 - (132.0 - (99.0 - 140.0 / w2) / w2) / w2) / w2) / w / 166320.) {
                    goto finis;
                }
            }
        }
    }

L_np_small:
    /*---------------------- np = n*p < 30 : ------------------------- */

    repeat {
        ix = 0;
        f = qn;
        u = igraph_rng_get_unif01(rng);
        repeat {
            if (u < f) {
                goto finis;
            }
            if (ix > 110) {
                break;
            }
            u -= f;
            ix++;
            f *= (g / ix - r);
        }
    }
finis:
    if (psave > 0.5) {
        ix = n - ix;
    }
    return (double)ix;
}

static igraph_real_t igraph_rexp(igraph_rng_t *rng, double rate) {
    igraph_real_t scale = 1.0 / rate;
    if (!IGRAPH_FINITE(scale) || scale <= 0.0) {
        if (scale == 0.0) {
            return 0.0;
        }
        return IGRAPH_NAN;
    }
    return scale * igraph_exp_rand(rng);
}

/*
 *  Mathlib : A C Library of Special Functions
 *  Copyright (C) 1998 Ross Ihaka
 *  Copyright (C) 2000      The R Core Team
 *  Copyright (C) 2003      The R Foundation
 *
 *  This program is free software; you can redistribute it and/or modify
 *  it under the terms of the GNU General Public License as published by
 *  the Free Software Foundation; either version 2 of the License, or
 *  (at your option) any later version.
 *
 *  This program is distributed in the hope that it will be useful,
 *  but WITHOUT ANY WARRANTY; without even the implied warranty of
 *  MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
 *  GNU General Public License for more details.
 *
 *  You should have received a copy of the GNU General Public License
 *  along with this program; if not, a copy is available at
 *  http://www.r-project.org/Licenses/
 *
 *  SYNOPSIS
 *
 *      double dnorm4(double x, double mu, double sigma, int give_log)
 *            {dnorm (..) is synonymous and preferred inside R}
 *
 *  DESCRIPTION
 *
 *      Compute the density of the normal distribution.
 */

double igraph_dnorm(double x, double mu, double sigma, int give_log) {
#ifdef IEEE_754
    if (ISNAN(x) || ISNAN(mu) || ISNAN(sigma)) {
        return x + mu + sigma;
    }
#endif
    if (!igraph_finite(sigma)) {
        return R_D__0;
    }
    if (!igraph_finite(x) && mu == x) {
        return ML_NAN;    /* x-mu is NaN */
    }
    if (sigma <= 0) {
        if (sigma < 0) {
            ML_ERR_return_NAN;
        }
        /* sigma == 0 */
        return (x == mu) ? ML_POSINF : R_D__0;
    }
    x = (x - mu) / sigma;

    if (!igraph_finite(x)) {
        return R_D__0;
    }
    return (give_log ?
            -(M_LN_SQRT_2PI  +  0.5 * x * x + log(sigma)) :
            M_1_SQRT_2PI * exp(-0.5 * x * x)  /   sigma);
    /* M_1_SQRT_2PI = 1 / sqrt(2 * pi) */
}

/* This is from nmath/rgamma.c */

/*
 *  Mathlib : A C Library of Special Functions
 *  Copyright (C) 1998 Ross Ihaka
 *  Copyright (C) 2000--2008 The R Core Team
 *
 *  This program is free software; you can redistribute it and/or modify
 *  it under the terms of the GNU General Public License as published by
 *  the Free Software Foundation; either version 2 of the License, or
 *  (at your option) any later version.
 *
 *  This program is distributed in the hope that it will be useful,
 *  but WITHOUT ANY WARRANTY; without even the implied warranty of
 *  MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
 *  GNU General Public License for more details.
 *
 *  You should have received a copy of the GNU General Public License
 *  along with this program; if not, a copy is available at
 *  http://www.r-project.org/Licenses/
 *
 *  SYNOPSIS
 *
 *    #include <Rmath.h>
 *    double rgamma(double a, double scale);
 *
 *  DESCRIPTION
 *
 *    Random variates from the gamma distribution.
 *
 *  REFERENCES
 *
 *    [1] Shape parameter a >= 1.  Algorithm GD in:
 *
 *    Ahrens, J.H. and Dieter, U. (1982).
 *    Generating gamma variates by a modified
 *    rejection technique.
 *    Comm. ACM, 25, 47-54.
 *
 *
 *    [2] Shape parameter 0 < a < 1. Algorithm GS in:
 *
 *    Ahrens, J.H. and Dieter, U. (1974).
 *    Computer methods for sampling from gamma, beta,
 *    poisson and binomial distributions.
 *    Computing, 12, 223-246.
 *
 *    Input: a = parameter (mean) of the standard gamma distribution.
 *    Output: a variate from the gamma(a)-distribution
 */

