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1 /* |
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2 |
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3 Copyright (C) 1996, 1997 John W. Eaton |
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4 |
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5 This file is part of Octave. |
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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 the |
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9 Free Software Foundation; either version 2, or (at your option) any |
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10 later version. |
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11 |
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12 Octave is distributed in the hope that it will be useful, but WITHOUT |
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13 ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or |
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14 FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License |
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15 for more details. |
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16 |
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17 You should have received a copy of the GNU General Public License |
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18 along with Octave; see the file COPYING. If not, write to the Free |
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19 Software Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA |
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20 02110-1301, USA. |
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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 <cfloat> |
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29 #include <cmath> |
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30 |
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31 #include <string> |
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32 |
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33 #include "lo-ieee.h" |
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34 #include "str-vec.h" |
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35 #include "quit.h" |
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36 |
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37 #include "Cell.h" |
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38 #include "defun.h" |
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39 #include "error.h" |
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40 #include "gripes.h" |
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41 #include "oct-map.h" |
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42 #include "oct-obj.h" |
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43 #include "ov.h" |
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44 #include "ov-complex.h" |
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45 #include "ov-cx-mat.h" |
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46 #include "parse.h" |
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47 #include "pt-mat.h" |
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48 #include "utils.h" |
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49 #include "variables.h" |
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50 |
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51 #define ANY_ALL(FCN) \ |
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52 \ |
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53 octave_value retval; \ |
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54 \ |
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55 int nargin = args.length (); \ |
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56 \ |
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57 if (nargin == 1 || nargin == 2) \ |
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58 { \ |
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59 int dim = (nargin == 1 ? -1 : args(1).int_value (true) - 1); \ |
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60 \ |
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61 if (! error_state) \ |
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62 { \ |
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63 if (dim >= -1) \ |
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64 retval = args(0).FCN (dim); \ |
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65 else \ |
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66 error (#FCN ": invalid dimension argument = %d", dim + 1); \ |
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67 } \ |
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68 else \ |
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69 error (#FCN ": expecting dimension argument to be an integer"); \ |
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70 } \ |
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71 else \ |
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72 print_usage (); \ |
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73 \ |
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74 return retval |
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75 |
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76 DEFUN (all, args, , |
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77 "-*- texinfo -*-\n\ |
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78 @deftypefn {Built-in Function} {} all (@var{x}, @var{dim})\n\ |
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79 The function @code{all} behaves like the function @code{any}, except\n\ |
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80 that it returns true only if all the elements of a vector, or all the\n\ |
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81 elements along dimension @var{dim} of a matrix, are nonzero.\n\ |
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82 @end deftypefn") |
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83 { |
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84 ANY_ALL (all); |
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85 } |
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86 |
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87 DEFUN (any, args, , |
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88 "-*- texinfo -*-\n\ |
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89 @deftypefn {Built-in Function} {} any (@var{x}, @var{dim})\n\ |
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90 For a vector argument, return 1 if any element of the vector is\n\ |
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91 nonzero.\n\ |
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92 \n\ |
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93 For a matrix argument, return a row vector of ones and\n\ |
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94 zeros with each element indicating whether any of the elements of the\n\ |
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95 corresponding column of the matrix are nonzero. For example,\n\ |
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96 \n\ |
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97 @example\n\ |
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98 @group\n\ |
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99 any (eye (2, 4))\n\ |
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100 @result{} [ 1, 1, 0, 0 ]\n\ |
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101 @end group\n\ |
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102 @end example\n\ |
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103 \n\ |
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104 If the optional argument @var{dim} is supplied, work along dimension\n\ |
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105 @var{dim}. For example,\n\ |
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106 \n\ |
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107 @example\n\ |
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108 @group\n\ |
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109 any (eye (2, 4), 2)\n\ |
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110 @result{} [ 1; 1 ]\n\ |
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111 @end group\n\ |
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112 @end example\n\ |
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113 @end deftypefn") |
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114 { |
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115 ANY_ALL (any); |
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116 } |
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117 |
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118 // These mapping functions may also be useful in other places, eh? |
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119 |
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120 typedef double (*d_dd_fcn) (double, double); |
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121 |
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122 static Matrix |
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123 map_d_m (d_dd_fcn f, double x, const Matrix& y) |
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124 { |
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125 octave_idx_type nr = y.rows (); |
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126 octave_idx_type nc = y.columns (); |
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127 |
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128 Matrix retval (nr, nc); |
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129 |
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130 for (octave_idx_type j = 0; j < nc; j++) |
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131 for (octave_idx_type i = 0; i < nr; i++) |
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132 { |
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133 OCTAVE_QUIT; |
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134 retval (i, j) = f (x, y (i, j)); |
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135 } |
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136 |
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137 return retval; |
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138 } |
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139 |
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140 static Matrix |
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141 map_m_d (d_dd_fcn f, const Matrix& x, double y) |
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649
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142 { |
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143 octave_idx_type nr = x.rows (); |
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144 octave_idx_type nc = x.columns (); |
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145 |
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146 Matrix retval (nr, nc); |
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147 |
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148 for (octave_idx_type j = 0; j < nc; j++) |
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149 for (octave_idx_type i = 0; i < nr; i++) |
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150 { |
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151 OCTAVE_QUIT; |
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152 retval (i, j) = f (x (i, j), y); |
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153 } |
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154 |
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155 return retval; |
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156 } |
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157 |
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158 static Matrix |
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159 map_m_m (d_dd_fcn f, const Matrix& x, const Matrix& y) |
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160 { |
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161 octave_idx_type x_nr = x.rows (); |
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162 octave_idx_type x_nc = x.columns (); |
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163 |
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164 octave_idx_type y_nr = y.rows (); |
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165 octave_idx_type y_nc = y.columns (); |
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166 |
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167 assert (x_nr == y_nr && x_nc == y_nc); |
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168 |
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169 Matrix retval (x_nr, x_nc); |
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170 |
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171 for (octave_idx_type j = 0; j < x_nc; j++) |
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172 for (octave_idx_type i = 0; i < x_nr; i++) |
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173 { |
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174 OCTAVE_QUIT; |
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175 retval (i, j) = f (x (i, j), y (i, j)); |
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176 } |
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177 |
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178 return retval; |
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179 } |
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180 |
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181 DEFUN (atan2, args, , |
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182 "-*- texinfo -*-\n\ |
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183 @deftypefn {Mapping Function} {} atan2 (@var{y}, @var{x})\n\ |
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184 Compute atan (@var{y} / @var{x}) for corresponding elements of @var{y}\n\ |
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185 and @var{x}. The result is in range -pi to pi.\n\ |
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186 @end deftypefn") |
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649
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187 { |
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188 octave_value retval; |
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189 |
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190 int nargin = args.length (); |
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191 |
