demo_balanced_cluster_GPS.m

%%
clc, clear all, close all
%%
% Load Data
load 'data\conv_feature.mat'
load 'data\activity_feature.mat'
load 'data\dark_feature.mat'
load 'data\phonecharge_feature.mat'
load 'data\phonelock_feature.mat'
load 'data\audio_feature.mat'
load 'data\pre_PHQ9.mat'
load 'data\post_PHQ9.mat'
load 'data\gps_Processesed_feature.mat'
% Path setting
prefix='\Depression_2\';
addpath([prefix '\utility_code_1\clustering\ssvd-code']);
addpath([prefix '\utility_code_1\clustering\spectralCoClustering']);
addpath([prefix '\utility_code_1\clustering']);
addpath([prefix '\utility_code_1\short_functions']);
addpath([prefix '\utility_code_1\InfoTheory']);
addpath([prefix '\utility_code_1\kernel']);
addpath([prefix '\utility_code_1\evaluate']);
addpath([prefix '\utility_code_1\code']);
addpath([prefix '\code\exsiting_algorithm']);
addpath([prefix '\code']);
prefix='\Multi-view-Clustering\';
addpath([prefix '\proximal_2']);
%% read the raw data for activity, conversation, dark, audio, phonelock records and give the average on day values as view1. Each row of view1 is an instance from the population.
% average one user/day - % for all 49 user get 1 value
view1(:, 1) = average_c(activity_feature, 3); % Activity  = No Movement
view1(:, 2) = average_c(conv_feature, 3); % Conversation duration
view1(:, 3) = average_c(dark_feature, 3); % Dark duration
view1(:, 4) = average_c(dark_feature, 4); % Dark count 
view1(:, 5) = average_c(conv_feature, 2); % Conversation count
view1(:, 6) = average_c(activity_feature, 4); % Activity  = Walk
view1(:, 7) = average_c(activity_feature, 5); % Activity  = Run
view1(:, 8) = average_c(audio_feature, 3); % Audio = Quite
view1(:, 9) = average_c(audio_feature, 4); % Audio = Noisy
view1(:, 10) = average_c(audio_feature, 5); % Audio = Loud
view1(:, 11) = average_c(phonelock_feature, 2); % Phone lock = Count
view1(:, 12) = average_c(phonelock_feature, 3); % Phone lock = Duration
%% read the sine parameters which fit the time series data of activity, audio, conversation, phonelock as view2. 
% amp, phase, intercept and freq ///for all 49 user get 4 value
view2(:, 1:4) = denoising_wl_sin(activity_feature, 4, [800 14 0 6100]); % Walking -> amp, freq, phase, intercept
view2(:, 5:8) = denoising_wl_sin(audio_feature, 4, [400 14 0 1000]); % Noisy -> amp, phase, intercept and frequency
view2(:, 9:12) = denoising_wl_sin(conv_feature, 3, [200 14 0 7000]); % Conve

% view2(:, 13:16) = denoising_wl_sin(phonelock_feature, 3);
% view2 = [sin_act_5, sin_audio_5, sin_conv_5, sin_lock_5];
%% View 3 have the GPS features that include: 
% Location variance, Time spent in each cluster (K = 3), Entropy, 
% Normalized entropy, percentage of time @ home, Transition time, and 
% distance traveled in km.
view3 = gpsFeatures(:, 1:end-2);% End = User ids
%% Reassigning to different variables - no need though
view1_data = view1;
view2_data = view2;
view3_data = view3;
% normalize the data.
n = size(view1_data, 1); % total rows - 49 users
% d1 = size(view1_data, 2);
% d2 = size(view2_data, 2);
% d3 = size(view3_data, 2);
M_1_norm = normc(view1_data);
M_2_norm = normc(view2_data);
M_3_norm = normc(view3_data);
% Reassign the normalized data
M_1 = M_1_norm;
M_2 = M_2_norm;
M_3 = M_3_norm;

%a small experiment
% [m,n] = size(M_1_norm);
% M_1(:,1) = M_1_norm(:,n-3);
% M_1(:,n-3) = M_1_norm(:,1);

% Getting all values as in a single cell
M = cell(1, 1);
M{1} = M_1;
M{2} = M_2;
M{3} = M_3;
%% using cv to find the proper parameters lambda_z, lambda_2 then run 
% Multiview Biclustering method to get the 1st cluster. lambda_z is for the
% size of the cluster we want. lambda_2 is for the number of features to 
% differentiate the cluster from the rest of the population. 
lambda_z = 9;%9;
lambda_2 = 5;%5;
iSeedV1 = 12;%12;

lambda_z2 = 7;
iSeedV2 = 1;
%% z1 is a binary vector as a idnetifier of our explored cluster. 1 means
% the ith instance on the ith row belong to the identified cluster. 0 means
% this instance does not belong to this cluster. V is a matrix shows the 
% significantly useful features that be detected to diffentiate the 
% identified cluster Clus1 from the rest by our method. 


