Example #1
0
File: beta.c Project: lemahdi/mglib
int
gsl_sf_beta_e(const double x, const double y, gsl_sf_result * result)
{
  if((x > 0 && y > 0) && x < 50.0 && y < 50.0) {
    /* Handle the easy case */
    gsl_sf_result gx, gy, gxy;
    gsl_sf_gamma_e(x, &gx);
    gsl_sf_gamma_e(y, &gy);
    gsl_sf_gamma_e(x+y, &gxy);
    result->val  = (gx.val*gy.val)/gxy.val;
    result->err  = gx.err * fabs(gy.val/gxy.val);
    result->err += gy.err * fabs(gx.val/gxy.val);
    result->err += fabs((gx.val*gy.val)/(gxy.val*gxy.val)) * gxy.err;
    result->err += 2.0 * GSL_DBL_EPSILON * fabs(result->val);
    return GSL_SUCCESS;
  }
  else if (isnegint(x) || isnegint(y)) {
    DOMAIN_ERROR(result);
  } else if (isnegint(x+y)) {  /* infinity in the denominator */
    result->val = 0.0;
    result->err = 0.0;
    return GSL_SUCCESS;
  } else {
    gsl_sf_result lb;
    double sgn;
    int stat_lb = gsl_sf_lnbeta_sgn_e(x, y, &lb, &sgn);
    if(stat_lb == GSL_SUCCESS) {
      int status = gsl_sf_exp_err_e(lb.val, lb.err, result);
      result->val *= sgn;
      return status;
    }
    else {
      result->val = 0.0;
      result->err = 0.0;
      return stat_lb;
    }
  }
}
Example #2
0
File: beta.c Project: lemahdi/mglib
int
gsl_sf_lnbeta_sgn_e(const double x, const double y, gsl_sf_result * result, double * sgn)
{
  /* CHECK_POINTER(result) */

  if(x == 0.0 || y == 0.0) {
    *sgn = 0.0;
    DOMAIN_ERROR(result);
  } else if (isnegint(x) || isnegint(y)) {
    *sgn = 0.0;
    DOMAIN_ERROR(result); /* not defined for negative integers */
  }

  /* See if we can handle the postive case with min/max < 0.2 */

  if (x > 0 && y > 0) {
    const double max = GSL_MAX(x,y);
    const double min = GSL_MIN(x,y);
    const double rat = min/max;
    
    if(rat < 0.2) {
      /* min << max, so be careful
       * with the subtraction
       */
      double lnpre_val;
      double lnpre_err;
      double lnpow_val;
      double lnpow_err;
      double t1, t2, t3;
      gsl_sf_result lnopr;
      gsl_sf_result gsx, gsy, gsxy;
      gsl_sf_gammastar_e(x, &gsx);
      gsl_sf_gammastar_e(y, &gsy);
      gsl_sf_gammastar_e(x+y, &gsxy);
      gsl_sf_log_1plusx_e(rat, &lnopr);
      lnpre_val = log(gsx.val*gsy.val/gsxy.val * M_SQRT2*M_SQRTPI);
      lnpre_err = gsx.err/gsx.val + gsy.err/gsy.val + gsxy.err/gsxy.val;
      t1 = min*log(rat);
      t2 = 0.5*log(min);
      t3 = (x+y-0.5)*lnopr.val;
      lnpow_val  = t1 - t2 - t3;
      lnpow_err  = GSL_DBL_EPSILON * (fabs(t1) + fabs(t2) + fabs(t3));
      lnpow_err += fabs(x+y-0.5) * lnopr.err;
      result->val  = lnpre_val + lnpow_val;
      result->err  = lnpre_err + lnpow_err;
      result->err += 2.0 * GSL_DBL_EPSILON * fabs(result->val);
      *sgn = 1.0;
      return GSL_SUCCESS;
    }
  }

