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annotate m-toolbox/classes/@ao/corr.m @ 33:5e7477b94d94 database-connection-manager

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author Daniele Nicolodi <nicolodi@science.unitn.it>
date Mon, 05 Dec 2011 16:20:06 +0100
parents f0afece42f48
children
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Daniele Nicolodi <nicolodi@science.unitn.it>
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1 % CORR estimate linear correlation coefficients.
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Daniele Nicolodi <nicolodi@science.unitn.it>
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2 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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Daniele Nicolodi <nicolodi@science.unitn.it>
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3 %
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Daniele Nicolodi <nicolodi@science.unitn.it>
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4 % DESCRIPTION: CORR estimate linear correlation coefficients.
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Daniele Nicolodi <nicolodi@science.unitn.it>
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5 %
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Daniele Nicolodi <nicolodi@science.unitn.it>
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6 % The method returns a P-by-P matrix containing the pairwise
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Daniele Nicolodi <nicolodi@science.unitn.it>
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7 % linear correlation coefficient between each pair of columns
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Daniele Nicolodi <nicolodi@science.unitn.it>
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8 % in the N-by-P matrix X formed from the length-N vectors of
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Daniele Nicolodi <nicolodi@science.unitn.it>
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9 % the P input AOs. The coefficients are calculated using
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Daniele Nicolodi <nicolodi@science.unitn.it>
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10 % Pearson's product-moment method.
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Daniele Nicolodi <nicolodi@science.unitn.it>
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11 %
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Daniele Nicolodi <nicolodi@science.unitn.it>
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12 % CALL: >> c = corr(a,b)
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Daniele Nicolodi <nicolodi@science.unitn.it>
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13 % >> c = corr(a,b,c,...)
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14 %
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Daniele Nicolodi <nicolodi@science.unitn.it>
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15 % INPUTS: a,b,c,... - input analysis objects
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Daniele Nicolodi <nicolodi@science.unitn.it>
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16 %
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Daniele Nicolodi <nicolodi@science.unitn.it>
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17 % OUTPUTS: c - output analysis object containing the correlation matrix.
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18 %
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19 % <a href="matlab:utils.helper.displayMethodInfo('ao', 'corr')">Parameters Description</a>
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Daniele Nicolodi <nicolodi@science.unitn.it>
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20 %
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Daniele Nicolodi <nicolodi@science.unitn.it>
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21 % VERSION: $Id: corr.m,v 1.9 2011/04/08 08:56:18 hewitson Exp $
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22 %
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Daniele Nicolodi <nicolodi@science.unitn.it>
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23 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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24
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Daniele Nicolodi <nicolodi@science.unitn.it>
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25 function varargout = corr(varargin)
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26
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Daniele Nicolodi <nicolodi@science.unitn.it>
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27 % Check if this is a call for parameters
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Daniele Nicolodi <nicolodi@science.unitn.it>
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28 if utils.helper.isinfocall(varargin{:})
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Daniele Nicolodi <nicolodi@science.unitn.it>
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29 varargout{1} = getInfo(varargin{3});
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Daniele Nicolodi <nicolodi@science.unitn.it>
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30 return
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31 end
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32
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Daniele Nicolodi <nicolodi@science.unitn.it>
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33 import utils.const.*
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Daniele Nicolodi <nicolodi@science.unitn.it>
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34 utils.helper.msg(msg.PROC3, 'running %s/%s', mfilename('class'), mfilename);
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35
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Daniele Nicolodi <nicolodi@science.unitn.it>
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36 % Collect input variable names
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Daniele Nicolodi <nicolodi@science.unitn.it>
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37 in_names = cell(size(varargin));
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Daniele Nicolodi <nicolodi@science.unitn.it>
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38 for ii = 1:nargin,in_names{ii} = inputname(ii);end
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Daniele Nicolodi <nicolodi@science.unitn.it>
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39
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Daniele Nicolodi <nicolodi@science.unitn.it>
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40 % Collect all AOs and plists
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Daniele Nicolodi <nicolodi@science.unitn.it>
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41 [as, ao_invars] = utils.helper.collect_objects(varargin(:), 'ao', in_names);
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42
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Daniele Nicolodi <nicolodi@science.unitn.it>
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43 if nargout == 0
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Daniele Nicolodi <nicolodi@science.unitn.it>
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44 error('### corr cannot be used as a modifier. Please give an output variable.');
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Daniele Nicolodi <nicolodi@science.unitn.it>
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45 end
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46
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Daniele Nicolodi <nicolodi@science.unitn.it>
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47 if numel(as) < 2
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Daniele Nicolodi <nicolodi@science.unitn.it>
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48 error('### corr requires at least two input AOs to work.');
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49 end
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50
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Daniele Nicolodi <nicolodi@science.unitn.it>
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51 % Convolute the data
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Daniele Nicolodi <nicolodi@science.unitn.it>
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52 smat = [];
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Daniele Nicolodi <nicolodi@science.unitn.it>
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53 inunits = unit;
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Daniele Nicolodi <nicolodi@science.unitn.it>
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54 name = '';
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Daniele Nicolodi <nicolodi@science.unitn.it>
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55 desc = '';
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Daniele Nicolodi <nicolodi@science.unitn.it>
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56 for jj=1:numel(as)
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Daniele Nicolodi <nicolodi@science.unitn.it>
