Home > manopt > tools > identify_linear_piece.m

identify_linear_piece

PURPOSE ^

Identify a segment of the curve (x, y) that appears to be linear.

SYNOPSIS ^

function [range, poly] = identify_linear_piece(x, y, window_length)

DESCRIPTION ^

 Identify a segment of the curve (x, y) that appears to be linear.

 function [range poly] = identify_linear_piece(x, y, window_length)

 This function attempts to identify a contiguous segment of the curve
 defined by the vectors x and y that appears to be linear. A line is fit
 through the data over all windows of length window_length and the best
 fit is retained. The output specifies the range of indices such that
 x(range) is the portion over which (x, y) is the most linear and the
 output poly specifies a first order polynomial that best fits (x, y) over
 that range, following the usual matlab convention for polynomials
 (highest degree coefficients first).

 See also: checkdiff checkgradient checkhessian

CROSS-REFERENCE INFORMATION ^

This function calls: This function is called by:

SOURCE CODE ^

0001 function [range, poly] = identify_linear_piece(x, y, window_length)
0002 % Identify a segment of the curve (x, y) that appears to be linear.
0003 %
0004 % function [range poly] = identify_linear_piece(x, y, window_length)
0005 %
0006 % This function attempts to identify a contiguous segment of the curve
0007 % defined by the vectors x and y that appears to be linear. A line is fit
0008 % through the data over all windows of length window_length and the best
0009 % fit is retained. The output specifies the range of indices such that
0010 % x(range) is the portion over which (x, y) is the most linear and the
0011 % output poly specifies a first order polynomial that best fits (x, y) over
0012 % that range, following the usual matlab convention for polynomials
0013 % (highest degree coefficients first).
0014 %
0015 % See also: checkdiff checkgradient checkhessian
0016 
0017 % This file is part of Manopt: www.manopt.org.
0018 % Original author: Nicolas Boumal, July 8, 2013.
0019 % Contributors:
0020 % Change log:
0021 
0022     residues = zeros(length(x)-window_length, 1);
0023     polys = zeros(2, length(residues));
0024     for i = 1 : length(residues)
0025         range = i:(i+window_length);
0026         [poly, meta] = polyfit(x(range), y(range), 1);
0027         residues(i) = meta.normr;
0028         polys(:, i) = poly';
0029     end
0030     [unused, best] = min(residues); %#ok<ASGLU>
0031     range = best:(best+window_length);
0032     poly = polys(:, best)';
0033 
0034 end

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