%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% This is an implementation of an ARTMAP variant,
% as described in:
% Gaddam, C. S. (2007).
% Feature Selection via transmitter depletion in ARTMAP. Online Document,
% xx(x) xxx-xxx.
% Boston, MA: Boston University.
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Programmed by Chaitanya Sai (August 2007-08)
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%
% The notation follows the article's notation,
% as does the headers for each step of the
% algorithm
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function updated_artmap = DEFARTMAP_LearnTwinV10dynamic(artmap, inputVectors, classes)
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% This is an implementation of Default ARTMAP,
% as described in CAS/CNS Technical Report TR-2003-008
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Programmed by Ogi Ogas
%%%%%%%%%%%%%%%%%%%%%%%%%
% The notation follows the article's notation,
% as does the headers for each step of the
% algorithm
% 02/21/05 Added search cycle count to artmap structure
% 08.31.05 Added list of nodes activated for each training input (This is
% only used for certain priming simulations; otherwise, this is ignored.)
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% WINNER-TAKE-ALL TRAINING
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
ON=1; OFF=0;
LEARN_TRAIL=OFF;
disp('Using Twin-Trail Catcher Version 28 (dyn) of DEFARTMAP_Learn!')
% Make sure that the data is appropriate for the given network.
[numFeatures, n] = size(inputVectors);
if (numFeatures ~= 2*artmap.M)
error('The input vector does not contain the same number of features as the network.');
end
if (LEARN_TRAIL==ON)
[artmap.learn_trail(1:n).A]=deal([]);
[artmap.learn_trail(1:n).K]=deal([]);
[artmap.learn_trail(1:n).winning_node]=deal([]);
[artmap.learn_trail(1:n).weight_before]=deal([]);
[artmap.learn_trail(1:n).weight_after]=deal([]);
[artmap.learn_trail(1:n).MT_nodes]=deal([]);
[artmap.learn_trail(1:n).MT_mismatch]=deal([]);
[artmap.learn_trail(1:n).MT_x_before]=deal([]);
[artmap.learn_trail(1:n).MT_e_before]=deal([]);
[artmap.learn_trail(1:n).MT_e_after]=deal([]);
[artmap.learn_trail(1:n).MT_Adash_before]=deal([]);
[artmap.learn_trail(1:n).MT_Adash_after]=deal([]);
end
artmap.compare_rho=zeros(n,3);
comp_rho=1;
% Initialize the return artmap network
%%%%%%%%%%%%Initializing e before every round e should start at 1
%%%%%%%%%%%%for version x
if ismember(artmap.model_num,[0 1 19 20 21 22 23 24 25 26 28 29 30 31 32])
e_AttentionDefault=zeros(2*artmap.M,1);
elseif ismember(artmap.model_num,[17 18])
e_AttentionDefault=ones(2*artmap.M,1);
end
search_cycles = 0;
artmap.e_mod_dist=zeros(n,1);
artmap.A_dep_dist=zeros(n,1);
% Classify and learn on each sample.
for sampleNumber = 1:n
if (mod(sampleNumber,400)==0)
disp('Sample Number:')
disp(sampleNumber')
end
% if sampleNumber==6
% disp('Start Debugging Here')
% end
%%%%%%%%%%%%%%%%%
% if sampleNumber==12268
% artmap.lambda_Attention=0;
% end
%%%%%%%%%%%%%%%
% Reset the activated node (NOTE: this is Moshe Bar addition)
activated_node=[];
% Get the next input vector.
