GNU Octave  9.1.0
A high-level interpreted language, primarily intended for numerical computations, mostly compatible with Matlab
conv2.cc
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25 
26 #if defined (HAVE_CONFIG_H)
27 # include "config.h"
28 #endif
29 
30 #include "oct-convn.h"
31 
32 #include "defun.h"
33 #include "error.h"
34 #include "ovl.h"
35 #include "utils.h"
36 
37 OCTAVE_BEGIN_NAMESPACE(octave)
38 
39 enum Shape { SHAPE_FULL, SHAPE_SAME, SHAPE_VALID };
40 
41 DEFUN (conv2, args, ,
42  doc: /* -*- texinfo -*-
43 @deftypefn {} {@var{C} =} conv2 (@var{A}, @var{B})
44 @deftypefnx {} {@var{C} =} conv2 (@var{v1}, @var{v2}, @var{m})
45 @deftypefnx {} {@var{C} =} conv2 (@dots{}, @var{shape})
46 Return the 2-D convolution of @var{A} and @var{B}.
47 
48 The size of the result is determined by the optional @var{shape} argument
49 which takes the following values
50 
51 @table @asis
52 @item @var{shape} = @qcode{"full"}
53 Return the full convolution. (default)
54 
55 @item @var{shape} = @qcode{"same"}
56 Return the central part of the convolution with the same size as @var{A}.
57 The central part of the convolution begins at the indices
58 @code{floor ([size(@var{B})/2] + 1)}.
59 
60 @item @var{shape} = @qcode{"valid"}
61 Return only the parts which do not include zero-padded edges.
62 The size of the result is @code{max (size (A) - size (B) + 1, 0)}.
63 @end table
64 
65 When the third argument is a matrix, return the convolution of the matrix
66 @var{m} by the vector @var{v1} in the column direction and by the vector
67 @var{v2} in the row direction.
68 @seealso{conv, convn}
69 @end deftypefn */)
70 {
71  int nargin = args.length ();
72 
73  if (nargin < 2 || nargin > 4)
74  print_usage ();
75 
76  std::string shape = "full"; // default
77  bool separable = false;
78  convn_type ct = convn_full;
79 
80  if (nargin == 3)
81  {
82  if (args(2).is_string ())
83  shape = args(2).string_value ();
84  else
85  separable = true;
86  }
87  else if (nargin == 4)
88  {
89  separable = true;
90  shape = args(3).string_value ();
91  }
92 
93  if (args(0).ndims () > 2 || args(1).ndims () > 2)
94  error ("conv2: A and B must be 1-D vectors or 2-D matrices");
95 
96  if (shape == "full")
97  ct = convn_full;
98  else if (shape == "same")
99  ct = convn_same;
100  else if (shape == "valid")
101  ct = convn_valid;
102  else
103  error ("conv2: SHAPE type not valid");
104 
105  octave_value retval;
106 
107  if (separable)
108  {
109  // If user requests separable, check first two params are vectors
110  if (! (1 == args(0).rows () || 1 == args(0).columns ())
111  || ! (1 == args(1).rows () || 1 == args(1).columns ()))
112  error ("conv2: arguments must be vectors for separable option");
113 
114  if (args(0).is_single_type () || args(1).is_single_type ()
115  || args(2).is_single_type ())
116  {
117  if (args(0).iscomplex () || args(1).iscomplex ()
118  || args(2).iscomplex ())
119  {
120  FloatComplexMatrix a (args(2).float_complex_matrix_value ());
121  if (args(1).isreal () && args(2).isreal ())
122  {
123  FloatColumnVector v1 (args(0).float_vector_value ());
124  FloatRowVector v2 (args(1).float_vector_value ());
125  retval = convn (a, v1, v2, ct);
126  }
127  else
128  {