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192 if (nargin == 2 && args(0).is_defined () && args(1).is_defined ()) |
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193 { |
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194 octave_value arg_y = args(0); |
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195 octave_value arg_x = args(1); |
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196 |
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197 octave_idx_type y_nr = arg_y.rows (); |
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198 octave_idx_type y_nc = arg_y.columns (); |
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199 |
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200 octave_idx_type x_nr = arg_x.rows (); |
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201 octave_idx_type x_nc = arg_x.columns (); |
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202 |
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203 int arg_y_empty = empty_arg ("atan2", y_nr, y_nc); |
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204 int arg_x_empty = empty_arg ("atan2", x_nr, x_nc); |
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205 |
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206 if (arg_y_empty > 0 && arg_x_empty > 0) |
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207 return octave_value (Matrix ()); |
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208 else if (arg_y_empty || arg_x_empty) |
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209 return retval; |
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210 |
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211 octave_idx_type y_is_scalar = (y_nr == 1 && y_nc == 1); |
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212 octave_idx_type x_is_scalar = (x_nr == 1 && x_nc == 1); |
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213 |
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214 if (y_is_scalar && x_is_scalar) |
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215 { |
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216 double y = arg_y.double_value (); |
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217 |
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218 if (! error_state) |
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219 { |
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220 double x = arg_x.double_value (); |
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221 |
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222 if (! error_state) |
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223 retval = atan2 (y, x); |
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224 } |
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225 } |
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226 else if (y_is_scalar) |
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227 { |
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228 double y = arg_y.double_value (); |
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229 |
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230 if (! error_state) |
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231 { |
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232 Matrix x = arg_x.matrix_value (); |
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233 |
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234 if (! error_state) |
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235 retval = map_d_m (atan2, y, x); |
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236 } |
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237 } |
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238 else if (x_is_scalar) |
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239 { |
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240 Matrix y = arg_y.matrix_value (); |
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241 |
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242 if (! error_state) |
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243 { |
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244 double x = arg_x.double_value (); |
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245 |
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246 if (! error_state) |
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247 retval = map_m_d (atan2, y, x); |
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248 } |
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249 } |
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250 else if (y_nr == x_nr && y_nc == x_nc) |
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251 { |
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252 Matrix y = arg_y.matrix_value (); |
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253 |
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254 if (! error_state) |
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255 { |
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256 Matrix x = arg_x.matrix_value (); |
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257 |
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258 if (! error_state) |
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259 retval = map_m_m (atan2, y, x); |
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260 } |
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261 } |
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262 else |
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263 error ("atan2: nonconformant matrices"); |
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264 } |
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265 else |
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266 print_usage (); |
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267 |
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268 return retval; |
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269 } |
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270 |
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271 DEFUN (fmod, args, , |
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272 "-*- texinfo -*-\n\ |
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273 @deftypefn {Mapping Function} {} fmod (@var{x}, @var{y})\n\ |
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274 Compute the floating point remainder of dividing @var{x} by @var{y}\n\ |
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275 using the C library function @code{fmod}. The result has the same\n\ |
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276 sign as @var{x}. If @var{y} is zero, the result implementation-defined.\n\ |
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277 @end deftypefn") |
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278 { |
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279 octave_value retval; |
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280 |
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281 int nargin = args.length (); |
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282 |
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283 if (nargin == 2 && args(0).is_defined () && args(1).is_defined ()) |
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284 { |
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285 octave_value arg_x = args(0); |
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286 octave_value arg_y = args(1); |
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287 |
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288 octave_idx_type y_nr = arg_y.rows (); |
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289 octave_idx_type y_nc = arg_y.columns (); |
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290 |
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291 octave_idx_type x_nr = arg_x.rows (); |
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292 octave_idx_type x_nc = arg_x.columns (); |
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293 |
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294 int arg_y_empty = empty_arg ("fmod", y_nr, y_nc); |
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295 int arg_x_empty = empty_arg ("fmod", x_nr, x_nc); |
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296 |
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297 if (arg_y_empty > 0 && arg_x_empty > 0) |
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298 return octave_value (Matrix ()); |
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299 else if (arg_y_empty || arg_x_empty) |
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300 return retval; |
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301 |
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302 octave_idx_type y_is_scalar = (y_nr == 1 && y_nc == 1); |
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303 octave_idx_type x_is_scalar = (x_nr == 1 && x_nc == 1); |
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304 |
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305 if (y_is_scalar && x_is_scalar) |
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306 { |
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307 double y = arg_y.double_value (); |
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308 |
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309 if (! error_state) |
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310 { |
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311 double x = arg_x.double_value (); |
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312 |
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313 if (! error_state) |
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314 retval = fmod (x, y); |
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315 } |
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316 } |
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317 else if (y_is_scalar) |
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318 { |
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319 double y = arg_y.double_value (); |
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320 |
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321 if (! error_state) |
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322 { |
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323 Matrix x = arg_x.matrix_value (); |
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324 |
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325 if (! error_state) |
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326 retval = map_m_d (fmod, x, y); |
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327 } |
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328 } |
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329 else if (x_is_scalar) |
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330 { |
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331 Matrix y = arg_y.matrix_value (); |
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332 |
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333 if (! error_state) |
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334 { |
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335 double x = arg_x.double_value (); |
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336 |
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337 if (! error_state) |
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338 retval = map_d_m (fmod, x, y); |
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339 } |
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340 } |
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341 else if (y_nr == x_nr && y_nc == x_nc) |
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342 { |
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343 Matrix y = arg_y.matrix_value (); |
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344 |
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345 if (! error_state) |
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346 { |
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347 Matrix x = arg_x.matrix_value (); |
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348 |
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349 if (! error_state) |
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350 retval = map_m_m (fmod, x, y); |
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351 } |
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352 } |
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353 else |
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354 error ("fmod: nonconformant matrices"); |
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355 } |
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356 else |
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357 print_usage (); |
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358 |
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359 return retval; |
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360 } |
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361 |
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3723
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362 #define DATA_REDUCTION(FCN) \ |
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363 \ |
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364 octave_value retval; \ |
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365 \ |
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366 int nargin = args.length (); \ |
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367 \ |
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368 if (nargin == 1 || nargin == 2) \ |
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369 { \ |
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370 octave_value arg = args(0); \ |
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371 \ |
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372 int dim = (nargin == 1 ? -1 : args(1).int_value (true) - 1); \ |
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373 \ |
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374 if (! error_state) \ |
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375 { \ |
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376 if (dim >= -1) \ |
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377 { \ |
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378 if (arg.is_real_type ()) \ |
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379 { \ |
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380 NDArray tmp = arg.array_value (); \ |
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3723
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381 \ |
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382 if (! error_state) \ |