% Depressed people identification, also find what features are important
% for our clustering method
% cluster people into group, and see how they are related to depression
[z1, U, V, obj] = proxi3_3(M, lambda_z, [lambda_2; lambda_2; lambda_2], iSeedV1);
rowClus1 = double(z1~=0);
Clus1 = rowClus1; % After finding one cluster, erase that from data and then move forward for next clustering

%% erase the instances in Clus1, then prepare for the next clustering process.
IND = find(z1~=0);
M2_1 = M_1;
M2_2 = M_2;
M2_3 = M_3;

M2_1(IND, :)=[];
M2_2(IND, :)=[];
M2_3(IND, :)=[];

M2{1} = M2_1;
M2{2} = M2_2;
M2{3} = M2_3;
%% run multiview biclustering method again to detect the 2nd cluster Clus2.

n_1_pre = [];
n_2_pre = [];
n_3_pre = [];
n_1_post = [];
n_2_post = [];
n_3_post = [];
[z2, U2, V2, obj] = proxi3_3(M2, lambda_z2, [lambda_2; lambda_2; lambda_2], iSeedV2);
rowClus2 = double(z2~=0);
Clus2 = zeros(n, 1);
Clus2(IND) = 0;
IND2 = 1:1:49;
IND2(IND) = [];
TN = find(z2==0);
IND2(TN) = [];
Clus2(IND2) = 1;
Clus3 = ones(n, 1) - Clus1 - Clus2;

% view 1 is ok
% view 2, wavelet filtering, f transformation -> raw data...can improve ft
% method. using ft to find most important point. Also we can add useful
% descrption - more features
% ------------------------------------------------------------------------
%% Asma starts here - After adding view 3 i.e. GPS
% ------------------------------------------------------------------------
%% Get data from View 1, 2 and 3 for Random forest
% First get all the values for view 1 based on V value
% For all users, get the average of each feature i.e. 49 rows/users, we
% will have 12 columns containing average of that perticular feature
for k = 1:49 %kth user
    view1Data(k,1:12) = view1(k,:); % Jin's feature
    % For testing purpose - delete later - successful test, id maches
%     view1Data(k, 13) = double(activity_feature{k}(1, 1)); % Activity user id
%     view1Data(k, 14) = double(conv_feature{k}(1, 1)); % conv user id
%     view1Data(k, 15) = double(dark_feature{k}(1, 1)); % dark feature user id
%     view1Data(k, 16) = double(audio_feature{k}(1, 1)); % audio user id
%     view1Data(k, 17) = double(phonelock_feature{k}(1, 1)); % ph lock user id
%     if (view1Data(k, 13) == view1Data(k, 15)) && (view1Data(k, 13) == view1Data(k, 16)) 
%         igetthevalues(k) = 1;
%     else 
%         igetthevalues(k) = 0;
%     end
end
% View 2 data
view1Data(:, 13:24) = view2(:, :); % amp, phase, intercept and freq of activity, Audio feature, Conversation feaure
% view1Data(:, 22:25) = view2(:, 5:8); % 
% view1Data(:, 26:29) = view2(:, 9:12); % 
% View 3 data - GPS Features
view1Data(:,25:33) = view3;
view1Data(:,34) = gpsFeatures(:, end-1); % the user ids - for matching
% purpose only - successful test, id maches
% Match user ids and assign both PHQ9 id and score, else keep -1
x = 1;
z = size(view1Data,2); % To add more columns
for i = 1:size(view1Data,1)
    if view1Data(i, 34) == pre_PHQ9(x,1)% == view1Data(:,39) %if ids match - pre-phq9
%         view1Data(i, z+1) = pre_PHQ9(x,1); % User id
        view1Data(i, z+1) = pre_PHQ9(x, 2); % User's pre-phq9 score
        x = x + 1;
    else % If user not exists
        view1Data(i, z+1) = -1; 
%         view1Data(i, z+2) = -1;
    end
end
% Same for post phq-9
x = 1;
for i = 1:size(view1Data,1)
    if view1Data(i, 34) == post_PHQ9(x,1)% == view1Data(i,39) %if ids match - post-phq9
%         view1Data(i, z+3) = post_PHQ9(x,1); % User id
        view1Data(i, z+2) = post_PHQ9(x, 2); % User's pre-phq9 score
        x = x + 1;
    else % If user not exists
        view1Data(i, z+2) = -1;
%         view1Data(i, z+4) = -1;
    end
end
plotData = view1Data;
% Feature selection through multiview - might have to change
fSetV1 = V{1}(:,1);
fSetV2 = V{2}(:,1);
fSetV3 = V{3}(:,1);

