--- a/scripts/sparse/sprand.m	Sat Sep 03 13:24:01 2011 -0400
+++ b/scripts/sparse/sprand.m	Sat Sep 03 10:25:25 2011 -0700
@@ -52,9 +52,9 @@
   endif
 
   if (nargin == 1)
-    [i, j, v] = find (m);
+    [i, j] = find (m);
     [nr, nc] = size (m);
-    S = sparse (i, j, rand (size (v)), nr, nc);
+    S = sparse (i, j, rand (size (i)), nr, nc);
     return;
   endif
 

--- a/scripts/sparse/sprandn.m	Sat Sep 03 13:24:01 2011 -0400
+++ b/scripts/sparse/sprandn.m	Sat Sep 03 10:25:25 2011 -0700
@@ -38,30 +38,75 @@
 ## Author: Paul Kienzle <pkienzle@users.sf.net>
 
 function S = sprandn (m, n, d)
-  if (nargin == 1)
-    [i, j, v] = find (m);
-    [nr, nc] = size (m);
-    S = sparse (i, j, randn (size (v)), nr, nc);
-  elseif (nargin == 3)
-    mn = m*n;
-    k = round (d*mn);
-    idx = unique (fix (rand (min (k*1.01, k+10), 1) * mn)) + 1;
-    ## idx contains random numbers in [1,mn]
-    ## generate 1% or 10 more random values than necessary in order to
-    ## reduce the probability that there are less than k distinct
-    ## values; maybe a better strategy could be used but I don't think
-    ## it's worth the price.
 
-    ## actual number of entries in S
-    k = min (length (idx), k);
-    j = floor ((idx(1:k)-1)/m);
-    i = idx(1:k) - j*m;
-    if (isempty (i))
-      S = sparse (m, n);
-    else
-      S = sparse (i, j+1, randn (k, 1), m, n);
-    endif
-  else
+  if (nargin != 1 && nargin != 3)
     print_usage ();
   endif
+
+  if (nargin == 1)
+    [i, j] = find (m);
+    [nr, nc] = size (m);
+    S = sparse (i, j, randn (size (i)), nr, nc);
+    return;
+  endif
+
+  if (!(isscalar (m) && m == fix (m) && m > 0))
+    error ("sprand: M must be an integer greater than 0");
+  endif
+
+  if (!(isscalar (n) && n == fix (n) && n > 0))
+    error ("sprand: N must be an integer greater than 0");
+  endif
+
+  if (d < 0 || d > 1)
+    error ("sprand: density D must be between 0 and 1");
+  endif
+
+  mn = m*n;
+  k = round (d*mn);
+  idx = unique (fix (rand (min (k*1.01, k+10), 1) * mn)) + 1;
+  ## idx contains random numbers in [1,mn]
+  ## generate 1% or 10 more random values than necessary in order to
+  ## reduce the probability that there are less than k distinct
+  ## values; maybe a better strategy could be used but I don't think
+  ## it's worth the price.
+
+  ## actual number of entries in S
+  k = min (length (idx), k);
+  j = floor ((idx(1:k)-1)/m);
+  i = idx(1:k) - j*m;
+  if (isempty (i))
+    S = sparse (m, n);
+  else
+    S = sparse (i, j+1, randn (k, 1), m, n);
+  endif
+
 endfunction
+
+
+## FIXME: Test for density can't happen until code of sprandn is improved
+%!test
+%! s = sprandn (4, 10, 0.1);
+%! assert (size (s), [4, 10]);
+##%! assert (nnz (s) / numel (s), 0.1, .01);
+
+%% Test 1-input calling form
+%!test
+%! s = sprandn (sparse ([1 2 3], [3 2 3], [2 2 2]));
+%! [i, j, v] = find (s);
+%! assert (sort (i), [1 2 3]');
+%! assert (sort (j), [2 3 3]');
+
+%% Test input validation
+%!error sprandn ()
+%!error sprandn (1, 2)
+%!error sprandn (1, 2, 3, 4)
+%!error sprandn (ones(3), 3, 0.5)
+%!error sprandn (3.5, 3, 0.5)
+%!error sprandn (0, 3, 0.5)
+%!error sprandn (3, ones(3), 0.5)
+%!error sprandn (3, 3.5, 0.5)
+%!error sprandn (3, 0, 0.5)
+%!error sprandn (3, 3, -1)
+%!error sprandn (3, 3, 2)
+