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383 retval = tmp.FCN (dim); \ |
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384 } \ |
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385 else if (arg.is_complex_type ()) \ |
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386 { \ |
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4569
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387 ComplexNDArray tmp = arg.complex_array_value (); \ |
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3723
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388 \ |
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389 if (! error_state) \ |
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390 retval = tmp.FCN (dim); \ |
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391 } \ |
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392 else \ |
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393 { \ |
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394 gripe_wrong_type_arg (#FCN, arg); \ |
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395 return retval; \ |
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396 } \ |
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397 } \ |
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398 else \ |
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399 error (#FCN ": invalid dimension argument = %d", dim + 1); \ |
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400 } \ |
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401 } \ |
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402 else \ |
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5823
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403 print_usage (); \ |
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3723
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404 \ |
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405 return retval |
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406 |
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1957
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407 DEFUN (cumprod, args, , |
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3428
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408 "-*- texinfo -*-\n\ |
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3723
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409 @deftypefn {Built-in Function} {} cumprod (@var{x}, @var{dim})\n\ |
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410 Cumulative product of elements along dimension @var{dim}. If\n\ |
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411 @var{dim} is omitted, it defaults to 1 (column-wise cumulative\n\ |
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412 products).\n\ |
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413 \n\ |
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414 As a special case, if @var{x} is a vector and @var{dim} is omitted,\n\ |
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415 return the cumulative product of the elements as a vector with the\n\ |
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416 same orientation as @var{x}.\n\ |
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3428
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417 @end deftypefn") |
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523
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418 { |
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3723
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419 DATA_REDUCTION (cumprod); |
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523
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420 } |
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421 |
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1957
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422 DEFUN (cumsum, args, , |
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3428
|
423 "-*- texinfo -*-\n\ |
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3723
|
424 @deftypefn {Built-in Function} {} cumsum (@var{x}, @var{dim})\n\ |
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425 Cumulative sum of elements along dimension @var{dim}. If @var{dim}\n\ |
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|
426 is omitted, it defaults to 1 (column-wise cumulative sums).\n\ |
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5061
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427 \n\ |
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428 As a special case, if @var{x} is a vector and @var{dim} is omitted,\n\ |
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429 return the cumulative sum of the elements as a vector with the\n\ |
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|
430 same orientation as @var{x}.\n\ |
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3428
|
431 @end deftypefn") |
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523
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432 { |
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3723
|
433 DATA_REDUCTION (cumsum); |
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523
|
434 } |
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435 |
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5775
|
436 // FIXME -- we could eliminate some duplicate code here with |
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3972
|
437 // some template functions or macros. |
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438 |
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2086
|
439 static octave_value |
|
5275
|
440 make_diag (const Matrix& v, octave_idx_type k) |
|
767
|
441 { |
|
5275
|
442 octave_idx_type nr = v.rows (); |
|
|
443 octave_idx_type nc = v.columns (); |
|
767
|
444 assert (nc == 1 || nr == 1); |
|
|
445 |
|
2086
|
446 octave_value retval; |
|
767
|
447 |
|
5275
|
448 octave_idx_type roff = 0; |
|
|
449 octave_idx_type coff = 0; |
|
767
|
450 if (k > 0) |
|
|
451 { |
|
|
452 roff = 0; |
|
|
453 coff = k; |
|
|
454 } |
|
|
455 else if (k < 0) |
|
|
456 { |
|
|
457 roff = -k; |
|
|
458 coff = 0; |
|
|
459 } |
|
|
460 |
|
|
461 if (nr == 1) |
|
|
462 { |
|
5275
|
463 octave_idx_type n = nc + std::abs (k); |
|
767
|
464 Matrix m (n, n, 0.0); |
|
5275
|
465 for (octave_idx_type i = 0; i < nc; i++) |
|
2305
|
466 m (i+roff, i+coff) = v (0, i); |
|
4233
|
467 retval = m; |
|
767
|
468 } |
|
|
469 else |
|
|
470 { |
|
5275
|
471 octave_idx_type n = nr + std::abs (k); |
|
767
|
472 Matrix m (n, n, 0.0); |
|
5275
|
473 for (octave_idx_type i = 0; i < nr; i++) |
|
2305
|
474 m (i+roff, i+coff) = v (i, 0); |
|
4233
|
475 retval = m; |
|
767
|
476 } |
|
|
477 |
|
|
478 return retval; |
|
|
479 } |
|
|
480 |
|
2086
|
481 static octave_value |
|
5275
|
482 make_diag (const ComplexMatrix& v, octave_idx_type k) |
|
767
|
483 { |
|
5275
|
484 octave_idx_type nr = v.rows (); |
|
|
485 octave_idx_type nc = v.columns (); |
|
767
|
486 assert (nc == 1 || nr == 1); |
|
|
487 |
|
2086
|
488 octave_value retval; |
|
767
|
489 |
|
5275
|
490 octave_idx_type roff = 0; |
|
|
491 octave_idx_type coff = 0; |
|
767
|
492 if (k > 0) |
|
|
493 { |
|
|
494 roff = 0; |
|
|
495 coff = k; |
|
|
496 } |
|
|
497 else if (k < 0) |
|
|
498 { |
|
|
499 roff = -k; |
|
|
500 coff = 0; |
|
|
501 } |
|
|
502 |
|
|
503 if (nr == 1) |
|
|
504 { |
|
5275
|
505 octave_idx_type n = nc + std::abs (k); |
|
767
|
506 ComplexMatrix m (n, n, 0.0); |
|
5275
|
507 for (octave_idx_type i = 0; i < nc; i++) |
|
2305
|
508 m (i+roff, i+coff) = v (0, i); |
|
4233
|
509 retval = m; |
|
767
|
510 } |
|
|
511 else |
|
|
512 { |
|
5275
|
513 octave_idx_type n = nr + std::abs (k); |
|
767
|
514 ComplexMatrix m (n, n, 0.0); |
|
5275
|
515 for (octave_idx_type i = 0; i < nr; i++) |
|
2305
|
516 m (i+roff, i+coff) = v (i, 0); |
|
4233
|
517 retval = m; |
|
767
|
518 } |
|
|
519 |
|
|
520 return retval; |
|
|
521 } |
|
|
522 |
|
2086
|
523 static octave_value |
|
|
524 make_diag (const octave_value& arg) |
|
767
|
525 { |
|
2086
|
526 octave_value retval; |
|
767
|
527 |
|
|
528 if (arg.is_real_type ()) |
|
|
529 { |
|
|
530 Matrix m = arg.matrix_value (); |
|
|
531 |
|
|
532 if (! error_state) |
|
|
533 { |
|
5275
|
534 octave_idx_type nr = m.rows (); |
|
|
535 octave_idx_type nc = m.columns (); |
|
767
|
536 |
|
|
537 if (nr == 0 || nc == 0) |
|
|
538 retval = Matrix (); |
|
|
539 else if (nr == 1 || nc == 1) |
|
|
540 retval = make_diag (m, 0); |
|
|
541 else |
|
|
542 { |
|
|
543 ColumnVector v = m.diag (); |
|
5164
|
544 if (v.numel () > 0) |
|
767
|
545 retval = v; |
|
|
546 } |
|
|
547 } |
|
|
548 else |
|
|
549 gripe_wrong_type_arg ("diag", arg); |
|
|
550 } |
|
|
551 else if (arg.is_complex_type ()) |
|
|
552 { |
|
|
553 ComplexMatrix cm = arg.complex_matrix_value (); |
|
|
554 |
|
|
555 if (! error_state) |
|
|
556 { |
|
5275
|
557 octave_idx_type nr = cm.rows (); |
|
|
558 octave_idx_type nc = cm.columns (); |
|
767
|
559 |
|
|
560 if (nr == 0 || nc == 0) |
|
|
561 retval = Matrix (); |
|
|
562 else if (nr == 1 || nc == 1) |
|
|
563 retval = make_diag (cm, 0); |
|
|
564 else |
|
|
565 { |
|
|
566 ComplexColumnVector v = cm.diag (); |
|
5164
|
567 if (v.numel () > 0) |
|
767
|
568 retval = v; |
|
|
569 } |
|
|
570 } |
|
|
571 else |
|
|
572 gripe_wrong_type_arg ("diag", arg); |
|
|
573 } |
|
|
574 else |
|
|
575 gripe_wrong_type_arg ("diag", arg); |
|
|
576 |
|
|
577 return retval; |
|
|
578 } |
|
|
579 |
|
2086
|
580 static octave_value |
|
|
581 make_diag (const octave_value& a, const octave_value& b) |
|
767
|
582 { |
|
2086
|
583 octave_value retval; |
|
767
|
584 |
|
5275
|
585 octave_idx_type k = b.int_value (); |
|
767
|
586 |
|
|
587 if (error_state) |
|
|
588 { |
|
|
589 error ("diag: invalid second argument"); |
|
|
590 return retval; |
|
|
591 } |
|
|
592 |
|
|
593 if (a.is_real_type ()) |
|
|
594 { |
|
3307
|
595 Matrix m = a.matrix_value (); |
|
767
|
596 |
|
3307
|
597 if (! error_state) |
|
767
|
598 { |
|
5275
|
599 octave_idx_type nr = m.rows (); |
|
|
600 octave_idx_type nc = m.columns (); |
|
767
|
601 |
|
3972
|
602 if (nr == 1 || nc == 1) |
|
|
603 retval = make_diag (m, k); |
|
|
604 else if (nr == 0 || nc == 0) |
|
767
|
605 retval = Matrix (); |
|
|
606 else |
|
|
607 { |
|
|
608 ColumnVector d = m.diag (k); |
|
|
609 retval = d; |
|
|
610 } |
|
|
611 } |
|
|
612 } |
|
|
613 else if (a.is_complex_type ()) |
|
|
614 { |
|
3307
|
615 ComplexMatrix cm = a.complex_matrix_value (); |
|
767
|
616 |
|
3307
|
617 if (! error_state) |
|
767
|
618 { |
|
5275
|
619 octave_idx_type nr = cm.rows (); |
|
|
620 octave_idx_type nc = cm.columns (); |
|
767
|
621 |
|
3972
|
622 if (nr == 1 || nc == 1) |
|
|
623 retval = make_diag (cm, k); |
|
|
624 else if (nr == 0 || nc == 0) |
|
767
|
625 retval = Matrix (); |
|
|
626 else |
|
|
627 { |
|
|
628 ComplexColumnVector d = cm.diag (k); |
|
|
629 retval = d; |
|
|
630 } |
|
|
631 } |
|
|
632 } |
|
|
633 else |
|
|
634 gripe_wrong_type_arg ("diag", a); |
|
|
635 |
|
|
636 return retval; |
|
|
637 } |
|
|
638 |
|
1957
|
639 DEFUN (diag, args, , |
|
3369
|
640 "-*- texinfo -*-\n\ |
|
|
641 @deftypefn {Built-in Function} {} diag (@var{v}, @var{k})\n\ |
|
|
642 Return a diagonal matrix with vector @var{v} on diagonal @var{k}. The\n\ |
|
|
643 second argument is optional. If it is positive, the vector is placed on\n\ |
|
|
644 the @var{k}-th super-diagonal. If it is negative, it is placed on the\n\ |
|
|
645 @var{-k}-th sub-diagonal. The default value of @var{k} is 0, and the\n\ |
|
|
646 vector is placed on the main diagonal. For example,\n\ |
|
|
647 \n\ |
|
|
648 @example\n\ |
|
|
649 @group\n\ |
|
|
650 diag ([1, 2, 3], 1)\n\ |
|
|
651 @result{} 0 1 0 0\n\ |
|
|
652 0 0 2 0\n\ |
|
|
653 0 0 0 3\n\ |
|
|
654 0 0 0 0\n\ |
|
|
655 @end group\n\ |
|
|
656 @end example\n\ |
|
6772
|
657 \n\ |
|
|
658 @noindent\n\ |
|
|
659 Given a matrix argument, instead of a vector, @code{diag} extracts the\n\ |
|
6774
|
660 @var{k}-th diagonal of the matrix.\n\ |
|
3369
|
661 @end deftypefn") |
|
523
|
662 { |
|
4233
|
663 octave_value retval; |
|
523
|
664 |
|
|
665 int nargin = args.length (); |
|
|
666 |
|
712
|
667 if (nargin == 1 && args(0).is_defined ()) |
|
767
|
668 retval = make_diag (args(0)); |
|
712
|
669 else if (nargin == 2 && args(0).is_defined () && args(1).is_defined ()) |
|
767
|
670 retval = make_diag (args(0), args(1)); |
|
523
|
671 else |
|
5823
|
672 print_usage (); |
|
523
|
673 |
|
|
674 return retval; |
|
|
675 } |
|
|
676 |
|
1957
|
677 DEFUN (prod, args, , |
|
3428
|
678 "-*- texinfo -*-\n\ |
|
3723
|
679 @deftypefn {Built-in Function} {} prod (@var{x}, @var{dim})\n\ |
|
|
680 Product of elements along dimension @var{dim}. If @var{dim} is\n\ |
|
|
681 omitted, it defaults to 1 (column-wise products).\n\ |
|
5061
|
682 \n\ |
|
|
683 As a special case, if @var{x} is a vector and @var{dim} is omitted,\n\ |
|
|
684 return the product of the elements.\n\ |
|
3428
|
685 @end deftypefn") |
|
523
|
686 { |
|
3723
|
687 DATA_REDUCTION (prod); |
|
523
|
688 } |
|
|
689 |
|
4824
|
690 static octave_value |
|
|
691 do_cat (const octave_value_list& args, std::string fname) |
|
4806
|
692 { |
|
|
693 octave_value retval; |
|
|
694 |
|
4824
|
695 int n_args = args.length (); |
|
4806
|
696 |
|
5714
|
697 if (n_args == 1) |
|
|
698 retval = Matrix (); |
|
|
699 else if (n_args == 2) |
|
|
700 retval = args(1); |
|
5507
|
701 else if (n_args > 2) |
|
4824
|
702 { |
|
5275
|
703 octave_idx_type dim = args(0).int_value () - 1; |
|
4806
|
704 |
|
4824
|
705 if (error_state) |
|
4806
|
706 { |
|
4824
|
707 error ("cat: expecting first argument to be a integer"); |
|
4806
|
708 return retval; |
|
|
709 } |
|
|
710 |
|
4824
|
711 if (dim >= 0) |
|
|
712 { |
|
4915
|
713 |
|
|
714 dim_vector dv = args(1).dims (); |
|
4824
|
715 |
|
4915
|
716 for (int i = 2; i < args.length (); i++) |
|
|
717 { |
|
|
718 // add_dims constructs a dimension vector which holds the |
|
4824
|
719 // dimensions of the final array after concatenation. |
|
4806
|
720 |
|
4915
|
721 if (! dv.concat (args(i).dims (), dim)) |
|
4806
|
722 { |
|
4824
|
723 // Dimensions do not match. |
|
4915
|
724 error ("cat: dimension mismatch"); |
|
4806
|
725 return retval; |
|
|
726 } |
|
4824
|
727 } |
|
|
728 |
|
4915
|
729 // The lines below might seem crazy, since we take a copy |
|
|
730 // of the first argument, resize it to be empty and then resize |
|
|
731 // it to be full. This is done since it means that there is no |
|
|
732 // recopying of data, as would happen if we used a single resize. |
|
|
733 // It should be noted that resize operation is also significantly |
|
|
734 // slower than the do_cat_op function, so it makes sense to have an |
|
|
735 // empty matrix and copy all data. |
|
4824
|
736 // |
|
4915
|
