Mercurial > octave
annotate scripts/polynomial/polyval.m @ 29359:7854d5752dd2
maint: merge stable to default.
author | John W. Eaton <jwe@octave.org> |
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date | Wed, 10 Feb 2021 10:10:40 -0500 |
parents | 90fea9cc9caa 0a5b15007766 |
children | 796f54d4ddbf |
rev | line source |
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1 ######################################################################## |
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2 ## |
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3 ## Copyright (C) 1994-2021 The Octave Project Developers |
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4 ## |
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5 ## See the file COPYRIGHT.md in the top-level directory of this |
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6 ## distribution or <https://octave.org/copyright/>. |
2313 | 7 ## |
8 ## This file is part of Octave. | |
9 ## | |
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10 ## Octave is free software: you can redistribute it and/or modify it |
2313 | 11 ## under the terms of the GNU General Public License as published by |
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12 ## the Free Software Foundation, either version 3 of the License, or |
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13 ## (at your option) any later version. |
2313 | 14 ## |
15 ## Octave is distributed in the hope that it will be useful, but | |
16 ## WITHOUT ANY WARRANTY; without even the implied warranty of | |
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17 ## MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the |
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18 ## GNU General Public License for more details. |
2313 | 19 ## |
20 ## You should have received a copy of the GNU General Public License | |
7016 | 21 ## along with Octave; see the file COPYING. If not, see |
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22 ## <https://www.gnu.org/licenses/>. |
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23 ## |
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24 ######################################################################## |
904 | 25 |
3368 | 26 ## -*- texinfo -*- |
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27 ## @deftypefn {} {@var{y} =} polyval (@var{p}, @var{x}) |
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28 ## @deftypefnx {} {@var{y} =} polyval (@var{p}, @var{x}, [], @var{mu}) |
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29 ## @deftypefnx {} {[@var{y}, @var{dy}] =} polyval (@var{p}, @var{x}, @var{s}) |
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30 ## @deftypefnx {} {[@var{y}, @var{dy}] =} polyval (@var{p}, @var{x}, @var{s}, @var{mu}) |
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31 ## |
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32 ## Evaluate the polynomial @var{p} at the specified values of @var{x}. |
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33 ## |
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34 ## If @var{x} is a vector or matrix, the polynomial is evaluated for each of |
3368 | 35 ## the elements of @var{x}. |
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36 ## |
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37 ## When @var{mu} is present, evaluate the polynomial for |
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38 ## @w{(@var{x} - @var{mu}(1)) / @var{mu}(2)}. |
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39 ## |
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40 ## In addition to evaluating the polynomial, the second output represents the |
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41 ## prediction interval, @var{y} +/- @var{dy}, which contains at least 50% of |
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42 ## the future predictions. To calculate the prediction interval, the |
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43 ## structured variable @var{s}, originating from @code{polyfit}, must be |
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44 ## supplied. |
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45 ## |
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46 ## @seealso{polyvalm, polyaffine, polyfit, roots, poly} |
3368 | 47 ## @end deftypefn |
1025 | 48 |
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49 function [y, dy] = polyval (p, x, s = [], mu) |
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51 if (nargin < 2 || (nargout == 2 && nargin < 3)) |
6046 | 52 print_usage (); |
561 | 53 endif |
54 | |
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55 ## Algorithm requires floating point values |
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56 if (! isfloat (p) || (! isvector (p) && ! isempty (p))) |
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57 error ("polyval: P must be a numeric floating point vector"); |
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58 endif |
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59 if (! isfloat (x)) |
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60 error ("polyval: X must be numeric floating point"); |
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61 endif |
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62 |
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63 if (nargout > 1) |
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64 if (isempty (s)) |
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65 error ("polyval: S input is required for DY output argument"); |
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66 elseif (isstruct (s)) |
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67 if (! all (ismember ({"R", "normr", "df"}, fieldnames (s)))) |
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68 error ("polyval: S input is missing required fields"); |
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69 endif |
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70 else |
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71 error ("polyval: S input must be a structure"); |
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72 endif |
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73 endif |
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74 |
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75 if (nargin == 4 && (! isfloat (mu) || numel (mu) < 2)) |
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76 error ("polyval: MU must be numeric floating point with 2 values"); |
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77 endif |
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78 |
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79 if (isempty (p) || isempty (x)) |
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80 if (isa (p, "single") || isa (x, "single")) |
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81 y = zeros (size (x), "single"); |
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82 else |
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83 y = zeros (size (x)); |
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84 endif |
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85 return; |
561 | 86 endif |
87 | |
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88 if (nargin == 4) |
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89 x = (x - mu(1)) / mu(2); |
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90 endif |
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91 |
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92 n = numel (p) - 1; |
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93 y = p(1) * ones (size (x), class (x)); |
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94 for i = 2:n+1 |
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95 y = y .* x + p(i); |
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96 endfor |
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97 |
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98 if (nargout > 1) |
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99 ## Note: the F-Distribution is generally considered to be single-sided. |
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100 ## http://www.itl.nist.gov/div898/handbook/eda/section3/eda3673.htm |
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101 ## t = finv (1-alpha, s.df, s.df); |
