1.1 --- /dev/null Thu Jan 01 00:00:00 1970 +0000
1.2 +++ b/media/libopus/silk/float/solve_LS_FLP.c Wed Dec 31 06:09:35 2014 +0100
1.3 @@ -0,0 +1,207 @@
1.4 +/***********************************************************************
1.5 +Copyright (c) 2006-2011, Skype Limited. All rights reserved.
1.6 +Redistribution and use in source and binary forms, with or without
1.7 +modification, are permitted provided that the following conditions
1.8 +are met:
1.9 +- Redistributions of source code must retain the above copyright notice,
1.10 +this list of conditions and the following disclaimer.
1.11 +- Redistributions in binary form must reproduce the above copyright
1.12 +notice, this list of conditions and the following disclaimer in the
1.13 +documentation and/or other materials provided with the distribution.
1.14 +- Neither the name of Internet Society, IETF or IETF Trust, nor the
1.15 +names of specific contributors, may be used to endorse or promote
1.16 +products derived from this software without specific prior written
1.17 +permission.
1.18 +THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
1.19 +AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
1.20 +IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
1.21 +ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
1.22 +LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
1.23 +CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
1.24 +SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
1.25 +INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
1.26 +CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
1.27 +ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
1.28 +POSSIBILITY OF SUCH DAMAGE.
1.29 +***********************************************************************/
1.30 +
1.31 +#ifdef HAVE_CONFIG_H
1.32 +#include "config.h"
1.33 +#endif
1.34 +
1.35 +#include "main_FLP.h"
1.36 +#include "tuning_parameters.h"
1.37 +
1.38 +/**********************************************************************
1.39 + * LDL Factorisation. Finds the upper triangular matrix L and the diagonal
1.40 + * Matrix D (only the diagonal elements returned in a vector)such that
1.41 + * the symmetric matric A is given by A = L*D*L'.
1.42 + **********************************************************************/
1.43 +static OPUS_INLINE void silk_LDL_FLP(
1.44 + silk_float *A, /* I/O Pointer to Symetric Square Matrix */
1.45 + opus_int M, /* I Size of Matrix */
1.46 + silk_float *L, /* I/O Pointer to Square Upper triangular Matrix */
1.47 + silk_float *Dinv /* I/O Pointer to vector holding the inverse diagonal elements of D */
1.48 +);
1.49 +
1.50 +/**********************************************************************
1.51 + * Function to solve linear equation Ax = b, when A is a MxM lower
1.52 + * triangular matrix, with ones on the diagonal.
1.53 + **********************************************************************/
1.54 +static OPUS_INLINE void silk_SolveWithLowerTriangularWdiagOnes_FLP(
1.55 + const silk_float *L, /* I Pointer to Lower Triangular Matrix */
1.56 + opus_int M, /* I Dim of Matrix equation */
1.57 + const silk_float *b, /* I b Vector */
1.58 + silk_float *x /* O x Vector */
1.59 +);
1.60 +
1.61 +/**********************************************************************
1.62 + * Function to solve linear equation (A^T)x = b, when A is a MxM lower
1.63 + * triangular, with ones on the diagonal. (ie then A^T is upper triangular)
1.64 + **********************************************************************/
1.65 +static OPUS_INLINE void silk_SolveWithUpperTriangularFromLowerWdiagOnes_FLP(
1.66 + const silk_float *L, /* I Pointer to Lower Triangular Matrix */
1.67 + opus_int M, /* I Dim of Matrix equation */
1.68 + const silk_float *b, /* I b Vector */
1.69 + silk_float *x /* O x Vector */
1.70 +);
1.71 +
1.72 +/**********************************************************************
1.73 + * Function to solve linear equation Ax = b, when A is a MxM
1.74 + * symmetric square matrix - using LDL factorisation
1.75 + **********************************************************************/
1.76 +void silk_solve_LDL_FLP(
1.77 + silk_float *A, /* I/O Symmetric square matrix, out: reg. */
1.78 + const opus_int M, /* I Size of matrix */
1.79 + const silk_float *b, /* I Pointer to b vector */
1.80 + silk_float *x /* O Pointer to x solution vector */
1.81 +)
1.82 +{
1.83 + opus_int i;
1.84 + silk_float L[ MAX_MATRIX_SIZE ][ MAX_MATRIX_SIZE ];
1.85 + silk_float T[ MAX_MATRIX_SIZE ];
