Mercurial > octave
annotate libinterp/corefcn/kron.cc @ 25485:38a07d930ccd
Improve kron performance by 17% by fewer calls to octave_quit() (bug #54005).
* kron.cc: Move octave_quit() from innermost of triple "for" loops to outermost
loop. Ctrl+C responsiveness is not impacted since humans expect results in
hundreds of milliseconds.
author | Rik <rik@octave.org> |
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date | Wed, 20 Jun 2018 10:51:45 -0700 |
parents | 6652d3823428 |
children | 00f796120a6d |
rev | line source |
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3910 | 1 /* |
2 | |
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3 Copyright (C) 2002-2018 John W. Eaton |
3910 | 4 |
5 This file is part of Octave. | |
6 | |
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7 Octave is free software: you can redistribute it and/or modify it |
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8 under the terms of the GNU General Public License as published by |
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9 the Free Software Foundation, either version 3 of the License, or |
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10 (at your option) any later version. |
3910 | 11 |
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12 Octave is distributed in the hope that it will be useful, but |
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13 WITHOUT ANY WARRANTY; without even the implied warranty of |
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14 MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the |
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15 GNU General Public License for more details. |
3910 | 16 |
17 You should have received a copy of the GNU General Public License | |
7016 | 18 along with Octave; see the file COPYING. If not, see |
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19 <https://www.gnu.org/licenses/>. |
3910 | 20 |
21 */ | |
22 | |
3911 | 23 // Author: Paul Kienzle <pkienzle@users.sf.net> |
24 | |
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25 #if defined (HAVE_CONFIG_H) |
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26 # include "config.h" |
3910 | 27 #endif |
28 | |
29 #include "dMatrix.h" | |
10461 | 30 #include "fMatrix.h" |
3910 | 31 #include "CMatrix.h" |
10461 | 32 #include "fCMatrix.h" |
33 | |
34 #include "dSparse.h" | |
35 #include "CSparse.h" | |
36 | |
37 #include "dDiagMatrix.h" | |
38 #include "fDiagMatrix.h" | |
39 #include "CDiagMatrix.h" | |
40 #include "fCDiagMatrix.h" | |
41 | |
42 #include "PermMatrix.h" | |
43 | |
44 #include "mx-inlines.cc" | |
4153 | 45 #include "quit.h" |
3910 | 46 |
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47 #include "defun.h" |
3910 | 48 #include "error.h" |
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49 #include "ovl.h" |
3910 | 50 |
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51 template <typename R, typename T> |
10461 | 52 static MArray<T> |
53 kron (const MArray<R>& a, const MArray<T>& b) | |
54 { | |
55 assert (a.ndims () == 2); | |
56 assert (b.ndims () == 2); | |
57 | |
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58 octave_idx_type nra = a.rows (); |
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59 octave_idx_type nrb = b.rows (); |
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60 octave_idx_type nca = a.cols (); |
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61 octave_idx_type ncb = b.cols (); |
3910 | 62 |
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63 MArray<T> c (dim_vector (nra*nrb, nca*ncb)); |
10461 | 64 T *cv = c.fortran_vec (); |
65 | |
66 for (octave_idx_type ja = 0; ja < nca; ja++) | |
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67 { |
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68 octave_quit (); |
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69 for (octave_idx_type jb = 0; jb < ncb; jb++) |
10461 | 70 { |
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71 for (octave_idx_type ia = 0; ia < nra; ia++) |
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72 { |
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73 mx_inline_mul (nrb, cv, a(ia, ja), b.data () + nrb*jb); |
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74 cv += nrb; |
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75 } |
10461 | 76 } |
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77 } |
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78 |
10461 | 79 return c; |
80 } | |
81 | |
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82 template <typename R, typename T> |
10461 | 83 static MArray<T> |
84 kron (const MDiagArray2<R>& a, const MArray<T>& b) | |
85 { | |
86 assert (b.ndims () == 2); | |
87 | |
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88 octave_idx_type nra = a.rows (); |
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89 octave_idx_type nrb = b.rows (); |
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90 octave_idx_type dla = a.diag_length (); |
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91 octave_idx_type nca = a.cols (); |
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92 octave_idx_type ncb = b.cols (); |
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93 |
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94 MArray<T> c (dim_vector (nra*nrb, nca*ncb), T ()); |
10461 | 95 |
96 for (octave_idx_type ja = 0; ja < dla; ja++) | |
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97 { |
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98 octave_quit (); |
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99 for (octave_idx_type jb = 0; jb < ncb; jb++) |
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100 { |
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101 mx_inline_mul (nrb, &c.xelem (ja*nrb, ja*ncb + jb), a.dgelem (ja), |
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102 b.data () + nrb*jb); |
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103 } |
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104 } |
10461 | 105 |
106 return c; | |
107 } | |
3910 | 108 |
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109 template <typename T> |
10461 | 110 static MSparse<T> |
111 kron (const MSparse<T>& A, const MSparse<T>& B) | |
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112 { |
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113 octave_idx_type idx = 0; |
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114 MSparse<T> C (A.rows () * B.rows (), A.columns () * B.columns (), |
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115 A.nnz () * B.nnz ()); |
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116 |
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117 C.cidx (0) = 0; |
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118 |
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119 for (octave_idx_type Aj = 0; Aj < A.columns (); Aj++) |
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120 { |
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121 octave_quit (); |
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122 for (octave_idx_type Bj = 0; Bj < B.columns (); Bj++) |
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123 { |
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124 for (octave_idx_type Ai = A.cidx (Aj); Ai < A.cidx (Aj+1); Ai++) |
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125 { |
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126 octave_idx_type Ci = A.ridx (Ai) * B.rows (); |
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127 const T v = A.data (Ai); |
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128 |
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129 for (octave_idx_type Bi = B.cidx (Bj); Bi < B.cidx (Bj+1); Bi++) |
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130 { |
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131 C.data (idx) = v * B.data (Bi); |