% B.3 / B.6
A = inputVectors(:, sampleNumber);
MT_nodes=0;
MT_ALL_nodes=0;
if LEARN_TRAIL==ON
artmap.learn_trail(sampleNumber).A=A;
end
% Get the next class
% B.3 / B.6
K = classes(sampleNumber);
if LEARN_TRAIL==ON
artmap.learn_trail(sampleNumber).K=K;
end
% B.5
artmap.rho = artmap.rho_bar;
artmap.rho_dep = artmap.rho_bar;
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%Try dynamically changing alpha
% artmap.alpha=10/(artmap.C+10);
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
if (artmap.start_conservative==1)
%artmap.rho = .98;
%artmap.rho_dep = .98;
artmap.alpha=10/(artmap.C+10);
end
if (artmap.C==0)
artmap.C=1;
artmap.w(:,1) = A;
artmap.W = zeros(1,artmap.numClasses);
artmap.W(1,K)=1;
if LEARN_TRAIL==ON
artmap.learn_trail(sampleNumber).winning_node=artmap.C;
artmap.learn_trail(sampleNumber).weight_before = A';
artmap.learn_trail(sampleNumber).weight_after = A';
end
y = zeros(1,artmap.C); % the code at F2. At each node j, activity reset to 0.
activated_node=1; % The first and only node is activated.
else
T = DEFARTMAP_choice_function(artmap,A);
[T_sorted, T_j] = sort(-T);
T_j = T_j(logical(-T_sorted>artmap.alpha.*artmap.M*(3.5)));
MATCH=0;
e_AttentionBefore=e_AttentionDefault;
A_dashBefore=A;
for j=T_j
x_sum=sum(min(A_dashBefore,artmap.w(:,j)));
z_sum=sum(min(A_dashBefore,1));
if sum(artmap.model_num==[24 25])==1
x_sum = sum(max(min(A_dashBefore,artmap.w(:,j))-artmap.lambda_Attention*e_AttentionBefore,0));
z_sum = sum(max(min(A_dashBefore,1)-artmap.lambda_Attention*e_AttentionBefore,0));
elseif sum(artmap.model_num==26)==1
x_sum=max(sum(min(A_dashBefore,artmap.w(:,j)))-(artmap.M-sum(A_dashBefore)),0);
z_sum=max(sum(min(A_dashBefore,1))-(artmap.M-sum(A_dashBefore)),0);
elseif sum(artmap.model_num==27)==1
x_sum = sum(max(min(A_dashBefore,artmap.w(:,j))-((2*artmap.M-sum(artmap.w(:,j)))/(2*artmap.M))*artmap.lambda_Attention*e_AttentionBefore,0));
z_sum = sum(max(min(A_dashBefore,1)-((2*artmap.M-2*artmap.M)/(2*artmap.M))*artmap.lambda_Attention*e_AttentionBefore,0));
elseif sum(artmap.model_num==28)==1
x_sum=sum(max(min(A_dashBefore,artmap.w(:,j))-(A-A_dashBefore),0));
z_sum=sum(max(min(A_dashBefore,1)-(A-A_dashBefore),0));
elseif sum(artmap.model_num==29)==1
x_sum=sum(min(max(artmap.w(:,j)-artmap.lambda_Attention*e_AttentionBefore,0),...
max(A_dashBefore-artmap.lambda_Attention*e_AttentionBefore,0)));
z_sum=sum(min(max(1-artmap.lambda_Attention*e_AttentionBefore,0),...
max(A_dashBefore-artmap.lambda_Attention*e_AttentionBefore,0)));
elseif sum(artmap.model_num==[30 31 32])==1
x_sum=sum(min(max(artmap.w(:,j)-artmap.lambda_Attention*e_AttentionBefore,0),...
max(A-artmap.lambda_Attention*e_AttentionBefore,0)));
z_sum=sum(min(max(1-artmap.lambda_Attention*e_AttentionBefore,0),...
