octave: src/DLD-FUNCTIONS/rand.cc comparison

comparison src/DLD-FUNCTIONS/rand.cc @ 5730:109fdf7b3dcb

[project @ 2006-04-03 19:18:26 by jwe]

author	jwe
date	Mon, 03 Apr 2006 19:18:26 +0000
parents	cf44c749ba52
children	8d7162924bd3

comparison

equal deleted inserted replaced

-:e065f7c18bdc
+:109fdf7b3dcb
 #include "oct-obj.h"
 #include "unwind-prot.h"
 #include "utils.h"
 static octave_value
-do_rand (const octave_value_list& args, int nargin, const char *fcn)
+do_rand (const octave_value_list& args, int nargin, const char *fcn,
+	 bool additional_arg = false)
 {
 octave_value retval;
+NDArray a;
+int idx = 0;
 dim_vector dims;
+if (additional_arg)
+{
+if (nargin == 0)
+	{
+	  error ("%s: expecting at least one argument", fcn);
+	  goto done;
+	}
+else if (args(0).is_string())
+	additional_arg = false;
+else
+	{
+	  a = args(0).array_value ();
+	  if (error_state)
+	    {
+	      error ("%s: expecting scalar or matrix arguments", fcn);
+	      goto done;
+	    }
+	  idx++;
+	  nargin--;
+	}
+}
 switch (nargin)
 {
 case 0:
 {
-	dims.resize (2);
+	if (additional_arg)
+	  dims = a.dims ();
-	dims(0) = 1;
+	else
-	dims(1) = 1;
+	  {
+	    dims.resize (2);
+	    dims(0) = 1;
+	    dims(1) = 1;
+	  }
 	goto gen_matrix;
 }
 break;
 case 1:
 {
-	octave_value tmp = args(0);
+	octave_value tmp = args(idx);
 	if (tmp.is_string ())
 	  {
 	    std::string s_arg = tmp.string_value ();
 	    if (s_arg == "dist")
 	      {
 		retval = octave_rand::distribution ();
 	      }
-	    else if (s_arg == "seed" || s_arg == "state")
+	    else if (s_arg == "seed")
 	      {
 		retval = octave_rand::seed ();
 	      }
+	    else if (s_arg == "state" || s_arg == "twister")
+	      {
+		retval = octave_rand::state ();
+	      }
 	    else if (s_arg == "uniform")
 	      {
 		octave_rand::uniform_distribution ();
 	      }
 	    else if (s_arg == "normal")
 	      {
 		octave_rand::normal_distribution ();
+	      }
+	    else if (s_arg == "exponential")
+	      {
+		octave_rand::exponential_distribution ();
+	      }
+	    else if (s_arg == "poisson")
+	      {
+		octave_rand::poisson_distribution ();
+	      }
+	    else if (s_arg == "gamma")
+	      {
+		octave_rand::gamma_distribution ();
 	      }
 	    else
 	      error ("%s: unrecognized string argument", fcn);
 	  }
 	else if (tmp.is_scalar_type ())
 }
 break;
 default:
 {
-	octave_value tmp = args(0);
+	octave_value tmp = args(idx);
 	if (nargin == 2 && tmp.is_string ())
 	  {
 	    std::string ts = tmp.string_value ();
-	    if (ts == "seed" || ts == "state")
+	    if (ts == "seed")
 	      {
-		double d = args(1).double_value ();
+		double d = args(idx+1).double_value ();
 		if (! error_state)
 		  octave_rand::seed (d);
+	      }
+	    else if (ts == "state" || ts == "twister")
+	      {
+		ColumnVector s =
+		  ColumnVector (args(idx+1).vector_value(false, true));
+		if (! error_state)
+		  octave_rand::state (s);
 	      }
 	    else
 	      error ("%s: unrecognized string argument", fcn);
 	  }
 	else
 	  {
 	    dims.resize (nargin);
 	    for (int i = 0; i < nargin; i++)
 	      {
-		dims(i) = (octave_idx_type)args(i).int_value ();
+		dims(i) = (octave_idx_type)args(idx+i).int_value ();
 		if (error_state)
 		  {
 		    error ("%s: expecting integer arguments", fcn);
 		    goto done;
 gen_matrix:
 dims.chop_trailing_singletons ();
-return octave_rand::nd_array (dims);
+if (additional_arg)
+{
+if (a.length() == 1)
+	return octave_rand::nd_array (dims, a(0));
+else
+	{
+	  if (a.dims() != dims)
+	    {
