Mercurial > hg > octave-nkf
annotate scripts/polynomial/polyfit.m @ 17744:d63878346099
maint: Update copyright notices for release.
author | John W. Eaton <jwe@octave.org> |
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date | Wed, 23 Oct 2013 22:09:27 -0400 |
parents | b327c19b6b16 |
children | 446c46af4b42 |
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1 ## Copyright (C) 1996-2013 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 (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 |
161 | |
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162 %!shared x |
7411 | 163 %! x = [-2, -1, 0, 1, 2]; |
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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 |