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| 1 /*********************************************************************** | |
| 2 Copyright (c) 2006-2011, Skype Limited. All rights reserved. | |
| 3 Redistribution and use in source and binary forms, with or without | |
| 4 modification, are permitted provided that the following conditions | |
| 5 are met: | |
| 6 - Redistributions of source code must retain the above copyright notice, | |
| 7 this list of conditions and the following disclaimer. | |
| 8 - Redistributions in binary form must reproduce the above copyright | |
| 9 notice, this list of conditions and the following disclaimer in the | |
| 10 documentation and/or other materials provided with the distribution. | |
| 11 - Neither the name of Internet Society, IETF or IETF Trust, nor the | |
| 12 names of specific contributors, may be used to endorse or promote | |
| 13 products derived from this software without specific prior written | |
| 14 permission. | |
| 15 THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" | |
| 16 AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE | |
| 17 IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE | |
| 18 ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE | |
| 19 LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR | |
| 20 CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF | |
| 21 SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS | |
| 22 INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN | |
| 23 CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) | |
| 24 ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE | |
| 25 POSSIBILITY OF SUCH DAMAGE. | |
| 26 ***********************************************************************/ | |
| 27 | |
| 28 #ifdef HAVE_CONFIG_H | |
| 29 #include "config.h" | |
| 30 #endif | |
| 31 | |
| 32 #include "main_FLP.h" | |
| 33 #include "tuning_parameters.h" | |
| 34 | |
| 35 /********************************************************************** | |
| 36 * LDL Factorisation. Finds the upper triangular matrix L and the diagonal | |
| 37 * Matrix D (only the diagonal elements returned in a vector)such that | |
| 38 * the symmetric matric A is given by A = L*D*L'. | |
| 39 **********************************************************************/ | |
| 40 static OPUS_INLINE void silk_LDL_FLP( | |
| 41 silk_float *A, /* I/O Pointer to Symetric Square Matrix
*/ | |
| 42 opus_int M, /* I Size of Matrix
*/ | |
| 43 silk_float *L, /* I/O Pointer to Square Upper triangular M
atrix */ | |
| 44 silk_float *Dinv /* I/O Pointer to vector holding the invers
e diagonal elements of D */ | |
| 45 ); | |
| 46 | |
| 47 /********************************************************************** | |
| 48 * Function to solve linear equation Ax = b, when A is a MxM lower | |
| 49 * triangular matrix, with ones on the diagonal. | |
| 50 **********************************************************************/ | |
| 51 static OPUS_INLINE void silk_SolveWithLowerTriangularWdiagOnes_FLP( | |
| 52 const silk_float *L, /* I Pointer to Lower Triangular Matrix
*/ | |
| 53 opus_int M, /* I Dim of Matrix equation
*/ | |
| 54 const silk_float *b, /* I b Vector
*/ | |
| 55 silk_float *x /* O x Vector
*/ | |
| 56 ); | |
| 57 | |
| 58 /********************************************************************** | |
| 59 * Function to solve linear equation (A^T)x = b, when A is a MxM lower | |
| 60 * triangular, with ones on the diagonal. (ie then A^T is upper triangular) | |
| 61 **********************************************************************/ | |
| 62 static OPUS_INLINE void silk_SolveWithUpperTriangularFromLowerWdiagOnes_FLP( | |
| 63 const silk_float *L, /* I Pointer to Lower Triangular Matrix
*/ | |
| 64 opus_int M, /* I Dim of Matrix equation
*/ | |
| 65 const silk_float *b, /* I b Vector
*/ | |
| 66 silk_float *x /* O x Vector
*/ | |
| 67 ); | |
| 68 | |
| 69 /********************************************************************** | |
| 70 * Function to solve linear equation Ax = b, when A is a MxM | |
| 71 * symmetric square matrix - using LDL factorisation | |
| 72 **********************************************************************/ | |
| 73 void silk_solve_LDL_FLP( | |
| 74 silk_float *A, /* I/O
Symmetric square matrix, out: reg. */ | |
| 75 const opus_int M, /* I
Size of matrix */ | |
| 76 const silk_float *b, /* I
Pointer to b vector */ | |
| 77 silk_float *x /* O
Pointer to x solution vector */ | |
| 78 ) | |
| 79 { | |
| 80 opus_int i; | |
| 81 silk_float L[ MAX_MATRIX_SIZE ][ MAX_MATRIX_SIZE ]; | |
| 82 silk_float T[ MAX_MATRIX_SIZE ]; | |
| 83 silk_float Dinv[ MAX_MATRIX_SIZE ]; /* inverse diagonal elements of D*/ | |
| 84 | |
| 85 silk_assert( M <= MAX_MATRIX_SIZE ); | |