  /* General case - Fallback */
  {
    gsl_sf_result lgx, lgy, lgxy;
    double sgx, sgy, sgxy, xy = x+y;
    int stat_gx  = gsl_sf_lngamma_sgn_e(x, &lgx, &sgx);
    int stat_gy  = gsl_sf_lngamma_sgn_e(y, &lgy, &sgy);
    int stat_gxy = gsl_sf_lngamma_sgn_e(xy, &lgxy, &sgxy);
    *sgn = sgx * sgy * sgxy;
    result->val  = lgx.val + lgy.val - lgxy.val;
    result->err  = lgx.err + lgy.err + lgxy.err;
    result->err += 2.0 * GSL_DBL_EPSILON * (fabs(lgx.val) + fabs(lgy.val) + fabs(lgxy.val));
    result->err += 2.0 * GSL_DBL_EPSILON * fabs(result->val);
    return GSL_ERROR_SELECT_3(stat_gx, stat_gy, stat_gxy);
  }
}
Example #3
0
int
gsl_sf_beta_inc_e(
    const double a,
    const double b,
    const double x,
    gsl_sf_result * result
)
{
    if(x < 0.0 || x > 1.0) {
        DOMAIN_ERROR(result);
    } else if (isnegint(a) || isnegint(b)) {
        DOMAIN_ERROR(result);
    } else if (isnegint(a+b)) {
        DOMAIN_ERROR(result);
    } else if(x == 0.0) {
        result->val = 0.0;
        result->err = 0.0;
        return GSL_SUCCESS;
    }
    else if(x == 1.0) {
        result->val = 1.0;
        result->err = 0.0;
        return GSL_SUCCESS;
    } else if (a <= 0 || b <= 0) {
        gsl_sf_result f, beta;
        int stat;
        const int stat_f = gsl_sf_hyperg_2F1_e(a, 1-b, a+1, x, &f);
        const int stat_beta = gsl_sf_beta_e(a, b, &beta);
        double prefactor = (pow(x, a) / a);
        result->val = prefactor * f.val / beta.val;
        result->err = fabs(prefactor) * f.err/ fabs(beta.val) + fabs(result->val/beta.val) * beta.err;

        stat = GSL_ERROR_SELECT_2(stat_f, stat_beta);
        if(stat == GSL_SUCCESS) {
            CHECK_UNDERFLOW(result);
        }
        return stat;
    } else {
        gsl_sf_result ln_beta;
        gsl_sf_result ln_x;
        gsl_sf_result ln_1mx;
        gsl_sf_result prefactor;
        const int stat_ln_beta = gsl_sf_lnbeta_e(a, b, &ln_beta);
        const int stat_ln_1mx = gsl_sf_log_1plusx_e(-x, &ln_1mx);
        const int stat_ln_x = gsl_sf_log_e(x, &ln_x);
        const int stat_ln = GSL_ERROR_SELECT_3(stat_ln_beta, stat_ln_1mx, stat_ln_x);

        const double ln_pre_val = -ln_beta.val + a * ln_x.val + b * ln_1mx.val;
        const double ln_pre_err =  ln_beta.err + fabs(a*ln_x.err) + fabs(b*ln_1mx.err);
        const int stat_exp = gsl_sf_exp_err_e(ln_pre_val, ln_pre_err, &prefactor);

        if(stat_ln != GSL_SUCCESS) {
            result->val = 0.0;
            result->err = 0.0;
            GSL_ERROR ("error", GSL_ESANITY);
        }

        if(x < (a + 1.0)/(a+b+2.0)) {
            /* Apply continued fraction directly. */
            gsl_sf_result cf;
            const int stat_cf = beta_cont_frac(a, b, x, &cf);
            int stat;
            result->val = prefactor.val * cf.val / a;
            result->err = (fabs(prefactor.err * cf.val) + fabs(prefactor.val * cf.err))/a;

            stat = GSL_ERROR_SELECT_2(stat_exp, stat_cf);
            if(stat == GSL_SUCCESS) {
                CHECK_UNDERFLOW(result);
            }
            return stat;
        }
        else {
            /* Apply continued fraction after hypergeometric transformation. */
            gsl_sf_result cf;
            const int stat_cf = beta_cont_frac(b, a, 1.0-x, &cf);
            int stat;
            const double term = prefactor.val * cf.val / b;
            result->val  = 1.0 - term;
            result->err  = fabs(prefactor.err * cf.val)/b;
            result->err += fabs(prefactor.val * cf.err)/b;
            result->err += 2.0 * GSL_DBL_EPSILON * (1.0 + fabs(term));
            stat = GSL_ERROR_SELECT_2(stat_exp, stat_cf);
            if(stat == GSL_SUCCESS) {
                CHECK_UNDERFLOW(result);
            }
            return stat;
        }
    }
}