C API用于随机¶

可以通过Cython或C-wrapper库（如CFFI）访问下面的各种发行版。所有函数都接受 bitgen_t 作为他们的第一个论点。要从Cython或C访问这些，必须链接 npyrandom 类库是NumPy发行版的一部分，位于 numpy/random/lib .

type bitgen_t¶

这个 bitgen_t 保存位生成器的当前状态，并指向在推进状态时返回标准C类型的函数的指针。

struct bitgen:
    void *state
    npy_uint64 (*next_uint64)(void *st) nogil
    uint32_t (*next_uint32)(void *st) nogil
    double (*next_double)(void *st) nogil
    npy_uint64 (*next_raw)(void *st) nogil

ctypedef bitgen bitgen_t

见延伸有关使用这些函数的示例。

这些函数使用以下约定命名：

“标准”是指任何参数的参考值。例如，“标准统一”是指间隔上的均匀分布 0.0 到 1.0
“填充”功能将填充所提供的 out 具有 cnt 价值观。
名称中没有“标准”的函数需要额外的参数来描述分布。
zig 在名称上都是基于一个ziggurat查找算法，是用来代替计算的 log ，速度明显加快。非ziggurat变体用于角落案例和遗留兼容性。

double random_standard_uniform(bitgen_t *bitgen_state)¶

void random_standard_uniform_fill(bitgen_t *bitgen_state, npy_intp cnt, double *out)¶

double random_standard_exponential(bitgen_t *bitgen_state)¶

void random_standard_exponential_fill(bitgen_t *bitgen_state, npy_intp cnt, double *out)¶

double random_standard_normal(bitgen_t *bitgen_state)¶

void random_standard_normal_fill(bitgen_t *bitgen_state, npy_intp count, double *out)¶

void random_standard_normal_fill_f(bitgen_t *bitgen_state, npy_intp count, float *out)¶

double random_standard_gamma(bitgen_t *bitgen_state, double shape)¶

float random_standard_uniform_f(bitgen_t *bitgen_state)¶

void random_standard_uniform_fill_f(bitgen_t *bitgen_state, npy_intp cnt, float *out)¶

float random_standard_exponential_f(bitgen_t *bitgen_state)¶

void random_standard_exponential_fill_f(bitgen_t *bitgen_state, npy_intp cnt, float *out)¶

float random_standard_normal_f(bitgen_t *bitgen_state)¶

float random_standard_gamma_f(bitgen_t *bitgen_state, float shape)¶

double random_normal(bitgen_t *bitgen_state, double loc, double scale)¶

double random_gamma(bitgen_t *bitgen_state, double shape, double scale)¶

float random_gamma_f(bitgen_t *bitgen_state, float shape, float scale)¶

double random_exponential(bitgen_t *bitgen_state, double scale)¶

double random_uniform(bitgen_t *bitgen_state, double lower, double range)¶

double random_beta(bitgen_t *bitgen_state, double a, double b)¶

double random_chisquare(bitgen_t *bitgen_state, double df)¶

double random_f(bitgen_t *bitgen_state, double dfnum, double dfden)¶

double random_standard_cauchy(bitgen_t *bitgen_state)¶

double random_pareto(bitgen_t *bitgen_state, double a)¶

double random_weibull(bitgen_t *bitgen_state, double a)¶

double random_power(bitgen_t *bitgen_state, double a)¶

double random_laplace(bitgen_t *bitgen_state, double loc, double scale)¶

double random_gumbel(bitgen_t *bitgen_state, double loc, double scale)¶

double random_logistic(bitgen_t *bitgen_state, double loc, double scale)¶

double random_lognormal(bitgen_t *bitgen_state, double mean, double sigma)¶

double random_rayleigh(bitgen_t *bitgen_state, double mode)¶

double random_standard_t(bitgen_t *bitgen_state, double df)¶

double random_noncentral_chisquare(bitgen_t *bitgen_state, double df, double nonc)¶

double random_noncentral_f(bitgen_t *bitgen_state, double dfnum, double dfden, double nonc)¶

double random_wald(bitgen_t *bitgen_state, double mean, double scale)¶

double random_vonmises(bitgen_t *bitgen_state, double mu, double kappa)¶

double random_triangular(bitgen_t *bitgen_state, double left, double mode, double right)¶

npy_int64 random_poisson(bitgen_t *bitgen_state, double lam)¶

npy_int64 random_negative_binomial(bitgen_t *bitgen_state, double n, double p)¶

type binomial_t¶

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;

npy_int64 random_binomial(bitgen_t *bitgen_state, double p, npy_int64 n, binomial_t *binomial)¶

npy_int64 random_logseries(bitgen_t *bitgen_state, double p)¶

npy_int64 random_geometric_search(bitgen_t *bitgen_state, double p)¶

npy_int64 random_geometric_inversion(bitgen_t *bitgen_state, double p)¶

npy_int64 random_geometric(bitgen_t *bitgen_state, double p)¶

npy_int64 random_zipf(bitgen_t *bitgen_state, double a)¶

npy_int64 random_hypergeometric(bitgen_t *bitgen_state, npy_int64 good, npy_int64 bad, npy_int64 sample)¶

npy_uint64 random_interval(bitgen_t *bitgen_state, npy_uint64 max)¶

void random_multinomial(bitgen_t *bitgen_state, npy_int64 n, npy_int64 *mnix, double *pix, npy_intp d, binomial_t *binomial)¶

int random_multivariate_hypergeometric_count(bitgen_t *bitgen_state, npy_int64 total, size_t num_colors, npy_int64 *colors, npy_int64 nsample, size_t num_variates, npy_int64 *variates)¶

void random_multivariate_hypergeometric_marginals(bitgen_t *bitgen_state, npy_int64 total, size_t num_colors, npy_int64 *colors, npy_int64 nsample, size_t num_variates, npy_int64 *variates)¶

生成单个整数

npy_int64 random_positive_int64(bitgen_t *bitgen_state)¶

npy_int32 random_positive_int32(bitgen_t *bitgen_state)¶

npy_int64 random_positive_int(bitgen_t *bitgen_state)¶

npy_uint64 random_uint(bitgen_t *bitgen_state)¶

以闭区间生成随机uint64数 [关，关+rng] .

npy_uint64 random_bounded_uint64(bitgen_t *bitgen_state, npy_uint64 off, npy_uint64 rng, npy_uint64 mask, bool use_masked)¶

性能延伸