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