Mercurial > hg > octave-nkf
annotate scripts/polynomial/polyfit.m @ 15063:36cbcc37fdb8
Refactor configure.ac to make it more understandable.
Use common syntax for messages in config.h
Correct typos, refer to libraries in all caps, use two spaces after period.
Follow Autoconf guidelines and place general tests before specific tests.
* configure.ac, m4/acinclude.m4: Use common syntax for messages in config.h
Correct typos, refer to libraries in all caps, use two spaces after period.
Follow Autoconf guidelines and place general tests before specific tests.
author | Rik <rik@octave.org> |
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date | Tue, 31 Jul 2012 10:28:51 -0700 |
parents | cd375519eab0 |
children | 7babcdb9bc13 |
rev | line source |
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1 ## Copyright (C) 1996-2012 John W. Eaton |
2313 | 2 ## |
3 ## This file is part of Octave. | |
4 ## | |
5 ## Octave is free software; you can redistribute it and/or modify it | |
6 ## under the terms of the GNU General Public License as published by | |
7016 | 7 ## the Free Software Foundation; either version 3 of the License, or (at |
8 ## your option) any later version. | |
2313 | 9 ## |
10 ## Octave is distributed in the hope that it will be useful, but | |
11 ## WITHOUT ANY WARRANTY; without even the implied warranty of | |
12 ## MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU | |
13 ## General Public License for more details. | |
14 ## | |
15 ## You should have received a copy of the GNU General Public License | |
7016 | 16 ## along with Octave; see the file COPYING. If not, see |
17 ## <http://www.gnu.org/licenses/>. | |
2303 | 18 |
3368 | 19 ## -*- texinfo -*- |
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20 ## @deftypefn {Function File} {@var{p} =} polyfit (@var{x}, @var{y}, @var{n}) |
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21 ## @deftypefnx {Function File} {[@var{p}, @var{s}] =} polyfit (@var{x}, @var{y}, @var{n}) |
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22 ## @deftypefnx {Function File} {[@var{p}, @var{s}, @var{mu}] =} polyfit (@var{x}, @var{y}, @var{n}) |
3368 | 23 ## Return the coefficients of a polynomial @var{p}(@var{x}) of degree |
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24 ## @var{n} that minimizes the least-squares-error of the fit to the points |
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25 ## @code{[@var{x}, @var{y}]}. If @var{n} is a logical vector, it is used |
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26 ## as a mask to selectively force the corresponding polynomial |
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27 ## coefficients to be used or ignored. |
3418 | 28 ## |
4491 | 29 ## The polynomial coefficients are returned in a row vector. |
30 ## | |
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31 ## The optional output @var{s} is a structure containing the following fields: |
3426 | 32 ## |
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33 ## @table @samp |
4491 | 34 ## @item R |
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35 ## Triangular factor R from the QR@tie{}decomposition. |
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36 ## |
4491 | 37 ## @item X |
7501 | 38 ## The Vandermonde matrix used to compute the polynomial coefficients. |
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39 ## |
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40 ## @item C |
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41 ## The unscaled covariance matrix, formally equal to the inverse of |
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42 ## @var{x'}*@var{x}, but computed in a way minimizing roundoff error |
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43 ## propagation. |
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44 ## |
4491 | 45 ## @item df |
7501 | 46 ## The degrees of freedom. |
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47 ## |
4491 | 48 ## @item normr |
7501 | 49 ## The norm of the residuals. |
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50 ## |
4491 | 51 ## @item yf |
7501 | 52 ## The values of the polynomial for each value of @var{x}. |
4491 | 53 ## @end table |
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54 ## |
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55 ## The second output may be used by @code{polyval} to calculate the |
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56 ## statistical error limits of the predicted values. In particular, the |
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57 ## standard deviation of @var{p} coefficients is given by @* |
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58 ## @code{sqrt (diag (s.C)/s.df)*s.normr}. |
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59 ## |
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60 ## When the third output, @var{mu}, is present the |
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61 ## coefficients, @var{p}, are associated with a polynomial in |
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62 ## @var{xhat} = (@var{x}-@var{mu}(1))/@var{mu}(2). |
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63 ## Where @var{mu}(1) = mean (@var{x}), and @var{mu}(2) = std (@var{x}). |
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64 ## This linear transformation of @var{x} improves the numerical |
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65 ## stability of the fit. |
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66 ## @seealso{polyval, polyaffine, roots, vander, zscore} |
3368 | 67 ## @end deftypefn |
2311 | 68 |
5428 | 69 ## Author: KH <Kurt.Hornik@wu-wien.ac.at> |
2312 | 70 ## Created: 13 December 1994 |
71 ## Adapted-By: jwe | |
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72 ## Modified on 20120204 by P. Dupuis; added the ability to specify a |
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73 ## polynomial mask instead of a polynomial degree. |
2312 | 74 |
4491 | 75 function [p, s, mu] = polyfit (x, y, n) |
2325 | 76 |
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77 if (nargin < 3 || nargin > 4) |
6046 | 78 print_usage (); |
2261 | 79 endif |
2325 | 80 |
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81 if (nargout > 2) |
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82 ## Normalized the x values. |
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83 mu = [mean(x), std(x)]; |
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84 x = (x - mu(1)) / mu(2); |
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85 endif |
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86 |
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87 if (! size_equal (x, y)) |
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88 error ("polyfit: X and Y must be vectors of the same size"); |
2261 | 89 endif |
2325 | 90 |
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91 if (islogical (n)) |
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92 polymask = n; |
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93 ## n is the polynomial degree as given the polymask size; m is the |
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94 ## effective number of used coefficients. |
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95 n = length (polymask) - 1; m = sum (polymask) - 1; |
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96 else |
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97 if (! (isscalar (n) && n >= 0 && ! isinf (n) && n == fix (n))) |
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98 error ("polyfit: N must be a non-negative integer"); |
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99 endif |
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100 polymask = logical (ones (1, n+1)); m = n; |