+	      error ("%s: mismatch in argument size", fcn);
+	      return retval;
+	    }
+	  octave_idx_type len = a.length ();
+	  NDArray m (dims);
+	  double *v = m.fortran_vec ();
+	  for (octave_idx_type i = 0; i < len; i++)
+	    v[i] = octave_rand::scalar (a(i));
+	  return m;
+	}
+}
+else
+return octave_rand::nd_array (dims);
 }
 DEFUN_DLD (rand, args, ,
 "-*- texinfo -*-\n\
 @deftypefn {Loadable Function} {} rand (@var{x})\n\
 @deftypefnx {Loadable Function} {} rand (@var{n}, @var{m})\n\
-@deftypefnx {Loadable Function} {} rand (@code{\"seed\"}, @var{x})\n\
+@deftypefnx {Loadable Function} {} rand (\"state\", @var{x})\n\
+@deftypefnx {Loadable Function} {} rand (\"seed\", @var{x})\n\
 Return a matrix with random elements uniformly distributed on the\n\
 interval (0, 1).  The arguments are handled the same as the arguments\n\
-for @code{eye}.  In\n\
+for @code{eye}.\n\
-addition, you can set the seed for the random number generator using the\n\
+\n\
+You can query the state of the random number generator using the\n\
 form\n\
 \n\
 @example\n\
-rand (\"seed\", @var{x})\n\
+v = rand (\"state\")\n\
+@end example\n\
+\n\
+This returns a column vector @var{v} of length 625. Later, you can\n\
+restore the random number generator to the state @var{v}\n\
+using the form\n\
+\n\
+@example\n\
+rand (\"state\", v)\n\
 @end example\n\
 \n\
 @noindent\n\
-where @var{x} is a scalar value.  If called as\n\
+You may also initialize the state vector from an arbitrary vector of\n\
-\n\
+length <= 625 for @var{v}.  This new state will be a hash based on the\n\
-@example\n\
+the value of @var{v}, not @var{v} itself.\n\
-rand (\"seed\")\n\
+\n\
-@end example\n\
+By default, the generator is initialized from @code{/dev/urandom} if it is\n\
-\n\
+available, otherwise from cpu time, wall clock time and the current\n\
-@noindent\n\
+fraction of a second.\n\
-@code{rand} returns the current value of the seed.\n\
+\n\
+@code{rand} uses the Mersenne Twister with a period of 2^19937-1\n\
+(See M. Matsumoto and T. Nishimura, ``Mersenne Twister: A 623-dimensionally\n\
+equidistributed uniform pseudorandom number generator'', ACM Trans. on\n\
+Modeling and Computer Simulation Vol. 8, No. 1, Januray pp.3-30 1998,\n\
+@url{http://www.math.keio.ac.jp/~matumoto/emt.html}).\n\
+Do NOT use for CRYPTOGRAPHY without securely hashing several returned\n\
+values together, otherwise the generator state can be learned after\n\
+reading 624 consecutive values.\n\
+\n\
+@code{rand} includes a second random number generator, that was the\n\
+previous generator used in octave. The new generator is used by default\n\
+as it is significantly faster than the old generator, and produces\n\
+random numebrs with a significantly longer cycle time. However, in\n\
+some circumstances it might be desireable to obtain the same random\n\
+sequences as used by the old generators. To do this the keyword\n\
+\"seed\" is used to specify that the old generators should be use,\n\
+as in\n\
+\n\
+@example\n\
+rand (\"seed\", val)\n\
+@end example\n\
+\n\
+which sets the seed of the generator to @var{val}. The seed of the\n\
+generator can be queried with\n\
+\n\
+@example\n\
+s = rand (\"seed\")\n\
+@end example\n\
+\n\
+However, it should be noted that querying the seed will not cause\n\
+@code{rand} to use the old generators, only setting the seed will.\n\
+To cause @code{rand} to once again use the new generators, the\n\
+keyword \"state\" should be used to reset the state of the @code{rand}.\n\
+@seealso{randn,rande,randg,randp}\n\