max(A-artmap.lambda_Attention*e_AttentionBefore,0)));
end
if (artmap.use_type_three_tracking==1)
if (sum(artmap.model_num==28)==1)
MATCH_TRACK_TEST=(((x_sum+artmap.gamma)/(sum(max(2*A_dashBefore-A,0))+eps+artmap.gamma)) >= artmap.rho);
elseif (sum(artmap.model_num==29)==1)
MATCH_TRACK_TEST=(((x_sum+artmap.gamma)/(sum(max(A_dashBefore-artmap.lambda_Attention*e_AttentionBefore,0))+eps+artmap.gamma)) >= artmap.rho);
elseif (sum(artmap.model_num==[30 31 32])==1)
MATCH_TRACK_TEST=(((x_sum+artmap.gamma)/(sum(max(A-artmap.lambda_Attention*e_AttentionBefore,0))+eps+artmap.gamma)) >= artmap.rho);
else
MATCH_TRACK_TEST=(((x_sum+artmap.gamma)/(sum(A_dashBefore)+eps+artmap.gamma)) >= artmap.rho);
end
%MATCH_FACTOR=(sum(A_dashBefore)-sum(min(A_dashBefore,artmap.w(:,j))))/((sum(A)-sum(min(A,artmap.w(:,j))))+eps);
%MATCH_TRACK_TEST=((sum(min(A,artmap.w(:,j)))*MATCH_FACTOR)/artmap.M >= artmap.rho);
elseif (artmap.use_type_three_tracking==0)
MATCH_TRACK_TEST=(x_sum/artmap.M >= artmap.rho);
elseif (artmap.use_type_three_tracking==2)
MATCH_TRACK_1= (((x_sum+artmap.gamma)/(sum(A_dashBefore)+eps+artmap.gamma)) >= artmap.rho_dep);
MATCH_TRACK_2= (x_sum/artmap.M >= artmap.rho);
artmap.compare_rho(comp_rho,:)=[MATCH_TRACK_1 MATCH_TRACK_2 sampleNumber];
comp_rho=comp_rho+1;
MATCH_TRACK_TEST = ((((x_sum+artmap.gamma)/(sum(A_dashBefore)+eps+artmap.gamma)) >= artmap.rho_dep) ...
&& (x_sum/artmap.M >= artmap.rho));
elseif (artmap.use_type_three_tracking==3)
MATCH_TRACK_TEST=((x_sum+artmap.gamma)/...
((1-sum(artmap.w(:,j))/(2*artmap.M))*artmap.M+sum(artmap.w(:,j))*sum(A_dashBefore)/(2*artmap.M)+artmap.gamma))>=artmap.rho;
end
if LEARN_TRAIL==ON
MT_ALL_nodes=MT_ALL_nodes+1;
artmap.learn_trail(sampleNumber).rho_calc{MT_ALL_nodes}=[(x_sum+artmap.gamma)./(sum(A_dashBefore)+eps+artmap.gamma) ...
x_sum./artmap.M ];
artmap.learn_trail(sampleNumber).rho{MT_ALL_nodes} = [artmap.rho_dep artmap.rho];
end
if MATCH_TRACK_TEST
search_cycles = search_cycles+1;
J=find(artmap.W(j,:));
if J==K
MATCH=1;
artmap.win_sequence(length(artmap.win_sequence)+1)=j;
if LEARN_TRAIL==ON
artmap.learn_trail(sampleNumber).winning_node=j;
artmap.learn_trail(sampleNumber).weight_before = artmap.w(:,j);
end
storeWeights=artmap.w;
artmap.w=DEFARTMAP_Update_Weights(artmap,j,A);
artmap.e_mod_dist(sampleNumber)=artmap.lambda_Attention*sum(e_AttentionBefore)/2;
artmap.A_dep_dist(sampleNumber)=artmap.lambda_Attention*sum(A_dashBefore);
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% %%THIS LOOP CHECKS IF THE NEXT_INPUT TEST IS MET
% y = zeros(artmap.C,1);
% T = DEFARTMAP_choice_function(artmap,A);
% Lambda = find(T>artmap.alpha.*artmap.M);
% Lambda_prime = find(T==artmap.M);
%
% if ~isempty(Lambda_prime) % Point Box Case -- w_j = A
% y(Lambda_prime)=(1./length(Lambda_prime));
% else
% sumL = sum(1./(artmap.M-T(Lambda)));
% y(Lambda)=(1./(artmap.M-T(Lambda)))./sumL;
% end
%