129  FloatComplexColumnVector v1 (args(0).float_complex_vector_value ());
130  FloatComplexRowVector v2 (args(1).float_complex_vector_value ());
131  retval = convn (a, v1, v2, ct);
132  }
133  }
134  else
135  {
136  FloatColumnVector v1 (args(0).float_vector_value ());
137  FloatRowVector v2 (args(1).float_vector_value ());
138  FloatMatrix a (args(2).float_matrix_value ());
139  retval = convn (a, v1, v2, ct);
140  }
141  }
142  else
143  {
144  if (args(0).iscomplex () || args(1).iscomplex ()
145  || args(2).iscomplex ())
146  {
147  ComplexMatrix a (args(2).complex_matrix_value ());
148  if (args(1).isreal () && args(2).isreal ())
149  {
150  ColumnVector v1 (args(0).vector_value ());
151  RowVector v2 (args(1).vector_value ());
152  retval = convn (a, v1, v2, ct);
153  }
154  else
155  {
156  ComplexColumnVector v1 (args(0).complex_vector_value ());
157  ComplexRowVector v2 (args(1).complex_vector_value ());
158  retval = convn (a, v1, v2, ct);
159  }
160  }
161  else
162  {
163  ColumnVector v1 (args(0).vector_value ());
164  RowVector v2 (args(1).vector_value ());
165  Matrix a (args(2).matrix_value ());
166  retval = convn (a, v1, v2, ct);
167  }
168  }
169  } // if (separable)
170  else
171  {
172  if (args(0).is_single_type () || args(1).is_single_type ())
173  {
174  if (args(0).iscomplex () || args(1).iscomplex ())
175  {
176  FloatComplexMatrix a (args(0).float_complex_matrix_value ());
177  if (args(1).isreal ())
178  {
179  FloatMatrix b (args(1).float_matrix_value ());
180  retval = convn (a, b, ct);
181  }
182  else
183  {
184  FloatComplexMatrix b (args(1).float_complex_matrix_value ());
185  retval = convn (a, b, ct);
186  }
187  }
188  else
189  {
190  FloatMatrix a (args(0).float_matrix_value ());
191  FloatMatrix b (args(1).float_matrix_value ());
192  retval = convn (a, b, ct);
193  }
194  }
195  else
196  {
197  if (args(0).iscomplex () || args(1).iscomplex ())
198  {
199  ComplexMatrix a (args(0).complex_matrix_value ());
200  if (args(1).isreal ())
201  {
202  Matrix b (args(1).matrix_value ());
203  retval = convn (a, b, ct);
204  }
205  else
206  {
207  ComplexMatrix b (args(1).complex_matrix_value ());
208  retval = convn (a, b, ct);
209  }
210  }
211  else
212  {
213  Matrix a (args(0).matrix_value ());
214  Matrix b (args(1).matrix_value ());
215  retval = convn (a, b, ct);
216  }
217  }
218 
219  } // if (separable)
220 
221  return retval;
222 }
223 
224 /*
225 %!test
226 %! c = [0,1,2,3;1,8,12,12;4,20,24,21;7,22,25,18];
227 %! assert (conv2 ([0,1;1,2], [1,2,3;4,5,6;7,8,9]), c);
228 
229 %!test
230 %! c = single ([0,1,2,3;1,8,12,12;4,20,24,21;7,22,25,18]);
231 %! assert (conv2 (single ([0,1;1,2]), single ([1,2,3;4,5,6;7,8,9])), c);
232 
233 %!test
234 %! c = [1,4,4;5,18,16;14,48,40;19,62,48;15,48,36];
235 %! assert (conv2 (1:3, 1:2, [1,2;3,4;5,6]), c);
236 
237 %!assert (conv2 (1:3, 1:2, [1,2;3,4;5,6], "full"),
238 %! conv2 (1:3, 1:2, [1,2;3,4;5,6]));
239 
240 ## Test shapes
241 %!shared A, B, C
242 %! old_state = rand ("state");
243 %! restore_state = onCleanup (@() rand ("state", old_state));
244 %! rand ("state", 12345); # initialize generator to make behavior reproducible
245 %! A = rand (3, 4);
246 %! B = rand (4);
247 %! C = conv2 (A, B);
248 %!assert (conv2 (A,B, "full"), C)