 @end deftypefn")
 {
 octave_value retval;
 int nargin = args.length ();
 retval = do_rand (args, nargin, "rand");
 return retval;
 }
+/*
+%!test # 'state' can be a scalar
+%! rand('state',12); x = rand(1,4);
+%! rand('state',12); y = rand(1,4);
+%! assert(x,y);
+%!test # 'state' can be a vector
+%! rand('state',[12,13]); x=rand(1,4);
+%! rand('state',[12;13]); y=rand(1,4);
+%! assert(x,y);
+%!test # querying 'state' doesn't disturb sequence
+%! rand('state',12); rand(1,2); x=rand(1,2);
+%! rand('state',12); rand(1,2);
+%! s=rand('state'); y=rand(1,2);
+%! assert(x,y);
+%! rand('state',s); z=rand(1,2);
+%! assert(x,z);
+%!test # 'seed' must be a scalar
+%! rand('seed',12); x = rand(1,4);
+%! rand('seed',12); y = rand(1,4);
+%! assert(x,y);
+%!error(rand('seed',[12,13]))
+%!test # querying 'seed' returns a value which can be used later
+%! s=rand('seed'); x=rand(1,2);
+%! rand('seed',s); y=rand(1,2);
+%! assert(x,y);
+%!test # querying 'seed' doesn't disturb sequence
+%! rand('seed',12); rand(1,2); x=rand(1,2);
+%! rand('seed',12); rand(1,2);
+%! s=rand('seed'); y=rand(1,2);
+%! assert(x,y);
+%! rand('seed',s); z=rand(1,2);
+%! assert(x,z);
+*/
+/*
+%!test
+%! % statistical tests may fail occasionally.
+%! rand("state",12);
+%! x = rand(100000,1);
+%! assert(max(x)<1.); %*** Please report this!!! ***
+%! assert(min(x)>0.); %*** Please report this!!! ***
+%! assert(mean(x),0.5,0.0024);
+%! assert(var(x),1/48,0.0632);
+%! assert(skewness(x),0,0.012);
+%! assert(kurtosis(x),-6/5,0.0094);
+%!test
+%! % statistical tests may fail occasionally.
+%! rand("seed",12);
+%! x = rand(100000,1);
+%! assert(max(x)<1.); %*** Please report this!!! ***
+%! assert(min(x)>0.); %*** Please report this!!! ***
+%! assert(mean(x),0.5,0.0024);
+%! assert(var(x),1/48,0.0632);
+%! assert(skewness(x),0,0.012);
+%! assert(kurtosis(x),-6/5,0.0094);
+*/
 static std::string current_distribution = octave_rand::distribution ();
 static void
 reset_rand_generator (void *)
 DEFUN_DLD (randn, args, ,
 "-*- texinfo -*-\n\
 @deftypefn {Loadable Function} {} randn (@var{x})\n\
 @deftypefnx {Loadable Function} {} randn (@var{n}, @var{m})\n\
-@deftypefnx {Loadable Function} {} randn (@code{\"seed\"}, @var{x})\n\
+@deftypefnx {Loadable Function} {} randn (\"state\", @var{x})\n\
-Return a matrix with normally distributed random elements.  The\n\
+@deftypefnx {Loadable Function} {} randn (\"seed\", @var{x})\n\
-arguments are handled the same as the arguments for @code{eye}.  In\n\
+Return a matrix with normally distributed random elements. The\n\
-addition, you can set the seed for the random number generator using the\n\
+arguments are handled the same as the arguments for @code{rand}.\n\
-form\n\
+\n\
-\n\
+By default, @code{randn} uses a Marsaglia and Tsang Ziggurat technique to\n\
-@example\n\
+transform from a uniform to a normal distribution. (G. Marsaglia and\n\
-randn (\"seed\", @var{x})\n\
+W.W. Tsang, 'Ziggurat method for generating random variables',\n\
-@end example\n\
+J. Statistical Software, vol 5, 2000,\n\
-\n\
+@url{http://www.jstatsoft.org/v05/i08/})\n\
-@noindent\n\
+\n\
-where @var{x} is a scalar value.  If called as\n\
+@seealso{rand,rande,randg,randp}\n\
-\n\
-@example\n\
-randn (\"seed\")\n\
-@end example\n\
-\n\
-@noindent\n\
-@code{randn} returns the current value of the seed.\n\
 @end deftypefn")
 {
 octave_value retval;
 int nargin = args.length ();
 unwind_protect::run_frame ("randn");
 return retval;
 }
+/*
+%!test
+%! % statistical tests may fail occasionally.
