#include "xinclude.h"
-
-
#define XDL_MAX_COST_MIN 256
#define XDL_HEUR_MIN_COST 256
#define XDL_LINE_MAX (long)((1UL << (CHAR_BIT * sizeof(long) - 1)) - 1)
#define XDL_SNAKE_CNT 20
#define XDL_K_HEUR 4
-
-
typedef struct s_xdpsplit {
long i1, i2;
int min_lo, min_hi;
} xdpsplit_t;
-
-
-
-static long xdl_split(unsigned long const *ha1, long off1, long lim1,
- unsigned long const *ha2, long off2, long lim2,
- long *kvdf, long *kvdb, int need_min, xdpsplit_t *spl,
- xdalgoenv_t *xenv);
-static xdchange_t *xdl_add_change(xdchange_t *xscr, long i1, long i2, long chg1, long chg2);
-
-
-
-
-
/*
* See "An O(ND) Difference Algorithm and its Variations", by Eugene Myers.
* Basically considers a "box" (off1, off2, lim1, lim2) and scan from both
*/
#define INDENT_WEIGHT 60
+/*
+ * How far do we slide a hunk at most?
+ */
+#define INDENT_HEURISTIC_MAX_SLIDING 100
+
/*
* Compute a badness score for the hypothetical split whose measurements are
* stored in m. The weight factors were determined empirically using the tools and
long shift, best_shift = -1;
struct split_score best_score;
- for (shift = earliest_end; shift <= g.end; shift++) {
+ shift = earliest_end;
+ if (g.end - groupsize - 1 > shift)
+ shift = g.end - groupsize - 1;
+ if (g.end - INDENT_HEURISTIC_MAX_SLIDING > shift)
+ shift = g.end - INDENT_HEURISTIC_MAX_SLIDING;
+ for (; shift <= g.end; shift++) {
struct split_measurement m;
struct split_score score = {0, 0};