249 %!assert (conv2 (A,B, "same"), C(3:5,3:6))
250 %!assert (conv2 (A,B, "valid"), zeros (0, 1))
251 %!assert (size (conv2 (B,A, "valid")), [2 1])
252 
253 %!test
254 %!shared A, B, C
255 %! old_state = rand ("state");
256 %! restore_state = onCleanup (@() rand ("state", old_state));
257 %! rand ("state", 12345); # initialize generator to make behavior reproducible
258 %! A = rand (3, 4);
259 %! B = rand (5);
260 %! C = conv2 (A, B);
261 %!assert (conv2 (A,B, "full"), C)
262 %!assert (conv2 (A,B, "same"), C(3:5,3:6))
263 %!assert (conv2 (A,B, "valid"), zeros (0, 0))
264 %!assert (size (conv2 (B,A, "valid")), [3 2])
265 
266 ## Clear shared variables so they are not reported for tests below
267 %!shared
268 
269 ## Test cases from Bug #34893
270 %!assert <*34893> (conv2 ([1:5;1:5], [1:2], "same"),
271 %! [4 7 10 13 10; 4 7 10 13 10])
272 %!assert <*34893> (conv2 ([1:5;1:5]', [1:2]', "same"),
273 %! [4 7 10 13 10; 4 7 10 13 10]')
274 %!assert <*34893> (conv2 ([1:5;1:5], [1:2], "valid"),
275 %! [4 7 10 13; 4 7 10 13])
276 %!assert <*34893> (conv2 ([1:5;1:5]', [1:2]', "valid"),
277 %! [4 7 10 13; 4 7 10 13]')
278 
279 %% Restore the rand "seed" and "state" values in order, so that the
280 %% new "state" algorithm remains active after these tests complete.
281 %!function restore_rand_state (seed, state)
282 %! rand ("seed", seed);
283 %! rand ("state", state);
284 %!endfunction
285 
286 %% FIXME: This test only passes when using the "old" random number
287 %% generator by setting the "seed" parameter to any value. If
288 %% the "state" parameter is used, the test fails. This probably
289 %% indicates that this test is particularly fragile. This might
290 %% need further investigation or a rewrite, for example using
291 %% random integer values to avoid precision overflow.
292 %!test
293 %! old_seed = rand ("seed");
294 %! old_state = rand ("state");
295 %! restore_state = onCleanup (@() restore_rand_state (old_seed, old_state));
296 %! rand ("seed", 42);
297 %! x = rand (100);
298 %! y = ones (5);
299 %! A = conv2 (x, y)(5:end-4,5:end-4);
300 %! B = conv2 (x, y, "valid");
301 %! assert (B, A); # Yes, this test is for *exact* equivalence.
302 
303 ## Test input validation
304 %!error conv2 ()
305 %!error conv2 (1)
306 %!error <must be 1-D vectors or 2-D matrices> conv2 (ones (2), ones (2,2,2))
307 %!error <SHAPE type not valid> conv2 (1,2, "NOT_A_SHAPE")
308 ## Test alternate calling form which should be 2 vectors and a matrix
309 %!error conv2 (ones (2), 1, 1)
310 %!error conv2 (1, ones (2), 1)
311 */
312 
313 DEFUN (convn, args, ,
314  doc: /* -*- texinfo -*-
315 @deftypefn {} {@var{C} =} convn (@var{A}, @var{B})
316 @deftypefnx {} {@var{C} =} convn (@var{A}, @var{B}, @var{shape})
317 Return the n-D convolution of @var{A} and @var{B}.
318 
319 The size of the result is determined by the optional @var{shape} argument
320 which takes the following values
321 
322 @table @asis
323 @item @var{shape} = @qcode{"full"}
324 Return the full convolution. (default)
325 
326 @item @var{shape} = @qcode{"same"}
327 Return central part of the convolution with the same size as @var{A}.
328 The central part of the convolution begins at the indices
329 @code{floor ([size(@var{B})/2] + 1)}.
