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Update %!tests in DLD-FUNCTIONS/ directory with Octave coding conventions. * __contourc__.cc, __delaunayn__.cc, __dispatch__.cc, __dsearchn__.cc, __fltk_uigetfile__.cc, __glpk__.cc, __lin_interpn__.cc, __magick_read__.cc, __pchip_deriv__.cc, __qp__.cc, __voronoi__.cc, besselj.cc, betainc.cc, bsxfun.cc, cellfun.cc, chol.cc, conv2.cc, convhulln.cc, dassl.cc, det.cc, dlmread.cc, dmperm.cc, dot.cc, eig.cc, eigs.cc, fft.cc, fft2.cc, filter.cc, find.cc, gammainc.cc, gcd.cc, givens.cc, hess.cc, hex2num.cc, inv.cc, kron.cc, lookup.cc, lsode.cc, lu.cc, luinc.cc, matrix_type.cc, max.cc, mgorth.cc, nproc.cc, qr.cc, quad.cc, quadcc.cc, qz.cc, rand.cc, rcond.cc, regexp.cc, schur.cc, spparms.cc, sqrtm.cc, str2double.cc, strfind.cc, sub2ind.cc, svd.cc, syl.cc, time.cc, tril.cc, tsearch.cc: Update %!tests in DLD-FUNCTIONS/ directory with Octave coding conventions.
author Rik <octave@nomad.inbox5.com>
date Tue, 27 Mar 2012 22:46:45 -0700
parents 846273dae16b
children 34f067bcac12
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1 /*
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2
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3 Copyright (C) 1999-2012 Andy Adler
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4 Copyright (C) 2010 VZLU Prague
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5
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6 This file is part of Octave.
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7
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8 Octave is free software; you can redistribute it and/or modify it
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9 under the terms of the GNU General Public License as published by the
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10 Free Software Foundation; either version 3 of the License, or (at your
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11 option) any later version.
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12
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13 Octave is distributed in the hope that it will be useful, but WITHOUT
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14 ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or
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15 FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License
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16 for more details.
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17
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18 You should have received a copy of the GNU General Public License
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19 along with Octave; see the file COPYING. If not, see
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20 <http://www.gnu.org/licenses/>.
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21
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22 */
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23
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24 #ifdef HAVE_CONFIG_H
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25 #include <config.h>
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26 #endif
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27
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28 #include "oct-convn.h"
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29
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30 #include "defun-dld.h"
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31 #include "error.h"
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32 #include "oct-obj.h"
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33 #include "utils.h"
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34
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35 enum Shape { SHAPE_FULL, SHAPE_SAME, SHAPE_VALID };
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36
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37 DEFUN_DLD (conv2, args, ,
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38 "-*- texinfo -*-\n\
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39 @deftypefn {Loadable Function} {} conv2 (@var{A}, @var{B})\n\
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40 @deftypefnx {Loadable Function} {} conv2 (@var{v1}, @var{v2}, @var{m})\n\
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41 @deftypefnx {Loadable Function} {} conv2 (@dots{}, @var{shape})\n\
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42 Return the 2-D convolution of @var{A} and @var{B}. The size of the result\n\
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43 is determined by the optional @var{shape} argument which takes the following\n\
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44 values\n\
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45 \n\
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46 @table @asis\n\
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47 @item @var{shape} = \"full\"\n\
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48 Return the full convolution. (default)\n\
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49 \n\
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50 @item @var{shape} = \"same\"\n\
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51 Return the central part of the convolution with the same size as @var{A}.\n\
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52 The central part of the convolution begins at the indices\n\
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53 @code{floor ([size(@var{B})/2] + 1)}.\n\
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54 \n\
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55 @item @var{shape} = \"valid\"\n\
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56 Return only the parts which do not include zero-padded edges.\n\
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57 The size of the result is @code{max (size (A) - size (B) + 1, 0)}.\n\
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58 @end table\n\
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59 \n\
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60 When the third argument is a matrix, return the convolution of the matrix\n\
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61 @var{m} by the vector @var{v1} in the column direction and by the vector\n\
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62 @var{v2} in the row direction.\n\
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63 @seealso{conv, convn}\n\
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64 @end deftypefn")
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65 {
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66 octave_value retval;
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67 octave_value tmp;
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68 int nargin = args.length ();
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69 std::string shape = "full"; // default
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70 bool separable = false;