+%! rand("state",12);
+%! x = randn(100000,1);
+%! assert(mean(x),0,0.01);
+%! assert(var(x),1,0.02);
+%! assert(skewness(x),0,0.02);
+%! assert(kurtosis(x),0,0.04);
+%!test
+%! % statistical tests may fail occasionally.
+%! rand("seed",12);
+%! x = randn(100000,1);
+%! assert(mean(x),0,0.01);
+%! assert(var(x),1,0.02);
+%! assert(skewness(x),0,0.02);
+%! assert(kurtosis(x),0,0.04);
+*/
+DEFUN_DLD (rande, args, ,
+"-*- texinfo -*-\n\
+@deftypefn {Loadable Function} {} rande (@var{x})\n\
+@deftypefnx {Loadable Function} {} rande (@var{n}, @var{m})\n\
+@deftypefnx {Loadable Function} {} rande (\"state\", @var{x})\n\
+@deftypefnx {Loadable Function} {} rande (\"seed\", @var{x})\n\
+Return a matrix with exponentially distributed random elements. The\n\
+arguments are handled the same as the arguments for @code{rand}.\n\
+\n\
+By default, @code{randn} uses a Marsaglia and Tsang Ziggurat technique to\n\
+transform from a uniform to a exponential distribution. (G. Marsaglia and\n\
+W.W. Tsang, 'Ziggurat method for generating random variables',\n\
+J. Statistical Software, vol 5, 2000,\n\
+@url{http://www.jstatsoft.org/v05/i08/})\n\
+@seealso{rand,randn,randg,randp}\n\
+@end deftypefn")
+{
+octave_value retval;
+int nargin = args.length ();
+unwind_protect::begin_frame ("rande");
+// This relies on the fact that elements are popped from the unwind
+// stack in the reverse of the order they are pushed
+// (i.e. current_distribution will be reset before calling
+// reset_rand_generator()).