fSetV1_2 = V2{1}(:,1);
fSetV2_2 = V2{2}(:,1);
fSetV3_2 = V2{3}(:,1);

j = 1;
v1_p = 0;
for i = 1:size(fSetV1,1)
   % if (fSetV1(i,1) ~= 0 | fSetV1_2(i,1) ~= 0 ) % If feature is selected by the clustering algorithm
        fSetV1Data(:,j) = view1Data(:,i); % Get the whole column of that feature
        j = j + 1;
        v1_p = v1_p + 1;
    %end
end
%View 2
v2_p = 0;
for i = 1:size(fSetV2,1)
    if (fSetV2(i,1) ~= 0 | fSetV2_2(i,1) ~= 0) % If feature is selected by the clustering algorithm
        fSetV1Data(:,j) = view1Data(:,12+i); % Get the whole column of that feature
        j = j + 1;
        v2_p = v2_p + 1;
    end
end
%View 3
v3_p = 0;
for i = 1:size(fSetV3,1) 
    if (fSetV3(i,1) >= 0 | fSetV3_2(i,1) ~= 0 ) % See if we need to handle the negitive values
        fSetV1Data(:,j) = view1Data(:,24+i); % Get the whole column of that feature
        j = j + 1;
        v3_p = v3_p + 1;
    end
end
b1 = size(fSetV1Data,2);
testAsma = fSetV1Data;
fSetV1Data(:,j) = view1Data(:,z+1); % pre Phq-9 score
fSetV1Data(:,j+1) = view1Data(:,z+2); % post Phq-9 score
for i = 1:size(fSetV1Data,1)
    fSetV1Data(i,j+2) = mean(fSetV1Data(i,j:j+1)); %PHQ-9 scores
end
% for i = 1:size(fSetV1Data,1)
%     if fSetV1Data(i,j+2) >= 10 % Cut off at 10 for the mean PHQ-9
%         fSetV1Data(i,j+3) = 1;
%     else
%         fSetV1Data(i,j+3) = 0;
%     end
% end
AllClusData = zeros(size(Clus1,1),1);
for i = 1:size(AllClusData,1)
    if (Clus1(i,1) == 1)
        AllClusData(i,1) = 1; % Cluster 1 (-1)
    elseif (Clus2(i,1) == 1)
        AllClusData(i,1) = 2; % Cluster 2 (1)
    elseif (Clus3(i,1) == 1)
        AllClusData(i,1) = 3; % Cluster 3 (0)
    end
end
fSetV1Data(:,end+1) = AllClusData; 
plotData(:,end+1) = AllClusData;
% fSetV1Data(:,end+1) = Clus1(:,1);
% fSetV1Data(:,end+1) = Clus2(:,1);
% fSetV1Data(:,end+1) = Clus3(:,1);
%%
% Creating a balanced data set
Clus1Assign = find(fSetV1Data(:,end) == 1);
x_d = fSetV1Data(Clus1Assign,:);
y_d = fSetV1Data(Clus1Assign,:);
z_d = fSetV1Data(Clus1Assign,:);
%%
Clus2Assign = find(fSetV1Data(:,end) == 2);
x_d1 = fSetV1Data(Clus2Assign,:);
y_d1 = fSetV1Data(Clus2Assign,:);
z_d1= fSetV1Data(Clus2Assign,:);

fSetV1Data = [fSetV1Data; x_d; y_d; z_d; x_d1; y_d1; z_d1; z_d1];
% fSetV1Data(50:end, end-3) = 1; % Self assignment
% Normalize the data set 
d3 = fSetV1Data(:,1:end-4); % Remove other cols - Self assignment
d3 = [normc(d3) fSetV1Data(:,end)]; % Normalize the data, but not labels - for random forest
%%
% Plotting - asma starts here
% Get the cluster wise data
plotData1 = plotData(:,1:33); % features
plotData1(:,end+1) = plotData(:,35);  % pre-phq
plotData1(:,end+1) = (plotData(:,36)+plotData(:,35)/2);% post-phq
plotData1(:,end+1) = plotData(:,end);% cluster label

p = find(plotData1(:, end) == 1);
q = find(plotData1(:, end) == 2);
r = find(plotData1(:, end) == 3);