% % %%%%%%%%%%%%%%%%DEFAULT TEST%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% if artmap.DEFAULT_RETEST
% class_val = find(artmap.W(y==max(y),:),1,'first');
% else
% %%%%%%%%%%%DISTRIBUTED TEST%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% [dum,class_val] = max(y'*artmap.W);
% end
%
% if K~=class_val
% artmap.NI_fail_addNode(end+1)=sampleNumber;
% %
% % %%%%%%%%%%%%UNDO RECENT LEARNING AND ADD NODE%%%%%%%%%%%
% % artmap.w=storeWeights;
%
%
% artmap.C=artmap.C+1;
% artmap.w(:,artmap.C)=A;
%
% if LEARN_TRAIL==ON
% artmap.learn_trail(sampleNumber).winning_node=artmap.C;
% artmap.learn_trail(sampleNumber).weight_before = A';
% artmap.learn_trail(sampleNumber).weight_after = A';
% end
%
%
% artmap.W=[artmap.W; zeros(1,artmap.numClasses)];
% artmap.W(artmap.C,K)=1;
%
% % Activated node is new node
%
% end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
if LEARN_TRAIL==ON
artmap.learn_trail(sampleNumber).weight_after = artmap.w(:,j);
end
activated_node=j;
break; % Don't check any more T_j
else % if J~=K
if (artmap.use_type_three_tracking==1)
if sum(artmap.model_num==28)==1
%%%If Model Number 28
artmap.rho = ((x_sum+artmap.gamma)./(sum(max(2*A_dashBefore-A,0))+eps+artmap.gamma)) + artmap.epsilon;
artmap.rho_uc = ((z_sum+artmap.gamma)./(sum(max(2*A_dashBefore-A,0))+eps+artmap.gamma));
elseif sum(artmap.model_num==29)==1
artmap.rho = ((x_sum+artmap.gamma)./(sum(max(A_dashBefore-artmap.lambda_Attention*e_AttentionBefore,0))+eps+artmap.gamma)) + artmap.epsilon;
artmap.rho_uc = ((z_sum+artmap.gamma)./(sum(max(A_dashBefore-artmap.lambda_Attention*e_AttentionBefore,0))+eps+artmap.gamma));
elseif sum(artmap.model_num==[30 31 32])==1
artmap.rho = ((x_sum+artmap.gamma)./(sum(max(A-artmap.lambda_Attention*e_AttentionBefore,0))+eps+artmap.gamma)) + artmap.epsilon;
artmap.rho_uc = ((z_sum+artmap.gamma)./(sum(max(A-artmap.lambda_Attention*e_AttentionBefore,0))+eps+artmap.gamma));
else
%%%If Model Number is not 28, 29, or 30
artmap.rho = ((x_sum+artmap.gamma)./(sum(A_dashBefore)+eps+artmap.gamma)) + artmap.epsilon;
artmap.rho_uc = ((z_sum+artmap.gamma)./(sum(A_dashBefore)+eps+artmap.gamma));
end
%MATCH_FACTOR=(sum(A_dashBefore)-sum(min(A_dashBefore,artmap.w(:,j))))/((sum(A)-sum(min(A,artmap.w(:,j))))+eps);
%artmap.rho=(sum(min(A,artmap.w(:,j)))*MATCH_FACTOR)/artmap.M;
elseif (artmap.use_type_three_tracking==0)
artmap.rho = x_sum./artmap.M + artmap.epsilon;
artmap.rho_uc = z_sum./artmap.M ;
elseif (artmap.use_type_three_tracking==2)
artmap.rho = x_sum./artmap.M + artmap.epsilon;
artmap.rho_uc = z_sum./artmap.M;
artmap.rho_dep = ((x_sum+artmap.gamma)./(sum(A_dashBefore)+eps+artmap.gamma)) + artmap.epsilon;
artmap.rho_dep_uc = ((z_sum+artmap.gamma)./(sum(A_dashBefore)+eps+artmap.gamma));
artmap.rho=min(artmap.rho_dep,artmap.rho);
artmap.rho_uc=min(artmap.rho_dep_uc,artmap.rho_uc);
artmap.rho_dep=artmap.rho;
elseif (artmap.use_type_three_tracking==3)
%THIS SEEMS TO BE WRONG%
artmap.rho = ((x_sum+artmap.gamma)./...
