Mercurial > hg > octave-nkf
annotate libinterp/corefcn/kron.cc @ 19527:cd2a75e5cd6e gui-release
use indentation width for the editor from the settings (bug #43592)
* file-editor-tab.cc (notice-settings): Read the indentation width from the
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author | Torsten <ttl@justmail.de> |
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date | Thu, 13 Nov 2014 07:02:33 +0100 |
parents | 175b392e91fe |
children | 6a71e5030df5 |
rev | line source |
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3910 | 1 /* |
2 | |
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3 Copyright (C) 2002-2013 John W. Eaton |
3910 | 4 |
5 This file is part of Octave. | |
6 | |
7 Octave is free software; you can redistribute it and/or modify it | |
8 under the terms of the GNU General Public License as published by the | |
7016 | 9 Free Software Foundation; either version 3 of the License, or (at your |
10 option) any later version. | |
3910 | 11 |
12 Octave is distributed in the hope that it will be useful, but WITHOUT | |
13 ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or | |
14 FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License | |
15 for more details. | |
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 |
19 <http://www.gnu.org/licenses/>. | |
3910 | 20 |
21 */ | |
22 | |
3911 | 23 // Author: Paul Kienzle <pkienzle@users.sf.net> |
24 | |
3910 | 25 #ifdef HAVE_CONFIG_H |
26 #include <config.h> | |
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" |
49 #include "oct-obj.h" | |
50 | |
10461 | 51 template <class R, class T> |
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 | |
58 octave_idx_type nra = a.rows (), nrb = b.rows (); | |
59 octave_idx_type nca = a.cols (), ncb = b.cols (); | |
3910 | 60 |
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61 MArray<T> c (dim_vector (nra*nrb, nca*ncb)); |
10461 | 62 T *cv = c.fortran_vec (); |
63 | |
64 for (octave_idx_type ja = 0; ja < nca; ja++) | |
65 for (octave_idx_type jb = 0; jb < ncb; jb++) | |
66 for (octave_idx_type ia = 0; ia < nra; ia++) | |
67 { | |
68 octave_quit (); | |
69 mx_inline_mul (nrb, cv, a(ia, ja), b.data () + nrb*jb); | |
70 cv += nrb; | |
71 } | |
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72 |
10461 | 73 return c; |
74 } | |
75 | |
76 template <class R, class T> | |
77 static MArray<T> | |
78 kron (const MDiagArray2<R>& a, const MArray<T>& b) | |
79 { | |
80 assert (b.ndims () == 2); | |
81 | |
82 octave_idx_type nra = a.rows (), nrb = b.rows (), dla = a.diag_length (); | |
83 octave_idx_type nca = a.cols (), ncb = b.cols (); | |
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84 |
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85 MArray<T> c (dim_vector (nra*nrb, nca*ncb), T ()); |
10461 | 86 |
87 for (octave_idx_type ja = 0; ja < dla; ja++) | |
88 for (octave_idx_type jb = 0; jb < ncb; jb++) | |
89 { | |
90 octave_quit (); | |
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91 mx_inline_mul (nrb, &c.xelem (ja*nrb, ja*ncb + jb), a.dgelem (ja), |
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92 b.data () + nrb*jb); |
10461 | 93 } |
94 | |
95 return c; | |
96 } | |
3910 | 97 |
98 template <class T> | |
10461 | 99 static MSparse<T> |
100 kron (const MSparse<T>& A, const MSparse<T>& B) | |
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101 { |
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102 octave_idx_type idx = 0; |
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103 MSparse<T> C (A.rows () * B.rows (), A.columns () * B.columns (), |
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104 A.nnz () * B.nnz ()); |
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105 |
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106 C.cidx (0) = 0; |
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107 |
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108 for (octave_idx_type Aj = 0; Aj < A.columns (); Aj++) |
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109 for (octave_idx_type Bj = 0; Bj < B.columns (); Bj++) |
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110 { |
10461 | 111 octave_quit (); |
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112 for (octave_idx_type Ai = A.cidx (Aj); Ai < A.cidx (Aj+1); Ai++) |
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113 { |
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114 octave_idx_type Ci = A.ridx (Ai) * B.rows (); |
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115 const T v = A.data (Ai); |
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116 |
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117 for (octave_idx_type Bi = B.cidx (Bj); Bi < B.cidx (Bj+1); Bi++) |
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118 { |
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119 C.data (idx) = v * B.data (Bi); |
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120 C.ridx (idx++) = Ci + B.ridx (Bi); |
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121 } |
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122 } |
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123 C.cidx (Aj * B.columns () + Bj + 1) = idx; |
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124 } |
10461 | 125 |
126 return C; | |
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127 } |
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128 |
10461 | 129 static PermMatrix |
130 kron (const PermMatrix& a, const PermMatrix& b) | |
131 { | |
132 octave_idx_type na = a.rows (), nb = b.rows (); | |
133 const octave_idx_type *pa = a.data (), *pb = b.data (); | |
134 PermMatrix c(na*nb); // Row permutation. | |
135 octave_idx_type *pc = c.fortran_vec (); | |
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136 |
10461 | 137 bool cola = a.is_col_perm (), colb = b.is_col_perm (); |
138 if (cola && colb) | |
139 { | |
140 for (octave_idx_type i = 0; i < na; i++) | |
141 for (octave_idx_type j = 0; j < nb; j++) | |
142 pc[pa[i]*nb+pb[j]] = i*nb+j; | |
143 } | |
144 else if (cola) | |
145 { | |
146 for (octave_idx_type i = 0; i < na; i++) | |
147 for (octave_idx_type j = 0; j < nb; j++) | |