737 // We might also start with a empty octave_value using |
|
|
738 // tmp = octave_value_typeinfo::lookup_type (args(1).type_name()); |
|
|
739 // and then directly resize. However, for some types there might be |
|
|
740 // some additional setup needed, and so this should be avoided. |
|
5533
|
741 |
|
4915
|
742 octave_value tmp; |
|
5533
|
743 |
|
6399
|
744 int i; |
|
|
745 for (i = 1; i < n_args; i++) |
|
5533
|
746 { |
|
|
747 if (! args (i).all_zero_dims ()) |
|
|
748 { |
|
|
749 tmp = args (i); |
|
|
750 break; |
|
|
751 } |
|
|
752 } |
|
5164
|
753 |
|
6401
|
754 if (i == n_args) |
|
|
755 retval = Matrix (); |
|
|
756 else |
|
4915
|
757 { |
|
6401
|
758 tmp = tmp.resize (dim_vector (0,0)).resize (dv); |
|
4824
|
759 |
|
4915
|
760 if (error_state) |
|
|
761 return retval; |
|
4806
|
762 |
|
6883
|
763 int dv_len = dv.length (); |
|
|
764 Array<octave_idx_type> ra_idx (dv_len, 0); |
|
6401
|
765 |
|
|
766 for (int j = i; j < n_args; j++) |
|
|
767 { |
|
6887
|
768 if (args (j). dims (). any_zero ()) |
|
6883
|
769 continue; |
|
|
770 |
|
6401
|
771 tmp = do_cat_op (tmp, args (j), ra_idx); |
|
|
772 |
|
|
773 if (error_state) |
|
|
774 return retval; |
|
|
775 |
|
|
776 dim_vector dv_tmp = args (j).dims (); |
|
|
777 |
|
6883
|
778 if (dim >= dv_len) |
|
|
779 { |
|
|
780 if (j > i) |
|
|
781 error ("%s: indexing error", fname.c_str ()); |
|
|
782 break; |
|
|
783 } |
|
|
784 else |
|
|
785 ra_idx (dim) += (dim < dv_tmp.length () ? |
|
|
786 dv_tmp (dim) : 1); |
|
6401
|
787 } |
|
|
788 |
|
|
789 retval = tmp; |
|
4915
|
790 } |
|
4806
|
791 } |
|
5533
|
792 else |
|
|
793 error ("%s: invalid dimension argument", fname.c_str ()); |
|
4806
|
794 } |
|
|
795 else |
|
5823
|
796 print_usage (); |
|
4806
|
797 |
|
|
798 return retval; |
|
|
799 } |
|
|
800 |
|
|
801 DEFUN (horzcat, args, , |
|
4824
|
802 "-*- texinfo -*-\n\ |
|
4806
|
803 @deftypefn {Built-in Function} {} horzcat (@var{array1}, @var{array2}, @dots{}, @var{arrayN})\n\ |
|
|
804 Return the horizontal concatenation of N-d array objects, @var{array1},\n\ |
|
|
805 @var{array2}, @dots{}, @var{arrayN} along dimension 2.\n\ |
|
5642
|
806 @seealso{cat, vertcat}\n\ |
|
|
807 @end deftypefn") |
|
4806
|
808 { |
|
|
809 octave_value_list args_tmp = args; |
|
|
810 |
|
|
811 int dim = 2; |
|
|
812 |
|
|
813 octave_value d (dim); |
|
|
814 |
|
|
815 args_tmp.prepend (d); |
|
|
816 |
|
4824
|
817 return do_cat (args_tmp, "horzcat"); |
|
4806
|
818 } |
|
|
819 |
|
|
820 DEFUN (vertcat, args, , |
|
|
821 "-*- texinfo -*-\n\ |
|
|
822 @deftypefn {Built-in Function} {} vertcat (@var{array1}, @var{array2}, @dots{}, @var{arrayN})\n\ |
|
|
823 Return the vertical concatenation of N-d array objects, @var{array1},\n\ |
|
|
824 @var{array2}, @dots{}, @var{arrayN} along dimension 1.\n\ |
|
5642
|
825 @seealso{cat, horzcat}\n\ |
|
|
826 @end deftypefn") |
|
4806
|
827 { |
|
|
828 octave_value_list args_tmp = args; |
|
|
829 |
|
|
830 int dim = 1; |
|
|
831 |
|
|
832 octave_value d (dim); |
|
|
833 |
|
|
834 args_tmp.prepend (d); |
|
|
835 |
|
4824
|
836 return do_cat (args_tmp, "vertcat"); |
|
4806
|
837 } |
|
|
838 |
|
4758
|
839 DEFUN (cat, args, , |
|
|
840 "-*- texinfo -*-\n\ |
|
|
841 @deftypefn {Built-in Function} {} cat (@var{dim}, @var{array1}, @var{array2}, @dots{}, @var{arrayN})\n\ |
|
4806
|
842 Return the concatenation of N-d array objects, @var{array1},\n\ |
|
|
843 @var{array2}, @dots{}, @var{arrayN} along dimension @var{dim}.\n\ |
|
4758
|
844 \n\ |
|
|
845 @example\n\ |
|
|
846 @group\n\ |
|
|
847 A = ones (2, 2);\n\ |
|
|
848 B = zeros (2, 2);\n\ |
|
|
849 cat (2, A, B)\n\ |
|
|
850 @result{} ans =\n\ |
|
|
851 \n\ |
|
|
852 1 1 0 0\n\ |
|
|
853 1 1 0 0\n\ |
|
|
854 @end group\n\ |
|
|
855 @end example\n\ |
|
|
856 \n\ |
|
|
857 Alternatively, we can concatenate @var{A} and @var{B} along the\n\ |
|
|
858 second dimension the following way:\n\ |
|
|
859 \n\ |
|
|
860 @example\n\ |
|
|
861 @group\n\ |
|
|
862 [A, B].\n\ |
|
|
863 @end group\n\ |
|
|
864 @end example\n\ |
|
|
865 \n\ |
|
|
866 @var{dim} can be larger than the dimensions of the N-d array objects\n\ |
|
|
867 and the result will thus have @var{dim} dimensions as the\n\ |
|
|
868 following example shows:\n\ |
|
|
869 @example\n\ |
|
|
870 @group\n\ |
|
|
871 cat (4, ones(2, 2), zeros (2, 2))\n\ |
|
|
872 @result{} ans =\n\ |
|
|
873 \n\ |
|
|
874 ans(:,:,1,1) =\n\ |
|
|
875 \n\ |
|
|
876 1 1\n\ |
|
|
877 1 1\n\ |
|
|
878 \n\ |
|
|
879 ans(:,:,1,2) =\n\ |
|
|
880 0 0\n\ |
|
|
881 0 0\n\ |
|
|
882 @end group\n\ |
|
|
883 @end example\n\ |
|
5642
|
884 @seealso{horzcat, vertcat}\n\ |
|
|
885 @end deftypefn") |
|
4758
|
886 { |
|
4824
|
887 return do_cat (args, "cat"); |
|
4758
|
888 } |
|
|
889 |
|
4593
|
890 static octave_value |
|
|
891 do_permute (const octave_value_list& args, bool inv, const std::string& fname) |
|
|
892 { |
|
|
893 octave_value retval; |
|
|
894 |
|
5148
|
895 if (args.length () == 2 && args(1).length () >= args(1).ndims ()) |
|
4593
|
896 { |
|
|
897 Array<int> vec = args(1).int_vector_value (); |
|
|
898 |
|
5775
|
899 // FIXME -- maybe we should create an idx_vector object |
|
5148
|
900 // here and pass that to permute? |
|
|
901 |
|
|
902 int n = vec.length (); |
|
|
903 |
|
|
904 for (int i = 0; i < n; i++) |
|
|
905 vec(i)--; |
|
|
906 |
|
4593
|
907 octave_value ret = args(0).permute (vec, inv); |
|
|
908 |
|
|
909 if (! error_state) |
|
|
910 retval = ret; |
|
|
911 } |
|
|
912 else |
|
5823
|
913 print_usage (); |
|
4593
|
914 |
|
|
915 return retval; |
|
|
916 } |
|
|
917 |
|
|
918 DEFUN (permute, args, , |
|
|
919 "-*- texinfo -*-\n\ |
|
|
920 @deftypefn {Built-in Function} {} permute (@var{a}, @var{perm})\n\ |
|
|
921 Return the generalized transpose for an N-d array object @var{a}.\n\ |
|
|
922 The permutation vector @var{perm} must contain the elements\n\ |
|
|
923 @code{1:ndims(a)} (in any order, but each element must appear just once).\n\ |
|
5642
|
924 @seealso{ipermute}\n\ |
|
|
925 @end deftypefn") |
|
4593
|
926 { |
|
|
927 return do_permute (args, false, "permute"); |
|
|
928 } |
|
|
929 |
|
|
930 DEFUN (ipermute, args, , |
|
|
931 "-*- texinfo -*-\n\ |
|
|
932 @deftypefn {Built-in Function} {} ipermute (@var{a}, @var{iperm})\n\ |
|
|
933 The inverse of the @code{permute} function. The expression\n\ |
|
|
934 \n\ |
|
|
935 @example\n\ |
|
|
936 ipermute (permute (a, perm), perm)\n\ |
|
|
937 @end example\n\ |
|
|
938 returns the original array @var{a}.\n\ |
|
5642
|
939 @seealso{permute}\n\ |
|
|
940 @end deftypefn") |
|
4593
|
941 { |
|
|
942 return do_permute (args, true, "ipermute"); |
|
|
943 } |
|
|
944 |
|
3195
|
945 DEFUN (length, args, , |
|
3373
|
946 "-*- texinfo -*-\n\ |
|
|
947 @deftypefn {Built-in Function} {} length (@var{a})\n\ |
|
4176
|
948 Return the `length' of the object @var{a}. For matrix objects, the\n\ |
|
3373
|
949 length is the number of rows or columns, whichever is greater (this\n\ |
|
6556
|
950 odd definition is used for compatibility with @sc{Matlab}).\n\ |
|
3373
|
951 @end deftypefn") |
|
3195
|
952 { |
|
|
953 octave_value retval; |
|
|
954 |
|
|
955 if (args.length () == 1) |
|
|
956 { |
|
|
957 int len = args(0).length (); |
|
|
958 |
|
|
959 if (! error_state) |
|
4233
|
960 retval = len; |
|
3195
|
961 } |
|
|
962 else |
|
5823
|
963 print_usage (); |
|
3195
|
964 |
|
|
965 return retval; |
|
|
966 } |
|
|
967 |
|
4554
|
968 DEFUN (ndims, args, , |
|
|
969 "-*- texinfo -*-\n\ |
|
|
970 @deftypefn {Built-in Function} {} ndims (@var{a})\n\ |
|
|
971 Returns the number of dimensions of array @var{a}.\n\ |
|
|
972 For any array, the result will always be larger than or equal to 2.\n\ |
|
|
973 Trailing singleton dimensions are not counted.\n\ |
|
|
974 @end deftypefn") |
|
|
975 { |
|
|
976 octave_value retval; |
|
|
977 |
|
|
978 if (args.length () == 1) |
|
|
979 { |
|
|
980 int n_dims = args(0).ndims (); |
|
|
981 |
|
|
982 if (! error_state) |
|
|
983 retval = n_dims; |
|
|
984 } |
|
|
985 else |
|
5823
|
986 print_usage (); |
|
4554
|
987 |
|
|
988 return retval; |
|
|
989 } |
|
|
990 |
|
4559
|
991 DEFUN (numel, args, , |
|
|
992 "-*- texinfo -*-\n\ |
|
|
993 @deftypefn {Built-in Function} {} numel (@var{a})\n\ |
|
|
994 Returns the number of elements in the object @var{a}.\n\ |
|
5724
|
995 @seealso{size}\n\ |
|
4559
|
996 @end deftypefn") |
|
|
997 { |
|
|
998 octave_value retval; |
|
|
999 |
|
|
1000 if (args.length () == 1) |
|
|
1001 { |
|
|
1002 int numel = args(0).numel (); |
|
|
1003 |
|
|
1004 if (! error_state) |
|
|
1005 { |
|
|
1006 if (numel < 0) |
|
|
1007 numel = 0; |
|
|
1008 |
|
|
1009 retval = numel; |
|
|
1010 } |
|
|
1011 } |
|
|
1012 else |
|
5823
|
1013 print_usage (); |
|
4559
|
1014 |
|
|
1015 return retval; |
|
|
1016 } |
|
|
1017 |
|
1957
|
1018 DEFUN (size, args, nargout, |
|
3373
|
1019 "-*- texinfo -*-\n\ |
|
|
1020 @deftypefn {Built-in Function} {} size (@var{a}, @var{n})\n\ |
|
|
1021 Return the number rows and columns of @var{a}.\n\ |
|
|
1022 \n\ |
|
|
1023 With one input argument and one output argument, the result is returned\n\ |
|
4741
|
1024 in a row vector. If there are multiple output arguments, the number of\n\ |
|
|
1025 rows is assigned to the first, and the number of columns to the second,\n\ |
|
|
1026 etc. For example,\n\ |
|
3373
|
1027 \n\ |
|
|
1028 @example\n\ |
|
|
1029 @group\n\ |
|
|
1030 size ([1, 2; 3, 4; 5, 6])\n\ |
|
|
1031 @result{} [ 3, 2 ]\n\ |
|
1031
|
1032 \n\ |
|
3373
|
1033 [nr, nc] = size ([1, 2; 3, 4; 5, 6])\n\ |
|
|
1034 @result{} nr = 3\n\ |
|
|
1035 @result{} nc = 2\n\ |
|
|
1036 @end group\n\ |
|
|
1037 @end example\n\ |
|
|
1038 \n\ |
|
4741
|
1039 If given a second argument, @code{size} will return the size of the\n\ |
|
|
1040 corresponding dimension. For example\n\ |
|
1031
|
1041 \n\ |
|
3373
|
1042 @example\n\ |
|
|
1043 size ([1, 2; 3, 4; 5, 6], 2)\n\ |
|
|
1044 @result{} 2\n\ |
|
|
1045 @end example\n\ |
|
|
1046 \n\ |
|
|
1047 @noindent\n\ |
|
|
1048 returns the number of columns in the given matrix.\n\ |
|
5724
|
1049 @seealso{numel}\n\ |
|
3373
|
1050 @end deftypefn") |
|
523
|
1051 { |
|
2086
|
1052 octave_value_list retval; |
|
523
|
1053 |
|
|
1054 int nargin = args.length (); |
|
|
1055 |
|
4513
|
1056 if (nargin == 1) |
|
523
|
1057 { |
|
4513
|
1058 dim_vector dimensions = args(0).dims (); |
|
|
1059 |
|
|
1060 int ndims = dimensions.length (); |
|
1031
|
1061 |
|
4513
|
1062 Matrix m (1, ndims); |
|
|
1063 |
|
|
1064 if (nargout > 1) |
|
523
|
1065 { |
|
5991
|
1066 for (int i = nargout-1; i >= ndims; i--) |
|
|
1067 retval(i) = 1; |
|
|
1068 |
|
6197
|
1069 if (ndims > nargout) |
|
|
1070 { |
|
|
1071 octave_idx_type d = 1; |
|
|
1072 |
|
|
1073 while (ndims >= nargout) |
|
|
1074 d *= dimensions(--ndims); |
|
|
1075 |
|
|
1076 retval(ndims) = d; |
|
|
1077 } |
|
|
1078 |
|
4513
|
1079 while (ndims--) |
|
|
1080 retval(ndims) = dimensions(ndims); |
|
523
|
1081 } |
|
4513
|
1082 else |
|
712
|
1083 { |
|
4513
|
1084 for (int i = 0; i < ndims; i++) |
|
|
1085 m(0, i) = dimensions(i); |
|
|
1086 |
|
|
1087 retval(0) = m; |
|
712
|
1088 } |
|
1031
|
1089 } |
|
|
1090 else if (nargin == 2 && nargout < 2) |
|
|
1091 { |
|
5275
|
1092 octave_idx_type nd = args(1).int_value (true); |
|
1031
|
1093 |
|
|
1094 if (error_state) |
|
|
1095 error ("size: expecting scalar as second argument"); |
|
712
|
1096 else |
|
1031
|
1097 { |
|
4741
|
1098 dim_vector dv = args(0).dims (); |
|
|
1099 |
|
4911
|
1100 if (nd > 0) |
|
|
1101 { |
|
|
1102 if (nd <= dv.length ()) |
|
|
1103 retval(0) = dv(nd-1); |
|
|
1104 else |
|
|
1105 retval(0) = 1; |
|
|
1106 } |
|
1031
|
1107 else |
|
4741
|
1108 error ("size: requested dimension (= %d) out of range", nd); |
|
1031
|
1109 } |
|
523
|
1110 } |
|
712
|
1111 else |
|
5823
|
1112 print_usage (); |
|
523
|
1113 |
|
|
1114 return retval; |
|
|
1115 } |
|
|
1116 |
|
6156
|
1117 DEFUN (size_equal, args, , |
|
|
1118 "-*- texinfo -*-\n\ |
|
6561
|
1119 @deftypefn {Built-in Function} {} size_equal (@var{a}, @var{b}, @dots{})\n\ |
|
|
1120 Return true if the dimensions of all arguments agree.\n\ |
|
6156
|
1121 Trailing singleton dimensions are ignored.\n\ |
|
|
1122 @seealso{size, numel}\n\ |
|
|
1123 @end deftypefn") |
|
|
1124 { |
|
|
1125 octave_value retval; |
|
|
1126 |
|
6561
|
1127 int nargin = args.length (); |
|
|
1128 |
|
|
1129 if (nargin >= 2) |
|
6156
|
1130 { |
|
6561
|
1131 retval = true; |
|
|
1132 |
|
6156
|
1133 dim_vector a_dims = args(0).dims (); |
|
|
1134 a_dims.chop_trailing_singletons (); |
|
6561
|
1135 |
|
|
1136 for (int i = 1; i < nargin; ++i) |
|
|
1137 { |
|
|
1138 dim_vector b_dims = args(i).dims (); |
|
|
1139 b_dims.chop_trailing_singletons (); |
|
|
1140 |
|
|
1141 if (a_dims != b_dims) |
|
|
1142 { |
|
|
1143 retval = false; |
|
|
1144 break; |
|
|
1145 } |
|
|
1146 } |
|
6156
|
1147 } |
|
|
1148 else |
|
|
1149 print_usage (); |
|
|
1150 |
|
|
1151 return retval; |
|
|
1152 } |
|
|
1153 |
|
5602
|
1154 DEFUN (nnz, args, , |
|
|
1155 "-*- texinfo -*-\n\ |
|
6156
|
1156 @deftypefn {Built-in Function} {@var{scalar} =} nnz (@var{a})\n\ |
|
|
1157 Returns the number of non zero elements in @var{a}.\n\ |
|
5602
|
1158 @seealso{sparse}\n\ |
|
|
1159 @end deftypefn") |
|
|
1160 { |
|
|
1161 octave_value retval; |
|
|
1162 |
|
|
1163 if (args.length () == 1) |
|
|
1164 retval = args(0).nnz (); |
|
|
1165 else |
|
5823
|
1166 print_usage (); |
|
5602
|
1167 |
|
|
1168 return retval; |
|
|
1169 } |
|
|
1170 |
|
5604
|
1171 DEFUN (nzmax, args, , |
|
|
1172 "-*- texinfo -*-\n\ |
|
6156
|
1173 @deftypefn {Built-in Function} {@var{scalar} =} nzmax (@var{SM})\n\ |
|
5604
|
1174 Return the amount of storage allocated to the sparse matrix @var{SM}.\n\ |
|
|
1175 Note that Octave tends to crop unused memory at the first oppurtunity\n\ |
|
|
1176 for sparse objects. There are some cases of user created sparse objects\n\ |
|
|
1177 where the value returned by @dfn{nzmaz} will not be the same as @dfn{nnz},\n\ |
|
|
1178 but in general they will give the same result.\n\ |
|
|
1179 @seealso{sparse, spalloc}\n\ |
|
|
1180 @end deftypefn") |
|
|
1181 { |
|
|
1182 octave_value retval; |
|
|
1183 |
|
|
1184 if (args.length() == 1) |
|
|
1185 retval = args(0).nzmax (); |
|
|
1186 else |
|
5823
|
1187 print_usage (); |
|
5604
|
1188 |
|
|
1189 return retval; |
|
|
1190 } |
|
|
1191 |
|
5677
|
1192 DEFUN (rows, args, , |
|
|
1193 "-*- texinfo -*-\n\ |
|
|
1194 @deftypefn {Built-in Function} {} rows (@var{a})\n\ |
|
|
1195 Return the number of rows of @var{a}.\n\ |
|
5724
|
1196 @seealso{size, numel, columns, length, isscalar, isvector, ismatrix}\n\ |
|
5677
|
1197 @end deftypefn") |
|
|
1198 { |
|
|
1199 octave_value retval; |
|
|
1200 |
|
|
1201 if (args.length () == 1) |
|
|
1202 retval = args(0).rows (); |
|
|
1203 else |
|
5823
|
1204 print_usage (); |
|
5677
|
1205 |
|
|
1206 return retval; |
|
|
1207 } |
|
|
1208 |
|
|