((1-sum(artmap.w(:,j))/(2*artmap.M))*artmap.M+sum(artmap.w(:,j))*sum(A_dashBefore)/(2*artmap.M)+artmap.gamma));
end
[e_AttentionAfter,A_dashAfter,x_Attention] =...
ART_depleter_new(sampleNumber,artmap.w(:,j),A,e_AttentionBefore,artmap.lambda_Attention,artmap.M,artmap.model_num,artmap.deplete_fast);
if sum(e_AttentionAfter)<.0001
artmap.e_realLow=artmap.e_realLow+1;
end
if sum(e_AttentionAfter)<2*eps
artmap.e_zero=artmap.e_zero+1;
end
if LEARN_TRAIL==ON
MT_nodes=MT_nodes+1;
artmap.learn_trail(sampleNumber).MT_nodes{MT_nodes}=j;
artmap.learn_trail(sampleNumber).MT_rho{MT_nodes} =artmap.rho;
artmap.learn_trail(sampleNumber).MT_x_before{MT_nodes} =x_Attention';
artmap.learn_trail(sampleNumber).MT_e_before{MT_nodes} =e_AttentionBefore';
artmap.learn_trail(sampleNumber).MT_e_after{MT_nodes} =e_AttentionAfter';
artmap.learn_trail(sampleNumber).MT_Adash_before{MT_nodes} =A_dashBefore';
artmap.learn_trail(sampleNumber).MT_Adash_after{MT_nodes} =A_dashAfter';
end
A_dashBefore=A_dashAfter;
e_AttentionBefore=e_AttentionAfter;
end % if J==K b
else % if (sum(min(A,artmap.w(:,j)))./artmap.M >= artmap.rho) %RESET, NO MATCH TRACKING
if (artmap.deplete_in_mismatch==1)
[e_AttentionAfter,A_dashAfter,x_Attention] =...
ART_depleter_new(sampleNumber,artmap.w(:,j),A,e_AttentionBefore,artmap.lambda_Attention,artmap.M,artmap.model_num,artmap.deplete_fast);
if sum(e_AttentionAfter)<.0001
artmap.e_realLow=artmap.e_realLow+1;
end
if sum(e_AttentionAfter)<2*eps
artmap.e_zero=artmap.e_zero+1;
end
if LEARN_TRAIL==ON
MT_nodes=MT_nodes+1;
artmap.learn_trail(sampleNumber).MT_mismatch{MT_nodes}=1;
artmap.learn_trail(sampleNumber).MT_nodes{MT_nodes}=j;
artmap.learn_trail(sampleNumber).MT_rho{MT_nodes} =artmap.rho;
artmap.learn_trail(sampleNumber).MT_x_before{MT_nodes} =x_Attention';
artmap.learn_trail(sampleNumber).MT_e_before{MT_nodes} =e_AttentionBefore';
artmap.learn_trail(sampleNumber).MT_e_after{MT_nodes} =e_AttentionAfter';
artmap.learn_trail(sampleNumber).MT_Adash_before{MT_nodes} =A_dashBefore';
artmap.learn_trail(sampleNumber).MT_Adash_after{MT_nodes} =A_dashAfter';
end
A_dashBefore=A_dashAfter;
e_AttentionBefore=e_AttentionAfter;
end
end % if (sum(min(A,artmap.w(:,j)))./artmap.M >= artmap.rho)
end % for J=T_j
if (MATCH==0) %
%sampleNumber
artmap.C=artmap.C+1;
artmap.w(:,artmap.C)=A;
artmap.node_created=[artmap.node_created sampleNumber];
artmap.e_mod_dist(sampleNumber)=artmap.lambda_Attention*sum(e_AttentionBefore)/2;
artmap.A_dep_dist(sampleNumber)=artmap.lambda_Attention*sum(A_dashBefore);
if (artmap.use_type_three_tracking==1)
if sum(artmap.model_num==28)==1
z_sum=sum(max(min(A_dashBefore,1)-(A-A_dashBefore),0));
rho_uc = ((z_sum+artmap.gamma)./(sum(max(2*A_dashBefore-A,0))+eps+artmap.gamma))+0*artmap.epsilon;
elseif sum(artmap.model_num==29)==1
z_sum=sum(min(max(1-artmap.lambda_Attention*e_AttentionBefore,0),...