330 
331 @item @var{shape} = @qcode{"valid"}
332 Return only the parts which do not include zero-padded edges.
333 The size of the result is @code{max (size (A) - size (B) + 1, 0)}.
334 @end table
335 
336 @seealso{conv2, conv}
337 @end deftypefn */)
338 {
339  int nargin = args.length ();
340 
341  if (nargin < 2 || nargin > 3)
342  print_usage ();
343 
344  std::string shape = "full"; // default
345  convn_type ct = convn_full;
346 
347  if (nargin == 3)
348  shape = args(2).xstring_value ("convn: SHAPE must be a string");
349 
350  if (shape == "full")
351  ct = convn_full;
352  else if (shape == "same")
353  ct = convn_same;
354  else if (shape == "valid")
355  ct = convn_valid;
356  else
357  error ("convn: SHAPE type not valid");
358 
359  octave_value retval;
360 
361  if (args(0).is_single_type () || args(1).is_single_type ())
362  {
363  if (args(0).iscomplex () || args(1).iscomplex ())
364  {
365  FloatComplexNDArray a (args(0).float_complex_array_value ());
366  if (args(1).isreal ())
367  {
368  FloatNDArray b (args(1).float_array_value ());
369  retval = convn (a, b, ct);
370  }
371  else
372  {
373  FloatComplexNDArray b (args(1).float_complex_array_value ());
374  retval = convn (a, b, ct);
375  }
376  }
377  else
378  {
379  FloatNDArray a (args(0).float_array_value ());
380  FloatNDArray b (args(1).float_array_value ());
381  retval = convn (a, b, ct);
382  }
383  }
384  else
385  {
386  if (args(0).iscomplex () || args(1).iscomplex ())
387  {
388  ComplexNDArray a (args(0).complex_array_value ());
389  if (args(1).isreal ())
390  {
391  NDArray b (args(1).array_value ());
392  retval = convn (a, b, ct);
393  }
394  else
395  {
396  ComplexNDArray b (args(1).complex_array_value ());
397  retval = convn (a, b, ct);
398  }
399  }
400  else
401  {
402  NDArray a (args(0).array_value ());
403  NDArray b (args(1).array_value ());
404  retval = convn (a, b, ct);
405  }
406  }
407 
408  return retval;
409 }
410 
411 /*
412 %!test <39314>
413 %! v = reshape ([1 2], [1 1 2]);
414 %! assert (convn (v, v), reshape ([1 4 4], [1 1 3]));
415 %! assert (convn (v, v, "same"), reshape ([4 4], [1 1 2]));
416 %! assert (convn (v, v, "valid"), 4);
417 
418 ## The following test may look weird since we are using the output
419 ## of convn to test itself. However, because calculations are done
420 ## differently based on the shape option, it will help to catch some
421 ## bugs. See also bug #39314.
422 ## FIXME: The "valid" option uses an entirely different code path
423 ## through C++ and Fortran to calculate inner convolution.
424 ## The terms in the convolution added in reverse order compared
425 ## to the "full" option. This produces differences on the order
426 ## of tens of eps. This should be fixed, but in the meantime
427 ## the tests will be marked as known failures.
428 %!shared a, b, c
429 %! ## test 3D by 3D
430 %! old_state = rand ("state");
431 %! restore_state = onCleanup (@() rand ("state", old_state));
432 %! rand ("state", 12345); # initialize generator to make behavior reproducible
433 %! a = rand (10, 10, 10);
434 %! b = rand (3, 3, 3);
435 %! c = convn (a, b, "full");
436 %!assert (convn (a, b, "same"), c(2:11,2:11,2:11))
437 %!test <39314>
438 %! assert (convn (a, b, "valid"), c(3:10,3:10,3:10));
439 %!