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132 C.ridx (idx++) = Ci + B.ridx (Bi); |
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133 } |
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134 } |
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135 C.cidx (Aj * B.columns () + Bj + 1) = idx; |
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136 } |
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137 } |
10461 | 138 |
139 return C; | |
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140 } |
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141 |
10461 | 142 static PermMatrix |
143 kron (const PermMatrix& a, const PermMatrix& b) | |
144 { | |
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145 octave_idx_type na = a.rows (); |
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146 octave_idx_type nb = b.rows (); |
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147 const Array<octave_idx_type>& pa = a.col_perm_vec (); |
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148 const Array<octave_idx_type>& pb = b.col_perm_vec (); |
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149 Array<octave_idx_type> res_perm (dim_vector (na * nb, 1)); |
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150 octave_idx_type rescol = 0; |
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151 for (octave_idx_type i = 0; i < na; i++) |
10461 | 152 { |
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153 octave_idx_type a_add = pa(i) * nb; |
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154 for (octave_idx_type j = 0; j < nb; j++) |
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155 res_perm.xelem (rescol++) = a_add + pb(j); |
10461 | 156 } |
157 | |
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158 return PermMatrix (res_perm, true); |
10461 | 159 } |
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160 |
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161 template <typename MTA, typename MTB> |
10461 | 162 octave_value |
163 do_kron (const octave_value& a, const octave_value& b) | |
164 { | |
165 MTA am = octave_value_extract<MTA> (a); | |
166 MTB bm = octave_value_extract<MTB> (b); | |
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167 |
10461 | 168 return octave_value (kron (am, bm)); |
169 } | |
170 | |
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171 octave_value |
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172 dispatch_kron (const octave_value& a, const octave_value& b) |
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173 { |
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174 octave_value retval; |
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175 if (a.is_perm_matrix () && b.is_perm_matrix ()) |
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176 retval = do_kron<PermMatrix, PermMatrix> (a, b); |
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177 else if (a.issparse () || b.issparse ()) |
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178 { |
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179 if (a.iscomplex () || b.iscomplex ()) |
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180 retval = do_kron<SparseComplexMatrix, SparseComplexMatrix> (a, b); |
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181 else |
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182 retval = do_kron<SparseMatrix, SparseMatrix> (a, b); |
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183 } |
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184 else if (a.is_diag_matrix ()) |
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185 { |
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186 if (b.is_diag_matrix () && a.rows () == a.columns () |
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187 && b.rows () == b.columns ()) |
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188 { |
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189 // We have two diagonal matrices, the product of those will be |
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190 // another diagonal matrix. To do that efficiently, extract |
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191 // the diagonals as vectors and compute the product. That |
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192 // will be another vector, which we then use to construct a |
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193 // diagonal matrix object. Note that this will fail if our |
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194 // digaonal matrix object is modified to allow the nonzero |
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195 // values to be stored off of the principal diagonal (i.e., if |
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196 // diag ([1,2], 3) is modified to return a diagonal matrix |
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197 // object instead of a full matrix object). |
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198 |
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199 octave_value tmp = dispatch_kron (a.diag (), b.diag ()); |
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200 retval = tmp.diag (); |
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201 } |
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202 else if (a.is_single_type () || b.is_single_type ()) |
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203 { |
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204 if (a.iscomplex ()) |
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205 retval = do_kron<FloatComplexDiagMatrix, FloatComplexMatrix> (a, b); |
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206 else if (b.iscomplex ()) |
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207 retval = do_kron<FloatDiagMatrix, FloatComplexMatrix> (a, b); |
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208 else |
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209 retval = do_kron<FloatDiagMatrix, FloatMatrix> (a, b); |
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210 } |
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211 else |
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212 { |
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213 if (a.iscomplex ()) |
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214 retval = do_kron<ComplexDiagMatrix, ComplexMatrix> (a, b); |
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215 else if (b.iscomplex ()) |
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216 retval = do_kron<DiagMatrix, ComplexMatrix> (a, b); |
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217 else |
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218 retval = do_kron<DiagMatrix, Matrix> (a, b); |
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219 } |
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220 } |
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221 else if (a.is_single_type () || b.is_single_type ()) |
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222 { |
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223 if (a.iscomplex ()) |
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224 retval = do_kron<FloatComplexMatrix, FloatComplexMatrix> (a, b); |
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225 else if (b.iscomplex ()) |
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226 retval = do_kron<FloatMatrix, FloatComplexMatrix> (a, b); |
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227 else |
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228 retval = do_kron<FloatMatrix, FloatMatrix> (a, b); |
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229 } |
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230 else |
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231 { |
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232 if (a.iscomplex ()) |