max(A_dashBefore-artmap.lambda_Attention*e_AttentionBefore,0)));
rho_uc = ((z_sum+artmap.gamma)./(sum(max(A_dashBefore-artmap.lambda_Attention*e_AttentionBefore,0))+eps+artmap.gamma));
%rho_uc = ((z_sum+artmap.gamma)./(sum(max(2*A_dashBefore-A,0))+eps+artmap.gamma))+0*artmap.epsilon;
elseif sum(artmap.model_num==[30 31 32])==1
z_sum=sum(min(max(1-artmap.lambda_Attention*e_AttentionBefore,0),...
max(A-artmap.lambda_Attention*e_AttentionBefore,0)));
rho_uc = ((z_sum+artmap.gamma)./(sum(max(A-artmap.lambda_Attention*e_AttentionBefore,0))+eps+artmap.gamma));
%rho_uc =
%((z_sum+artmap.gamma)./(sum(max(2*A_dashBefore-A,0))+eps+artmap.gamma))+0*artmap.epsilon;
elseif sum(artmap.model_num==[24 25])==1
z_sum = sum(max(min(A_dashBefore,1)-artmap.lambda_Attention*e_AttentionBefore,0));
rho_uc = ((z_sum+artmap.gamma)./(sum(A_dashBefore)+eps+artmap.gamma));
elseif sum(artmap.model_num==26)==1
z_sum=max(sum(min(A_dashBefore,1))-(artmap.M-sum(A_dashBefore)),0);
rho_uc = ((z_sum+artmap.gamma)./(sum(A_dashBefore)+eps+artmap.gamma));
elseif sum(artmap.model_num==27)==1
z_sum = sum(max(min(A_dashBefore,1)-((2*artmap.M-2*artmap.M)/(2*artmap.M))*artmap.lambda_Attention*e_AttentionBefore,0));
rho_uc = ((z_sum+artmap.gamma)./(sum(A_dashBefore)+eps+artmap.gamma));
end
end
if artmap.rho>(rho_uc+.00001)
artmap.uncommitted_fail=artmap.uncommitted_fail+1;
disp('It is happening!');
end
if LEARN_TRAIL==ON
artmap.learn_trail(sampleNumber).winning_node=artmap.C;
artmap.learn_trail(sampleNumber).weight_before = A';
artmap.learn_trail(sampleNumber).weight_after = A';
end
artmap.W=[artmap.W; zeros(1,artmap.numClasses)];
artmap.W(artmap.C,K)=1;
activated_node=artmap.C; % Activated node is new node
end % (MATCH==0)
end % if (artmap.C==0)
artmap.NodeList = [artmap.NodeList, activated_node];
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%THIS LOOP CHECKS IF THE NEXT_INPUT TEST IS MET
y = zeros(artmap.C,1);
T = DEFARTMAP_choice_function(artmap,A);
Lambda = find(T>artmap.alpha.*artmap.M);
Lambda_prime = find(T==artmap.M);
if ~isempty(Lambda_prime) % Point Box Case -- w_j = A
y(Lambda_prime)=(1./length(Lambda_prime));
else
sumL = sum(1./(artmap.M-T(Lambda)));
y(Lambda)=(1./(artmap.M-T(Lambda)))./sumL;
end
class_val = find(artmap.W(y==max(y),:),1,'first');
if K~=class_val
artmap.NI_fail(end+1)=sampleNumber;
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% if (sampleNumber<=20)
% drawRectsARTMAP_internal
% end
end % for sampleNumber = 1:numSamples
artmap.search_cycles = search_cycles;
updated_artmap = artmap;
return
