Use stdbool.h, bool, true, false instead of manually defining

git-svn-id: svn://mattst88.com/svn/cleanbench/trunk@60 0d43b9a7-5ab2-4d7b-af9d-f64450cef757

1 files changed, 13 insertions, 15 deletions

diff --git a/neural.c b/neural.c
index c3fe5e5..cf03dac 100644
--- a/neural.c
+++ b/neural.c
@@ -1,6 +1,7 @@
 #include <stdio.h>
 #include <stdlib.h>
 #include <stdint.h>
+#include <stdbool.h>
 #include <string.h>
 #include <math.h>
 #include <limits.h>
@@ -45,8 +46,6 @@
 */
 #define LOOP_MAX  500000L
 
-#define T 1                     /* TRUE */
-#define F 0                     /* FALSE */
 #define ERR -1
 #define MAXPATS 10              /* max number of patterns in data file */
 #define IN_X_SIZE 5             /* number of neurodes/row of input layer */
@@ -86,7 +85,7 @@ double  avg_out_error[MAXPATS]; /* average error each pattern */
 int iteration_count;    /* number of passes thru network so far */
 int numpats;            /* number of patterns in data file */
 int numpasses;          /* number of training passes through data file */
-int learned;            /* flag--if TRUE, network has learned all patterns */
+int learned;            /* flag--if true, network has learned all patterns */
 
 static clock_t DoNNetIteration(unsigned long nloops);
 static void do_mid_forward(int patt);
@@ -122,7 +121,7 @@ DoNNET(void)
 /*        const char*     context="CPU:NNET"; */ /* Since we never fprintf errors here, we don't need this */
         clock_t         total_time = 0;
         int             iterations = 0;
-        static int      is_adjusted = FALSE;
+        static bool     is_adjusted = false;
         static int      loops = 1;
 
         /*
@@ -147,8 +146,8 @@ DoNNET(void)
         /*
         ** See if we need to perform self adjustment loop.
         */
-        if (is_adjusted == FALSE) {
-                is_adjusted = TRUE;
+        if (is_adjusted == false) {
+                is_adjusted = true;
                 /*
                 ** Do self-adjustment.  This involves initializing the
                 ** # of loops and increasing the loop count until we
@@ -196,9 +195,9 @@ while(nloops--)
 	randomize_wts();
 	zero_changes();
 	iteration_count=1;
-	learned = F;
+	learned = false;
 	numpasses = 0;
-	while (learned == F)
+	while (!learned)
 	{
 		for (patt=0; patt<numpats; patt++)
 		{
@@ -275,7 +274,6 @@ for (neurode=0; neurode<OUT_SIZE; neurode++)
 	sum = 1.0/(1.0+exp(-sum));
 	out_out[neurode] = sum;
 }
-return;
 }
 
 /*************************
@@ -284,7 +282,7 @@ return;
 ** Display the actual output vs. the desired output of the
 ** network.
 ** Once the training is complete, and the "learned" flag set
-** to TRUE, then display_output sends its output to both
+** to true, then display_output sends its output to both
 ** the screen and to a text output file.
 **
 ** NOTE: This routine has been disabled in the benchmark
@@ -548,8 +546,8 @@ static int check_out_error()
 {
 int result,i,error;
 
-result  = T;
-error   = F;
+result  = true;
+error   = false;
 worst_pass_error();     /* identify the worst error in this pass */
 
 /*
@@ -566,11 +564,11 @@ for (i=0; i<numpats; i++)
 	 i+1,tot_out_error[i]);
 */
 
-	if (worst_error >= STOP) result = F;
-	if (tot_out_error[i] >= 16.0) error = T;
+	if (worst_error >= STOP) result = false;
+	if (tot_out_error[i] >= 16.0) error = true;
 }
 
-if (error == T) result = ERR;
+if (error) result = ERR;
 
 
 #ifdef DEBUG