1 files changed, 0 insertions, 82 deletions
diff --git a/libm/double/levnsn.c b/libm/double/levnsn.c
deleted file mode 100644
index 3fda5d6bd..000000000
--- a/libm/double/levnsn.c
+++ /dev/null
@@ -1,82 +0,0 @@
-/*		Levnsn.c		*/
-/* Levinson-Durbin LPC
- *
- * | R0 R1 R2 ... RN-1 |   | A1 |       | -R1 |
- * | R1 R0 R1 ... RN-2 |   | A2 |       | -R2 |
- * | R2 R1 R0 ... RN-3 |   | A3 |   =   | -R3 |
- * |          ...      |   | ...|       | ... |
- * | RN-1 RN-2... R0   |   | AN |       | -RN |
- *
- * Ref: John Makhoul, "Linear Prediction, A Tutorial Review"
- * Proc. IEEE Vol. 63, PP 561-580 April, 1975.
- *
- * R is the input autocorrelation function.  R0 is the zero lag
- * term.  A is the output array of predictor coefficients.  Note
- * that a filter impulse response has a coefficient of 1.0 preceding
- * A1.  E is an array of mean square error for each prediction order
- * 1 to N.  REFL is an output array of the reflection coefficients.
- */
-
-#define abs(x) ( (x) < 0 ? -(x) : (x) )
-
-int levnsn( n, r, a, e, refl )
-int n;
-double r[], a[], e[], refl[];
-{
-int k, km1, i, kmi, j;
-double ai, akk, err, err1, r0, t, akmi;
-double *pa, *pr;
-
-for( i=0; i<n; i++ )
-	{
-	a[i] = 0.0;
-	e[i] = 0.0;
-	refl[i] = 0.0;
-	}
-r0 = r[0];
-e[0] = r0;
-err = r0;
-
-akk = -r[1]/err;
-err = (1.0 - akk*akk) * err;
-e[1] = err;
-a[1] = akk;
-refl[1] = akk;
-
-if( err < 1.0e-2 )
-	return 0;
-
-for( k=2; k<n; k++ )
-	{
-	t = 0.0;
-	pa = &a[1];
-	pr = &r[k-1];
-	for( j=1; j<k; j++ )
-		t += *pa++ * *pr--;
-	akk = -( r[k] + t )/err;
-	refl[k] = akk;
-	km1 = k/2;
-	for( j=1; j<=km1; j++ )
-		{
-		kmi = k-j;
-		ai = a[j];
-		akmi = a[kmi];
-		a[j] = ai + akk*akmi;
-		if( i == kmi )
-			goto nxtk;
-		a[kmi] = akmi + akk*ai;
-		}
-nxtk:
-	a[k] = akk;
-	err1 = (1.0 - akk*akk)*err;
-	e[k] = err1;
-	if( err1 < 0 )
-		err1 = -err1;
-/*	err1 = abs(err1);*/
-/*	if( (err1 < 1.0e-2) || (err1 >= err) )*/
-	if( err1 < 1.0e-2 )
-		return 0;
-	err = err1;
-	}
-  return 0;
-}