1209 DEFUN (columns, args, , |
|
|
1210 "-*- texinfo -*-\n\ |
|
|
1211 @deftypefn {Built-in Function} {} columns (@var{a})\n\ |
|
|
1212 Return the number of columns of @var{a}.\n\ |
|
5724
|
1213 @seealso{size, numel, rows, length, isscalar, isvector, and ismatrix}\n\ |
|
5677
|
1214 @end deftypefn") |
|
|
1215 { |
|
|
1216 octave_value retval; |
|
|
1217 |
|
|
1218 if (args.length () == 1) |
|
|
1219 retval = args(0).columns (); |
|
|
1220 else |
|
5823
|
1221 print_usage (); |
|
5677
|
1222 |
|
|
1223 return retval; |
|
|
1224 } |
|
|
1225 |
|
1957
|
1226 DEFUN (sum, args, , |
|
3428
|
1227 "-*- texinfo -*-\n\ |
|
3723
|
1228 @deftypefn {Built-in Function} {} sum (@var{x}, @var{dim})\n\ |
|
|
1229 Sum of elements along dimension @var{dim}. If @var{dim} is\n\ |
|
|
1230 omitted, it defaults to 1 (column-wise sum).\n\ |
|
5061
|
1231 \n\ |
|
|
1232 As a special case, if @var{x} is a vector and @var{dim} is omitted,\n\ |
|
|
1233 return the sum of the elements.\n\ |
|
3428
|
1234 @end deftypefn") |
|
523
|
1235 { |
|
3723
|
1236 DATA_REDUCTION (sum); |
|
523
|
1237 } |
|
|
1238 |
|
1957
|
1239 DEFUN (sumsq, args, , |
|
3428
|
1240 "-*- texinfo -*-\n\ |
|
3723
|
1241 @deftypefn {Built-in Function} {} sumsq (@var{x}, @var{dim})\n\ |
|
|
1242 Sum of squares of elements along dimension @var{dim}. If @var{dim}\n\ |
|
|
1243 is omitted, it defaults to 1 (column-wise sum of squares).\n\ |
|
3095
|
1244 \n\ |
|
5061
|
1245 As a special case, if @var{x} is a vector and @var{dim} is omitted,\n\ |
|
|
1246 return the sum of squares of the elements.\n\ |
|
|
1247 \n\ |
|
|
1248 This function is conceptually equivalent to computing\n\ |
|
3723
|
1249 @example\n\ |
|
|
1250 sum (x .* conj (x), dim)\n\ |
|
|
1251 @end example\n\ |
|
|
1252 but it uses less memory and avoids calling conj if @var{x} is real.\n\ |
|
3428
|
1253 @end deftypefn") |
|
523
|
1254 { |
|
3723
|
1255 DATA_REDUCTION (sumsq); |
|
523
|
1256 } |
|
|
1257 |
|
6688
|
1258 DEFUN (islogical, args, , |
|
3428
|
1259 "-*- texinfo -*-\n\ |
|
6688
|
1260 @deftypefn {Built-in Functio} {} islogical (@var{x})\n\ |
|
|
1261 Return true if @var{x} is a logical object.\n\ |
|
3439
|
1262 @end deftypefn") |
|
3209
|
1263 { |
|
|
1264 octave_value retval; |
|
|
1265 |
|
|
1266 if (args.length () == 1) |
|
3258
|
1267 retval = args(0).is_bool_type (); |
|
3209
|
1268 else |
|
5823
|
1269 print_usage (); |
|
3209
|
1270 |
|
|
1271 return retval; |
|
|
1272 } |
|
|
1273 |
|
6688
|
1274 DEFALIAS (isbool, islogical); |
|
3209
|
1275 |
|
6223
|
1276 DEFUN (isinteger, args, , |
|
|
1277 "-*- texinfo -*-\n\ |
|
6230
|
1278 @deftypefn {Built-in Function} {} isinteger (@var{x})\n\ |
|
6223
|
1279 Return true if @var{x} is an integer object (int8, uint8, int16, etc.).\n\ |
|
|
1280 Note that @code{isinteger (14)} is false because numeric constants in\n\ |
|
|
1281 are double precision floating point values.\n\ |
|
|
1282 @seealso{isreal, isnumeric, class, isa}\n\ |
|
|
1283 @end deftypefn") |
|
|
1284 { |
|
|
1285 octave_value retval; |
|
|
1286 |
|
|
1287 if (args.length () == 1) |
|
|
1288 retval = args(0).is_integer_type (); |
|
|
1289 else |
|
|
1290 print_usage (); |
|
|
1291 |
|
|
1292 return retval; |
|
|
1293 } |
|
|
1294 |
|
4028
|
1295 DEFUN (iscomplex, args, , |
|
3428
|
1296 "-*- texinfo -*-\n\ |
|
4028
|
1297 @deftypefn {Built-in Function} {} iscomplex (@var{x})\n\ |
|
3428
|
1298 Return true if @var{x} is a complex-valued numeric object.\n\ |
|
|
1299 @end deftypefn") |
|
3186
|
1300 { |
|
|
1301 octave_value retval; |
|
|
1302 |
|
|
1303 if (args.length () == 1) |
|
3258
|
1304 retval = args(0).is_complex_type (); |
|
3186
|
1305 else |
|
5823
|
1306 print_usage (); |
|
3186
|
1307 |
|
|
1308 return retval; |
|
|
1309 } |
|
|
1310 |
|
5775
|
1311 // FIXME -- perhaps this should be implemented with an |
|
5476
|
1312 // octave_value member function? |
|
|
1313 |
|
|
1314 DEFUN (complex, args, , |
|
|
1315 "-*- texinfo -*-\n\ |
|
|
1316 @deftypefn {Built-in Function} {} complex (@var{val})\n\ |
|
|
1317 @deftypefnx {Built-in Function} {} complex (@var{re}, @var{im})\n\ |
|
|
1318 Convert @var{x} to a complex value.\n\ |
|
|
1319 @end deftypefn") |
|
|
1320 { |
|
|
1321 octave_value retval; |
|
|
1322 |
|
|
1323 int nargin = args.length (); |
|
|
1324 |
|
|
1325 if (nargin == 1) |
|
|
1326 { |
|
|
1327 octave_value arg = args(0); |
|
|
1328 |
|
|
1329 if (arg.is_complex_type ()) |
|
|
1330 retval = arg; |
|
|
1331 else |
|
|
1332 { |
|
|
1333 if (arg.numel () == 1) |
|
|
1334 { |
|
|
1335 Complex val = arg.complex_value (); |
|
|
1336 |
|
|
1337 if (! error_state) |
|
|
1338 retval = octave_value (new octave_complex (val)); |
|
|
1339 } |
|
|
1340 else |
|
|
1341 { |
|
|
1342 ComplexNDArray val = arg.complex_array_value (); |
|
|
1343 |
|
|
1344 if (! error_state) |
|
|
1345 retval = octave_value (new octave_complex_matrix (val)); |
|
|
1346 } |
|
|
1347 |
|
|
1348 if (error_state) |
|
|
1349 error ("complex: invalid conversion"); |
|
|
1350 } |
|
|
1351 } |
|
|
1352 else if (nargin == 2) |
|
|
1353 { |
|
|
1354 octave_value re = args(0); |
|
|
1355 octave_value im = args(1); |
|
|
1356 |
|
|
1357 if (re.numel () == 1) |
|
|
1358 { |
|
|
1359 double re_val = re.double_value (); |
|
|
1360 |
|
|
1361 if (im.numel () == 1) |
|
|
1362 { |
|
|
1363 double im_val = im.double_value (); |
|
|
1364 |
|
|
1365 if (! error_state) |
|
|
1366 retval = octave_value (new octave_complex (Complex (re_val, im_val))); |
|
|
1367 } |
|
|
1368 else |
|
|
1369 { |
|
|
1370 const NDArray im_val = im.array_value (); |
|
|
1371 |
|
|
1372 if (! error_state) |
|
|
1373 { |
|
|
1374 ComplexNDArray result (im_val.dims (), Complex ()); |
|
|
1375 |
|
|
1376 for (octave_idx_type i = 0; i < im_val.numel (); i++) |
|
|
1377 result.xelem (i) = Complex (re_val, im_val(i)); |
|
|
1378 |
|
|
1379 retval = octave_value (new octave_complex_matrix (result)); |
|
|
1380 } |
|
|
1381 } |
|
|
1382 } |
|
|
1383 else |
|
|
1384 { |
|
|
1385 const NDArray re_val = re.array_value (); |
|
|
1386 |
|
|
1387 if (im.numel () == 1) |
|
|
1388 { |
|
|
1389 double im_val = im.double_value (); |
|
|
1390 |
|
|
1391 if (! error_state) |
|
|
1392 { |
|
|
1393 ComplexNDArray result (re_val.dims (), Complex ()); |
|
|
1394 |
|
|
1395 for (octave_idx_type i = 0; i < re_val.numel (); i++) |
|
|
1396 result.xelem (i) = Complex (re_val(i), im_val); |
|
|
1397 |
|
|
1398 retval = octave_value (new octave_complex_matrix (result)); |
|
|
1399 } |
|
|
1400 } |
|
|
1401 else |
|
|
1402 { |
|
|
1403 const NDArray im_val = im.array_value (); |
|
|
1404 |
|
|
1405 if (! error_state) |
|
|
1406 { |
|
|
1407 if (re_val.dims () == im_val.dims ()) |
|
|
1408 { |
|
|
1409 ComplexNDArray result (re_val.dims (), Complex ()); |
|
|
1410 |
|
|
1411 for (octave_idx_type i = 0; i < re_val.numel (); i++) |
|
|
1412 result.xelem (i) = Complex (re_val(i), im_val(i)); |
|
|
1413 |
|
|
1414 retval = octave_value (new octave_complex_matrix (result)); |
|
|
1415 } |
|
|
1416 else |
|
|
1417 error ("complex: dimension mismatch"); |
|
|
1418 } |
|
|
1419 } |
|
|
1420 } |
|
|
1421 |
|
|
1422 if (error_state) |
|
|
1423 error ("complex: invalid conversion"); |
|
|
1424 } |
|
|
1425 else |
|
5823
|
1426 print_usage (); |
|
5476
|
1427 |
|
|
1428 return retval; |
|
|
1429 } |
|
|
1430 |
|
3258
|
1431 DEFUN (isreal, args, , |
|
3428
|
1432 "-*- texinfo -*-\n\ |
|
|
1433 @deftypefn {Built-in Function} {} isreal (@var{x})\n\ |
|
|
1434 Return true if @var{x} is a real-valued numeric object.\n\ |
|
|
1435 @end deftypefn") |
|
3258
|
1436 { |
|
|
1437 octave_value retval; |
|
|
1438 |
|
|
1439 if (args.length () == 1) |
|
|
1440 retval = args(0).is_real_type (); |
|
|
1441 else |
|
5823
|
1442 print_usage (); |
|
3258
|
1443 |
|
|
1444 return retval; |
|
|
1445 } |
|
|
1446 |
|
3202
|
1447 DEFUN (isempty, args, , |
|
3373
|
1448 "-*- texinfo -*-\n\ |
|
|
1449 @deftypefn {Built-in Function} {} isempty (@var{a})\n\ |
|
|
1450 Return 1 if @var{a} is an empty matrix (either the number of rows, or\n\ |
|
|
1451 the number of columns, or both are zero). Otherwise, return 0.\n\ |
|
|
1452 @end deftypefn") |
|
3202
|
1453 { |
|
4233
|
1454 octave_value retval = false; |
|
3202
|
1455 |
|
|
1456 if (args.length () == 1) |
|
4559
|
1457 retval = args(0).is_empty (); |
|
3202
|
1458 else |
|
5823
|
1459 print_usage (); |
|
3202
|
1460 |
|
|
1461 return retval; |
|
|
1462 } |
|
|
1463 |
|
3206
|
1464 DEFUN (isnumeric, args, , |
|
3428
|
1465 "-*- texinfo -*-\n\ |
|
|
1466 @deftypefn {Built-in Function} {} isnumeric (@var{x})\n\ |
|
|
1467 Return nonzero if @var{x} is a numeric object.\n\ |
|
|
1468 @end deftypefn") |
|
3206
|
1469 { |
|
|
1470 octave_value retval; |
|
|
1471 |
|
|
1472 if (args.length () == 1) |
|
3258
|
1473 retval = args(0).is_numeric_type (); |
|
3206
|
1474 else |
|
5823
|
1475 print_usage (); |
|
3206
|
1476 |
|
|
1477 return retval; |
|
|
1478 } |
|
|
1479 |
|
4028
|
1480 DEFUN (islist, args, , |
|
3526
|
1481 "-*- texinfo -*-\n\ |
|
4028
|
1482 @deftypefn {Built-in Function} {} islist (@var{x})\n\ |
|
3428
|
1483 Return nonzero if @var{x} is a list.\n\ |
|
|
1484 @end deftypefn") |
|
3204
|
1485 { |
|
|
1486 octave_value retval; |
|
|
1487 |
|
|
1488 if (args.length () == 1) |
|
3258
|
1489 retval = args(0).is_list (); |
|
3204
|
1490 else |
|
5823
|
1491 print_usage (); |
|
3204
|
1492 |
|
|
1493 return retval; |
|
|
1494 } |
|
|
1495 |
|
4028
|
1496 DEFUN (ismatrix, args, , |
|
3321
|
1497 "-*- texinfo -*-\n\ |
|
4028
|
1498 @deftypefn {Built-in Function} {} ismatrix (@var{a})\n\ |
|
3321
|
1499 Return 1 if @var{a} is a matrix. Otherwise, return 0.\n\ |
|
3333
|
1500 @end deftypefn") |
|
3202
|
1501 { |
|
4233
|
1502 octave_value retval = false; |
|
3202
|
1503 |
|
|
1504 if (args.length () == 1) |
|
|
1505 { |
|
|
1506 octave_value arg = args(0); |
|
|
1507 |
|
3212
|
1508 if (arg.is_scalar_type () || arg.is_range ()) |
|
4233
|
1509 retval = true; |
|
3202
|
1510 else if (arg.is_matrix_type ()) |
|
4233
|
1511 retval = (arg.rows () >= 1 && arg.columns () >= 1); |
|
3202
|
1512 } |
|
|
1513 else |
|
5823
|
1514 print_usage (); |
|
3202
|
1515 |
|
|
1516 return retval; |
|
|
1517 } |
|
|
1518 |
|
3354
|
1519 static octave_value |
|
5747
|
1520 fill_matrix (const octave_value_list& args, int val, const char *fcn) |
|
523
|
1521 { |
|
3354
|
1522 octave_value retval; |
|
523
|
1523 |
|
|
1524 int nargin = args.length (); |
|
|
1525 |
|
4946
|
1526 oct_data_conv::data_type dt = oct_data_conv::dt_double; |
|
4481
|
1527 |
|
4946
|
1528 dim_vector dims (1, 1); |
|
4481
|
1529 |
|
|
1530 if (nargin > 0 && args(nargin-1).is_string ()) |
|
|
1531 { |
|
4946
|
1532 std::string nm = args(nargin-1).string_value (); |
|
4481
|
1533 nargin--; |
|
|
1534 |
|
4946
|
1535 dt = oct_data_conv::string_to_data_type (nm); |
|
|
1536 |
|
|
1537 if (error_state) |
|
|
1538 return retval; |
|
4481
|
1539 } |
|
|
1540 |
|
523
|
1541 switch (nargin) |
|
|
1542 { |
|
712
|
1543 case 0: |
|
|
1544 break; |
|
777
|
1545 |
|
610
|
1546 case 1: |
|
4481
|
1547 get_dimensions (args(0), fcn, dims); |
|
610
|
1548 break; |
|
777
|
1549 |
|
4563
|
1550 default: |
|
|
1551 { |
|
|
1552 dims.resize (nargin); |
|
4481
|
1553 |
|
4563
|
1554 for (int i = 0; i < nargin; i++) |
|
|
1555 { |
|
6133
|
1556 dims(i) = args(i).is_empty () ? 0 : args(i).idx_type_value (); |
|
4481
|
1557 |
|
4563
|
1558 if (error_state) |
|
|
1559 { |
|
4732
|
1560 error ("%s: expecting scalar integer arguments", fcn); |
|
4563
|
1561 break; |
|
|
1562 } |
|
|
1563 } |
|
|
1564 } |
|
|
1565 break; |
|
4481
|
1566 } |
|
|
1567 |
|
|
1568 if (! error_state) |
|
|
1569 { |
|
4946
|
1570 dims.chop_trailing_singletons (); |
|
4565
|
1571 |
|
4481
|
1572 check_dimensions (dims, fcn); |
|
3354
|
1573 |
|
5775
|
1574 // FIXME -- perhaps this should be made extensible by |
|
4946
|
1575 // using the class name to lookup a function to call to create |
|
|
1576 // the new value. |
|
|
1577 |
|
|
1578 // Note that automatic narrowing will handle conversion from |
|
|
1579 // NDArray to scalar. |
|
|
1580 |
|
4481
|
1581 if (! error_state) |
|
|
1582 { |
|
4946
|
1583 switch (dt) |
|
|
1584 { |
|
|
1585 case oct_data_conv::dt_int8: |
|
|
1586 retval = int8NDArray (dims, val); |
|
|
1587 break; |
|
4481
|
1588 |
|
4946
|
1589 case oct_data_conv::dt_uint8: |
|
|
1590 retval = uint8NDArray (dims, val); |
|
|
1591 break; |
|
|
1592 |
|
|
1593 case oct_data_conv::dt_int16: |
|
|
1594 retval = int16NDArray (dims, val); |
|
|
1595 break; |
|
|
1596 |
|
|
1597 case oct_data_conv::dt_uint16: |
|
|
1598 retval = uint16NDArray (dims, val); |
|
|
1599 break; |
|
|
1600 |
|
|
1601 case oct_data_conv::dt_int32: |
|
|
1602 retval = int32NDArray (dims, val); |
|
|
1603 break; |
|
777
|
1604 |
|
4946
|
1605 case oct_data_conv::dt_uint32: |
|
|
1606 retval = uint32NDArray (dims, val); |
|
|
1607 break; |
|
|
1608 |
|
|
1609 case oct_data_conv::dt_int64: |
|
|
1610 retval = int64NDArray (dims, val); |
|
|
1611 break; |
|
4481
|
1612 |
|
4946
|
1613 case oct_data_conv::dt_uint64: |
|
|
1614 retval = uint64NDArray (dims, val); |
|
|
1615 break; |
|
4481
|
1616 |
|
5775
|
1617 case oct_data_conv::dt_single: // FIXME |
|
4946
|
1618 case oct_data_conv::dt_double: |
|
|
1619 retval = NDArray (dims, val); |
|
|
1620 break; |
|
|
1621 |
|
4986
|
1622 case oct_data_conv::dt_logical: |
|
|
1623 retval = boolNDArray (dims, val); |
|
|
1624 break; |
|
|
1625 |
|
4946
|
1626 default: |
|
|
1627 error ("%s: invalid class name", fcn); |
|
|
1628 break; |
|
4481
|
1629 } |
|
|
1630 } |
|
523
|
1631 } |
|
|
1632 |
|
|
1633 return retval; |
|
|
1634 } |
|
|
1635 |
|
5747
|
1636 static octave_value |
|
|
1637 fill_matrix (const octave_value_list& args, double val, const char *fcn) |
|
|
1638 { |
|
|
1639 octave_value retval; |
|
|
1640 |
|
|
1641 int nargin = args.length (); |
|
|
1642 |
|
|
1643 oct_data_conv::data_type dt = oct_data_conv::dt_double; |
|
|
1644 |
|
|
1645 dim_vector dims (1, 1); |
|
|
1646 |
|
|
1647 if (nargin > 0 && args(nargin-1).is_string ()) |
|
|
1648 { |
|
|
1649 std::string nm = args(nargin-1).string_value (); |
|
|
1650 nargin--; |
|
|
1651 |
|
|
1652 dt = oct_data_conv::string_to_data_type (nm); |
|
|
1653 |
|
|
1654 if (error_state) |
|
|
1655 return retval; |
|
|
1656 } |
|
|
1657 |
|
|
1658 switch (nargin) |
|
|
1659 { |
|
|
1660 case 0: |
|
|
1661 break; |
|
|
1662 |
|
|
1663 case 1: |
|
|
1664 get_dimensions (args(0), fcn, dims); |
|
|
1665 break; |
|
|
1666 |
|
|
1667 default: |
|
|
1668 { |
|
|
1669 dims.resize (nargin); |
|
|
1670 |
|
|
1671 for (int i = 0; i < nargin; i++) |
|
|
1672 { |
|
6133
|
1673 dims(i) = args(i).is_empty () ? 0 : args(i).idx_type_value (); |
|
5747
|
1674 |
|
|
1675 if (error_state) |
|
|
1676 { |
|
|
1677 error ("%s: expecting scalar integer arguments", fcn); |
|
|
1678 break; |
|
|
1679 } |
|
|
1680 } |
|
|
1681 } |
|
|
1682 break; |
|
|
1683 } |