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57 smat = [smat as(jj).data.getY];
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Daniele Nicolodi <nicolodi@science.unitn.it>
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58 inunits = inunits .* as(jj).data.yunits;
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Daniele Nicolodi <nicolodi@science.unitn.it>
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59 name = strcat(name, [',' ao_invars{jj}]);
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60 desc = strcat(desc, [' ' as(jj).description]);
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61 end
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Daniele Nicolodi <nicolodi@science.unitn.it>
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62 desc = strtrim(desc);
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63
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Daniele Nicolodi <nicolodi@science.unitn.it>
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64 % compute the sample correlation using Pearson's product-moment coefficient
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Daniele Nicolodi <nicolodi@science.unitn.it>
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65 Cv = cov(smat);
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Daniele Nicolodi <nicolodi@science.unitn.it>
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66 C = zeros(size(Cv));
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Daniele Nicolodi <nicolodi@science.unitn.it>
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67 for ii=1:size(Cv, 1)
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Daniele Nicolodi <nicolodi@science.unitn.it>
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68 for kk=1:size(Cv,2)
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Daniele Nicolodi <nicolodi@science.unitn.it>
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69 C(ii,kk) = Cv(ii,kk) ./ (sqrt(Cv(ii,ii))*sqrt(Cv(kk,kk)));
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70 end
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71 end
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72
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Daniele Nicolodi <nicolodi@science.unitn.it>
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73 bs = ao(cdata(C));
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Daniele Nicolodi <nicolodi@science.unitn.it>
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74 bs.name = sprintf('corr(%s)', name(2:end));
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75 bs.description = desc;
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76 bs.data.setYunits(inunits);
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77 bs.addHistory(getInfo('None'), getDefaultPlist, ao_invars, [as(:).hist]);
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78
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Daniele Nicolodi <nicolodi@science.unitn.it>
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79 % Set output
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Daniele Nicolodi <nicolodi@science.unitn.it>
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80 if nargout == numel(bs)
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Daniele Nicolodi <nicolodi@science.unitn.it>
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81 % List of outputs
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82 for ii = 1:numel(bs)
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83 varargout{ii} = bs(ii);
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84 end
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Daniele Nicolodi <nicolodi@science.unitn.it>
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85 else
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Daniele Nicolodi <nicolodi@science.unitn.it>
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86 % Single output
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87 varargout{1} = bs;
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88 end
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89 end
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90
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Daniele Nicolodi <nicolodi@science.unitn.it>
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91 %--------------------------------------------------------------------------
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Daniele Nicolodi <nicolodi@science.unitn.it>
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92 % Get Info Object
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93 %--------------------------------------------------------------------------
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Daniele Nicolodi <nicolodi@science.unitn.it>
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94 function ii = getInfo(varargin)
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Daniele Nicolodi <nicolodi@science.unitn.it>
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95 if nargin == 1 && strcmpi(varargin{1}, 'None')
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Daniele Nicolodi <nicolodi@science.unitn.it>
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96 sets = {};
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Daniele Nicolodi <nicolodi@science.unitn.it>
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97 pls = [];
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Daniele Nicolodi <nicolodi@science.unitn.it>
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98 else
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Daniele Nicolodi <nicolodi@science.unitn.it>
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99 sets = {'Default'};
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Daniele Nicolodi <nicolodi@science.unitn.it>
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100 pls = getDefaultPlist;
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101 end
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Daniele Nicolodi <nicolodi@science.unitn.it>
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102 % Build info object
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Daniele Nicolodi <nicolodi@science.unitn.it>
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103 ii = minfo(mfilename, 'ao', 'ltpda', utils.const.categories.sigproc, '$Id: corr.m,v 1.9 2011/04/08 08:56:18 hewitson Exp $', sets, pls);
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Daniele Nicolodi <nicolodi@science.unitn.it>
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104 ii.setModifier(false);
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Daniele Nicolodi <nicolodi@science.unitn.it>
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105 ii.setArgsmin(2);
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106 end
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Daniele Nicolodi <nicolodi@science.unitn.it>
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107
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Daniele Nicolodi <nicolodi@science.unitn.it>
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108 %--------------------------------------------------------------------------
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Daniele Nicolodi <nicolodi@science.unitn.it>
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109 % Get Default Plist
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Daniele Nicolodi <nicolodi@science.unitn.it>
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110 %--------------------------------------------------------------------------
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Daniele Nicolodi <nicolodi@science.unitn.it>
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111
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Daniele Nicolodi <nicolodi@science.unitn.it>
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112 function plout = getDefaultPlist()
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Daniele Nicolodi <nicolodi@science.unitn.it>
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113 persistent pl;
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114 if exist('pl', 'var')==0 || isempty(pl)
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Daniele Nicolodi <nicolodi@science.unitn.it>
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115 pl = buildplist();
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116 end
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Daniele Nicolodi <nicolodi@science.unitn.it>
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117 plout = pl;
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118 end
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119
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Daniele Nicolodi <nicolodi@science.unitn.it>
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120 function pl_default = buildplist()
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121 pl_default = plist.EMPTY_PLIST;
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122 end
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123