static double igraph_rgamma(igraph_rng_t *rng, double a, double scale) {
    /* Constants : */
    static const double sqrt32 = 5.656854;
    static const double exp_m1 = 0.36787944117144232159;/* exp(-1) = 1/e */

    /* Coefficients q[k] - for q0 = sum(q[k]*a^(-k))
     * Coefficients a[k] - for q = q0+(t*t/2)*sum(a[k]*v^k)
     * Coefficients e[k] - for exp(q)-1 = sum(e[k]*q^k)
     */
    static const double q1 = 0.04166669;
    static const double q2 = 0.02083148;
    static const double q3 = 0.00801191;
    static const double q4 = 0.00144121;
    static const double q5 = -7.388e-5;
    static const double q6 = 2.4511e-4;
    static const double q7 = 2.424e-4;

    static const double a1 = 0.3333333;
    static const double a2 = -0.250003;
    static const double a3 = 0.2000062;
    static const double a4 = -0.1662921;
    static const double a5 = 0.1423657;
    static const double a6 = -0.1367177;
    static const double a7 = 0.1233795;

    /* State variables: */
    static IGRAPH_THREAD_LOCAL double aa = 0.;
    static IGRAPH_THREAD_LOCAL double aaa = 0.;
    static IGRAPH_THREAD_LOCAL double s, s2, d;    /* no. 1 (step 1) */
    static IGRAPH_THREAD_LOCAL double q0, b, si, c;/* no. 2 (step 4) */

    double e, p, q, r, t, u, v, w, x, ret_val;

    if (!igraph_finite(a) || !igraph_finite(scale) || a < 0.0 || scale <= 0.0) {
        if (scale == 0.) {
            return 0.;
        }
        ML_ERR_return_NAN;
    }

    if (a < 1.) { /* GS algorithm for parameters a < 1 */
        if (a == 0) {
            return 0.;
        }
        e = 1.0 + exp_m1 * a;
        repeat {
            p = e * igraph_rng_get_unif01(rng);
            if (p >= 1.0) {
                x = -log((e - p) / a);
                if (igraph_exp_rand(rng) >= (1.0 - a) * log(x)) {
                    break;
                }
            } else {
                x = exp(log(p) / a);
                if (igraph_exp_rand(rng) >= x) {
                    break;
                }
            }
        }
        return scale * x;
    }

    /* --- a >= 1 : GD algorithm --- */

    /* Step 1: Recalculations of s2, s, d if a has changed */
    if (a != aa) {
        aa = a;
        s2 = a - 0.5;
        s = sqrt(s2);
        d = sqrt32 - s * 12.0;
    }
    /* Step 2: t = standard normal deviate,
               x = (s,1/2) -normal deviate. */

    /* immediate acceptance (i) */
    t = igraph_norm_rand(rng);
    x = s + 0.5 * t;
    ret_val = x * x;
    if (t >= 0.0) {
        return scale * ret_val;
    }

    /* Step 3: u = 0,1 - uniform sample. squeeze acceptance (s) */
    u = igraph_rng_get_unif01(rng);
    if (d * u <= t * t * t) {
        return scale * ret_val;
    }