|
|
1684 |
|
|
1685 if (! error_state) |
|
|
1686 { |
|
|
1687 dims.chop_trailing_singletons (); |
|
|
1688 |
|
|
1689 check_dimensions (dims, fcn); |
|
|
1690 |
|
|
1691 // Note that automatic narrowing will handle conversion from |
|
|
1692 // NDArray to scalar. |
|
|
1693 |
|
|
1694 if (! error_state) |
|
|
1695 { |
|
|
1696 switch (dt) |
|
|
1697 { |
|
5775
|
1698 case oct_data_conv::dt_single: // FIXME |
|
5747
|
1699 case oct_data_conv::dt_double: |
|
|
1700 retval = NDArray (dims, val); |
|
|
1701 break; |
|
|
1702 |
|
|
1703 default: |
|
|
1704 error ("%s: invalid class name", fcn); |
|
|
1705 break; |
|
|
1706 } |
|
|
1707 } |
|
|
1708 } |
|
|
1709 |
|
|
1710 return retval; |
|
|
1711 } |
|
|
1712 |
|
|
1713 static octave_value |
|
|
1714 fill_matrix (const octave_value_list& args, const Complex& val, |
|
|
1715 const char *fcn) |
|
|
1716 { |
|
|
1717 octave_value retval; |
|
|
1718 |
|
|
1719 int nargin = args.length (); |
|
|
1720 |
|
|
1721 oct_data_conv::data_type dt = oct_data_conv::dt_double; |
|
|
1722 |
|
|
1723 dim_vector dims (1, 1); |
|
|
1724 |
|
|
1725 if (nargin > 0 && args(nargin-1).is_string ()) |
|
|
1726 { |
|
|
1727 std::string nm = args(nargin-1).string_value (); |
|
|
1728 nargin--; |
|
|
1729 |
|
|
1730 dt = oct_data_conv::string_to_data_type (nm); |
|
|
1731 |
|
|
1732 if (error_state) |
|
|
1733 return retval; |
|
|
1734 } |
|
|
1735 |
|
|
1736 switch (nargin) |
|
|
1737 { |
|
|
1738 case 0: |
|
|
1739 break; |
|
|
1740 |
|
|
1741 case 1: |
|
|
1742 get_dimensions (args(0), fcn, dims); |
|
|
1743 break; |
|
|
1744 |
|
|
1745 default: |
|
|
1746 { |
|
|
1747 dims.resize (nargin); |
|
|
1748 |
|
|
1749 for (int i = 0; i < nargin; i++) |
|
|
1750 { |
|
6133
|
1751 dims(i) = args(i).is_empty () ? 0 : args(i).idx_type_value (); |
|
5747
|
1752 |
|
|
1753 if (error_state) |
|
|
1754 { |
|
|
1755 error ("%s: expecting scalar integer arguments", fcn); |
|
|
1756 break; |
|
|
1757 } |
|
|
1758 } |
|
|
1759 } |
|
|
1760 break; |
|
|
1761 } |
|
|
1762 |
|
|
1763 if (! error_state) |
|
|
1764 { |
|
|
1765 dims.chop_trailing_singletons (); |
|
|
1766 |
|
|
1767 check_dimensions (dims, fcn); |
|
|
1768 |
|
|
1769 // Note that automatic narrowing will handle conversion from |
|
|
1770 // NDArray to scalar. |
|
|
1771 |
|
|
1772 if (! error_state) |
|
|
1773 { |
|
|
1774 switch (dt) |
|
|
1775 { |
|
5775
|
1776 case oct_data_conv::dt_single: // FIXME |
|
5747
|
1777 case oct_data_conv::dt_double: |
|
|
1778 retval = ComplexNDArray (dims, val); |
|
|
1779 break; |
|
|
1780 |
|
|
1781 default: |
|
|
1782 error ("%s: invalid class name", fcn); |
|
|
1783 break; |
|
|
1784 } |
|
|
1785 } |
|
|
1786 } |
|
|
1787 |
|
|
1788 return retval; |
|
|
1789 } |
|
|
1790 |
|
|
1791 static octave_value |
|
|
1792 fill_matrix (const octave_value_list& args, bool val, const char *fcn) |
|
|
1793 { |
|
|
1794 octave_value retval; |
|
|
1795 |
|
|
1796 int nargin = args.length (); |
|
|
1797 |
|
|
1798 dim_vector dims (1, 1); |
|
|
1799 |
|
|
1800 switch (nargin) |
|
|
1801 { |
|
|
1802 case 0: |
|
|
1803 break; |
|
|
1804 |
|
|
1805 case 1: |
|
|
1806 get_dimensions (args(0), fcn, dims); |
|
|
1807 break; |
|
|
1808 |
|
|
1809 default: |
|
|
1810 { |
|
|
1811 dims.resize (nargin); |
|
|
1812 |
|
|
1813 for (int i = 0; i < nargin; i++) |
|
|
1814 { |
|
6133
|
1815 dims(i) = args(i).is_empty () ? 0 : args(i).idx_type_value (); |
|
5747
|
1816 |
|
|
1817 if (error_state) |
|
|
1818 { |
|
|
1819 error ("%s: expecting scalar integer arguments", fcn); |
|
|
1820 break; |
|
|
1821 } |
|
|
1822 } |
|
|
1823 } |
|
|
1824 break; |
|
|
1825 } |
|
|
1826 |
|
|
1827 if (! error_state) |
|
|
1828 { |
|
|
1829 dims.chop_trailing_singletons (); |
|
|
1830 |
|
|
1831 check_dimensions (dims, fcn); |
|
|
1832 |
|
|
1833 // Note that automatic narrowing will handle conversion from |
|
|
1834 // NDArray to scalar. |
|
|
1835 |
|
|
1836 if (! error_state) |
|
|
1837 retval = boolNDArray (dims, val); |
|
|
1838 } |
|
|
1839 |
|
|
1840 return retval; |
|
|
1841 } |
|
|
1842 |
|
3354
|
1843 DEFUN (ones, args, , |
|
3369
|
1844 "-*- texinfo -*-\n\ |
|
|
1845 @deftypefn {Built-in Function} {} ones (@var{x})\n\ |
|
|
1846 @deftypefnx {Built-in Function} {} ones (@var{n}, @var{m})\n\ |
|
4948
|
1847 @deftypefnx {Built-in Function} {} ones (@var{n}, @var{m}, @var{k}, @dots{})\n\ |
|
|
1848 @deftypefnx {Built-in Function} {} ones (@dots{}, @var{class})\n\ |
|
4481
|
1849 Return a matrix or N-dimensional array whose elements are all 1.\n\ |
|
|
1850 The arguments are handled the same as the arguments for @code{eye}.\n\ |
|
3369
|
1851 \n\ |
|
|
1852 If you need to create a matrix whose values are all the same, you should\n\ |
|
|
1853 use an expression like\n\ |
|
|
1854 \n\ |
|
|
1855 @example\n\ |
|
|
1856 val_matrix = val * ones (n, m)\n\ |
|
|
1857 @end example\n\ |
|
4945
|
1858 \n\ |
|
|
1859 The optional argument @var{class}, allows @code{ones} to return an array of\n\ |
|
5747
|
1860 the specified type, for example\n\ |
|
4945
|
1861 \n\ |
|
|
1862 @example\n\ |
|
|
1863 val = ones (n,m, \"uint8\")\n\ |
|
|
1864 @end example\n\ |
|
3369
|
1865 @end deftypefn") |
|
523
|
1866 { |
|
5747
|
1867 return fill_matrix (args, 1, "ones"); |
|
523
|
1868 } |
|
|
1869 |
|
3354
|
1870 DEFUN (zeros, args, , |
|
3369
|
1871 "-*- texinfo -*-\n\ |
|
|
1872 @deftypefn {Built-in Function} {} zeros (@var{x})\n\ |
|
|
1873 @deftypefnx {Built-in Function} {} zeros (@var{n}, @var{m})\n\ |
|
4948
|
1874 @deftypefnx {Built-in Function} {} zeros (@var{n}, @var{m}, @var{k}, @dots{})\n\ |
|
|
1875 @deftypefnx {Built-in Function} {} zeros (@dots{}, @var{class})\n\ |
|
4481
|
1876 Return a matrix or N-dimensional array whose elements are all 0.\n\ |
|
|
1877 The arguments are handled the same as the arguments for @code{eye}.\n\ |
|
4945
|
1878 \n\ |
|
|
1879 The optional argument @var{class}, allows @code{zeros} to return an array of\n\ |
|
5747
|
1880 the specified type, for example\n\ |
|
4945
|
1881 \n\ |
|
|
1882 @example\n\ |
|
|
1883 val = zeros (n,m, \"uint8\")\n\ |
|
|
1884 @end example\n\ |
|
3369
|
1885 @end deftypefn") |
|
523
|
1886 { |
|
5747
|
1887 return fill_matrix (args, 0, "zeros"); |
|
|
1888 } |
|
|
1889 |
|
|
1890 DEFUN (Inf, args, , |
|
|
1891 "-*- texinfo -*-\n\ |
|
|
1892 @deftypefn {Built-in Function} {} Inf (@var{x})\n\ |
|
|
1893 @deftypefnx {Built-in Function} {} Inf (@var{n}, @var{m})\n\ |
|
|
1894 @deftypefnx {Built-in Function} {} Inf (@var{n}, @var{m}, @var{k}, @dots{})\n\ |
|
|
1895 @deftypefnx {Built-in Function} {} Inf (@dots{}, @var{class})\n\ |
|
|
1896 Return a matrix or N-dimensional array whose elements are all Infinity.\n\ |
|
|
1897 The arguments are handled the same as the arguments for @code{eye}.\n\ |
|
|
1898 The optional argument @var{class} may be either @samp{\"single\"} or\n\ |
|
5798
|
1899 @samp{\"double\"}. The default is @samp{\"double\"}.\n\ |
|
5747
|
1900 @end deftypefn") |
|
|
1901 { |
|
|
1902 return fill_matrix (args, lo_ieee_inf_value (), "Inf"); |
|
|
1903 } |
|
|
1904 |
|
|
1905 DEFALIAS (inf, Inf); |
|
|
1906 |
|
|
1907 DEFUN (NaN, args, , |
|
|
1908 "-*- texinfo -*-\n\ |
|
|
1909 @deftypefn {Built-in Function} {} NaN (@var{x})\n\ |
|
|
1910 @deftypefnx {Built-in Function} {} NaN (@var{n}, @var{m})\n\ |
|
|
1911 @deftypefnx {Built-in Function} {} NaN (@var{n}, @var{m}, @var{k}, @dots{})\n\ |
|
|
1912 @deftypefnx {Built-in Function} {} NaN (@dots{}, @var{class})\n\ |
|
|
1913 Return a matrix or N-dimensional array whose elements are all NaN\n\ |
|
|
1914 (Not a Number). The value NaN is the result of an operation like\n\ |
|
|
1915 @iftex\n\ |
|
|
1916 @tex\n\ |
|
|
1917 $0/0$, or $\\infty - \\infty$,\n\ |
|
|
1918 @end tex\n\ |
|
|
1919 @end iftex\n\ |
|
|
1920 @ifinfo\n\ |
|
|
1921 0/0, or @samp{Inf - Inf},\n\ |
|
|
1922 @end ifinfo\n\ |
|
|
1923 or any operation with a NaN.\n\ |
|
|
1924 \n\ |
|
|
1925 Note that NaN always compares not equal to NaN. This behavior is\n\ |
|
|
1926 specified by the IEEE standard for floating point arithmetic. To\n\ |
|
|
1927 find NaN values, you must use the @code{isnan} function.\n\ |
|
|
1928 \n\ |
|
|
1929 The arguments are handled the same as the arguments for @code{eye}.\n\ |
|
|
1930 The optional argument @var{class} may be either @samp{\"single\"} or\n\ |
|
5798
|
1931 @samp{\"double\"}. The default is @samp{\"double\"}.\n\ |
|
5747
|
1932 @end deftypefn") |
|
|
1933 { |
|
|
1934 return fill_matrix (args, lo_ieee_nan_value (), "NaN"); |
|
|
1935 } |
|
|
1936 |
|
|
1937 DEFALIAS (nan, NaN); |
|
|
1938 |
|
|
1939 DEFUN (e, args, , |
|
|
1940 "-*- texinfo -*-\n\ |
|
|
1941 @deftypefn {Built-in Function} {} e (@var{x})\n\ |
|
|
1942 @deftypefnx {Built-in Function} {} e (@var{n}, @var{m})\n\ |
|
|
1943 @deftypefnx {Built-in Function} {} e (@var{n}, @var{m}, @var{k}, @dots{})\n\ |
|
|
1944 @deftypefnx {Built-in Function} {} e (@dots{}, @var{class})\n\ |
|
|
1945 Return a matrix or N-dimensional array whose elements are all equal\n\ |
|
|
1946 to the base of natural logarithms. The constant\n\ |
|
|
1947 @iftex\n\ |
|
|
1948 @tex\n\ |
|
|
1949 $e$\n\ |
|
|
1950 @end tex\n\ |
|
|
1951 @end iftex\n\ |
|
|
1952 @ifinfo\n\ |
|
|
1953 @var{e}\n\ |
|
|
1954 @end ifinfo\n\ |
|
|
1955 satisfies the equation\n\ |
|
|
1956 @iftex\n\ |
|
|
1957 @tex\n\ |
|
|
1958 $\\log (e) = 1$.\n\ |
|
|
1959 @end tex\n\ |
|
|
1960 @end iftex\n\ |
|
|
1961 @ifinfo\n\ |
|
|
1962 @code{log} (@var{e}) = 1.\n\ |
|
|
1963 @end ifinfo\n\ |
|
|
1964 @end deftypefn") |
|
|
1965 { |
|
|
1966 #if defined (M_E) |
|
|
1967 double e_val = M_E; |
|
|
1968 #else |
|
|
1969 double e_val = exp (1.0); |
|
|
1970 #endif |
|
|
1971 |
|
|
1972 return fill_matrix (args, e_val, "e"); |
|
|
1973 } |
|
|
1974 |
|
|
1975 DEFUN (eps, args, , |
|
|
1976 "-*- texinfo -*-\n\ |
|
|
1977 @deftypefn {Built-in Function} {} eps (@var{x})\n\ |
|
|
1978 @deftypefnx {Built-in Function} {} eps (@var{n}, @var{m})\n\ |
|
|
1979 @deftypefnx {Built-in Function} {} eps (@var{n}, @var{m}, @var{k}, @dots{})\n\ |
|
|
1980 @deftypefnx {Built-in Function} {} eps (@dots{}, @var{class})\n\ |
|
|
1981 Return a matrix or N-dimensional array whose elements are all eps,\n\ |
|
|
1982 the machine precision. More precisely, @code{eps} is the largest\n\ |
|
|
1983 relative spacing between any two adjacent numbers in the machine's\n\ |
|
|
1984 floating point system. This number is obviously system-dependent. On\n\ |
|
|
1985 machines that support 64 bit IEEE floating point arithmetic, @code{eps}\n\ |
|
|
1986 is approximately\n\ |
|
|
1987 @ifinfo\n\ |
|
|
1988 2.2204e-16.\n\ |
|
|
1989 @end ifinfo\n\ |
|
|
1990 @iftex\n\ |
|
|
1991 @tex\n\ |
|
|
1992 $2.2204\\times10^{-16}$.\n\ |
|
|
1993 @end tex\n\ |
|
|
1994 @end iftex\n\ |
|
|
1995 @end deftypefn") |
|
|
1996 { |
|
|
1997 return fill_matrix (args, DBL_EPSILON, "eps"); |
|
|
1998 } |
|
|
1999 |
|
|
2000 DEFUN (pi, args, , |
|
|
2001 "-*- texinfo -*-\n\ |
|
|
2002 @deftypefn {Built-in Function} {} pi (@var{x})\n\ |
|
|
2003 @deftypefnx {Built-in Function} {} pi (@var{n}, @var{m})\n\ |
|
|
2004 @deftypefnx {Built-in Function} {} pi (@var{n}, @var{m}, @var{k}, @dots{})\n\ |
|
|
2005 @deftypefnx {Built-in Function} {} pi (@dots{}, @var{class})\n\ |
|
|
2006 Return a matrix or N-dimensional array whose elements are all equal\n\ |
|
|
2007 to the ratio of the circumference of a circle to its diameter.\n\ |
|
|
2008 Internally, @code{pi} is computed as @samp{4.0 * atan (1.0)}.\n\ |
|
|
2009 @end deftypefn") |
|
|
2010 { |
|
|
2011 #if defined (M_PI) |
|
|
2012 double pi_val = M_PI; |
|
|
2013 #else |
|
|
2014 double pi_val = 4.0 * atan (1.0); |
|
|
2015 #endif |
|
|
2016 |
|
|
2017 return fill_matrix (args, pi_val, "pi"); |
|
|
2018 } |
|
|
2019 |
|
|
2020 DEFUN (realmax, args, , |
|
|
2021 "-*- texinfo -*-\n\ |
|
|
2022 @deftypefn {Built-in Function} {} realmax (@var{x})\n\ |
|
|
2023 @deftypefnx {Built-in Function} {} realmax (@var{n}, @var{m})\n\ |
|
|
2024 @deftypefnx {Built-in Function} {} realmax (@var{n}, @var{m}, @var{k}, @dots{})\n\ |
|
|
2025 @deftypefnx {Built-in Function} {} realmax (@dots{}, @var{class})\n\ |
|
|
2026 Return a matrix or N-dimensional array whose elements are all equal\n\ |
|
|
2027 to the largest floating point number that is representable. The actual\n\ |
|
|
2028 value is system-dependent. On machines that support 64-bit IEEE\n\ |
|
|
2029 floating point arithmetic, @code{realmax} is approximately\n\ |
|
|
2030 @ifinfo\n\ |
|
|
2031 1.7977e+308\n\ |
|
|
2032 @end ifinfo\n\ |
|
|
2033 @iftex\n\ |
|
|
2034 @tex\n\ |
|
|
2035 $1.7977\\times10^{308}$.\n\ |
|
|
2036 @end tex\n\ |
|
|
2037 @end iftex\n\ |
|
|
2038 @seealso{realmin}\n\ |
|
|
2039 @end deftypefn") |
|
|
2040 { |
|
|
2041 return fill_matrix (args, DBL_MAX, "realmax"); |
|
|
2042 } |
|
|
2043 |
|
|
2044 DEFUN (realmin, args, , |
|
|
2045 "-*- texinfo -*-\n\ |
|
|
2046 @deftypefn {Built-in Function} {} realmin (@var{x})\n\ |
|
|
2047 @deftypefnx {Built-in Function} {} realmin (@var{n}, @var{m})\n\ |
|
|
2048 @deftypefnx {Built-in Function} {} realmin (@var{n}, @var{m}, @var{k}, @dots{})\n\ |
|
|
2049 @deftypefnx {Built-in Function} {} realmin (@dots{}, @var{class})\n\ |
|
|
2050 Return a matrix or N-dimensional array whose elements are all equal\n\ |
|
|
2051 to the smallest normalized floating point number that is representable.\n\ |
|
|
2052 The actual value is system-dependent. On machines that support\n\ |
|
|
2053 64-bit IEEE floating point arithmetic, @code{realmin} is approximately\n\ |
|
|
2054 @ifinfo\n\ |
|
|
2055 2.2251e-308\n\ |
|
|
2056 @end ifinfo\n\ |
|
|
2057 @iftex\n\ |
|
|
2058 @tex\n\ |
|
|
2059 $2.2251\\times10^{-308}$.\n\ |
|
|
2060 @end tex\n\ |
|
|
2061 @end iftex\n\ |
|
|
2062 @seealso{realmax}\n\ |
|
|
2063 @end deftypefn") |
|
|
2064 { |
|
|
2065 return fill_matrix (args, DBL_MIN, "realmin"); |
|
|
2066 } |
|
|
2067 |
|
|
2068 DEFUN (I, args, , |
|
|
2069 "-*- texinfo -*-\n\ |
|
|
2070 @deftypefn {Built-in Function} {} I (@var{x})\n\ |
|
|
2071 @deftypefnx {Built-in Function} {} I (@var{n}, @var{m})\n\ |
|
|
2072 @deftypefnx {Built-in Function} {} I (@var{n}, @var{m}, @var{k}, @dots{})\n\ |
|
|
2073 @deftypefnx {Built-in Function} {} I (@dots{}, @var{class})\n\ |
|
|
2074 Return a matrix or N-dimensional array whose elements are all equal\n\ |
|
|
2075 to the pure imaginary unit, defined as\n\ |
|
|
2076 @iftex\n\ |
|
|
2077 @tex\n\ |
|
|
2078 $\\sqrt{-1}$.\n\ |
|
|
2079 @end tex\n\ |
|
|
2080 @end iftex\n\ |
|
|
2081 @ifinfo\n\ |
|
|
2082 @code{sqrt (-1)}.\n\ |
|
|
2083 @end ifinfo\n\ |
|
|
2084 Since I (also i, J, and J) is a function, you can use the name(s) for\n\ |
|
|
2085 other purposes.\n\ |
|
|
2086 @end deftypefn") |
|
|
2087 { |
|
|
2088 return fill_matrix (args, Complex (0.0, 1.0), "I"); |
|
|
2089 } |
|
|
2090 |
|
|
2091 DEFALIAS (i, I); |
|
|
2092 DEFALIAS (J, I); |
|
|
2093 DEFALIAS (j, I); |
|
|
2094 |
|
|
