*
* Copyright (c) 2006 Michael Niedermayer <michaelni@gmx.at>
*
- * This file is part of FFmpeg.
+ * This file is part of Libav.
*
- * FFmpeg is free software; you can redistribute it and/or
+ * Libav is free software; you can redistribute it and/or
* modify it under the terms of the GNU Lesser General Public
* License as published by the Free Software Foundation; either
* version 2.1 of the License, or (at your option) any later version.
*
- * FFmpeg is distributed in the hope that it will be useful,
+ * Libav is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
* Lesser General Public License for more details.
*
* You should have received a copy of the GNU Lesser General Public
- * License along with FFmpeg; if not, write to the Free Software
+ * License along with Libav; if not, write to the Free Software
* Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
*/
-#ifndef LLS_H
-#define LLS_H
+#ifndef AVUTIL_LLS_H
+#define AVUTIL_LLS_H
+
+#include "macros.h"
+#include "mem.h"
+#include "version.h"
#define MAX_VARS 32
+#define MAX_VARS_ALIGN FFALIGN(MAX_VARS+1,4)
//FIXME avoid direct access to LLSModel from outside
/**
* Linear least squares model.
*/
-typedef struct LLSModel{
- double covariance[MAX_VARS+1][MAX_VARS+1];
- double coeff[MAX_VARS][MAX_VARS];
+typedef struct LLSModel {
+ DECLARE_ALIGNED(32, double, covariance[MAX_VARS_ALIGN][MAX_VARS_ALIGN]);
+ DECLARE_ALIGNED(32, double, coeff[MAX_VARS][MAX_VARS]);
double variance[MAX_VARS];
int indep_count;
-}LLSModel;
+ /**
+ * Take the outer-product of var[] with itself, and add to the covariance matrix.
+ * @param m this context
+ * @param var training samples, starting with the value to be predicted
+ * 32-byte aligned, and any padding elements must be initialized
+ * (i.e not denormal/nan).
+ */
+ void (*update_lls)(struct LLSModel *m, double *var);
+ /**
+ * Inner product of var[] and the LPC coefs.
+ * @param m this context
+ * @param var training samples, excluding the value to be predicted. unaligned.
+ * @param order lpc order
+ */
+ double (*evaluate_lls)(struct LLSModel *m, double *var, int order);
+} LLSModel;
-void av_init_lls(LLSModel *m, int indep_count);
-void av_update_lls(LLSModel *m, double *param, double decay);
-void av_solve_lls(LLSModel *m, double threshold, int min_order);
-double av_evaluate_lls(LLSModel *m, double *param, int order);
+void avpriv_init_lls(LLSModel *m, int indep_count);
+void ff_init_lls_x86(LLSModel *m);
+void avpriv_solve_lls(LLSModel *m, double threshold, unsigned short min_order);
-#endif
+#endif /* AVUTIL_LLS_H */