gpols evaluate

C:\Users\Makise Kurisu\Downloads\gpols_v2\GpOls\gpols_evaluate.m Saturday, 14 March 2015 8:18 PM

function popu = gpn_evaluate(popuin,ixs,X,Y,Q,optv);%Evaluates individulas and identificates their linear parameters% popu = gpols_evaluate(popuin,ixs,X,Y,Q,optv)% popu <- result (population)% popuin -> input population% ixs -> vector of indexes of individulas to evaluate% X -> Regression matrix without bias (!)% Y -> Output vector% Q -> Weighting vector (set empty if not used)% optv -> evaluation parameters (set empty for default)% [optv(1) optv(2)]: a1, a2 tree-size penalty parameters (default: 0,0)% optv(3): OLS treshold value, range: 0-1 (default: 0)% optv(4): if == 1 then polynomial evaluation else normal (default: 0)%%Remark:% The loss function is:% mse = (1/n)*E'*diag(Q)*E% where n is the number of data points, E is the error vector (Y-Ym),% diag(Q) is the weighting diagonal matrix (e.g. inverse of noise% covariance matrix or relative weights for relative mse)% The Y is the given output vector and the Em is the estimated output:% Y = Ym + E, Ym = [X ones(size(X,1),1)]*Theta% where X is the regression matrix, Theta is the linear parameter vector% and Theta(end) = bias%% The ixs contains the indexes of individuals must be evaluated.% If you want evaluate every individudal then set ixs = [1:popu.size]%% The applied tree-size penalty function:% penalty = 1/(1+exp(a1*(L-a2)))% where a1 and a2 the penalty function parameters, and L is the size of% the tree (number of nodes). If a1=0 or a2=0 then penalty = 1% Then the fitness is calucalted as% fitness = penalty * corrcoef(sqrt(diag(Q))*Y,sqrt(diag(Q))*Ym)%% The OLS treshold value determines the minimum allowed error reduction% ratio (err). If a sub-tree has smaller err then it will be eliminated.%% The polynomial evaluation means that every non-polynomial tree will be% transformed to polynomial before evaluation.%

% (c) Janos Madar, University of Veszprem, 2005

%Outputpopu = popuin;

%Options and parametersif isempty(optv),optv = zeros(1,4);

enda1 = optv(1);a2 = optv(2);olslimit = optv(3);polye = optv(4);

%WLS matrices

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if isempty(Q),Q = ones(size(Y,1),1);

endQ = diag(Q);X = sqrt(Q)*X;Y = sqrt(Q)*Y;

%Symbolum listfor i = 1:length(popu.symbols{1}),s = popu.symbols{1}{i}(1);if s=='*' | s=='/' | s=='^' | s=='\',symbols{1}{i} = strcat('.',popu.symbols{1}{i});

elsesymbols{1}{i} = popu.symbols{1}{i};

endendfor i = 1:size(X,2),symbols{2}{i} = sprintf('X(:,%i)',i);

end

%MAIN loopfor j = ixs,

%Get the treetree = popu.chrom{j}.tree;

%Exhange '+' -> '*' under non-'+' (polynom-operation)if (polye == 1),tree = polytree(tree);

end

%Collect the '+ parts'[vv,fs,vvdel] = fsgen(tree,symbols);

%Prune redundant partstree = prunetree(vvdel,tree,symbols);

%Collect the '+ parts'[vv,fs,vvdel] = fsgen(tree,symbols);if ~isempty(vvdel),error('Fatal error: redundant strings after deleting');

end

%OLSQ[vfsdel,err] = gpols_olsq(fs,X,Y,olslimit);tree.err = err;vvdel = vv(vfsdel);

%Prune redundant partstree = prunetree(vvdel,tree,symbols);

%Collect the '+ parts'[vv,fs,vvdel] = fsgen(tree,symbols);if ~isempty(vvdel),error('Fatal error: redundant strings after deleting');

end

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%LSQ[mse,cfsq,theta] = gpols_lsq(fs,X,Y);fit = cfsq;

%Tree-size penaltyif a1~=0 & a2~=0,Sl = tree_size(tree);fit = fit / (1+exp(a1*(Sl-a2)));

end

%Chrompopu.chrom{j}.tree = tree; %write back the treepopu.chrom{j}.mse = mse;popu.chrom{j}.fitness = fit;popu.chrom{j}.tree.param(1:length(theta)) = theta;popu.chrom{j}.tree.paramn = length(theta);

end

%---------------------------------------------------------------function [tree] = polytree(treein);tree = treein;v = [1];vv = [];i = 1;while i <= length(v),ii = v(i);if tree.nodetyp(ii)==1 & tree.node(ii)==1,v = [v, ii*2, ii*2+1];

elsevv = [vv, ii];

endi = i+1;

endfor ii = [vv],v = [ii];i = 1;while i <= length(v),

if tree.nodetyp(v(i))==1,if tree.node(v(i))==1,tree.node(v(i)) = 2;

endif v(i)*2+1 <= tree.maxsize,v = [v, v(i)*2, v(i)*2+1];

endendi = i+1;

endend

%---------------------------------------------------------------function [vv,fs,vvdel] = fsgen(tree,symbols);%Search the '+ parts'v = [1];vv = [];i = 1;while i <= length(v),

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ii = v(i);if tree.nodetyp(ii)==1 & tree.node(ii)==1,v = [v, ii*2, ii*2+1];

elsevv = [vv, ii];

endi = i+1;

endfs = [];i = 1;for ii = [vv],fs{i} = strcat('(',tree_stringrc(tree,ii,symbols),')');i = i+1;

end%Search the redundant '+ parts'vvdel = [];vvv = [];i = 1;while i <= length(fs),ok = 0;ii = 1;while ii<i & ok==0,ok = strcmp(fs{i},fs{ii});ii = ii+1;

endif ok==1,vvdel = [vvdel, vv(i)];

elsevvv = [vvv, i];

endi = i+1;

end

%---------------------------------------------------------------function tree = prunetree(vvdel,treein,symbols);%Delete subtreesnn = [length(symbols{1}), length(symbols{2})];tree = treein;n = floor(tree.maxsize/2);tree.nodetyp(vvdel) = 0;ok = 1;while ok,ok = 0;i = 1;while i<=n & ok==0,

if (tree.nodetyp(i)==1) & (tree.nodetyp(i*2)==0 | tree.nodetyp(i*2+1)==0),ok = 1;if tree.nodetyp(i*2)==0 & tree.nodetyp(i*2+1)==0,tree.nodetyp(i*2) = treein.nodetyp(i*2);tree.nodetyp(i*2+1) = treein.nodetyp(i*2+1);tree.nodetyp(i) = 0;

elseif tree.nodetyp(i*2)==0,tree.nodetyp(i*2) = treein.nodetyp(i*2);subtree = tree_getsubtree(tree,i*2+1);tree = tree_inserttree(subtree,tree,i,nn(2));

elsetree.nodetyp(i*2+1) = treein.nodetyp(i*2+1);

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subtree = tree_getsubtree(tree,i*2);tree = tree_inserttree(subtree,tree,i,nn(2));

endelseif (tree.nodetyp(i*2)==0 | tree.nodetyp(i*2+1)==0),ok = 1;if tree.nodetyp(i*2)==0,tree.nodetyp(i*2) = treein.nodetyp(i*2);

endif tree.nodetyp(i*2+1)==0,tree.nodetyp(i*2+1) = treein.nodetyp(i*2+1);

endendi = i+1;

endend

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