440 %!test
441 %! old_state = rand ("state");
442 %! restore_state = onCleanup (@() rand ("state", old_state));
443 %! rand ("state", 12345); # initialize generator to make behavior reproducible
444 %! ## test 3D by 2D
445 %! a = rand (10, 10, 10);
446 %! b = rand (3, 3);
447 %! c = convn (a, b, "full");
448 %!assert (convn (a, b, "same"), c(2:11,2:11,:))
449 %!test <39314>
450 %! assert (convn (a, b, "valid"), c(3:10,3:10,:));
451 %!
452 %!test
453 %! old_state = rand ("state");
454 %! restore_state = onCleanup (@() rand ("state", old_state));
455 %! rand ("state", 12345); # initialize generator to make behavior reproducible
456 %! ## test 2D by 3D
457 %! a = rand (10, 10);
458 %! b = rand (3, 3, 3);
459 %! c = convn (a, b, "full");
460 %!assert (convn (a, b, "same"), c(2:11,2:11,2))
461 %!assert (convn (a, b, "valid"), c(3:10,3:10,3:2)) # a 7x7x0 matrix
462 %!
463 %!test
464 %! old_state = rand ("state");
465 %! restore_state = onCleanup (@() rand ("state", old_state));
466 %! rand ("state", 12345); # initialize generator to make behavior reproducible
467 %! ## test multiple different number of dimensions, with odd and even numbers
468 %! a = rand (10, 15, 7, 8, 10);
469 %! b = rand (4, 3, 2, 3);
470 %! c = convn (a, b, "full");
471 %!assert (convn (a, b, "same"), c(3:12,2:16,2:8,2:9,:))
472 %!test <39314>
473 %! assert (convn (a, b, "valid"), c(4:10,3:15,2:7,3:8,:));
474 
475 %!test
476 %! a = reshape (floor (magic (16) /10), [4 8 4 2]);
477 %! b = reshape (magic (6), [4 3 3]);
478 %! c = zeros (7, 10, 6, 2);
479 %! c(:,:,1,1) = [
480 %! 875 1415 1215 741 288 264 635 1109 687 171
481 %! 110 467 1551 1790 1891 1651 1165 900 659 568
482 %! 883 1047 1475 1964 2181 2302 2117 1674 579 234
483 %! 940 2330 3099 2573 2306 2207 2442 2918 2272 1004
484 %! 161 500 1564 2066 2355 2270 2099 1621 1144 831
485 %! 644 622 886 1121 1652 1967 1907 1668 529 228
486 %! 160 752 1232 768 360 284 668 1132 1380 864];
487 %! c(:,:,2,1) = [
488 %! 150 1174 1903 1971 2030 1719 1467 1420 1220 472
489 %! 986 2243 2603 2385 2308 2530 2971 3181 2266 768
490 %! 914 2443 3750 3782 3976 3821 3723 3709 2599 1178
491 %! 1922 3374 5198 5472 5563 5853 5794 5543 3578 1820
492 %! 1060 2471 3846 3724 3682 3803 3812 3927 2876 1390
493 %! 470 2078 3283 3225 2701 2265 2165 2261 2324 1124
494 %! 700 1130 1486 1515 1830 2097 2081 2028 1009 348];
495 %! c(:,:,3,1) = [
496 %! 1350 2127 2461 2082 1694 1909 2230 2621 1681 683
497 %! 877 2473 4362 4556 4543 4314 3879 3703 2863 1497
498 %! 1934 4219 5874 6117 5966 6051 5984 5714 3891 1562
499 %! 1927 5997 8573 8456 8517 8025 7957 8101 6121 2500
500 %! 1558 3533 5595 6064 6453 6491 6275 5743 3794 1832
501 %! 1950 2762 3455 3423 4019 4578 4807 4857 2304 907
502 %! 525 1860 2731 2392 1872 1724 1961 2312 2315 1141];
503 %! c(:,:,4,1) = [