148 pc[pa[i]*nb+j] = i*nb+pb[j]; | |
149 } | |
150 else if (colb) | |
151 { | |
152 for (octave_idx_type i = 0; i < na; i++) | |
153 for (octave_idx_type j = 0; j < nb; j++) | |
154 pc[i*nb+pb[j]] = pa[i]*nb+j; | |
155 } | |
156 else | |
157 { | |
158 for (octave_idx_type i = 0; i < na; i++) | |
159 for (octave_idx_type j = 0; j < nb; j++) | |
160 pc[i*nb+j] = pa[i]*nb+pb[j]; | |
161 } | |
162 | |
163 return c; | |
164 } | |
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165 |
10461 | 166 template <class MTA, class MTB> |
167 octave_value | |
168 do_kron (const octave_value& a, const octave_value& b) | |
169 { | |
170 MTA am = octave_value_extract<MTA> (a); | |
171 MTB bm = octave_value_extract<MTB> (b); | |
172 return octave_value (kron (am, bm)); | |
173 } | |
174 | |
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175 octave_value |
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176 dispatch_kron (const octave_value& a, const octave_value& b) |
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177 { |
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178 octave_value retval; |
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179 if (a.is_perm_matrix () && b.is_perm_matrix ()) |
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180 retval = do_kron<PermMatrix, PermMatrix> (a, b); |
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181 else if (a.is_sparse_type () || b.is_sparse_type ()) |
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182 { |
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183 if (a.is_complex_type () || b.is_complex_type ()) |
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184 retval = do_kron<SparseComplexMatrix, SparseComplexMatrix> (a, b); |
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185 else |
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186 retval = do_kron<SparseMatrix, SparseMatrix> (a, b); |
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187 } |
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188 else if (a.is_diag_matrix ()) |
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189 { |
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190 if (b.is_diag_matrix () && a.rows () == a.columns () |
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191 && b.rows () == b.columns ()) |
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192 { |
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193 // We have two diagonal matrices, the product of those will be |
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194 // another diagonal matrix. To do that efficiently, extract |
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195 // the diagonals as vectors and compute the product. That |
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196 // will be another vector, which we then use to construct a |
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197 // diagonal matrix object. Note that this will fail if our |
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198 // digaonal matrix object is modified to allow the non-zero |
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199 // values to be stored off of the principal diagonal (i.e., if |
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200 // diag ([1,2], 3) is modified to return a diagonal matrix |
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201 // object instead of a full matrix object). |
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202 |
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203 octave_value tmp = dispatch_kron (a.diag (), b.diag ()); |
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204 retval = tmp.diag (); |
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205 } |
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206 else if (a.is_single_type () || b.is_single_type ()) |
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207 { |
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208 if (a.is_complex_type ()) |
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209 retval = do_kron<FloatComplexDiagMatrix, FloatComplexMatrix> (a, b); |
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210 else if (b.is_complex_type ()) |
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211 retval = do_kron<FloatDiagMatrix, FloatComplexMatrix> (a, b); |
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212 else |
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213 retval = do_kron<FloatDiagMatrix, FloatMatrix> (a, b); |
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214 } |
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215 else |
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216 { |
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217 if (a.is_complex_type ()) |
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218 retval = do_kron<ComplexDiagMatrix, ComplexMatrix> (a, b); |
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219 else if (b.is_complex_type ()) |
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220 retval = do_kron<DiagMatrix, ComplexMatrix> (a, b); |
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221 else |
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222 retval = do_kron<DiagMatrix, Matrix> (a, b); |
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223 } |
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224 } |
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225 else if (a.is_single_type () || b.is_single_type ()) |
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226 { |
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227 if (a.is_complex_type ()) |
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228 retval = do_kron<FloatComplexMatrix, FloatComplexMatrix> (a, b); |
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229 else if (b.is_complex_type ()) |
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230 retval = do_kron<FloatMatrix, FloatComplexMatrix> (a, b); |
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231 else |