2095 DEFUN (NA, args, , |
|
|
2096 "-*- texinfo -*-\n\ |
|
|
2097 @deftypefn {Built-in Function} {} NA (@var{x})\n\ |
|
|
2098 @deftypefnx {Built-in Function} {} NA (@var{n}, @var{m})\n\ |
|
|
2099 @deftypefnx {Built-in Function} {} NA (@var{n}, @var{m}, @var{k}, @dots{})\n\ |
|
|
2100 @deftypefnx {Built-in Function} {} NA (@dots{}, @var{class})\n\ |
|
|
2101 Return a matrix or N-dimensional array whose elements are all equal\n\ |
|
|
2102 to the special constant used to designate missing values.\n\ |
|
|
2103 @end deftypefn") |
|
|
2104 { |
|
|
2105 return fill_matrix (args, lo_ieee_na_value (), "NA"); |
|
|
2106 } |
|
|
2107 |
|
|
2108 DEFUN (false, args, , |
|
|
2109 "-*- texinfo -*-\n\ |
|
|
2110 @deftypefn {Built-in Function} {} false (@var{x})\n\ |
|
|
2111 @deftypefnx {Built-in Function} {} false (@var{n}, @var{m})\n\ |
|
|
2112 @deftypefnx {Built-in Function} {} false (@var{n}, @var{m}, @var{k}, @dots{})\n\ |
|
|
2113 Return a matrix or N-dimensional array whose elements are all logical 0.\n\ |
|
|
2114 The arguments are handled the same as the arguments for @code{eye}.\n\ |
|
|
2115 @end deftypefn") |
|
|
2116 { |
|
|
2117 return fill_matrix (args, false, "false"); |
|
|
2118 } |
|
|
2119 |
|
|
2120 DEFUN (true, args, , |
|
|
2121 "-*- texinfo -*-\n\ |
|
|
2122 @deftypefn {Built-in Function} {} true (@var{x})\n\ |
|
|
2123 @deftypefnx {Built-in Function} {} true (@var{n}, @var{m})\n\ |
|
|
2124 @deftypefnx {Built-in Function} {} true (@var{n}, @var{m}, @var{k}, @dots{})\n\ |
|
|
2125 Return a matrix or N-dimensional array whose elements are all logical 1.\n\ |
|
|
2126 The arguments are handled the same as the arguments for @code{eye}.\n\ |
|
|
2127 @end deftypefn") |
|
|
2128 { |
|
|
2129 return fill_matrix (args, true, "true"); |
|
3354
|
2130 } |
|
523
|
2131 |
|
4946
|
2132 template <class MT> |
|
|
2133 octave_value |
|
|
2134 identity_matrix (int nr, int nc) |
|
|
2135 { |
|
|
2136 octave_value retval; |
|
|
2137 |
|
|
2138 typename octave_array_type_traits<MT>::element_type one (1); |
|
|
2139 |
|
|
2140 if (nr == 1 && nc == 1) |
|
|
2141 retval = one; |
|
|
2142 else |
|
|
2143 { |
|
|
2144 dim_vector dims (nr, nc); |
|
|
2145 |
|
|
2146 typename octave_array_type_traits<MT>::element_type zero (0); |
|
|
2147 |
|
|
2148 MT m (dims, zero); |
|
|
2149 |
|
|
2150 if (nr > 0 && nc > 0) |
|
|
2151 { |
|
|
2152 int n = std::min (nr, nc); |
|
|
2153 |
|
|
2154 for (int i = 0; i < n; i++) |
|
|
2155 m(i,i) = one; |
|
|
2156 } |
|
|
2157 |
|
|
2158 retval = m; |
|
|
2159 } |
|
|
2160 |
|
|
2161 return retval; |
|
|
2162 } |
|
|
2163 |
|
5058
|
2164 #define INSTANTIATE_EYE(T) \ |
|
|
2165 template octave_value identity_matrix<T> (int, int) |
|
|
2166 |
|
|
2167 INSTANTIATE_EYE (int8NDArray); |
|
|
2168 INSTANTIATE_EYE (uint8NDArray); |
|
|
2169 INSTANTIATE_EYE (int16NDArray); |
|
|
2170 INSTANTIATE_EYE (uint16NDArray); |
|
|
2171 INSTANTIATE_EYE (int32NDArray); |
|
|
2172 INSTANTIATE_EYE (uint32NDArray); |
|
|
2173 INSTANTIATE_EYE (int64NDArray); |
|
|
2174 INSTANTIATE_EYE (uint64NDArray); |
|
|
2175 INSTANTIATE_EYE (NDArray); |
|
|
2176 INSTANTIATE_EYE (boolNDArray); |
|
|
2177 |
|
4945
|
2178 static octave_value |
|
4948
|
2179 identity_matrix (int nr, int nc, oct_data_conv::data_type dt) |
|
4945
|
2180 { |
|
|
2181 octave_value retval; |
|
|
2182 |
|
5775
|
2183 // FIXME -- perhaps this should be made extensible by using |
|
4946
|
2184 // the class name to lookup a function to call to create the new |
|
|
2185 // value. |
|
|
2186 |
|
|
2187 if (! error_state) |
|
|
2188 { |
|
|
2189 switch (dt) |
|
|
2190 { |
|
|
2191 case oct_data_conv::dt_int8: |
|
|
2192 retval = identity_matrix<int8NDArray> (nr, nc); |
|
|
2193 break; |
|
|
2194 |
|
|
2195 case oct_data_conv::dt_uint8: |
|
|
2196 retval = identity_matrix<uint8NDArray> (nr, nc); |
|
|
2197 break; |
|
|
2198 |
|
|
2199 case oct_data_conv::dt_int16: |
|
|
2200 retval = identity_matrix<int16NDArray> (nr, nc); |
|
|
2201 break; |
|
4945
|
2202 |
|
4946
|
2203 case oct_data_conv::dt_uint16: |
|
|
2204 retval = identity_matrix<uint16NDArray> (nr, nc); |
|
|
2205 break; |
|
|
2206 |
|
|
2207 case oct_data_conv::dt_int32: |
|
|
2208 retval = identity_matrix<int32NDArray> (nr, nc); |
|
|
2209 break; |
|
|
2210 |
|
|
2211 case oct_data_conv::dt_uint32: |
|
|
2212 retval = identity_matrix<uint32NDArray> (nr, nc); |
|
|
2213 break; |
|
4945
|
2214 |
|
4946
|
2215 case oct_data_conv::dt_int64: |
|
|
2216 retval = identity_matrix<int64NDArray> (nr, nc); |
|
|
2217 break; |
|
|
2218 |
|
|
2219 case oct_data_conv::dt_uint64: |
|
|
2220 retval = identity_matrix<uint64NDArray> (nr, nc); |
|
|
2221 break; |
|
4945
|
2222 |
|
5775
|
2223 case oct_data_conv::dt_single: // FIXME |
|
4946
|
2224 case oct_data_conv::dt_double: |
|
|
2225 retval = identity_matrix<NDArray> (nr, nc); |
|
|
2226 break; |
|
4945
|
2227 |
|
4986
|
2228 case oct_data_conv::dt_logical: |
|
|
2229 retval = identity_matrix<boolNDArray> (nr, nc); |
|
|
2230 break; |
|
|
2231 |
|
4946
|
2232 default: |
|
|
2233 error ("eye: invalid class name"); |
|
|
2234 break; |
|
4945
|
2235 } |
|
|
2236 } |
|
|
2237 |
|
|
2238 return retval; |
|
|
2239 } |
|
|
2240 |
|
4946
|
2241 #undef INT_EYE_MATRIX |
|
|
2242 |
|
1957
|
2243 DEFUN (eye, args, , |
|
3369
|
2244 "-*- texinfo -*-\n\ |
|
|
2245 @deftypefn {Built-in Function} {} eye (@var{x})\n\ |
|
|
2246 @deftypefnx {Built-in Function} {} eye (@var{n}, @var{m})\n\ |
|
4948
|
2247 @deftypefnx {Built-in Function} {} eye (@dots{}, @var{class})\n\ |
|
3369
|
2248 Return an identity matrix. If invoked with a single scalar argument,\n\ |
|
|
2249 @code{eye} returns a square matrix with the dimension specified. If you\n\ |
|
|
2250 supply two scalar arguments, @code{eye} takes them to be the number of\n\ |
|
|
2251 rows and columns. If given a vector with two elements, @code{eye} uses\n\ |
|
|
2252 the values of the elements as the number of rows and columns,\n\ |
|
|
2253 respectively. For example,\n\ |
|
|
2254 \n\ |
|
|
2255 @example\n\ |
|
|
2256 @group\n\ |
|
|
2257 eye (3)\n\ |
|
|
2258 @result{} 1 0 0\n\ |
|
|
2259 0 1 0\n\ |
|
|
2260 0 0 1\n\ |
|
|
2261 @end group\n\ |
|
|
2262 @end example\n\ |
|
|
2263 \n\ |
|
|
2264 The following expressions all produce the same result:\n\ |
|
|
2265 \n\ |
|
|
2266 @example\n\ |
|
|
2267 @group\n\ |
|
|
2268 eye (2)\n\ |
|
|
2269 @equiv{}\n\ |
|
|
2270 eye (2, 2)\n\ |
|
|
2271 @equiv{}\n\ |
|
|
2272 eye (size ([1, 2; 3, 4])\n\ |
|
|
2273 @end group\n\ |
|
|
2274 @end example\n\ |
|
|
2275 \n\ |
|
4945
|
2276 The optional argument @var{class}, allows @code{eye} to return an array of\n\ |
|
|
2277 the specified type, like\n\ |
|
|
2278 \n\ |
|
|
2279 @example\n\ |
|
|
2280 val = zeros (n,m, \"uint8\")\n\ |
|
|
2281 @end example\n\ |
|
|
2282 \n\ |
|
6556
|
2283 Calling @code{eye} with no arguments is equivalent to calling it\n\ |
|
|
2284 with an argument of 1. This odd definition is for compatibility\n\ |
|
|
2285 with @sc{Matlab}.\n\ |
|
3369
|
2286 @end deftypefn") |
|
523
|
2287 { |
|
3354
|
2288 octave_value retval; |
|
523
|
2289 |
|
4948
|
2290 int nargin = args.length (); |
|
4945
|
2291 |
|
4948
|
2292 oct_data_conv::data_type dt = oct_data_conv::dt_double; |
|
523
|
2293 |
|
4945
|
2294 // Check for type information. |
|
|
2295 |
|
|
2296 if (nargin > 0 && args(nargin-1).is_string ()) |
|
|
2297 { |
|
4948
|
2298 std::string nm = args(nargin-1).string_value (); |
|
4945
|
2299 nargin--; |
|
4948
|
2300 |
|
|
2301 dt = oct_data_conv::string_to_data_type (nm); |
|
|
2302 |
|
|
2303 if (error_state) |
|
|
2304 return retval; |
|
4945
|
2305 } |
|
|
2306 |
|
523
|
2307 switch (nargin) |
|
|
2308 { |
|
712
|
2309 case 0: |
|
4948
|
2310 retval = identity_matrix (1, 1, dt); |
|
712
|
2311 break; |
|
777
|
2312 |
|
610
|
2313 case 1: |
|
3354
|
2314 { |
|
5275
|
2315 octave_idx_type nr, nc; |
|
3354
|
2316 get_dimensions (args(0), "eye", nr, nc); |
|
|
2317 |
|
|
2318 if (! error_state) |
|
4948
|
2319 retval = identity_matrix (nr, nc, dt); |
|
3354
|
2320 } |
|
610
|
2321 break; |
|
777
|
2322 |
|
523
|
2323 case 2: |
|
3354
|
2324 { |
|
5275
|
2325 octave_idx_type nr, nc; |
|
3354
|
2326 get_dimensions (args(0), args(1), "eye", nr, nc); |
|
|
2327 |
|
|
2328 if (! error_state) |
|
4948
|
2329 retval = identity_matrix (nr, nc, dt); |
|
3354
|
2330 } |
|
523
|
2331 break; |
|
777
|
2332 |
|
523
|
2333 default: |
|
5823
|
2334 print_usage (); |
|
523
|
2335 break; |
|
|
2336 } |
|
|
2337 |
|
|
2338 return retval; |
|
|
2339 } |
|
|
2340 |
|
1957
|
2341 DEFUN (linspace, args, , |
|
3369
|
2342 "-*- texinfo -*-\n\ |
|
|
2343 @deftypefn {Built-in Function} {} linspace (@var{base}, @var{limit}, @var{n})\n\ |
|
|
2344 Return a row vector with @var{n} linearly spaced elements between\n\ |
|
6630
|
2345 @var{base} and @var{limit}. If the number of elements is greater than one,\n\ |
|
|
2346 then the @var{base} and @var{limit} are always included in\n\ |
|
3369
|
2347 the range. If @var{base} is greater than @var{limit}, the elements are\n\ |
|
|
2348 stored in decreasing order. If the number of points is not specified, a\n\ |
|
|
2349 value of 100 is used.\n\ |
|
1100
|
2350 \n\ |
|
4455
|
2351 The @code{linspace} function always returns a row vector.\n\ |
|
6630
|
2352 \n\ |
|
|
2353 For compatibility with @sc{Matlab}, return the second argument if\n\ |
|
|
2354 fewer than two values are requested.\n\ |
|
3369
|
2355 @end deftypefn") |
|
1100
|
2356 { |
|
3418
|
2357 octave_value retval; |
|
1100
|
2358 |
|
|
2359 int nargin = args.length (); |
|
|
2360 |
|
6133
|
2361 octave_idx_type npoints = 100; |
|
1100
|
2362 |
|
1940
|
2363 if (nargin != 2 && nargin != 3) |
|
|
2364 { |
|
5823
|
2365 print_usage (); |
|
1940
|
2366 return retval; |
|
|
2367 } |
|
|
2368 |
|
1100
|
2369 if (nargin == 3) |
|
6133
|
2370 npoints = args(2).idx_type_value (); |
|
1100
|
2371 |
|
|
2372 if (! error_state) |
|
|
2373 { |
|
3322
|
2374 octave_value arg_1 = args(0); |
|
|
2375 octave_value arg_2 = args(1); |
|
1100
|
2376 |
|
3322
|
2377 if (arg_1.is_complex_type () || arg_2.is_complex_type ()) |
|
|
2378 { |
|
|
2379 Complex x1 = arg_1.complex_value (); |
|
|
2380 Complex x2 = arg_2.complex_value (); |
|
|
2381 |
|
|
2382 if (! error_state) |
|
1100
|
2383 { |
|
3322
|
2384 ComplexRowVector rv = linspace (x1, x2, npoints); |
|
1100
|
2385 |
|
|
2386 if (! error_state) |
|
3418
|
2387 retval = rv; |
|
1100
|
2388 } |
|
|
2389 } |
|
|
2390 else |
|
3322
|
2391 { |
|
|
2392 double x1 = arg_1.double_value (); |
|
|
2393 double x2 = arg_2.double_value (); |
|
|
2394 |
|
|
2395 if (! error_state) |
|
|
2396 { |
|
|
2397 RowVector rv = linspace (x1, x2, npoints); |
|
|
2398 |
|
|
2399 if (! error_state) |
|
3418
|
2400 retval = rv; |
|
3322
|
2401 } |
|
|
2402 } |
|
1100
|
2403 } |
|
4732
|
2404 else |
|
|
2405 error ("linspace: expecting third argument to be an integer"); |
|
1100
|
2406 |
|
|
2407 return retval; |
|
|
2408 } |
|
|
2409 |
|
5775
|
2410 // FIXME -- should accept dimensions as separate args for N-d |
|
5734
|
2411 // arrays as well as 1-d and 2-d arrays. |
|
|
2412 |
|
5731
|
2413 DEFUN (resize, args, , |
|
|
2414 "-*- texinfo -*-\n\ |
|
|
2415 @deftypefn {Built-in Function} {} resize (@var{x}, @var{m})\n\ |
|
|
2416 @deftypefnx {Built-in Function} {} resize (@var{x}, @var{m}, @var{n})\n\ |
|
6174
|
2417 Destructively resize @var{x}.\n\ |
|
|
2418 \n\ |
|
|
2419 @strong{Values in @var{x} are not preserved as they are with\n\ |
|
6175
|
2420 @code{reshape}.}\n\ |
|
6174
|
2421 \n\ |
|
|
2422 If only @var{m} is supplied and it is a scalar, the dimension of the\n\ |
|
|
2423 result is @var{m}-by-@var{m}. If @var{m} is a vector, then the\n\ |
|
|
2424 dimensions of the result are given by the elements of @var{m}.\n\ |
|
|
2425 If both @var{m} and @var{n} are scalars, then the dimensions of\n\ |
|
|
2426 the result are @var{m}-by-@var{n}.\n\ |
|
|
2427 @seealso{reshape}\n\ |
|
5731
|
2428 @end deftypefn") |
|
|
2429 { |
|
|
2430 octave_value retval; |
|
|
2431 int nargin = args.length (); |
|
|
2432 |
|
|
2433 if (nargin == 2) |
|
|
2434 { |
|
|
2435 Array<double> vec = args(1).vector_value (); |
|
|
2436 int ndim = vec.length (); |
|
|
2437 if (ndim == 1) |
|
|
2438 { |
|
|
2439 octave_idx_type m = static_cast<octave_idx_type> (vec(0)); |
|
|
2440 retval = args(0); |
|
|
2441 retval = retval.resize (dim_vector (m, m), true); |
|
|
2442 } |
|
|
2443 else |
|
|
2444 { |
|
|
2445 dim_vector dv; |
|
|
2446 dv.resize (ndim); |
|
|
2447 for (int i = 0; i < ndim; i++) |
|
|
2448 dv(i) = static_cast<octave_idx_type> (vec(i)); |
|
|
2449 retval = args(0); |
|
|
2450 retval = retval.resize (dv, true); |
|
|
2451 } |
|
|
2452 } |
|
|
2453 else if (nargin == 3) |
|
|
2454 { |
|
|
2455 octave_idx_type m = static_cast<octave_idx_type> |
|
|
2456 (args(1).scalar_value()); |
|
|
2457 octave_idx_type n = static_cast<octave_idx_type> |
|
|
2458 (args(2).scalar_value()); |
|
|
2459 if (!error_state) |
|
|
2460 { |
|
|
2461 retval = args(0); |
|
|
2462 retval = retval.resize (dim_vector (m, n), true); |
|
|
2463 } |
|
|
2464 } |
|
|
2465 else |
|
5823
|
2466 print_usage (); |
|
5731
|
2467 return retval; |
|
|
2468 } |
|
|
2469 |
|
5775
|
2470 // FIXME -- should use octave_idx_type for dimensions. |
|
5734
|
2471 |
|
4567
|
2472 DEFUN (reshape, args, , |
|
|
2473 "-*- texinfo -*-\n\ |
|
6671
|
2474 @deftypefn {Built-in Function} {} reshape (@var{a}, @var{m}, @var{n}, @dots{})\n\ |
|
|
2475 @deftypefnx {Built-in Function} {} reshape (@var{a}, @var{siz})\n\ |
|
4567
|
2476 Return a matrix with the given dimensions whose elements are taken\n\ |
|
6671
|
2477 from the matrix @var{a}. The elements of the matrix are accessed in\n\ |
|
4567
|
2478 column-major order (like Fortran arrays are stored).\n\ |
|
|
2479 \n\ |
|
|
2480 For example,\n\ |
|
|
2481 \n\ |
|
|
2482 @example\n\ |
|
|
2483 @group\n\ |
|
|
2484 reshape ([1, 2, 3, 4], 2, 2)\n\ |
|
|
2485 @result{} 1 3\n\ |
|
|
2486 2 4\n\ |
|
|
2487 @end group\n\ |
|
|
2488 @end example\n\ |
|
|
2489 \n\ |
|
|
2490 @noindent\n\ |
|
|
2491 Note that the total number of elements in the original\n\ |
|
|
2492 matrix must match the total number of elements in the new matrix.\n\ |
|
5013
|
2493 \n\ |
|
|
2494 A single dimension of the return matrix can be unknown and is flagged\n\ |
|
|
2495 by an empty argument.\n\ |
|
4567
|
2496 @end deftypefn") |
|
|
2497 { |
|
|
2498 octave_value retval; |
|
|
2499 |
|
|
2500 int nargin = args.length (); |
|
|
2501 |
|
|
2502 Array<int> new_size; |
|
|
2503 |
|
|
2504 if (nargin == 2) |
|
|
2505 new_size = args(1).int_vector_value (); |
|
|
2506 else if (nargin > 2) |
|
|
2507 { |
|
|
2508 new_size.resize (nargin-1); |
|
5013
|
2509 int empty_dim = -1; |
|
|
2510 |
|
4567
|
2511 for (int i = 1; i < nargin; i++) |
|
|
2512 { |
|
5013
|
2513 if (args(i).is_empty ()) |
|
|
2514 if (empty_dim > 0) |
|
|
2515 { |
|
|