1.86 + silk_float Dinv[ MAX_MATRIX_SIZE ]; /* inverse diagonal elements of D*/
1.87 +
1.88 + silk_assert( M <= MAX_MATRIX_SIZE );
1.89 +
1.90 + /***************************************************
1.91 + Factorize A by LDL such that A = L*D*(L^T),
1.92 + where L is lower triangular with ones on diagonal
1.93 + ****************************************************/
1.94 + silk_LDL_FLP( A, M, &L[ 0 ][ 0 ], Dinv );
1.95 +
1.96 + /****************************************************
1.97 + * substitute D*(L^T) = T. ie:
1.98 + L*D*(L^T)*x = b => L*T = b <=> T = inv(L)*b
1.99 + ******************************************************/
1.100 + silk_SolveWithLowerTriangularWdiagOnes_FLP( &L[ 0 ][ 0 ], M, b, T );
1.101 +
1.102 + /****************************************************
1.103 + D*(L^T)*x = T <=> (L^T)*x = inv(D)*T, because D is
1.104 + diagonal just multiply with 1/d_i
1.105 + ****************************************************/
1.106 + for( i = 0; i < M; i++ ) {
1.107 + T[ i ] = T[ i ] * Dinv[ i ];
1.108 + }
1.109 + /****************************************************
1.110 + x = inv(L') * inv(D) * T
1.111 + *****************************************************/
1.112 + silk_SolveWithUpperTriangularFromLowerWdiagOnes_FLP( &L[ 0 ][ 0 ], M, T, x );
1.113 +}
1.114 +
1.115 +static OPUS_INLINE void silk_SolveWithUpperTriangularFromLowerWdiagOnes_FLP(
1.116 + const silk_float *L, /* I Pointer to Lower Triangular Matrix */
1.117 + opus_int M, /* I Dim of Matrix equation */
1.118 + const silk_float *b, /* I b Vector */
1.119 + silk_float *x /* O x Vector */
1.120 +)
1.121 +{
1.122 + opus_int i, j;
1.123 + silk_float temp;
1.124 + const silk_float *ptr1;
1.125 +
1.126 + for( i = M - 1; i >= 0; i-- ) {
1.127 + ptr1 = matrix_adr( L, 0, i, M );
1.128 + temp = 0;
1.129 + for( j = M - 1; j > i ; j-- ) {
1.130 + temp += ptr1[ j * M ] * x[ j ];
1.131 + }
1.132 + temp = b[ i ] - temp;
1.133 + x[ i ] = temp;
1.134 + }
1.135 +}
1.136 +
1.137 +static OPUS_INLINE void silk_SolveWithLowerTriangularWdiagOnes_FLP(
1.138 + const silk_float *L, /* I Pointer to Lower Triangular Matrix */
1.139 + opus_int M, /* I Dim of Matrix equation */
1.140 + const silk_float *b, /* I b Vector */
1.141 + silk_float *x /* O x Vector */
1.142 +)
1.143 +{
1.144 + opus_int i, j;
1.145 + silk_float temp;
1.146 + const silk_float *ptr1;
1.147 +
1.148 + for( i = 0; i < M; i++ ) {
1.149 + ptr1 = matrix_adr( L, i, 0, M );
1.150 + temp = 0;
1.151 + for( j = 0; j < i; j++ ) {
1.152 + temp += ptr1[ j ] * x[ j ];
1.153 + }
1.154 + temp = b[ i ] - temp;
1.155 + x[ i ] = temp;
1.156 + }
1.157 +}
1.158 +
1.159 +static OPUS_INLINE void silk_LDL_FLP(
1.160 + silk_float *A, /* I/O Pointer to Symetric Square Matrix */
1.161 + opus_int M, /* I Size of Matrix */
1.162 + silk_float *L, /* I/O Pointer to Square Upper triangular Matrix */
1.163 + silk_float *Dinv /* I/O Pointer to vector holding the inverse diagonal elements of D */
1.164 +)
1.165 +{
1.166 + opus_int i, j, k, loop_count, err = 1;
1.167 + silk_float *ptr1, *ptr2;
1.168 + double temp, diag_min_value;
1.169 + silk_float v[ MAX_MATRIX_SIZE ], D[ MAX_MATRIX_SIZE ]; /* temp arrays*/
1.170 +
1.171 + silk_assert( M <= MAX_MATRIX_SIZE );
1.172 +
1.173 + diag_min_value = FIND_LTP_COND_FAC * 0.5f * ( A[ 0 ] + A[ M * M - 1 ] );
1.174 + for( loop_count = 0; loop_count < M && err == 1; loop_count++ ) {
1.175 + err = 0;
1.176 + for( j = 0; j < M; j++ ) {
1.177 + ptr1 = matrix_adr( L, j, 0, M );
1.178 + temp = matrix_ptr( A, j, j, M ); /* element in row j column j*/
1.179 + for( i = 0; i < j; i++ ) {
1.180 + v[ i ] = ptr1[ i ] * D[ i ];
1.181 + temp -= ptr1[ i ] * v[ i ];
1.182 + }
1.183 + if( temp < diag_min_value ) {
1.184 + /* Badly conditioned matrix: add white noise and run again */
1.185 + temp = ( loop_count + 1 ) * diag_min_value - temp;
1.186 + for( i = 0; i < M; i++ ) {
1.187 + matrix_ptr( A, i, i, M ) += ( silk_float )temp;
1.188 + }
1.189 + err = 1;
1.190 + break;
1.191 + }
1.192 + D[ j ] = ( silk_float )temp;
1.193 + Dinv[ j ] = ( silk_float )( 1.0f / temp );
1.194 + matrix_ptr( L, j, j, M ) = 1.0f;
1.195 +
1.196 + ptr1 = matrix_adr( A, j, 0, M );
1.197 + ptr2 = matrix_adr( L, j + 1, 0, M);
1.198 + for( i = j + 1; i < M; i++ ) {
1.199 + temp = 0.0;
1.200 + for( k = 0; k < j; k++ ) {
1.201 + temp += ptr2[ k ] * v[ k ];
1.202 + }
1.203 + matrix_ptr( L, i, j, M ) = ( silk_float )( ( ptr1[ i ] - temp ) * Dinv[ j ] );
1.204 + ptr2 += M; /* go to next column*/
1.205 + }
1.206 + }
1.207 + }
1.208 + silk_assert( err == 0 );
1.209 +}
1.210 +
