1 files changed, 107 insertions, 0 deletions
diff --git a/gsl-1.9/randist/gausstail.c b/gsl-1.9/randist/gausstail.c
new file mode 100644
index 0000000..a5b4727
--- /dev/null
+++ b/gsl-1.9/randist/gausstail.c
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+/* randist/gausstail.c
+ * 
+ * Copyright (C) 1996, 1997, 1998, 1999, 2000 James Theiler, Brian Gough
+ * 
+ * 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 <config.h>
+#include <math.h>
+#include <gsl/gsl_math.h>
+#include <gsl/gsl_rng.h>
+#include <gsl/gsl_randist.h>
+#include <gsl/gsl_sf_erf.h>
+
+double
+gsl_ran_gaussian_tail (const gsl_rng * r, const double a, const double sigma)
+{
+  /* Returns a gaussian random variable larger than a
+   * This implementation does one-sided upper-tailed deviates.
+   */
+
+  double s = a / sigma;
+
+  if (s < 1)
+    {
+      /* For small s, use a direct rejection method. The limit s < 1
+         can be adjusted to optimise the overall efficiency */
+
+      double x;
+
+      do
+        {
+          x = gsl_ran_gaussian (r, 1.0);
+        }
+      while (x < s);
+      return x * sigma;
+    }
+  else
+    {
+      /* Use the "supertail" deviates from the last two steps
+       * of Marsaglia's rectangle-wedge-tail method, as described
+       * in Knuth, v2, 3rd ed, pp 123-128.  (See also exercise 11, p139,
+       * and the solution, p586.)
+       */
+
+      double u, v, x;
+
+      do
+        {
+          u = gsl_rng_uniform (r);
+          do
+            {
+              v = gsl_rng_uniform (r);
+            }
+          while (v == 0.0);
+          x = sqrt (s * s - 2 * log (v));
+        }
+      while (x * u > s);
+      return x * sigma;
+    }
+}
+
+double
+gsl_ran_gaussian_tail_pdf (const double x, const double a, const double sigma)
+{
+  if (x < a)
+    {
+      return 0;
+    }
+  else
+    {
+      double N, p;
+      double u = x / sigma ;
+
+      double f = gsl_sf_erfc (a / (sqrt (2.0) * sigma));
+
+      N = 0.5 * f;
+
+      p = (1 / (N * sqrt (2 * M_PI) * sigma)) * exp (-u * u / 2);
+
+      return p;
+    }
+}
+
+double
+gsl_ran_ugaussian_tail (const gsl_rng * r, const double a)
+{
+  return gsl_ran_gaussian_tail (r, a, 1.0) ;
+}
+
+double
+gsl_ran_ugaussian_tail_pdf (const double x, const double a)
+{
+  return gsl_ran_gaussian_tail_pdf (x, a, 1.0) ;
+}