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+ − % POLYFIT overloads polyfit() function of MATLAB for Analysis Objects.
+ − %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+ − %
+ − % DESCRIPTION: POLYFIT overloads polyfit() function of MATLAB for Analysis
+ − % Objects. It finds the coefficients of a polynomial P(X) of
+ − % degree N that fits the data Y best in a least-squares sense:
+ − % P(1)*X^N + P(2)*X^(N-1) +...+ P(N)*X + P(N+1)
+ − %
+ − % CALL: bs = polyfit(a1, a2, a3, ..., pl)
+ − % bs = polyfit(as,pl)
+ − % bs = as.polyfit(pl)
+ − %
+ − % INPUTS: aN - input analysis objects with data to be fitted.
+ − % X will be a.x
+ − % Y will be a.y
+ − % as - input analysis objects array
+ − % pl - input parameter list
+ − %
+ − % OUTPUTs: bs - An array of pest objects, each with the N+1 fitting coefficients P(j)
+ − %
+ − % <a href="matlab:utils.helper.displayMethodInfo('ao', 'polyfit')">Parameters Description</a>
+ − %
+ − % VERSION: $Id: polyfit.m,v 1.48 2011/05/12 03:37:08 mauro Exp $
+ − %
+ − % EXAMPLES:
+ − %
+ − % %% Make fake AO from polyval
+ − % nsecs = 100;
+ − % fs = 10;
+ − %
+ − % u = unit('fm s^-2');
+ − %
+ − % pl = plist('nsecs', nsecs, 'fs', fs, ...
+ − % 'tsfcn', 'polyval([3 2 1 ], t) + 1000*randn(size(t))', ...
+ − % 'xunits', 's', 'yunits', u);
+ − %
+ − % a1 = ao(pl);
+ − %
+ − % %% Fit a polynomial
+ − % N = 3;
+ − % p1 = polyfit(a1, plist('N', N));
+ − % p2 = polyfit(a1, plist('N', N, 'rescale', true));
+ − %
+ − % %% Compute fit: evaluating pest
+ − % %% Here we need to specify that we want to use the 'x' field of
+ − % %% the AO a to build the output AO
+ − %
+ − % b1 = p1.eval(plist('type', 'tsdata', 'XData', a1, 'Xfield', 'x'));
+ − % b2 = p2.eval(a1, plist('type', 'tsdata', 'Xfield', 'x'));
+ − %
+ − % %% Plot fit
+ − % iplot(a1, b1, plist('LineStyles', {'', '--'}));
+ − %
+ − % %% Remove polynomial
+ − % c = a1-b1;
+ − % iplot(c)
+ − %
+ − %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+ −
+ − function varargout = polyfit(varargin)
+ −
+ − % Check if this is a call for parameters
+ − if utils.helper.isinfocall(varargin{:})
+ − varargout{1} = getInfo(varargin{3});
+ − return
+ − end
+ −
+ − import utils.const.*
+ − utils.helper.msg(msg.PROC3, 'running %s/%s', mfilename('class'), mfilename);
+ −
+ − % Collect input variable names
+ − in_names = cell(size(varargin));
+ − for ii = 1:nargin,in_names{ii} = inputname(ii);end
+ −
+ − % Collect all AOs and plists
+ − [as, ao_invars] = utils.helper.collect_objects(varargin(:), 'ao', in_names);
+ − pl = utils.helper.collect_objects(varargin(:), 'plist', in_names);
+ −
+ − if nargout == 0
+ − error('### polyfit can not be used as a modifier method. Please give at least one output');
+ − end
+ −
+ − % Combine plists
+ − use_pl = parse(pl, getDefaultPlist);
+ −
+ − % Degree of polynomial to fit
+ − N = find(use_pl, 'N');
+ −
+ − % Center and rescale the data
+ − rescale = utils.prog.yes2true(find(use_pl, 'rescale'));
+ −
+ − % Loop over input AOs
+ − for jj = 1 : numel(as)
+ −
+ − if isa(as(jj).data, 'cdata')
+ − warning('!!! Can''t fit to cdata objects. Skipping AO %s', ao_invars{jj});
+ − bs = [];
+ − else
+ − % Fit polynomial
+ − mu = [];
+ − if rescale
+ − [p,s,mu] = polyfit(as(jj).x, as(jj).y, N);
+ − else
+ − [p,s] = polyfit(as(jj).x, as(jj).y, N);
+ − end
+ −
+ − % prepare models, units, names
+ − model = [];
+ − for kk = 1:N+1
+ − names{kk} = ['P' num2str(kk)];
+ − units{kk} = as(jj).yunits ./ ((as(jj).xunits).^(N-kk+1));
+ − if kk == 1
+ − model = [model 'P' num2str(kk) '*X.^' num2str(N-kk+1)];
+ − else
+ − model = [model ' + P' num2str(kk) '*X.^' num2str(N-kk+1)];
+ − end
+ − end
+ − model = smodel(plist('expression', model, ...
+ − 'params', names, ...
+ − 'values', p, ...
+ − 'xvar', 'X', ...
+ − 'xunits', as(jj).xunits, ...
+ − 'yunits', as(jj).yunits ...
+ − ));
+ −
+ − % Build new pest objects from these N+1 coefficients
+ − bs(jj) = pest;
+ − bs(jj).setY(p);
+ − bs(jj).setDof(s.df);
+ − bs(jj).setNames(names{:});
+ − bs(jj).setYunits(units);
+ − bs(jj).setModels(model);
+ − bs(jj).name = sprintf('polyfit(%s)', ao_invars{jj});
+ − bs(jj).addHistory(getInfo('None'), use_pl, ao_invars(jj), as(jj).hist);
+ − % Set procinfo object with some data
+ − bs(jj).procinfo = plist('S', s, 'mu', mu);
+ −
+ − end
+ −
+ − end
+ −
+ − % Set output
+ − varargout = utils.helper.setoutputs(nargout, bs);
+ −
+ − end
+ −
+ − %--------------------------------------------------------------------------
+ − % Get Info Object
+ − %--------------------------------------------------------------------------
+ − function ii = getInfo(varargin)
+ − if nargin == 1 && strcmpi(varargin{1}, 'None')
+ − sets = {};
+ − pl = [];
+ − else
+ − sets = {'Default'};
+ − pl = getDefaultPlist();
+ − end
+ − % Build info object
+ − ii = minfo(mfilename, 'ao', 'ltpda', utils.const.categories.sigproc, '$Id: polyfit.m,v 1.48 2011/05/12 03:37:08 mauro Exp $', sets, pl);
+ − ii.setModifier(false);
+ − ii.setArgsmin(1);
+ − end
+ −
+ − %--------------------------------------------------------------------------
+ − % Get Default Plist
+ − %--------------------------------------------------------------------------
+ − function plout = getDefaultPlist()
+ − persistent pl;
+ − if ~exist('pl', 'var') || isempty(pl)
+ − pl = buildplist();
+ − end
+ − plout = pl;
+ − end
+ −
+ − function pl = buildplist()
+ − pl = plist();
+ −
+ − % N
+ − p = param({'N','Degree of polynomial to fit.'}, {2, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9}, paramValue.SINGLE});
+ − pl.append(p);
+ −
+ − % Rescale
+ − p = param({'rescale',['set to ''true'' or ''false'' to center and ', ...
+ − 'rescale the data before fitting.<br>', ...
+ − 'See "help polyfit" for further details.']}, paramValue.FALSE_TRUE);
+ − pl.append(p);
+ −
+ − end
+ −
+ − % END
+ −