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102 ## dy = t * sqrt (1 + sumsq (A/s.R, 2)) * s.normr / sqrt (s.df) |
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103 ## If my inference is correct, then t must equal 1 for polyval. |
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104 ## This is because finv (0.5, n, n) = 1.0 for any n. |
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105 k = numel (x); |
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106 A = (x(:) * ones (1, n+1)) .^ (ones (k, 1) * (n:-1:0)); |
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107 dy = sqrt (1 + sumsq (A/s.R, 2)) * s.normr / sqrt (s.df); |
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108 dy = reshape (dy, size (x)); |
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109 endif |
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561 | 111 endfunction |
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113 |
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114 %!test |
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115 %! r = 0:10:50; |
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116 %! p = poly (r); |
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117 %! p = p / max (abs (p)); |
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118 %! x = linspace (0,50,11); |
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119 %! y = polyval (p,x) + 0.25*sin (100*x); |
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120 %! [pf, s] = polyfit (x, y, numel (r)); |
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121 %! [y1, delta] = polyval (pf, x, s); |
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122 %! expected = [0.37235, 0.35854, 0.32231, 0.32448, 0.31328, ... |
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123 %! 0.32036, 0.31328, 0.32448, 0.32231, 0.35854, 0.37235]; |
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124 %! assert (delta, expected, 0.00001); |
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126 %!test |
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127 %! x = 10 + (-2:2); |
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128 %! y = [0, 0, 1, 0, 2]; |
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129 %! p = polyfit (x, y, numel (x) - 1); |
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130 %! [pn, s, mu] = polyfit (x, y, numel (x) - 1); |
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131 %! y1 = polyval (p, x); |
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132 %! yn = polyval (pn, x, [], mu); |
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133 %! assert (y1, y, sqrt (eps)); |
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134 %! assert (yn, y, sqrt (eps)); |
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136 %!test |
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137 %! p = [0, 1, 0]; |
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138 %! x = 1:10; |
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139 %! assert (x, polyval (p,x), eps); |
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140 %! x = x(:); |
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141 %! assert (x, polyval (p,x), eps); |
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142 %! x = reshape (x, [2, 5]); |
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143 %! assert (x, polyval (p,x), eps); |
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144 %! x = reshape (x, [5, 2]); |
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145 %! assert (x, polyval (p,x), eps); |
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146 %! x = reshape (x, [1, 1, 5, 2]); |
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147 %! assert (x, polyval (p,x), eps); |
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149 %!test |
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150 %! p = [1]; |
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151 %! x = 1:10; |
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152 %! y = ones (size (x)); |
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153 %! assert (y, polyval (p,x), eps); |
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154 %! x = x(:); |
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155 %! y = ones (size (x)); |
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156 %! assert (y, polyval (p,x), eps); |
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157 %! x = reshape (x, [2, 5]); |
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158 %! y = ones (size (x)); |
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159 %! assert (y, polyval (p,x), eps); |
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160 %! x = reshape (x, [5, 2]); |
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161 %! y = ones (size (x)); |
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162 %! assert (y, polyval (p,x), eps); |
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163 %! x = reshape (x, [1, 1, 5, 2]); |
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164 |
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165 ## Test empty combinations |
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166 %!assert (polyval ([], 1:10), zeros (1, 10)) |
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167 %!assert (class (polyval (single ([]), 1:10)), "single") |
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168 %!assert (class (polyval ([], single (1:10))), "single") |
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169 %!assert (polyval (1, []), []) |
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170 %!assert (polyval ([], []), []) |
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171 %!assert (polyval (1, zeros (0,3)), zeros (0, 3)) |
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172 %!assert (class (polyval (single (1), [])), "single") |
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173 %!assert (class (polyval (1, single ([]))), "single") |
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174 %!assert (class (polyval (single ([]), [])), "single") |
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175 %!assert (class (polyval ([], single ([]))), "single") |
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176 |
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177 ## Test input validation |
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178 %!error <Invalid call> polyval () |
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179 %!error <Invalid call> polyval (1) |
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180 %!error <Invalid call> [y, dy] = polyval (1, 2) |
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181 %!error <P must be a numeric floating point vector> polyval ({1, 0}, 0:10) |
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182 %!error <P must be a numeric floating point vector> polyval (int8 ([1]), 0:10) |
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183 %!error <P must be a numeric floating point vector> polyval ([1,0;0,1], 0:10) |
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184 %!error <X must be numeric floating point> polyval ([1,0], {0:10}) |
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185 %!error <X must be numeric floating point> polyval ([1,0], int8 (0:10)) |
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186 %!error <S input is required> [y, dy] = polyval (1, 1, []) |
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187 %!error <S input is missing required fields> |
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188 %! [y, dy] = polyval (1, 1, struct ("T", 0, "normr", 1, "df", 2)); |
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189 %!error <S input must be a structure> [y, dy] = polyval (1, 1, 2) |
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190 %!error <MU must be numeric floating point with 2 values> |
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191 %! polyval (1, 1, [], {1, 2}); |
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192 %!error <MU must be numeric floating point with 2 values> |
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193 %! polyval (1, 1, [], int8 ([1,2])); |
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194 %!error <MU must be numeric floating point with 2 values> |
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195 %! polyval (1, 1, [], [1]); |