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71 convn_type ct;
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72
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73 if (nargin < 2)
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74 {
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75 print_usage ();
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76 return retval;
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77 }
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78 else if (nargin == 3)
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79 {
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80 if (args(2).is_string ())
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81 shape = args(2).string_value ();
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82 else
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83 separable = true;
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84 }
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85 else if (nargin >= 4)
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86 {
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87 separable = true;
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88 shape = args(3).string_value ();
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89 }
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90
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91 if (shape == "full")
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92 ct = convn_full;
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93 else if (shape == "same")
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94 ct = convn_same;
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95 else if (shape == "valid")
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96 ct = convn_valid;
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97 else
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98 {
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99 error ("conv2: SHAPE type not valid");
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100 print_usage ();
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101 return retval;
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102 }
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103
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104 if (separable)
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105 {
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106 // If user requests separable, check first two params are vectors
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107
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108 if (! (1 == args(0).rows () || 1 == args(0).columns ())
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109 || ! (1 == args(1).rows () || 1 == args(1).columns ()))
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110 {
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111 print_usage ();
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112 return retval;
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113 }
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114
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115 if (args(0).is_single_type () || args(1).is_single_type ()
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116 || args(2).is_single_type ())
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117 {
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118 if (args(0).is_complex_type () || args(1).is_complex_type ()
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119 || args(2).is_complex_type ())
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120 {
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121 FloatComplexMatrix a (args(2).float_complex_matrix_value ());
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122 if (args(1).is_real_type () && args(2).is_real_type ())
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123 {
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124 FloatColumnVector v1 (args(0).float_vector_value ());
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125 FloatRowVector v2 (args(1).float_vector_value ());
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126 retval = convn (a, v1, v2, ct);
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127 }
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128 else
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129 {
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130 FloatComplexColumnVector v1 (args(0).float_complex_vector_value ());
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131 FloatComplexRowVector v2 (args(1).float_complex_vector_value ());
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132 retval = convn (a, v1, v2, ct);
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133 }
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134 }
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135 else
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136 {
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137 FloatColumnVector v1 (args(0).float_vector_value ());
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138 FloatRowVector v2 (args(1).float_vector_value ());
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139 FloatMatrix a (args(2).float_matrix_value ());
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140 retval = convn (a, v1, v2, ct);
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141 }
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142 }
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143 else
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144 {
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145 if (args(0).is_complex_type () || args(1).is_complex_type ()
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146 || args(2).is_complex_type ())
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147 {
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148 ComplexMatrix a (args(2).complex_matrix_value ());
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149 if (args(1).is_real_type () && args(2).is_real_type ())
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150 {
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151 ColumnVector v1 (args(0).vector_value ());
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152 RowVector v2 (args(1).vector_value ());
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153 retval = convn (a, v1, v2, ct);
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154 }
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155 else
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156 {
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157 ComplexColumnVector v1 (args(0).complex_vector_value ());
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158 ComplexRowVector v2 (args(1).complex_vector_value ());
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159 retval = convn (a, v1, v2, ct);
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160 }
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161 }
