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78b5479633
* commit '502ab21af0ca68f76d6112722c46d2f35c004053': x86: lpc: simd av_update_lls The versions are bumped due to changes in lls.h which is used across libraries affecting intra library ABI (This version bump also covers changes to lls.h in the immedeatly previous commits) Merged-by: Michael Niedermayer <michaelni@gmx.at>
72 lines
2.4 KiB
C
72 lines
2.4 KiB
C
/*
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* linear least squares model
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*
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* Copyright (c) 2006 Michael Niedermayer <michaelni@gmx.at>
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*
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* This file is part of FFmpeg.
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*
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* FFmpeg is free software; you can redistribute it and/or
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* modify it under the terms of the GNU Lesser General Public
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* License as published by the Free Software Foundation; either
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* version 2.1 of the License, or (at your option) any later version.
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*
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* FFmpeg is distributed in the hope that it will be useful,
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* but WITHOUT ANY WARRANTY; without even the implied warranty of
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* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
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* Lesser General Public License for more details.
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*
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* You should have received a copy of the GNU Lesser General Public
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* License along with FFmpeg; if not, write to the Free Software
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* Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
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*/
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#ifndef AVUTIL_LLS_H
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#define AVUTIL_LLS_H
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#include "common.h"
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#include "mem.h"
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#include "version.h"
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#define MAX_VARS 32
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#define MAX_VARS_ALIGN FFALIGN(MAX_VARS+1,4)
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//FIXME avoid direct access to LLSModel from outside
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/**
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* Linear least squares model.
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*/
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typedef struct LLSModel {
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DECLARE_ALIGNED(32, double, covariance[MAX_VARS_ALIGN][MAX_VARS_ALIGN]);
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DECLARE_ALIGNED(32, double, coeff[MAX_VARS][MAX_VARS]);
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double variance[MAX_VARS];
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int indep_count;
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/**
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* Take the outer-product of var[] with itself, and add to the covariance matrix.
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* @param m this context
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* @param var training samples, starting with the value to be predicted
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* 32-byte aligned, and any padding elements must be initialized
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* (i.e not denormal/nan).
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*/
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void (*update_lls)(struct LLSModel *m, double *var);
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/**
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* Inner product of var[] and the LPC coefs.
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* @param m this context
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* @param var training samples, excluding the value to be predicted. unaligned.
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* @param order lpc order
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*/
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double (*evaluate_lls)(struct LLSModel *m, double *var, int order);
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} LLSModel;
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void avpriv_init_lls(LLSModel *m, int indep_count);
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void ff_init_lls_x86(LLSModel *m);
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void avpriv_solve_lls(LLSModel *m, double threshold, unsigned short min_order);
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#if FF_API_LLS_PRIVATE
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void av_init_lls(LLSModel *m, int indep_count);
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void av_update_lls(LLSModel *m, double *param, double decay);
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void av_solve_lls(LLSModel *m, double threshold, int min_order);
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double av_evaluate_lls(LLSModel *m, double *param, int order);
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#endif /* FF_API_LLS_PRIVATE */
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#endif /* AVUTIL_LLS_H */
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