504 %! 150 1317 2230 2621 2996 2767 2472 2049 1514 583
505 %! 1429 3056 3879 3703 3756 3964 4394 4570 3111 1250
506 %! 1833 4037 5984 5714 5846 5788 5883 6129 4157 2011
507 %! 3143 5469 7957 8101 8063 8475 8564 8439 5306 2538
508 %! 2001 4514 6275 5743 5391 5389 5578 6110 4473 1953
509 %! 817 3196 4807 4857 4229 3659 3477 3375 3208 1400
510 %! 750 1365 1961 2312 2840 2993 2722 2344 1092 323];
511 %! c(:,:,5,1) = [
512 %! 475 734 1296 1352 1400 1595 1557 1517 960 490
513 %! 751 1977 2831 2746 2607 2665 2733 2833 2186 912
514 %! 1065 3142 4344 4150 3768 3734 3876 4086 3366 1327
515 %! 976 3712 5530 5921 6158 5802 5481 5071 3821 1491
516 %! 1397 2996 3971 4003 4088 4180 4199 4146 2649 985
517 %! 1273 2121 2555 2247 2378 2624 2908 3229 1788 705
518 %! 365 1108 1530 1652 1550 1407 1274 1127 889 264];
519 %! c(:,:,6,1) = [
520 %! 0 133 345 683 982 1058 960 623 310 100
521 %! 437 806 1313 1332 1383 1391 1397 1370 864 495
522 %! 928 1573 2201 1928 1864 1932 2183 2445 1557 855
523 %! 1199 2083 2739 2573 2507 2656 2786 2928 1795 736
524 %! 912 1997 2404 2028 1692 1591 1803 2159 1603 599
525 %! 345 1092 1526 1666 1593 1437 1275 1116 863 253
526 %! 50 235 510 811 998 894 615 318 77 0];
527 %! c(:,:,1,2) = [
528 %! 840 1350 1176 697 293 320 674 1153 717 180
529 %! 142 490 1563 1824 1929 1604 1132 857 624 587
530 %! 890 1084 1539 1979 2238 2333 2072 1610 509 202
531 %! 966 2263 3034 2518 2250 2235 2512 2992 2305 1016
532 %! 200 561 1607 2107 2361 2277 2030 1548 1102 818
533 %! 652 631 922 1128 1670 1997 1895 1665 467 197
534 %! 160 744 1192 692 292 256 708 1208 1448 900];
535 %! c(:,:,2,2) = [
536 %! 179 1199 1886 1987 1997 1716 1479 1383 1215 485
537 %! 988 2213 2552 2358 2304 2615 3011 3210 2246 744
538 %! 921 2483 3747 3768 3960 3835 3712 3698 2588 1183
539 %! 1903 3416 5254 5490 5572 5826 5761 5505 3502 1814
540 %! 1064 2507 3825 3666 3680 3748 3821 3958 2892 1395
541 %! 495 2129 3277 3228 2566 2216 2154 2250 2390 1154
542 %! 700 1105 1472 1524 1856 2113 2059 2019 975 325];
543 %! c(:,:,3,2) = [
544 %! 1302 2104 2439 2006 1723 1931 2280 2685 1678 690
545 %! 877 2507 4408 4580 4523 4233 3852 3647 2850 1516
546 %! 1949 4238 5895 6143 6018 6063 5930 5656 3847 1538
547 %! 1953 5975 8547 8433 8407 8060 7955 8069 6170 2506
548 %! 1621 3536 5624 6117 6459 6456 6180 5666 3735 1815
549 %! 1904 2751 3429 3366 4122 4622 4840 4864 2242 882
550 %! 517 1843 2674 2337 1777 1686 2005 2367 2385 1175];
551 %! c(:,:,4,2) = [
552 %! 198 1346 2280 2685 2980 2759 2396 1982 1497 576
553 %! 1413 2994 3852 3647 3756 4035 4418 4595 3109 1231
554 %! 1873 4025 5930 5656 5792 5772 5909 6152 4185 2035
555 %! 3110 5510 7955 8069 8139 8456 8541 8439 5276 2541
556 %! 1964 4462 6180 5666 5315 5409 5631 6178 4536 1998
557 %! 869 3215 4840 4864 4121 3579 3420 3386 3271 1430
558 %! 725 1361 2005 2367 2925 3006 2667 2297 1054 325];