| 86 | |
| 87 /*************************************************** | |
| 88 Factorize A by LDL such that A = L*D*(L^T), | |
| 89 where L is lower triangular with ones on diagonal | |
| 90 ****************************************************/ | |
| 91 silk_LDL_FLP( A, M, &L[ 0 ][ 0 ], Dinv ); | |
| 92 | |
| 93 /**************************************************** | |
| 94 * substitute D*(L^T) = T. ie: | |
| 95 L*D*(L^T)*x = b => L*T = b <=> T = inv(L)*b | |
| 96 ******************************************************/ | |
| 97 silk_SolveWithLowerTriangularWdiagOnes_FLP( &L[ 0 ][ 0 ], M, b, T ); | |
| 98 | |
| 99 /**************************************************** | |
| 100 D*(L^T)*x = T <=> (L^T)*x = inv(D)*T, because D is | |
| 101 diagonal just multiply with 1/d_i | |
| 102 ****************************************************/ | |
| 103 for( i = 0; i < M; i++ ) { | |
| 104 T[ i ] = T[ i ] * Dinv[ i ]; | |
| 105 } | |
| 106 /**************************************************** | |
| 107 x = inv(L') * inv(D) * T | |
| 108 *****************************************************/ | |
| 109 silk_SolveWithUpperTriangularFromLowerWdiagOnes_FLP( &L[ 0 ][ 0 ], M, T, x )
; | |
| 110 } | |
| 111 | |
| 112 static OPUS_INLINE void silk_SolveWithUpperTriangularFromLowerWdiagOnes_FLP( | |
| 113 const silk_float *L, /* I Pointer to Lower Triangular Matrix
*/ | |
| 114 opus_int M, /* I Dim of Matrix equation
*/ | |
| 115 const silk_float *b, /* I b Vector
*/ | |
| 116 silk_float *x /* O x Vector
*/ | |
| 117 ) | |
| 118 { | |
| 119 opus_int i, j; | |
| 120 silk_float temp; | |
| 121 const silk_float *ptr1; | |
| 122 | |
| 123 for( i = M - 1; i >= 0; i-- ) { | |
| 124 ptr1 = matrix_adr( L, 0, i, M ); | |
| 125 temp = 0; | |
| 126 for( j = M - 1; j > i ; j-- ) { | |
| 127 temp += ptr1[ j * M ] * x[ j ]; | |
| 128 } | |
| 129 temp = b[ i ] - temp; | |
| 130 x[ i ] = temp; | |
| 131 } | |
| 132 } | |
| 133 | |
| 134 static OPUS_INLINE void silk_SolveWithLowerTriangularWdiagOnes_FLP( | |
| 135 const silk_float *L, /* I Pointer to Lower Triangular Matrix
*/ | |
| 136 opus_int M, /* I Dim of Matrix equation
*/ | |
| 137 const silk_float *b, /* I b Vector
*/ | |
| 138 silk_float *x /* O x Vector
*/ | |
| 139 ) | |
| 140 { | |
| 141 opus_int i, j; | |
| 142 silk_float temp; | |
| 143 const silk_float *ptr1; | |
| 144 | |
| 145 for( i = 0; i < M; i++ ) { | |
| 146 ptr1 = matrix_adr( L, i, 0, M ); | |
| 147 temp = 0; | |
| 148 for( j = 0; j < i; j++ ) { | |
| 149 temp += ptr1[ j ] * x[ j ]; | |
| 150 } | |
| 151 temp = b[ i ] - temp; | |
| 152 x[ i ] = temp; | |
| 153 } | |
| 154 } | |
| 155 | |
| 156 static OPUS_INLINE void silk_LDL_FLP( | |
| 157 silk_float *A, /* I/O Pointer to Symetric Square Matrix
*/ | |
| 158 opus_int M, /* I Size of Matrix
*/ | |
| 159 silk_float *L, /* I/O Pointer to Square Upper triangular M
atrix */ | |
| 160 silk_float *Dinv /* I/O Pointer to vector holding the invers
e diagonal elements of D */ | |
| 161 ) | |
| 162 { | |
| 163 opus_int i, j, k, loop_count, err = 1; | |
| 164 silk_float *ptr1, *ptr2; | |
| 165 double temp, diag_min_value; | |
| 166 silk_float v[ MAX_MATRIX_SIZE ], D[ MAX_MATRIX_SIZE ]; /* temp arrays*/ | |
| 167 | |
| 168 silk_assert( M <= MAX_MATRIX_SIZE ); | |
| 169 | |
| 170 diag_min_value = FIND_LTP_COND_FAC * 0.5f * ( A[ 0 ] + A[ M * M - 1 ] ); | |
| 171 for( loop_count = 0; loop_count < M && err == 1; loop_count++ ) { | |
| 172 err = 0; | |
| 173 for( j = 0; j < M; j++ ) { | |
| 174 ptr1 = matrix_adr( L, j, 0, M ); | |
| 175 temp = matrix_ptr( A, j, j, M ); /* element in row j column j*/ | |
| 176 for( i = 0; i < j; i++ ) { | |
| 177 v[ i ] = ptr1[ i ] * D[ i ]; | |
| 178 temp -= ptr1[ i ] * v[ i ]; | |
| 179 } | |
| 180 if( temp < diag_min_value ) { | |
| 181 /* Badly conditioned matrix: add white noise and run again */ | |
| 182 temp = ( loop_count + 1 ) * diag_min_value - temp; | |
| 183 for( i = 0; i < M; i++ ) { | |
| 184 matrix_ptr( A, i, i, M ) += ( silk_float )temp; | |
| 185 } | |
| 186 err = 1; | |
| 187 break; | |
| 188 } | |
| 189 D[ j ] = ( silk_float )temp; | |
| 190 Dinv[ j ] = ( silk_float )( 1.0f / temp ); | |
| 191 matrix_ptr( L, j, j, M ) = 1.0f; | |
| 192 | |
| 193 ptr1 = matrix_adr( A, j, 0, M ); | |
| 194 ptr2 = matrix_adr( L, j + 1, 0, M); | |
| 195 for( i = j + 1; i < M; i++ ) { | |
| 196 temp = 0.0; | |
| 197 for( k = 0; k < j; k++ ) { | |
| 198 temp += ptr2[ k ] * v[ k ]; | |
| 199 } | |
| 200 matrix_ptr( L, i, j, M ) = ( silk_float )( ( ptr1[ i ] - temp )
* Dinv[ j ] ); | |
| 201 ptr2 += M; /* go to next column*/ | |
| 202 } | |
| 203 } | |
| 204 } | |
| 205 silk_assert( err == 0 ); | |
| 206 } | |
| 207 | |
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Chromium Code Reviews
Issue 2962373002:
[Opus] Update to v1.2.1 (Closed)