+unwind_protect::add (reset_rand_generator, 0);
+unwind_protect_str (current_distribution);
+current_distribution = "exponential";
+octave_rand::distribution (current_distribution);
+retval = do_rand (args, nargin, "rande");
+unwind_protect::run_frame ("rande");
+return retval;
+}
+/*
+%!test
+%! % statistical tests may fail occasionally
+%! rand("state",12);
+%! x = rande(100000,1);
+%! assert(min(x)>0); % *** Please report this!!! ***
+%! assert(mean(x),1,0.01);
+%! assert(var(x),1,0.03);
+%! assert(skewness(x),2,0.06);
+%! assert(kurtosis(x),6,0.7);
+%!test
+%! % statistical tests may fail occasionally
+%! rand("seed",12);
+%! x = rande(100000,1);
+%! assert(min(x)>0); % *** Please report this!!! ***
+%! assert(mean(x),1,0.01);
+%! assert(var(x),1,0.03);
+%! assert(skewness(x),2,0.06);
+%! assert(kurtosis(x),6,0.7);
+*/
+DEFUN_DLD (randg, args, ,
+"-*- texinfo -*-\n\
+@deftypefn {Loadable Function} {} randg (@var{a}, @var{x})\n\
+@deftypefnx {Loadable Function} {} randg (@var{a}, @var{n}, @var{m})\n\
+@deftypefnx {Loadable Function} {} randg (\"state\", @var{x})\n\
+@deftypefnx {Loadable Function} {} randg (\"seed\", @var{x})\n\
+Return a matrix with @code{gamma(@var{a},1)} distributed random elements.\n\
+The arguments are handled the same as the arguments for @code{rand},\n\
+except for the argument @var{a}.\n\
+\n\
+This can be used to generate many distributions:\n\
+\n\
+@table @asis\n\
+@item @code{gamma (a,b)} for @code{a > -1}, @code{b > 0}\n\
+@example\n\
+r = b*randg(a)\n\
+@end example\n\
+@item @code{beta(a,b)} for @code{a > -1}, @code{b > -1}\n\
+@example\n\
+r1 = randg(a,1)\n\
+r = r1 / (r1 + randg(b,1))\n\
+@end example\n\
+@item @code{Erlang(a, n)}\n\
+@example\n\
+r = a*randg(n)\n\
+@end example\n\
+@item @code{chisq(df)} for @code{df > 0}\n\
+@example\n\
+r = 2*randg(df/2)\n\
+@end example\n\
+@item @code{t(df)} for @code{0 < df < inf} (use randn if df is infinite)\n\
+@example\n\
+r = randn() / sqrt(2*randg(df/2)/df)\n\
+@end example\n\
+@item @code{F(n1,n2)} for @code{0 < n1}, @code{0 < n2}\n\
+@example\n\
+r1 = 2*randg(n1/2)/n1 or 1 if n1 is infinite\n\
+r2 = 2*randg(n2/2)/n2 or 1 if n2 is infinite\n\
+r = r1 / r2\n\n\
+@end example\n\
+@item negative @code{binomial (n, p)} for @code{n > 0}, @code{0 < p <= 1}\n\
+@example\n\
+r = randp((1-p)/p * randg(n))\n\
+@end example\n\
+@item non-central @code{chisq(df,L)}, for @code{df >= 0} and @code{L > 0}\n\
+(use chisq if @code{L = 0})\n\
+@example\n\
+r = randp(L/2)\n\
+r(r > 0) = 2*randg(r(r > 0))\n\
+r(df > 0) += 2*randg(df(df > 0)/2)\n\
+@end example\n\
+@item @code{Dirichlet(a1,...,ak)}\n\
+@example\n\
+r = (randg(a1),...,randg(ak))\n\
+r = r / sum(r)\n\
+@end example\n\
+@end table\n\
+@seealso{rand,randn,rande,randp}\n\
+@end deftypefn")
+{
+octave_value retval;
+int nargin = args.length ();
+if (nargin < 1)
+error ("randg: insufficient arguments");
+else
+{
+unwind_protect::begin_frame ("randg");
+// This relies on the fact that elements are popped from the unwind
+// stack in the reverse of the order they are pushed
+// (i.e. current_distribution will be reset before calling
+// reset_rand_generator()).
+unwind_protect::add (reset_rand_generator, 0);
+unwind_protect_str (current_distribution);
+current_distribution = "gamma";
+octave_rand::distribution (current_distribution);
+retval = do_rand (args, nargin, "randg", true);
+unwind_protect::run_frame ("randg");
+}
+return retval;
+}
+/*
+%!test
+%! rand("state",12)
+%!assert(randg([-inf,-1,0,inf,nan]),[nan,nan,nan,nan,nan]) % *** Please report
+%!test
+%! % statistical tests may fail occasionally.
+%! a=0.1; x = randg(a,100000,1);
+%! assert(mean(x),    a,         0.01);
+%! assert(var(x),     a,         0.01);
+%! assert(skewness(x),2/sqrt(a), 1.);
+%! assert(kurtosis(x),6/a,       50.);
+%!test
+%! % statistical tests may fail occasionally.
