novelty-detection-ncm/Agnotron-model/Clustering Network/pcaNetworkLoops.m

close all;
clear all;

avg_taus = [];
taus_3 = [];
mm_etas = [0:0.0125:2];

%% one-time setup
seed = 12345; %10 was working for many of these demos %56; %23, 10 for 100D 10 clusters
    % seed choice affects initial weight direction, which affects peaks of
    % error plots
    
trackVars = 1; %turning on slows things WAY DOWN


% Select Dataset
%load('genData1.mat')
% load('genData2D_4c_200ppc.mat')
load('genData2D_3c_500ppc.mat')
% load('genData100D_10.mat')
% load('genData_realSpec_try2.mat')
% load('genData_realSpec_5c.mat')


% Define how data will be presented
twoClust_dm_unshuff = allPts_dm_unshuff(1:100, :);
twoClust_unshuff = allPts_unshuff(1:100, :);

shuffIdx = 1:100;%randperm(500);

part1_dm = twoClust_dm_unshuff(shuffIdx,:);
part1 = twoClust_unshuff(shuffIdx, :);

% bigY is input fed into clustering network - note that it's de-meaned
bigY = [allPts_dm_unshuff']; %[part1_dm' allPts_dm'];%_unshuff'];% allPts_dm' allPts_dm'];%(1:500,:)'];% allPts_dm'];

% mmY is input fed into mismatch network - just a non-de-meaned version of
% bigY
mmY = [allPts_unshuff']; %[part1' allPts'];%_unshuff'];% allPts' allPts'];%(1:500,:)'];%  allPts'];

% PCA CONFIGS
pcaNet.changeThresh = 1e-4; % for output convergence
initWeightMag = 0.1; %0.8 for some demos % should be 0.1
pcaNet.capW = 10;%500; %max weight to each output cell -- CHANGING THIS DIDN'T MAKE A DIFFERENCE
pcaNet.inhibCap = 10; %NEED 10 FOR SYNTHETIC, 25 FOR REAL... -- CHANGING THIS DOES MATTER

pcaNet.etaW = 0.05; %originally each was 0.5 -- MAKING THIS TOO HIGH DOESN'T MATTER MUCH
pcaNet.etaM = 0.05; % originally 0.5 -- CHANGING THIS DOESN'T MATTER MUCH
pcaNet.maxM = 1; 
pcaNet.maxW = 1;

% MISMATCH CONFIGS
% CHANGING THIS DRASTICALLY AFFECTS SHAPE OF ERROR PLOTS
mmNet.thresh = 0.05; % CHANGING THIS AFFECTS SHAPE OF ERROR PLOTS

% MISTMATCH STRUCTURE CONFIGS
mmNet.signed_synapses = 1; %force positive weight coeffs 
mmNet.c_plastic = 1; % 
mmNet.y_plastic = 0;

% MISMATCH ARCHITECTURE CONFIGS
yE_type = 'rand'; %'rand' or 'randnorm'
cE_type = 'rand';  
yI_type = 'rand';
cI_type = 'rand';
yR_type = 'direct'; %'rand' or 'direct' 
cR_type = 'direct'; %'rand' or 'direct'

% MISMATCH NETWORK CONFIGS
nCells_y = size(mmY,1); %one input per input dim
nCells_c = 10; % (500 works on synthetic) 10 clusters in 100D case (nb 100d is a lot)
nCells_Ny = 5; %not super sure how high or low this needs to be
nCells_Nc = 5; %but 100 and 100 should be enough to give us convex cone
iterations = size(bigY,2);


% Total Configs -- this doesn't matter, we're always learning
% (learningThresh = 0)
sigmaThresh = 2.5; %1 works for seed 10 %2.5;%2.5;%%0.25;%25; %1 worked well 
deltaLearn = 0.2; %(0.1) % no buffer -> incorrect clustering (not obvious why needed for shuffled data)
learningThresh = 0;%1; %make it deltaLearn to basically eliminate deltaLearn %0.5; %always learning -> incorrect clustering
pcaNet.sigmaThresh = sigmaThresh;


pcaNet.learning = 1; %this is set to 0 if mismatch is low, 1 if high
learningSig = 1;
rng(seed) 

%% looping sections
for mIdx = 1:length(mm_etas)
    mmNet.eta = mm_etas(mIdx);
    disp(['loop ' num2str(mIdx) ' of ' num2str(length(mm_etas))])
    
    
    W_init = initWeightMag*randn(nCells_c, nCells_y);
    M_init = 0*initWeightMag*rand(nCells_c); %initially 0
    
    for idx = 1:nCells_c
        M_init(idx, idx) = 0; %nrns don't drive selves
    end
    
    D_init = zeros(nCells_c,1);
    pcaNet.bigC = zeros(nCells_c, iterations);
    
    pcaNet.W = W_init;
    pcaNet.M = M_init;
    pcaNet.D = D_init;
    
    c = updateC_v5(...
        pcaNet.W, pcaNet.M, bigY(:,1), pcaNet.changeThresh);
    
    [maxx, thisCluster] = max(c);
    
    if maxx > 0
        pcaNet.clusters = thisCluster;
        pcaNet.bigC(thisCluster, 1) = c(thisCluster);
    else
        pcaNet.clusters = 0;
    end
    
    cT = zeros(size(c));
    cT(thisCluster) = c(thisCluster); % c at timestep t
    y = bigY(:,1);
    
    rng(seed);
    