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162 else
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163 {
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164 ColumnVector v1 (args(0).vector_value ());
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165 RowVector v2 (args(1).vector_value ());
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166 Matrix a (args(2).matrix_value ());
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167 retval = convn (a, v1, v2, ct);
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168 }
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169 }
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170 } // if (separable)
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171 else
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172 {
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173 if (args(0).is_single_type () || args(1).is_single_type ())
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174 {
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175 if (args(0).is_complex_type () || args(1).is_complex_type ())
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176 {
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177 FloatComplexMatrix a (args(0).float_complex_matrix_value ());
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178 if (args(1).is_real_type ())
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179 {
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180 FloatMatrix b (args(1).float_matrix_value ());
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181 retval = convn (a, b, ct);
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182 }
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183 else
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184 {
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185 FloatComplexMatrix b (args(1).float_complex_matrix_value ());
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186 retval = convn (a, b, ct);
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187 }
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188 }
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189 else
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190 {
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191 FloatMatrix a (args(0).float_matrix_value ());
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192 FloatMatrix b (args(1).float_matrix_value ());
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193 retval = convn (a, b, ct);
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194 }
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195 }
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196 else
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197 {
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198 if (args(0).is_complex_type () || args(1).is_complex_type ())
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199 {
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200 ComplexMatrix a (args(0).complex_matrix_value ());
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201 if (args(1).is_real_type ())
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202 {
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203 Matrix b (args(1).matrix_value ());
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204 retval = convn (a, b, ct);
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205 }
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206 else
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207 {
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208 ComplexMatrix b (args(1).complex_matrix_value ());
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209 retval = convn (a, b, ct);
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210 }
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211 }
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212 else
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213 {
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214 Matrix a (args(0).matrix_value ());
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215 Matrix b (args(1).matrix_value ());
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216 retval = convn (a, b, ct);
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217 }
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218 }
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219
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220 } // if (separable)
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221
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222 return retval;
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223 }
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224
14209
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225 /*
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226 %!test
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227 %! c = [0,1,2,3;1,8,12,12;4,20,24,21;7,22,25,18];
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228 %! assert (conv2 ([0,1;1,2], [1,2,3;4,5,6;7,8,9]), c);
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229
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230 %!test
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231 %! c = single ([0,1,2,3;1,8,12,12;4,20,24,21;7,22,25,18]);
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232 %! assert (conv2 (single ([0,1;1,2]), single ([1,2,3;4,5,6;7,8,9])), c);
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233
846273dae16b Return correct part of convolution for 'same' parameter in conv2, convn (Bug #34893).
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234 %!test
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235 %! c = [1,4,4;5,18,16;14,48,40;19,62,48;15,48,36];
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236 %! assert (conv2 (1:3, 1:2, [1,2;3,4;5,6]), c);
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237
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238 %!assert (conv2 (1:3, 1:2, [1,2;3,4;5,6], "full"),
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239 %! conv2 (1:3, 1:2, [1,2;3,4;5,6]));
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240
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241 %% Test shapes
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242 %!shared A, B, C
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243 %! A = rand (3, 4);
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244 %! B = rand (4);
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245 %! C = conv2 (A, B);
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246 %!assert (conv2 (A,B, "full"), C)
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247 %!assert (conv2 (A,B, "same"), C(3:5,3:6))
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248 %!assert (conv2 (A,B, "valid"), zeros (0, 1))
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249 %!assert (size (conv2 (B,A, "valid")), [2 1])
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250
846273dae16b Return correct part of convolution for 'same' parameter in conv2, convn (Bug #34893).