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232 retval = do_kron<FloatMatrix, FloatMatrix> (a, b); |
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233 } |
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234 else |
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235 { |
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236 if (a.is_complex_type ()) |
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237 retval = do_kron<ComplexMatrix, ComplexMatrix> (a, b); |
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238 else if (b.is_complex_type ()) |
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239 retval = do_kron<Matrix, ComplexMatrix> (a, b); |
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240 else |
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241 retval = do_kron<Matrix, Matrix> (a, b); |
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242 } |
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243 return retval; |
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244 } |
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245 |
10461 | 246 |
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247 DEFUN (kron, args, , "-*- texinfo -*-\n\ |
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248 @deftypefn {Built-in Function} {} kron (@var{A}, @var{B})\n\ |
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249 @deftypefnx {Built-in Function} {} kron (@var{A1}, @var{A2}, @dots{})\n\ |
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250 Form the Kronecker product of two or more matrices, defined block by \n\ |
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251 block as\n\ |
3910 | 252 \n\ |
253 @example\n\ | |
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254 x = [ a(i,j)*b ]\n\ |
3910 | 255 @end example\n\ |
256 \n\ | |
10840 | 257 For example:\n\ |
3910 | 258 \n\ |
259 @example\n\ | |
260 @group\n\ | |
261 kron (1:4, ones (3, 1))\n\ | |
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262 @result{} 1 2 3 4\n\ |
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263 1 2 3 4\n\ |
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264 1 2 3 4\n\ |
3910 | 265 @end group\n\ |
266 @end example\n\ | |
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267 \n\ |
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268 If there are more than two input arguments @var{A1}, @var{A2}, @dots{}, \n\ |
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269 @var{An} the Kronecker product is computed as\n\ |
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270 \n\ |
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271 @example\n\ |
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272 kron (kron (@var{A1}, @var{A2}), @dots{}, @var{An})\n\ |
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273 @end example\n\ |
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274 \n\ |
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275 @noindent\n\ |
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276 Since the Kronecker product is associative, this is well-defined.\n\ |
3910 | 277 @end deftypefn") |
278 { | |
10461 | 279 octave_value retval; |
3910 | 280 |
281 int nargin = args.length (); | |
282 | |
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283 if (nargin >= 2) |
3910 | 284 { |
10461 | 285 octave_value a = args(0), b = args(1); |
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286 retval = dispatch_kron (a, b); |
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287 for (octave_idx_type i = 2; i < nargin; i++) |
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288 retval = dispatch_kron (retval, args(i)); |
3910 | 289 } |
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290 else |
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291 print_usage (); |
3910 | 292 |
293 return retval; | |
294 } | |
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295 |
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296 |
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297 /* |
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298 %!test |
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299 %! x = ones (2); |
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300 %! assert (kron (x, x), ones (4)); |
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301 |
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302 %!shared x, y, z |
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303 %! x = [1, 2]; |
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304 %! y = [-1, -2]; |
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305 %! z = [1, 2, 3, 4; 1, 2, 3, 4; 1, 2, 3, 4]; |
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306 %!assert (kron (1:4, ones (3, 1)), z) |
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307 %!assert (kron (x, y, z), kron (kron (x, y), z)) |
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308 %!assert (kron (x, y, z), kron (x, kron (y, z))) |
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309 |
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310 %!assert (kron (diag ([1, 2]), diag ([3, 4])), diag ([3, 4, 6, 8])) |
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311 |
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312 %% Test for two diag matrices. See the comments above in |
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313 %% dispatch_kron for this case. |
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314 %% |
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315 %!test |
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316 %! expected = zeros (16, 16); |
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317 %! expected (1, 11) = 3; |
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318 %! expected (2, 12) = 4; |
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319 %! expected (5, 15) = 6; |
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320 %! expected (6, 16) = 8; |
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321 %! assert (kron (diag ([1, 2], 2), diag ([3, 4], 2)), expected) |
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322 */ |