*** regression.h	Fri Aug 16 16:33:38 2002
--- new_regression.h	Mon Sep  9 18:45:07 2002
***************
*** 66,68 ****
--- 66,125 ----
  			    gnum_float *res, regression_stat_t *stat);
  
+ /**
+  * logarithmic_regression:
+  * @xss: x-vectors.  (Ie., independent data.)
+  * @dim: number of x-vectors.
+  * @ys: y-vector.  (Dependent data.)
+  * @n: number of data points.
+  * @affine: if true, a non-zero constant is allowed.
+  * @res: output place for constant[0] and factor1[1], factor2[2], ...
+  *   There will be dim+1 results.
+  *
+  * This is almost a copy of linear_regression and produces multi-dimensional
+  * linear regressions on the input points after transforming xss to log(xss).
+  * Fits to "y = b + a1 * z1 + ... ad * zd" with "zi = log (xi)".
+  * Problems with arrays: see comment to gnumeric_linest in functions/fn-stat.c.
+  *
+  * Returns 0 for ok, non-zero otherwise.  (Errors: less than two points,
+  * all points on a vertical line, non-positive x data.)
+  */
+ 
+ int logarithmic_regression (gnum_float **xss, int dim,
+ 			    const gnum_float *ys, int n,
+ 			    int affine,
+ 			    gnum_float *res, regression_stat_t *stat);
+ 
+ /**
+  * logarithmic_fit:
+  * @xs: x-vector.  (Ie., independent data.)
+  * @ys: y-vector.  (Dependent data.)
+  * @n: number of data points.
+  * @res: output place for sign[0], a[1], b[2], c[3], and
+  * sum of squared residuals[4].
+  *
+  * This performs a two-dimensional non-linear fitting on the input points.
+  * Fits to "y = a + b * log (sign * (x - c))", with sign in {-1, +1}.
+  * The graph is a logarithmic curve moved horizontally by -c and possibly
+  * mirrored across the y-axis (if sign = -1).
+  *
+  * Returns 0 for OK, non-zero otherwise.
+  * (Requires: at least 3 different x values, at least 3 different y values.)
+  *
+  * Fits c (and sign) by iterative trials, but seems to be fast enough even
+  * for automatic recomputation. Accuracy of c is: Width of x-range rounded
+  * to the next smaller (10^integer) times 0.000005.
+  *
+  * Adapts c until a local minimum of squared residuals is reached. For each
+  * new c tried out the corresponding a and b are calculated by linear
+  * regression. If no local minimum is found, an error is returned. If there
+  * is more than one local minimum, the one found is not necessarily the
+  * smallest (i.e., there might be cases in which the returned fit is not the
+  * best possible). If the shape of the point cloud is to different from
+  * ``logarithmic'', either sign can not be determined (error returned) or no
+  * local minimum will be found.
+  */
+ 
+ int logarithmic_fit (gnum_float *xs,
+ 		     const gnum_float *ys, int n,
+ 		     gnum_float *res);
  #endif