2516 error ("reshape: only a single dimension can be unknown"); |
|
|
2517 break; |
|
|
2518 } |
|
|
2519 else |
|
|
2520 { |
|
|
2521 empty_dim = i; |
|
|
2522 new_size(i-1) = 1; |
|
|
2523 } |
|
|
2524 else |
|
|
2525 { |
|
6133
|
2526 new_size(i-1) = args(i).idx_type_value (); |
|
4567
|
2527 |
|
5013
|
2528 if (error_state) |
|
|
2529 break; |
|
|
2530 } |
|
|
2531 } |
|
|
2532 |
|
|
2533 if (! error_state && (empty_dim > 0)) |
|
|
2534 { |
|
|
2535 int nel = 1; |
|
|
2536 for (int i = 0; i < nargin - 1; i++) |
|
|
2537 nel *= new_size(i); |
|
|
2538 |
|
|
2539 if (nel == 0) |
|
|
2540 new_size(empty_dim-1) = 0; |
|
|
2541 else |
|
|
2542 { |
|
|
2543 int size_empty_dim = args(0).numel () / nel; |
|
|
2544 |
|
|
2545 if (args(0).numel () != size_empty_dim * nel) |
|
|
2546 error ("reshape: size is not divisble by the product of known dimensions (= %d)", nel); |
|
|
2547 else |
|
|
2548 new_size(empty_dim-1) = size_empty_dim; |
|
|
2549 } |
|
4567
|
2550 } |
|
|
2551 } |
|
|
2552 else |
|
|
2553 { |
|
5823
|
2554 print_usage (); |
|
4567
|
2555 return retval; |
|
|
2556 } |
|
|
2557 |
|
|
2558 if (error_state) |
|
|
2559 { |
|
|
2560 error ("reshape: invalid arguments"); |
|
|
2561 return retval; |
|
|
2562 } |
|
|
2563 |
|
4739
|
2564 // Remove trailing singletons in new_size, but leave at least 2 |
|
|
2565 // elements. |
|
|
2566 |
|
4567
|
2567 int n = new_size.length (); |
|
|
2568 |
|
4739
|
2569 while (n > 2) |
|
|
2570 { |
|
|
2571 if (new_size(n-1) == 1) |
|
|
2572 n--; |
|
|
2573 else |
|
|
2574 break; |
|
|
2575 } |
|
|
2576 |
|
|
2577 new_size.resize (n); |
|
|
2578 |
|
4567
|
2579 if (n < 2) |
|
|
2580 { |
|
|
2581 error ("reshape: expecting size to be vector with at least 2 elements"); |
|
|
2582 return retval; |
|
|
2583 } |
|
|
2584 |
|
|
2585 dim_vector new_dims; |
|
|
2586 |
|
|
2587 new_dims.resize (n); |
|
|
2588 |
|
5275
|
2589 for (octave_idx_type i = 0; i < n; i++) |
|
4567
|
2590 new_dims(i) = new_size(i); |
|
|
2591 |
|
|
2592 octave_value arg = args(0); |
|
|
2593 |
|
|
2594 if (new_dims.numel () == arg.numel ()) |
|
|
2595 retval = (new_dims == arg.dims ()) ? arg : arg.reshape (new_dims); |
|
|
2596 else |
|
|
2597 error ("reshape: size mismatch"); |
|
|
2598 |
|
|
2599 return retval; |
|
|
2600 } |
|
|
2601 |
|
4532
|
2602 DEFUN (squeeze, args, , |
|
|
2603 "-*- texinfo -*-\n\ |
|
|
2604 @deftypefn {Built-in Function} {} squeeze (@var{x})\n\ |
|
|
2605 Remove singleton dimensions from @var{x} and return the result.\n\ |
|
|
2606 @end deftypefn") |
|
|
2607 { |
|
|
2608 octave_value retval; |
|
|
2609 |
|
|
2610 if (args.length () == 1) |
|
4545
|
2611 retval = args(0).squeeze (); |
|
4532
|
2612 else |
|
5823
|
2613 print_usage (); |
|
4532
|
2614 |
|
|
2615 return retval; |
|
|
2616 } |
|
|
2617 |
|
6953
|
2618 /* |
|
|
2619 %!shared x |
|
|
2620 %! x = [1, -3, 4, 5, -7]; |
|
|
2621 %!assert(norm(x,1), 20); |
|
|
2622 %!assert(norm(x,2), 10); |
|
|
2623 %!assert(norm(x,3), 8.24257059961711, -4*eps); |
|
|
2624 %!assert(norm(x,Inf), 7); |
|
|
2625 %!assert(norm(x,-Inf), 1); |
|
|
2626 %!assert(norm(x,"inf"), 7); |
|
|
2627 %!assert(norm(x,"fro"), 10); |
|
|
2628 %!assert(norm(x), 10); |
|
|
2629 %!assert(norm([1e200, 1]), 1e200); |
|
|
2630 %!assert(norm([3+4i, 3-4i, sqrt(31)]), 9, -4*eps); |
|
|
2631 %!shared m |
|
|
2632 %! m = magic (4); |
|
|
2633 %!assert(norm(m,1), 34); |
|
|
2634 %!assert(norm(m,2), 34); |
|
|
2635 %!assert(norm(m,Inf), 34); |
|
|
2636 %!assert(norm(m,"inf"), 34); |
|
|
2637 */ |
|
|
2638 |
|
6945
|
2639 // Compute various norms of the vector X. |
|
|
2640 |
|
6953
|
2641 DEFUN (norm, args, , |
|
6508
|
2642 "-*- texinfo -*-\n\ |
|
6953
|
2643 @deftypefn {Function File} {} norm (@var{a}, @var{p})\n\ |
|
|
2644 Compute the p-norm of the matrix @var{a}. If the second argument is\n\ |
|
|
2645 missing, @code{p = 2} is assumed.\n\ |
|
|
2646 \n\ |
|
|
2647 If @var{a} is a matrix:\n\ |
|
|
2648 \n\ |
|
|
2649 @table @asis\n\ |
|
|
2650 @item @var{p} = @code{1}\n\ |
|
|
2651 1-norm, the largest column sum of the absolute values of @var{a}.\n\ |
|
|
2652 \n\ |
|
|
2653 @item @var{p} = @code{2}\n\ |
|
|
2654 Largest singular value of @var{a}.\n\ |
|
|
2655 \n\ |
|
|
2656 @item @var{p} = @code{Inf}\n\ |
|
|
2657 @cindex infinity norm\n\ |
|
|
2658 Infinity norm, the largest row sum of the absolute values of @var{a}.\n\ |
|
|
2659 \n\ |
|
|
2660 @item @var{p} = @code{\"fro\"}\n\ |
|
|
2661 @cindex Frobenius norm\n\ |
|
|
2662 Frobenius norm of @var{a}, @code{sqrt (sum (diag (@var{a}' * @var{a})))}.\n\ |
|
|
2663 @end table\n\ |
|
|
2664 \n\ |
|
|
2665 If @var{a} is a vector or a scalar:\n\ |
|
|
2666 \n\ |
|
|
2667 @table @asis\n\ |
|
|
2668 @item @var{p} = @code{Inf}\n\ |
|
|
2669 @code{max (abs (@var{a}))}.\n\ |
|
|
2670 \n\ |
|
|
2671 @item @var{p} = @code{-Inf}\n\ |
|
|
2672 @code{min (abs (@var{a}))}.\n\ |
|
|
2673 \n\ |
|
|
2674 @item other\n\ |
|
|
2675 p-norm of @var{a}, @code{(sum (abs (@var{a}) .^ @var{p})) ^ (1/@var{p})}.\n\ |
|
|
2676 @end table\n\ |
|
|
2677 @seealso{cond, svd}\n\ |
|
6508
|
2678 @end deftypefn") |
|
|
2679 { |
|
6953
|
2680 // Currently only handles vector norms for full double/complex |
|
|
2681 // vectors internally. Other cases are handled by __norm__.m. |
|
|
2682 |
|
|
2683 octave_value_list retval; |
|
6508
|
2684 |
|
|
2685 int nargin = args.length (); |
|
|
2686 |
|
|
2687 if (nargin == 1 || nargin == 2) |
|
|
2688 { |
|
6953
|
2689 octave_value x_arg = args(0); |
|
|
2690 |
|
|
2691 if (x_arg.is_empty ()) |
|
|
2692 retval(0) = 0.0; |
|
|
2693 else if (x_arg.ndims () == 2) |
|
6508
|
2694 { |
|
6953
|
2695 if ((x_arg.rows () == 1 || x_arg.columns () == 1) |
|
|
2696 && ! (x_arg.is_sparse_type () || x_arg.is_integer_type ())) |
|
|
2697 { |
|
|
2698 double p_val; |
|
|
2699 |
|
|
2700 octave_value p_arg; |
|
|
2701 |
|
|
2702 if (nargin == 1) |
|
|
2703 p_arg = 2; |
|
|
2704 else |
|
|
2705 p_arg = args(1); |
|
|
2706 |
|
|
2707 if (p_arg.is_string ()) |
|
|
2708 { |
|
|
2709 std::string p = args(1).string_value (); |
|
|
2710 |
|
|
2711 if (p == "inf") |
|
|
2712 p_val = octave_Inf; |
|
|
2713 else if (p == "fro") |
|
|
2714 p_val = -1; |
|
|
2715 else |
|
|
2716 error ("norm: unrecognized norm `%s'", p.c_str ()); |
|
|
2717 } |
|
|
2718 else |
|
|
2719 { |
|
|
2720 p_val = p_arg.double_value (); |
|
|
2721 |
|
|
2722 if (error_state) |
|
|
2723 error ("norm: unrecognized norm value"); |
|
|
2724 } |
|
|
2725 |
|
|
2726 if (! error_state) |
|
|
2727 { |
|
|
2728 if (x_arg.is_real_type ()) |
|
|
2729 { |
|
|
2730 MArray<double> x (x_arg.array_value ()); |
|
|
2731 |
|
|
2732 if (! error_state) |
|
|
2733 retval(0) = x.norm (p_val); |
|
|
2734 else |
|
|
2735 error ("norm: expecting real vector"); |
|
|
2736 } |
|
|
2737 else |
|
|
2738 { |
|
|
2739 MArray<Complex> x (x_arg.complex_array_value ()); |
|
|
2740 |
|
|
2741 if (! error_state) |
|
|
2742 retval(0) = x.norm (p_val); |
|
|
2743 else |
|
|
2744 error ("norm: expecting complex vector"); |
|
|
2745 } |
|
|
2746 } |
|
|
2747 } |
|
6508
|
2748 else |
|
6953
|
2749 retval = feval ("__norm__", args); |
|
6508
|
2750 } |
|
|
2751 else |
|
6953
|
2752 error ("norm: only valid for 2-D objects"); |
|
6508
|
2753 } |
|
|
2754 else |
|
|
2755 print_usage (); |
|
|
2756 |
|
|
2757 return retval; |
|
|
2758 } |
|
|
2759 |
|
6518
|
2760 #define UNARY_OP_DEFUN_BODY(F) \ |
|
|
2761 \ |
|
|
2762 octave_value retval; \ |
|
|
2763 \ |
|
|
2764 if (args.length () == 1) \ |
|
|
2765 retval = F (args(0)); \ |
|
|
2766 else \ |
|
|
2767 print_usage (); \ |
|
|
2768 \ |
|
|
2769 return retval |
|
|
2770 |
|
|
2771 DEFUN (not, args, , |
|
|
2772 "-*- texinfo -*-\n\ |
|
|
2773 @deftypefn {Built-in Function} {} not (@var{x})\n\ |
|
|
2774 This function is equivalent to @code{! x}.\n\ |
|
|
2775 @end deftypefn") |
|
|
2776 { |
|
|
2777 UNARY_OP_DEFUN_BODY (op_not); |
|
|
2778 } |
|
|
2779 |
|
|
2780 DEFUN (uplus, args, , |
|
|
2781 "-*- texinfo -*-\n\ |
|
|
2782 @deftypefn {Built-in Function} {} uplus (@var{x})\n\ |
|
|
2783 This function is equivalent to @code{+ x}.\n\ |
|
|
2784 @end deftypefn") |
|
|
2785 { |
|
|
2786 UNARY_OP_DEFUN_BODY (op_uplus); |
|
|
2787 } |
|
|
2788 |
|
|
2789 DEFUN (uminus, args, , |
|
|
2790 "-*- texinfo -*-\n\ |
|
|
2791 @deftypefn {Built-in Function} {} uminus (@var{x})\n\ |
|
|
2792 This function is equivalent to @code{- x}.\n\ |
|
|
2793 @end deftypefn") |
|
|
2794 { |
|
|
2795 UNARY_OP_DEFUN_BODY (op_uminus); |
|
|
2796 } |
|
|
2797 |
|
|
2798 DEFUN (transpose, args, , |
|
|
2799 "-*- texinfo -*-\n\ |
|
|
2800 @deftypefn {Built-in Function} {} transpose (@var{x})\n\ |
|
|
2801 This function is equivalent to @code{x.'}.\n\ |
|
|
2802 @end deftypefn") |
|
|
2803 { |
|
|
2804 UNARY_OP_DEFUN_BODY (op_transpose); |
|
|
2805 } |
|
|
2806 |
|
|
2807 DEFUN (ctranspose, args, , |
|
|
2808 "-*- texinfo -*-\n\ |
|
|
2809 @deftypefn {Built-in Function} {} ctranspose (@var{x})\n\ |
|
|
2810 This function is equivalent to @code{x'}.\n\ |
|
|
2811 @end deftypefn") |
|
|
2812 { |
|
|
2813 UNARY_OP_DEFUN_BODY (op_hermitian); |
|
|
2814 } |
|
|
2815 |
|
|
2816 #define BINARY_OP_DEFUN_BODY(F) \ |
|
|
2817 \ |
|
|
2818 octave_value retval; \ |
|
|
2819 \ |
|
|
2820 if (args.length () == 2) \ |
|
|
2821 retval = F (args(0), args(1)); \ |
|
|
2822 else \ |
|
|
2823 print_usage (); \ |
|
|
2824 \ |
|
|
2825 return retval |
|
|
2826 |
|
|
2827 DEFUN (plus, args, , |
|
|
2828 "-*- texinfo -*-\n\ |
|
|
2829 @deftypefn {Built-in Function} {} plus (@var{x}, @var{y})\n\ |
|
|
2830 This function is equivalent to @code{x + y}.\n\ |
|
|
2831 @end deftypefn") |
|
|
2832 { |
|
|
2833 BINARY_OP_DEFUN_BODY (op_add); |
|
|
2834 } |
|
|
2835 |
|
|
2836 DEFUN (minus, args, , |
|
|
2837 "-*- texinfo -*-\n\ |
|
|
2838 @deftypefn {Built-in Function} {} minus (@var{x}, @var{y})\n\ |
|
|
2839 This function is equivalent to @code{x - y}.\n\ |
|
|
2840 @end deftypefn") |
|
|
2841 { |
|
|
2842 BINARY_OP_DEFUN_BODY (op_sub); |
|
|
2843 } |
|
|
2844 |
|
|
2845 DEFUN (mtimes, args, , |
|
|
2846 "-*- texinfo -*-\n\ |
|
|
2847 @deftypefn {Built-in Function} {} mtimes (@var{x}, @var{y})\n\ |
|
|
2848 This function is equivalent to @code{x * y}.\n\ |
|
|
2849 @end deftypefn") |
|
|
2850 { |
|
|
2851 BINARY_OP_DEFUN_BODY (op_mul); |
|
|
2852 } |
|
|
2853 |
|
|
2854 DEFUN (mrdivide, args, , |
|
|
2855 "-*- texinfo -*-\n\ |
|
|
2856 @deftypefn {Built-in Function} {} mrdivide (@var{x}, @var{y})\n\ |
|
|
2857 This function is equivalent to @code{x / y}.\n\ |
|
|
2858 @end deftypefn") |
|
|
2859 { |
|
|
2860 BINARY_OP_DEFUN_BODY (op_div); |
|
|
2861 } |
|
|
2862 |
|
|
2863 DEFUN (mpower, args, , |
|
|
2864 "-*- texinfo -*-\n\ |
|
|
2865 @deftypefn {Built-in Function} {} mpower (@var{x}, @var{y})\n\ |
|
|
2866 This function is equivalent to @code{x ^ y}.\n\ |
|
|
2867 @end deftypefn") |
|
|
2868 { |
|
|
2869 BINARY_OP_DEFUN_BODY (op_pow); |
|
|
2870 } |
|
|
2871 |
|
|
2872 DEFUN (mldivide, args, , |
|
|
2873 "-*- texinfo -*-\n\ |
|
|
2874 @deftypefn {Built-in Function} {} mldivide (@var{x}, @var{y})\n\ |
|
|
2875 This function is equivalent to @code{x \\ y}.\n\ |
|
|
2876 @end deftypefn") |
|
|
2877 { |
|
|
2878 BINARY_OP_DEFUN_BODY (op_ldiv); |
|
|
2879 } |
|
|
2880 |
|
|
2881 DEFUN (lt, args, , |
|
|
2882 "-*- texinfo -*-\n\ |
|
|
2883 @deftypefn {Built-in Function} {} lt (@var{x}, @var{y})\n\ |
|
|
2884 This function is equivalent to @code{x < y}.\n\ |
|
|
2885 @end deftypefn") |
|
|
2886 { |
|
|
2887 BINARY_OP_DEFUN_BODY (op_lt); |
|
|
2888 } |
|
|
2889 |
|
|
2890 DEFUN (le, args, , |
|
|
2891 "-*- texinfo -*-\n\ |
|
|
2892 @deftypefn {Built-in Function} {} le (@var{x}, @var{y})\n\ |
|
|
2893 This function is equivalent to @code{x <= y}.\n\ |
|
|
2894 @end deftypefn") |
|
|
2895 { |
|
|
2896 BINARY_OP_DEFUN_BODY (op_le); |
|
|
2897 } |
|
|
2898 |
|
|
2899 DEFUN (eq, args, , |
|
|
2900 "-*- texinfo -*-\n\ |
|
|
2901 @deftypefn {Built-in Function} {} eq (@var{x}, @var{y})\n\ |
|
|
2902 This function is equivalent to @code{x == y}.\n\ |
|
|
2903 @end deftypefn") |
|
|
2904 { |
|
|
2905 BINARY_OP_DEFUN_BODY (op_eq); |
|
|
2906 } |
|
|
2907 |
|
|
2908 DEFUN (ge, args, , |
|
|
2909 "-*- texinfo -*-\n\ |
|
|
2910 @deftypefn {Built-in Function} {} ge (@var{x}, @var{y})\n\ |
|
|
2911 This function is equivalent to @code{x >= y}.\n\ |
|
|
2912 @end deftypefn") |
|
|
2913 { |
|
|
2914 BINARY_OP_DEFUN_BODY (op_ge); |
|
|
2915 } |
|
|
2916 |
|
|
2917 DEFUN (gt, args, , |
|
|
2918 "-*- texinfo -*-\n\ |
|
|
2919 @deftypefn {Built-in Function} {} gt (@var{x}, @var{y})\n\ |
|
|
2920 This function is equivalent to @code{x > y}.\n\ |
|
|
2921 @end deftypefn") |
|
|
2922 { |
|
|
2923 BINARY_OP_DEFUN_BODY (op_gt); |
|
|
2924 } |
|
|
2925 |
|
|
2926 DEFUN (ne, args, , |
|
|
2927 "-*- texinfo -*-\n\ |
|
|
2928 @deftypefn {Built-in Function} {} ne (@var{x}, @var{y})\n\ |
|
|
2929 This function is equivalent to @code{x != y}.\n\ |
|
|
2930 @end deftypefn") |
|
|
2931 { |
|
|
2932 BINARY_OP_DEFUN_BODY (op_ne); |
|
|
2933 } |
|
|
2934 |
|
|
2935 DEFUN (times, args, , |
|
|
2936 "-*- texinfo -*-\n\ |
|
|
2937 @deftypefn {Built-in Function} {} times (@var{x}, @var{y})\n\ |
|
|
2938 This function is equivalent to @code{x .* y}.\n\ |
|
|
2939 @end deftypefn") |
|
|
2940 { |
|
|
2941 BINARY_OP_DEFUN_BODY (op_el_mul); |
|
|
2942 } |
|
|
2943 |
|
|
2944 DEFUN (rdivide, args, , |
|
|
2945 "-*- texinfo -*-\n\ |
|
|
2946 @deftypefn {Built-in Function} {} rdivide (@var{x}, @var{y})\n\ |
|
|
2947 This function is equivalent to @code{x ./ y}.\n\ |
|
|
2948 @end deftypefn") |
|
|
2949 { |
|
|
2950 BINARY_OP_DEFUN_BODY (op_el_div); |
|
|
2951 } |
|
|
2952 |
|
|
2953 DEFUN (power, args, , |
|
|
2954 "-*- texinfo -*-\n\ |
|
|
2955 @deftypefn {Built-in Function} {} power (@var{x}, @var{y})\n\ |
|
|
2956 This function is equivalent to @code{x .\\ y}.\n\ |
|
|
2957 @end deftypefn") |
|
|
2958 { |
|
|
2959 BINARY_OP_DEFUN_BODY (op_el_pow); |
|
|
2960 } |
|
|
2961 |
|
|
2962 DEFUN (ldivide, args, , |
|
|
2963 "-*- texinfo -*-\n\ |
|
|
2964 @deftypefn {Built-in Function} {} ldivide (@var{x}, @var{y})\n\ |
|
|
2965 @end deftypefn") |
|
|
2966 { |
|
|
2967 BINARY_OP_DEFUN_BODY (op_el_ldiv); |
|
|
2968 } |
|
|
2969 |
|
|
2970 DEFUN (and, args, , |
|
|
2971 "-*- texinfo -*-\n\ |
|
|
2972 @deftypefn {Built-in Function} {} and (@var{x}, @var{y})\n\ |
|
|
2973 This function is equivalent to @code{x & y}.\n\ |
|
|
2974 @end deftypefn") |
|
|
2975 { |
|
|
2976 BINARY_OP_DEFUN_BODY (op_el_and); |
|
|
2977 } |
|
|
2978 |
|
|
2979 DEFUN (or, args, , |
|
|
2980 "-*- texinfo -*-\n\ |
|
|
2981 @deftypefn {Built-in Function} {} or (@var{x}, @var{y})\n\ |
|
|
2982 This function is equivalent to @code{x | y}.\n\ |
|
|
2983 @end deftypefn") |
|
|
2984 { |
|
|
2985 BINARY_OP_DEFUN_BODY (op_el_or); |
|
|
2986 } |
|
|
2987 |
|
523
|
2988 /* |
|
|
2989 ;;; Local Variables: *** |
|
|
2990 ;;; mode: C++ *** |
|
|
2991 ;;; End: *** |
|
|
2992 */ |