    /* Step 4: recalculations of q0, b, si, c if necessary */

    if (a != aaa) {
        aaa = a;
        r = 1.0 / a;
        q0 = ((((((q7 * r + q6) * r + q5) * r + q4) * r + q3) * r
               + q2) * r + q1) * r;

        /* Approximation depending on size of parameter a */
        /* The constants in the expressions for b, si and c */
        /* were established by numerical experiments */

        if (a <= 3.686) {
            b = 0.463 + s + 0.178 * s2;
            si = 1.235;
            c = 0.195 / s - 0.079 + 0.16 * s;
        } else if (a <= 13.022) {
            b = 1.654 + 0.0076 * s2;
            si = 1.68 / s + 0.275;
            c = 0.062 / s + 0.024;
        } else {
            b = 1.77;
            si = 0.75;
            c = 0.1515 / s;
        }
    }
    /* Step 5: no quotient test if x not positive */

    if (x > 0.0) {
        /* Step 6: calculation of v and quotient q */
        v = t / (s + s);
        if (fabs(v) <= 0.25)
            q = q0 + 0.5 * t * t * ((((((a7 * v + a6) * v + a5) * v + a4) * v
                                      + a3) * v + a2) * v + a1) * v;
        else {
            q = q0 - s * t + 0.25 * t * t + (s2 + s2) * log(1.0 + v);
        }


        /* Step 7: quotient acceptance (q) */
        if (log(1.0 - u) <= q) {
            return scale * ret_val;
        }
    }

    repeat {
        /* Step 8: e = standard exponential deviate
         *  u =  0,1 -uniform deviate
         *  t = (b,si)-double exponential (laplace) sample */
        e = igraph_exp_rand(rng);
        u = igraph_rng_get_unif01(rng);
        u = u + u - 1.0;
        if (u < 0.0) {
            t = b - si * e;
        } else {
            t = b + si * e;
        }
        /* Step  9:  rejection if t < tau(1) = -0.71874483771719 */
        if (t >= -0.71874483771719) {
            /* Step 10:  calculation of v and quotient q */
            v = t / (s + s);
            if (fabs(v) <= 0.25)
                q = q0 + 0.5 * t * t *
                ((((((a7 * v + a6) * v + a5) * v + a4) * v + a3) * v
                  + a2) * v + a1) * v;
            else {
                q = q0 - s * t + 0.25 * t * t + (s2 + s2) * log(1.0 + v);
            }
            /* Step 11:  hat acceptance (h) */
            /* (if q not positive go to step 8) */
            if (q > 0.0) {
                w = expm1(q);
                /*  ^^^^^ original code had approximation with rel.err < 2e-7 */
                /* if t is rejected sample again at step 8 */
                if (c * fabs(u) <= w * exp(e - 0.5 * t * t)) {
                    break;
                }
            }
        }
    } /* repeat .. until  `t' is accepted */
    x = s + 0.5 * t;
    return scale * x * x;
}

int igraph_rng_get_dirichlet(igraph_rng_t *rng,
                             const igraph_vector_t *alpha,
                             igraph_vector_t *result) {

    igraph_integer_t len = igraph_vector_size(alpha);
    igraph_integer_t j;
    igraph_real_t sum = 0.0;

    if (len < 2) {
        IGRAPH_ERROR("Dirichlet parameter vector too short, must "
                     "have at least two entries", IGRAPH_EINVAL);
    }
    if (igraph_vector_min(alpha) <= 0) {
        IGRAPH_ERROR("Dirichlet concentration parameters must be positive",
                     IGRAPH_EINVAL);
    }

    IGRAPH_CHECK(igraph_vector_resize(result, len));

    RNG_BEGIN();

    for (j = 0; j < len; j++) {
        VECTOR(*result)[j] = igraph_rng_get_gamma(rng, VECTOR(*alpha)[j], 1.0);
        sum += VECTOR(*result)[j];
    }
    for (j = 0; j < len; j++) {
        VECTOR(*result)[j] /= sum;
    }

    RNG_END();

    return IGRAPH_SUCCESS;
}