+%! a=0.95; x = randg(a,100000,1);
+%! assert(mean(x),    a,         0.01);
+%! assert(var(x),     a,         0.04);
+%! assert(skewness(x),2/sqrt(a), 0.2);
+%! assert(kurtosis(x),6/a,       2.);
+%!test
+%! % statistical tests may fail occasionally.
+%! a=1; x = randg(a,100000,1);
+%! assert(mean(x),a,             0.01);
+%! assert(var(x),a,              0.04);
+%! assert(skewness(x),2/sqrt(a), 0.2);
+%! assert(kurtosis(x),6/a,       2.);
+%!test
+%! % statistical tests may fail occasionally.
+%! a=10; x = randg(a,100000,1);
+%! assert(mean(x),    a,         0.1);
+%! assert(var(x),     a,         0.5);
+%! assert(skewness(x),2/sqrt(a), 0.1);
+%! assert(kurtosis(x),6/a,       0.5);
+%!test
+%! % statistical tests may fail occasionally.
+%! a=100; x = randg(a,100000,1);
+%! assert(mean(x),    a,         0.2);
+%! assert(var(x),     a,         2.);
+%! assert(skewness(x),2/sqrt(a), 0.05);
+%! assert(kurtosis(x),6/a,       0.2);
+%!test
+%! rand("seed",12)
+%!assert(randg([-inf,-1,0,inf,nan]),[nan,nan,nan,nan,nan]) % *** Please report
+%!test
+%! % statistical tests may fail occasionally.
+%! a=0.1; x = randg(a,100000,1);
+%! assert(mean(x),    a,         0.01);
+%! assert(var(x),     a,         0.01);
+%! assert(skewness(x),2/sqrt(a), 1.);
+%! assert(kurtosis(x),6/a,       50.);
+%!test
+%! % statistical tests may fail occasionally.
+%! a=0.95; x = randg(a,100000,1);
+%! assert(mean(x),    a,         0.01);
+%! assert(var(x),     a,         0.04);
+%! assert(skewness(x),2/sqrt(a), 0.2);
+%! assert(kurtosis(x),6/a,       2.);
+%!test
+%! % statistical tests may fail occasionally.
+%! a=1; x = randg(a,100000,1);
+%! assert(mean(x),a,             0.01);
+%! assert(var(x),a,              0.04);
+%! assert(skewness(x),2/sqrt(a), 0.2);
+%! assert(kurtosis(x),6/a,       2.);
+%!test
+%! % statistical tests may fail occasionally.
+%! a=10; x = randg(a,100000,1);
+%! assert(mean(x),    a,         0.1);
+%! assert(var(x),     a,         0.5);
+%! assert(skewness(x),2/sqrt(a), 0.1);
+%! assert(kurtosis(x),6/a,       0.5);
+%!test
+%! % statistical tests may fail occasionally.
+%! a=100; x = randg(a,100000,1);
+%! assert(mean(x),    a,         0.2);
+%! assert(var(x),     a,         2.);
+%! assert(skewness(x),2/sqrt(a), 0.05);
+%! assert(kurtosis(x),6/a,       0.2);
+*/
+DEFUN_DLD (randp, args, ,
+"-*- texinfo -*-\n\
+@deftypefn {Loadable Function} {} randp (@var{l}, @var{x})\n\
+@deftypefnx {Loadable Function} {} randp (@var{l}, @var{n}, @var{m})\n\
+@deftypefnx {Loadable Function} {} randp (\"state\", @var{x})\n\
+@deftypefnx {Loadable Function} {} randp (\"seed\", @var{x})\n\
+Return a matrix with Poisson distributed random elements. The arguments\n\
+are handled the same as the arguments for @code{rand}, except for the\n\
+argument @var{l}.\n\
+\n\
+Five different algorithms are used depending on the range of @var{l}\n\
+and whether or not @var{l} is a scalar or a matrix.\n\
+\n\
+@table @asis\n\
+@item For scalar @var{l} <= 12, use direct method.\n\
+Press, et al., 'Numerical Recipes in C', Cambridge University Press, 1992.\n\