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251 %!test
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252 %! B = rand (5);
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253 %! C = conv2 (A, B);
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254 %!assert (conv2 (A,B, "full"), C)
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255 %!assert (conv2 (A,B, "same"), C(3:5,3:6))
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256 %!assert (conv2 (A,B, "valid"), zeros (0, 0))
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257 %!assert (size (conv2 (B,A, "valid")), [3 2])
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258
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259 %% Clear shared variables so they are not reported for tests below
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260 %!shared
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261
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262 %% Test cases from Bug #34893
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263 %!assert (conv2 ([1:5;1:5], [1:2], "same"), [4 7 10 13 10; 4 7 10 13 10])
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264 %!assert (conv2 ([1:5;1:5]', [1:2]', "same"), [4 7 10 13 10; 4 7 10 13 10]')
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265 %!#assert (conv2 ([1:5;1:5], [1:2], "valid"), [4 7 10 13; 4 7 10 13])
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266 %!assert (conv2 ([1:5;1:5]', [1:2]', "valid"), [4 7 10 13; 4 7 10 13]')
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267
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268 %% Test input validation
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269 %!error conv2 ()
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270 %!error conv2 (1)
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271 %!error <SHAPE type not valid> conv2 (1,2, "NOT_A_SHAPE")
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272 %% Test alternate calling form which should be 2 vectors and a matrix
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273 %!error conv2 (ones (2), 1, 1)
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274 %!error conv2 (1, ones (2), 1)
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275 */
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276
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277 DEFUN_DLD (convn, args, ,
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278 "-*- texinfo -*-\n\
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279 @deftypefn {Loadable Function} {@var{C} =} convn (@var{A}, @var{B})\n\
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280 @deftypefnx {Loadable Function} {@var{C} =} convn (@var{A}, @var{B}, @var{shape})\n\
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281 Return the n-D convolution of @var{A} and @var{B}. The size of the result\n\
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282 is determined by the optional @var{shape} argument which takes the following\n\
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283 values\n\
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284 \n\
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285 @table @asis\n\
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286 @item @var{shape} = \"full\"\n\
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287 Return the full convolution. (default)\n\
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288 \n\
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289 @item @var{shape} = \"same\"\n\
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290 Return central part of the convolution with the same size as @var{A}.\n\
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291 The central part of the convolution begins at the indices\n\
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292 @code{floor ([size(@var{B})/2] + 1)}.\n\
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293 \n\
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294 @item @var{shape} = \"valid\"\n\
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295 Return only the parts which do not include zero-padded edges.\n\
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296 The size of the result is @code{max (size (A) - size (B) + 1, 0)}.\n\
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297 @end table\n\
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298 \n\
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299 @seealso{conv2, conv}\n\
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300 @end deftypefn")
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301 {
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302 octave_value retval;
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303 octave_value tmp;
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304 int nargin = args.length ();
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305 std::string shape = "full"; // default
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306 convn_type ct;
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307
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308 if (nargin < 2 || nargin > 3)
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309 {
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310 print_usage ();
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311 return retval;
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312 }
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313 else if (nargin == 3)
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314 {
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315 if (args(2).is_string ())
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316 shape = args(2).string_value ();
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317 }
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318
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319 if (shape == "full")
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320 ct = convn_full;
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321 else if (shape == "same")
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322 ct = convn_same;
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323 else if (shape == "valid")
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324 ct = convn_valid;
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325 else
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326 {
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327 error ("convn: SHAPE type not valid");
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328 print_usage ();
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329 return retval;
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330 }
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331
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332 if (args(0).is_single_type () || args(1).is_single_type ())
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333 {
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334 if (args(0).is_complex_type () || args(1).is_complex_type ())
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335 {
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336 FloatComplexNDArray a (args(0).float_complex_array_value ());
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337 if (args(1).is_real_type ())
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338 {
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339 FloatNDArray b (args(1).float_array_value ());
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340 retval = convn (a, b, ct);
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341 }
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342 else
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343 {
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344 FloatComplexNDArray b (args(1).float_complex_array_value ());
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345 retval = convn (a, b, ct);
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346 }
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347 }
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348 else
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349 {
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350 FloatNDArray a (args(0).float_array_value ());
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351 FloatNDArray b (args(1).float_array_value ());
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352 retval = convn (a, b, ct);
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353 }
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354 }
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355 else
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356 {
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357 if (args(0).is_complex_type () || args(1).is_complex_type ())
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358 {
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359 ComplexNDArray a (args(0).complex_array_value ());
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360 if (args(1).is_real_type ())
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361 {
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362 NDArray b (args(1).array_value ());
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363 retval = convn (a, b, ct);
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364 }
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365 else
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366 {
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367 ComplexNDArray b (args(1).complex_array_value ());
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368 retval = convn (a, b, ct);
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369 }
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370 }
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371 else
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372 {
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373 NDArray a (args(0).array_value ());
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374 NDArray b (args(1).array_value ());
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375 retval = convn (a, b, ct);
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376 }
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377 }
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378
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379 return retval;
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380 }
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381
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382 /*
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383 FIXME: Need tests for convn in addition to conv2.
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384 */