- random population and sample selection method for swaps
copyright 2009-2012 Blair Armstrong, Christine Watson, David Plaut
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GNU General Public License for more details.0001 % - random population and sample selection method for swaps 0002 % 0003 % copyright 2009-2012 Blair Armstrong, Christine Watson, David Plaut 0004 % 0005 % This file is part of SOS 0006 % 0007 % SOS is free software: you can redistribute it and/or modify 0008 % it for academic and non-commercial purposes 0009 % under the terms of the GNU General Public License as published by 0010 % the Free Software Foundation, either version 3 of the License, or 0011 % (at your option) any later version. For commercial or for-profit 0012 % uses, please contact the authors (sos@cnbc.cmu.edu). 0013 % 0014 % SOS is distributed in the hope that it will be useful, 0015 % but WITHOUT ANY WARRANTY; without even the implied warranty of 0016 % MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 0017 % GNU General Public License for more details. 0018 0019 % You should have received a copy of the GNU General Public License 0020 % along with SOS (see COPYING.txt). 0021 % If not, see <http://www.gnu.org/licenses/>. 0022 0023 0024 0025 classdef randPopulationAndSampleCandidateSelection < genericFeederCandidateSelection 0026 % selects a neighbor item from a sample's population or other children samples of that pop. 0027 % sample's population 0028 % 0029 % 0030 % PROPERTIES: 0031 % samples - list of all samples swaps could occur in 0032 % sampleItems - list of samples and items that could be swapped into those samples 0033 % 0034 % METHODS: 0035 % randPopulationAndSampleCandidateSelection() % Constructor 0036 % init() %initializes the sample method by generating a list of swap candidates for this sample. 0037 % [feederdf,feederdfIndex] = getCandidateIndex(obj,sample) % returns a dataframe and row index for a swap candidate 0038 0039 0040 0041 properties 0042 samples 0043 sampleItems 0044 end 0045 0046 methods 0047 %% randPopulationAndSampleCandidateSelection() CONSTRUCTOR 0048 function obj = randPopulationAndSampleCandidateSelection(sosObj) 0049 % Constructor 0050 0051 obj.init(sosObj); 0052 0053 verbosePrint('Random Population And Sample Feeder Candidate Selection Ready', ... 0054 'randPopulationAndSampleCandidateSelection_const'); 0055 end 0056 0057 0058 %% init(sosObj) METHOD 0059 function init(obj,sosObj) 0060 %initializes the sample method by generating a list of swap candidates for this sample. 0061 % 0062 %PARAMETERS: 0063 % sosObj - the SOS object the candidate selection method is linked to 0064 0065 obj.samples = []; 0066 obj.sampleItems = {}; 0067 0068 for i=1:length(sosObj.samples) 0069 curSample = sosObj.samples(i); 0070 obj.samples = [obj.samples curSample]; 0071 0072 alreadyMerged = {}; 0073 0074 candidateItemIndicies = {}; 0075 %add it's own items to the list. 0076 0077 for j=1:curSample.n 0078 if curSample.locks(j) == 0 0079 candidateItemIndicies = [candidateItemIndicies ; {curSample j}]; %#ok<AGROW> 0080 end 0081 end 0082 0083 alreadyMerged = [alreadyMerged {curSample}]; %#ok<AGROW> 0084 0085 %add it's population's item to the list 0086 0087 if(isempty(curSample.population) == false && isempty(curSample.population.data) == false) 0088 tempIndicies = cell(length(curSample.population.data{1}),2); 0089 for j=1:length(curSample.population.data{1}) 0090 tempIndicies{j,1} = curSample.population; 0091 tempIndicies{j,2} = j; 0092 end 0093 0094 candidateItemIndicies = [candidateItemIndicies ; tempIndicies]; %#ok<AGROW> 0095 alreadyMerged = [alreadyMerged {curSample.population}]; %#ok<AGROW> 0096 0097 end 0098 % add other samples which are part of this SOS obj and 0099 % which share the sample population to the current sample. 0100 0101 %no siblings if there is no population linked with the 0102 %object 0103 if isempty(curSample.population) ~= 1 0104 numSiblings=length(curSample.population.samples); 0105 else 0106 numSiblings = 0; 0107 end 0108 0109 for k=1:numSiblings 0110 siblingSample = curSample.population.samples(k); 0111 0112 doneAlready = false; 0113 for l=1:length(alreadyMerged) 0114 if alreadyMerged{l} == siblingSample 0115 doneAlready = true; 0116 end 0117 end 0118 0119 0120 inSOSObj = max(ismember(siblingSample,sosObj.samples)); 0121 %doneAlready = max(ismember(siblingSample,alreadyMerged)); 0122 0123 if inSOSObj == true && doneAlready == false 0124 for j=1:siblingSample.n 0125 %also exclude locked items from the sibling 0126 %sample from entering into the computation. 0127 if siblingSample.locks(j) == 0 0128 candidateItemIndicies = [candidateItemIndicies ; {siblingSample j}]; %#ok<AGROW> 0129 end 0130 end 0131 end 0132 0133 alreadyMerged = [alreadyMerged {siblingSample}]; %#ok<AGROW> 0134 0135 end 0136 0137 obj.sampleItems{i} = candidateItemIndicies; 0138 end 0139 0140 end %init() 0141 0142 0143 %% [feederdf,feederdfIndex] = getCandidateIndex(obj,sample) METHOD 0144 function [feederdf,feederdfIndex] = getCandidateIndex(obj,sample) 0145 % returns a dataframe and row index for a swap candidate 0146 % 0147 % PARAMETERS: 0148 % sample - the target sample to swap into 0149 % 0150 % RETURNS: 0151 % feederdf - the feeder dataframe 0152 % feederdfIndex -row index of item in feederdf 0153 0154 if(strcmp(class(sample),'sample') == false) 0155 error('Input must be a sample'); 0156 end 0157 0158 sampleIndex = -1; 0159 for i=1:length(obj.samples) 0160 if obj.samples(i) == sample 0161 sampleIndex = i; 0162 end 0163 end 0164 0165 %find the sample 0166 if sampleIndex == -1 0167 error('Unable to find sample'); 0168 end 0169 0170 %randomly select an item to swap with 0171 0172 lookupIndex = floor((length(obj.sampleItems{sampleIndex})*rand)+1); 0173 0174 feederdf = obj.sampleItems{1,sampleIndex}{lookupIndex,1}; 0175 feederdfIndex = obj.sampleItems{1,sampleIndex}{lookupIndex,2}; 0176 0177 end 0178 0179 end 0180 0181 end 0182