    % Mismatch Setup
    switch yE_type
        case 'rand'
            mmNet.we_yn = (rand(nCells_Ny, nCells_y))./nCells_y;
        case 'randnorm'
            mmNet.we_yn = (randn(nCells_Ny, nCells_y))./nCells_y;
    end
    
    switch yI_type
        case 'rand'
            mmNet.wi_yn = (rand(nCells_Nc, nCells_y))./nCells_y;
        case 'randnorm'
            mmNet.wi_yn = (randn(nCells_Nc, nCells_y))./nCells_y;
    end
    
    switch yR_type
        case 'rand'
            mmNet.r_yn = rand(nCells_y);
        case 'direct'
            mmNet.r_yn = eye(nCells_y);
    end
    
    switch cE_type
        case 'rand'
            mmNet.we_cn = (rand(nCells_Nc, nCells_c));%./nCells_c;
            %
        case 'randnorm'
            mmNet.we_cn = (randn(nCells_Nc, nCells_c));%./nCells_c;
    end
    
    switch cI_type
        case 'rand'
            mmNet.wi_cn = (rand(nCells_Ny, nCells_c));%./nCells_c;
            %
        case 'randnorm'
            mmNet.wi_cn = (randn(nCells_Ny, nCells_c));%./nCells_c;
    end
    
    switch cR_type
        case 'rand'
            mmNet.r_cn = rand(nCells_c);
        case 'direct'
            mmNet.r_cn = eye(nCells_c);
    end
    
    timesteps = length(bigY(1,:));
    
    if trackVars == 1
        mmNet.Vs_y = zeros(nCells_Ny, timesteps);
        mmNet.Vs_c = zeros(nCells_Nc, timesteps);
        mmNet.Fs_y = zeros(nCells_Ny, timesteps);
        mmNet.Fs_c = zeros(nCells_Nc, timesteps);
        
        
        mmNet.yWs_e = zeros(nCells_Ny, nCells_y, timesteps+1);
        mmNet.cWs_e = zeros(nCells_Nc, nCells_c, timesteps+1);
        mmNet.yWs_i = zeros(nCells_Nc, nCells_y, timesteps+1);
        mmNet.cWs_i = zeros(nCells_Ny, nCells_c, timesteps+1);
        
        mmNet.wyChanges_e = zeros(nCells_Ny, nCells_y, timesteps);
        mmNet.wcChanges_e = zeros(nCells_Nc, nCells_c, timesteps);
        mmNet.wyChanges_i = zeros(nCells_Nc, nCells_y, timesteps);
        mmNet.wcChanges_i = zeros(nCells_Ny, nCells_c, timesteps);
        
        mmNet.yWs_e(:,:,1) = mmNet.we_yn;
        mmNet.cWs_e(:,:,1) = mmNet.we_cn;
        mmNet.yWs_i(:,:,1) = mmNet.wi_yn;
        mmNet.cWs_i(:,:,1) = mmNet.wi_cn;
    end
    
    mmNet.errors_y = zeros(timesteps, 1);
    mmNet.errors_c = zeros(timesteps, 1);
    mmNet.allErrors = zeros(timesteps,1);
    
    % we've already done 1 iter of pca, so here do 1 iter of mm
    mmNet = mismatchIter_v2(cT, mmY, 1, mmNet, trackVars);
    
    
    disp('setup complete')
    
    % Run through algorithm (make functions for PCAIter and MNIter)
    for ts_idx = 2:iterations
        pcaNet = pcaIter_v6(bigY, ts_idx, pcaNet);
        
        
        pcaC = pcaNet.bigC(:, ts_idx);
        
        pcaC = pcaC>eps;
        
        mmNet = mismatchIter_v2(pcaC, mmY, ts_idx, mmNet, trackVars);
        
        sigmaNode = mmNet.allErrors(ts_idx);
        if sigmaNode > sigmaThresh
            learningSig = learningSig + deltaLearn;
            if learningSig > 1
                learningSig = 1;
            end
        else
            learningSig = learningSig - deltaLearn;
            if learningSig < 0
                learningSig = 0;
            end
        end
        
        pcaNet.learning = 1;
        
        

    end
    
    % what are taus?
    down1 = mmNet.allErrors(1:2);
    down2 = mmNet.allErrors(501:502);
    down3 = mmNet.allErrors(1001:1002);
    
    decay1 = (down1(1) - down1(end));
    decay2 = (down2(1) - down2(end));
    decay3 = (down3(1) - down3(end));
    
    mean_decay = (decay1+decay2+decay3)/3;
    taus_3 = [taus_3; decay3/down3(1)];
    avg_taus = [avg_taus; mean_decay];
    
%     colors = {'r.', 'y.', 'c.', 'g.', 'm.', 'k.', 'b.'};
%     for cIdx = 1:7
%         thisClust = find(pcaNet.clusters == cIdx);
%         figure(5);
%         plot(bigY(1, thisClust), bigY(2, thisClust), colors{cIdx})
%         %         if ts_idx == 2
%         hold on;
%         %         end
%     end
  figure(888); plot(mmNet.allErrors, 'k-'); xlim([995, 1025]); hold on;
  plot(1001:1002,mmNet.allErrors(1001:1002),'b*'); hold off;
  
  figure(999); plot(mm_etas(1:mIdx), taus_3(1:mIdx)); makepretty; xlim([0,max(mm_etas)]); pause(eps);
  %     close(5);
end