2261 | 101 endif |
2325 | 102 |
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103 y_is_row_vector = (rows (y) == 1); |
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104 |
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105 ## Reshape x & y into column vectors. |
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106 l = numel (x); |
9138 | 107 x = x(:); |
108 y = y(:); | |
2325 | 109 |
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110 ## Construct the Vandermonde matrix. |
9138 | 111 v = vander (x, n+1); |
2261 | 112 |
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113 ## Solve by QR decomposition. |
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114 [q, r, k] = qr (v(:, polymask), 0); |
9138 | 115 p = r \ (q' * y); |
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116 p(k) = p; |
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117 |
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118 if (n ~= m) |
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119 q = p; p = zeros (n+1, 1); |
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120 p(polymask) = q; |
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121 endif |
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122 |
4491 | 123 if (nargout > 1) |
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124 yf = v*p; |
3105 | 125 |
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126 if (y_is_row_vector) |
4491 | 127 s.yf = yf.'; |
128 else | |
129 s.yf = yf; | |
3105 | 130 endif |
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131 s.X = v; |
3091 | 132 |
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133 ## r.'*r is positive definite if X(:, polymask) is of full rank. |
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134 ## Invert it by cholinv to avoid taking the square root of squared |
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135 ## quantities. If cholinv fails, then X(:, polymask) is rank |
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136 ## deficient and not invertible. |
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137 try |
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138 C = cholinv (r.'*r)(k, k); |
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139 catch |
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140 C = NaN * ones (m+1, m+1); |
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141 end_try_catch |
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142 |
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143 if (n ~= m) |
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144 ## fill matrices if required |
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145 s.X(:, ~polymask) = 0; |
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146 s.R = zeros (n+1, n+1); s.R(polymask, polymask) = r; |
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147 s.C = zeros (n+1, n+1); s.C(polymask, polymask) = C; |
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148 else |
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149 s.R = r; |
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150 s.C = C; |
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151 endif |
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152 s.df = l - m - 1; |
4491 | 153 s.normr = norm (yf - y); |
2261 | 154 endif |
155 | |
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156 ## Return a row vector. |
5395 | 157 p = p.'; |
158 | |
2261 | 159 endfunction |
7411 | 160 |
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162 %!shared x |
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164 %!assert (polyfit (x, x.^2+x+1, 2), [1, 1, 1], sqrt (eps)) |
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165 %!assert (polyfit (x, x.^2+x+1, 3), [0, 1, 1, 1], sqrt (eps)) |
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166 %!fail ("polyfit (x, x.^2+x+1)") |
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167 %!fail ("polyfit (x, x.^2+x+1, [])") |
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169 ## Test difficult case where scaling is really needed. This example |
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170 ## demonstrates the rather poor result which occurs when the dependent |
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171 ## variable is not normalized properly. |
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172 ## Also check the usage of 2nd & 3rd output arguments. |
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173 %!test |
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174 %! x = [ -1196.4, -1195.2, -1194, -1192.8, -1191.6, -1190.4, -1189.2, -1188, \ |
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175 %! -1186.8, -1185.6, -1184.4, -1183.2, -1182]; |
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176 %! y = [ 315571.7086, 315575.9618, 315579.4195, 315582.6206, 315585.4966, \ |
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177 %! 315588.3172, 315590.9326, 315593.5934, 315596.0455, 315598.4201, \ |
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178 %! 315600.7143, 315602.9508, 315605.1765 ]; |
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179 %! [p1, s1] = polyfit (x, y, 10); |
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180 %! [p2, s2, mu] = polyfit (x, y, 10); |
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181 %! assert (s2.normr < s1.normr); |
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182 |
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183 %!test |
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184 %! x = 1:4; |
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185 %! p0 = [1i, 0, 2i, 4]; |
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186 %! y0 = polyval (p0, x); |
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187 %! p = polyfit (x, y0, numel (p0) - 1); |
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188 %! assert (p, p0, 1000*eps); |
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189 |
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190 %!test |
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191 %! x = 1000 + (-5:5); |
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192 %! xn = (x - mean (x)) / std (x); |
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193 %! pn = ones (1,5); |
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194 %! y = polyval (pn, xn); |
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195 %! [p, s, mu] = polyfit (x, y, numel (pn) - 1); |
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196 %! [p2, s2] = polyfit (x, y, numel (pn) - 1); |
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197 %! assert (p, pn, s.normr); |
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198 %! assert (s.yf, y, s.normr); |
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199 %! assert (mu, [mean(x), std(x)]); |
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200 %! assert (s.normr/s2.normr < sqrt (eps)); |
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201 |
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202 %!test |
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203 %! x = [1, 2, 3; 4, 5, 6]; |
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204 %! y = [0, 0, 1; 1, 0, 0]; |
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205 %! p = polyfit (x, y, 5); |
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206 %! expected = [0, 1, -14, 65, -112, 60] / 12; |
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207 %! assert (p, expected, sqrt (eps)); |
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208 |
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209 %!error <vectors of the same size> polyfit ([1, 2; 3, 4], [1, 2, 3, 4], 2) |
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210 |