559 %! c(:,:,5,2) = [
560 %! 462 754 1285 1359 1441 1605 1556 1488 938 488
561 %! 729 1967 2788 2732 2608 2683 2744 2830 2195 912
562 %! 1052 3139 4302 4101 3742 3730 3895 4103 3403 1335
563 %! 1007 3725 5577 5964 6165 5754 5407 5006 3846 1507
564 %! 1375 2969 3951 3990 4144 4183 4200 4150 2661 998
565 %! 1258 2090 2495 2188 2403 2664 2954 3279 1814 723
566 %! 388 1127 1551 1673 1525 1390 1253 1139 912 275];
567 %! c(:,:,6,2) = [
568 %! 19 147 384 716 1016 1059 927 570 276 80
569 %! 441 791 1298 1320 1401 1396 1409 1367 865 500
570 %! 932 1537 2155 1870 1860 1946 2221 2487 1584 874
571 %! 1201 2067 2705 2538 2512 2687 2806 2971 1812 756
572 %! 925 1976 2363 1971 1636 1600 1844 2239 1664 626
573 %! 372 1133 1558 1687 1570 1401 1243 1122 883 264
574 %! 60 270 556 857 1024 870 569 282 66 0];
575 %!assert (convn (a, b, "full"), c)
576 %!assert (convn (a, b, "same"), c(3:6,2:9,2:5,:))
577 %!assert (convn (a, b, "valid"), c(4,3:8,3:4,:))
578 
579 ## test correct class
580 %!shared a, b, c, d
581 %! old_state = rand ("state");
582 %! restore_state = onCleanup (@() rand ("state", old_state));
583 %! rand ("state", 12345); # initialize generator to make behavior reproducible
584 %! a = rand (5);
585 %! b = rand (3);
586 %! c = rand (5, "single");
587 %! d = rand (3, "single");
588 %!assert (class (convn (a, b)), "double")
589 %!assert (class (convn (c, b)), "single")
590 %!assert (class (convn (a, d)), "single")
591 %!assert (class (convn (true (5), b)), "double")
592 %!assert (class (convn (true (5), d)), "single")
593 %!assert (class (convn (ones (5, "uint8"), b)), "double")
594 %!assert (class (convn (d, ones (5, "uint8"))), "single")
595 
596 %!error convn ()
597 %!error convn (1)
598 %!error <SHAPE type not valid> convn (1,2, "NOT_A_SHAPE")
599 %!error convn (b, 1, 1)
600 */
601 
602 OCTAVE_END_NAMESPACE(octave)
Matrix
Definition: dMatrix.h:42
octave_value
Definition: ov.h:75
Shape
Shape
Definition: conv2.cc:39
SHAPE_VALID
@ SHAPE_VALID
Definition: conv2.cc:39
SHAPE_SAME
@ SHAPE_SAME
Definition: conv2.cc:39
SHAPE_FULL
@ SHAPE_FULL
Definition: conv2.cc:39
OCTAVE_BEGIN_NAMESPACE
OCTAVE_BEGIN_NAMESPACE(octave) static octave_value daspk_fcn
print_usage
void print_usage(void)
Definition: defun-int.h:72
DEFUN
#define DEFUN(name, args_name, nargout_name, doc)
Macro to define a builtin function.
Definition: defun.h:56
error
void() error(const char *fmt,...)
Definition: error.cc:988
convn
NDArray convn(const NDArray &a, const NDArray &b, convn_type ct)
Definition: oct-convn.cc:217
convn_type
convn_type
Definition: oct-convn.h:52
convn_same
@ convn_same
Definition: oct-convn.h:54
convn_valid
@ convn_valid
Definition: oct-convn.h:55
convn_full
@ convn_full
Definition: oct-convn.h:53
v2
const octave_char_matrix & v2
Definition: op-int-concat.cc:170