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233 retval = do_kron<ComplexMatrix, ComplexMatrix> (a, b); |
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234 else if (b.iscomplex ()) |
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235 retval = do_kron<Matrix, ComplexMatrix> (a, b); |
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236 else |
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237 retval = do_kron<Matrix, Matrix> (a, b); |
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238 } |
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239 return retval; |
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240 } |
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241 |
10461 | 242 |
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243 DEFUN (kron, args, , |
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244 doc: /* -*- texinfo -*- |
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245 @deftypefn {} {} kron (@var{A}, @var{B}) |
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246 @deftypefnx {} {} kron (@var{A1}, @var{A2}, @dots{}) |
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247 Form the Kronecker product of two or more matrices. |
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248 |
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249 This is defined block by block as |
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250 |
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251 @example |
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252 x = [ a(i,j)*b ] |
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253 @end example |
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254 |
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255 For example: |
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256 |
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257 @example |
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258 @group |
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259 kron (1:4, ones (3, 1)) |
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260 @result{} 1 2 3 4 |
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261 1 2 3 4 |
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262 1 2 3 4 |
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263 @end group |
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264 @end example |
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265 |
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266 If there are more than two input arguments @var{A1}, @var{A2}, @dots{}, |
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267 @var{An} the Kronecker product is computed as |
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268 |
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269 @example |
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270 kron (kron (@var{A1}, @var{A2}), @dots{}, @var{An}) |
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271 @end example |
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272 |
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273 @noindent |
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274 Since the Kronecker product is associative, this is well-defined. |
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275 @end deftypefn */) |
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277 int nargin = args.length (); | |
278 | |
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279 if (nargin < 2) |
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280 print_usage (); |
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282 octave_value retval; |
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283 |
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284 octave_value a = args(0); |
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285 octave_value b = args(1); |
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286 |
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287 retval = dispatch_kron (a, b); |
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288 |
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289 for (octave_idx_type i = 2; i < nargin; i++) |
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290 retval = dispatch_kron (retval, args(i)); |
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291 |
3910 | 292 return retval; |
293 } | |
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294 |
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295 /* |
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296 %!test |
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297 %! x = ones (2); |
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298 %! assert (kron (x, x), ones (4)); |
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299 |
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300 %!shared x, y, z, p1, p2, d1, d2 |
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301 %! x = [1, 2]; |
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302 %! y = [-1, -2]; |
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303 %! z = [1, 2, 3, 4; 1, 2, 3, 4; 1, 2, 3, 4]; |
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304 %! p1 = eye (3)([2, 3, 1], :); ## Permutation matrix |
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305 %! p2 = [0 1 0; 0 0 1; 1 0 0]; ## Non-permutation equivalent |
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306 %! d1 = diag ([1 2 3]); ## Diag type matrix |
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307 %! d2 = [1 0 0; 0 2 0; 0 0 3]; ## Non-diag equivalent |
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308 %!assert (kron (1:4, ones (3, 1)), z) |
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309 %!assert (kron (single (1:4), ones (3, 1)), single (z)) |
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310 %!assert (kron (sparse (1:4), ones (3, 1)), sparse (z)) |
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311 %!assert (kron (complex (1:4), ones (3, 1)), z) |
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312 %!assert (kron (complex (single(1:4)), ones (3, 1)), single(z)) |
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313 %!assert (kron (x, y, z), kron (kron (x, y), z)) |
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314 %!assert (kron (x, y, z), kron (x, kron (y, z))) |
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315 %!assert (kron (p1, p1), kron (p2, p2)) |
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316 %!assert (kron (p1, p2), kron (p2, p1)) |
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317 %!assert (kron (d1, d1), kron (d2, d2)) |
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318 %!assert (kron (d1, d2), kron (d2, d1)) |
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319 |
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320 %!assert (kron (diag ([1, 2]), diag ([3, 4])), diag ([3, 4, 6, 8])) |
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321 |
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322 ## Test for two diag matrices. |
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323 ## See the comments above in dispatch_kron for this case. |
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324 %!test |
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325 %! expected = zeros (16, 16); |
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326 %! expected (1, 11) = 3; |
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327 %! expected (2, 12) = 4; |
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328 %! expected (5, 15) = 6; |
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329 %! expected (6, 16) = 8; |
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330 %! assert (kron (diag ([1, 2], 2), diag ([3, 4], 2)), expected); |
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331 */ |