+@item For scalar @var{l} > 12, use rejection method.[1]\n\
+Press, et al., 'Numerical Recipes in C', Cambridge University Press, 1992.\n\
+@item For matrix @var{l} <= 10, use inversion method.[2]\n\
+Stadlober E., et al., WinRand source code, available via FTP.\n\
+@item For matrix @var{l} > 10, use patchwork rejection method.\n\
+Stadlober E., et al., WinRand source code, available via FTP, or\n\
+H. Zechner, 'Efficient sampling from continuous and discrete\n\
+unimodal distributions', Doctoral Dissertaion, 156pp., Technical\n\
+University Graz, Austria, 1994.\n\
+@item For @var{l} > 1e8, use normal approximation.\n\
+L. Montanet, et al., 'Review of Particle Properties', Physical Review\n\
+D 50 p1284, 1994\n\
+@end table\n\
+@seealso{rand,randn,rande,randg}\n\
+@end deftypefn")
+{
+octave_value retval;
+int nargin = args.length ();
+if (nargin < 1)
+error ("randp: insufficient arguments");
+else
+{
+unwind_protect::begin_frame ("randp");
+// This relies on the fact that elements are popped from the unwind
+// stack in the reverse of the order they are pushed
+// (i.e. current_distribution will be reset before calling
+// reset_rand_generator()).
+unwind_protect::add (reset_rand_generator, 0);
+unwind_protect_str (current_distribution);
+current_distribution = "poisson";
+octave_rand::distribution (current_distribution);
+retval = do_rand (args, nargin, "randp", true);
+unwind_protect::run_frame ("randp");
+}
+return retval;
+}
+/*
+%!test
+%! rand("state",12)
+%!assert(randp([-inf,-1,0,inf,nan]),[nan,nan,0,nan,nan]); % *** Please report
+%!test
+%! % statistical tests may fail occasionally.
+%! for a=[5 15]
+%!   x = randp(a,100000,1);
+%!   assert(min(x)>=0); % *** Please report this!!! ***
+%!   assert(mean(x),a,0.03);
+%!   assert(var(x),a,0.2);
+%!   assert(skewness(x),1/sqrt(a),0.03);
+%!   assert(kurtosis(x),1/a,0.08);
+%! end
+%!test
+%! % statistical tests may fail occasionally.
+%! for a=[5 15]
+%!   x = randp(a*ones(100000,1),100000,1);
+%!   assert(min(x)>=0); % *** Please report this!!! ***
+%!   assert(mean(x),a,0.03);
+%!   assert(var(x),a,0.2);
+%!   assert(skewness(x),1/sqrt(a),0.03);
+%!   assert(kurtosis(x),1/a,0.08);
+%! end
+%!test
+%! rand("seed",12)
+%!assert(randp([-inf,-1,0,inf,nan]),[nan,nan,0,nan,nan]); % *** Please report
+%!test
+%! % statistical tests may fail occasionally.
+%! for a=[5 15]
+%!   x = randp(a,100000,1);
+%!   assert(min(x)>=0); % *** Please report this!!! ***
+%!   assert(mean(x),a,0.03);
+%!   assert(var(x),a,0.2);
+%!   assert(skewness(x),1/sqrt(a),0.03);
+%!   assert(kurtosis(x),1/a,0.08);
+%! end
+%!test
+%! % statistical tests may fail occasionally.
+%! for a=[5 15]
+%!   x = randp(a*ones(100000,1),100000,1);
+%!   assert(min(x)>=0); % *** Please report this!!! ***
+%!   assert(mean(x),a,0.03);
+%!   assert(var(x),a,0.2);
+%!   assert(skewness(x),1/sqrt(a),0.03);
+%!   assert(kurtosis(x),1/a,0.08);
+%! end
+*/
 /*
 ;;; Local Variables: ***
 ;;; mode: C++ ***
 ;;; End: ***

Mercurial > octave

comparison src/DLD-FUNCTIONS/rand.cc @ 5730:109fdf7b3dcb