for i = 1:(size(plotData1,2)-1)
    meanAll(i) = mean(plotData1(:,i)); % mean of all, not cluster wise
    stdAll(i) = std(plotData1(:,i)); % std of all, not cluster wise
    meanc1(i) = mean(plotData1(p,i)); % mean of cluster 1 in view 1
    val1(i) = (meanc1(i) - meanAll(i))/stdAll(i);
    meanc2(i) = mean(plotData1(q,i)); % mean of cluster 2
    val2(i) = (meanc2(i) - meanAll(i))/stdAll(i);
    meanc3(i) = mean(plotData1(r,i)); % mean of cluster 3
    val3(i) = (meanc3(i) - meanAll(i))/stdAll(i);
end
% Lets plot view 1 - 1:12, 3 clusters
% Titles - amp, freq, phase, intercept
view1Title2 = {'PHQ9_{avg}','Activity_s', 'Conv_d', 'Dark_d', 'Dark_c', 'Conv_c', 'Activity_w', 'Activity_r', 'Audio_q', 'Audio_n','Audio_v','PhoneLock_c','PhoneLock_d'}';
view2Title2 = {'PHQ9_{avg}','Walk_a', 'Walk_p','Walk_{ph}','Walk_i','Noise_a', 'Noise_p','Noise_{ph}','Noise_i', 'ConD_a','ConD_p','ConD_{ph}','ConD_i'};
view3Title2 = {'PHQ9_{avg}','Location_{var}','Time_c_1','Time_c_2', 'Time_c_3','Entropy','Entropy_N','Home_d','Move_{percent}', 'Dist'}';
% Get view 1, cluster 1
clus1v1 = val1(1,1:12); % cluster 1
clus2v1 = val2(1,1:12); % cluster 2
clus3v1 = val3(1,1:12); % cluster 3
clusPHQc1 = val1(1,end-1);%end-2:
clusPHQc2 = val2(1,end-1);%end-2:
clusPHQc3 = val3(1,end-1);%end-2:
TT = [clusPHQc1, clus1v1];
TT2 = [clusPHQc2, clus2v1];
TT3 = [clusPHQc3, clus3v1];
figure(1),
hdl1 = bar(1:3, [TT; TT2; TT3]);
set(gca,'XTickLabel',{'Cluster1','Cluster2','Cluster3'}, 'FontSize',20)
xlabel('Average View','FontSize',20),ylabel('Relative Mean Value','FontSize',20)
gridLegend(hdl1,6,view1Title2, 'Fontsize',16);%, 'location','northoutside','Fontsize',12);%,'Box','off'););%,'location','northoutside','Fontsize',10,'Box','off');
%axis([0,4 ,-1,2 ]);
grid on
grid minor
% view 2
clus1v2 = val1(1,13:24); % cluster 1
clus2v2 = val2(1,13:24); % cluster 2
clus3v2 = val3(1,13:24); % cluster 3
clusPHQc1 = val1(1,end-1);%end-2:
clusPHQc2 = val2(1,end-1);%end-2:
clusPHQc3 = val3(1,end-1);%end-2:
TT = [clusPHQc1, clus1v2];
TT2 = [clusPHQc2, clus2v2];
TT3 = [clusPHQc3, clus3v2];
figure(2),
hdl2 = bar(1:3, [TT; TT2; TT3]);
set(gca,'XTickLabel',{'Cluster1','Cluster2','Cluster3'}, 'FontSize',20)
xlabel('Trend View','FontSize',20),ylabel('Relative Mean Value','FontSize',20)
gridLegend(hdl2,6,view2Title2, 'Fontsize',16);%, 'location','northoutside','Fontsize',12);%,'Box','off');
%axis([0,4 ,-1,2 ]);
grid on
grid minor
% view 3
clus1v3 = val1(1,25:33); % cluster 1
clus2v3 = val2(1,25:33); % cluster 2
clus3v3 = val3(1,25:33); % cluster 3
clusPHQc1 = val1(1,end-1);%end-2:
clusPHQc2 = val2(1,end-1);%end-2:
clusPHQc3 = val3(1,end-1);%end-2:
TT = [clusPHQc1, clus1v3];
TT2 = [clusPHQc2, clus2v3];
TT3 = [clusPHQc3, clus3v3];
figure(3),
hdl3 = bar(1:3, [TT; TT2; TT3]);
set(gca,'XTickLabel',{'Cluster1','Cluster2','Cluster3'}, 'FontSize',20)
xlabel('Mobility View','FontSize',20),ylabel('Relative Mean Value','FontSize',20)
gridLegend(hdl3,5,view3Title2, 'Fontsize',16);%, 'location','northoutside','Fontsize',12);%,'Box','off'););%,'location','northoutside','Fontsize',10,'Box','off');
%axis([0,4 ,-1,2 ]);
grid on
grid minor
%% Plot average PHQ-9 score and features for each view
%%
% phqmat = -ones(60,5);
%     for i = 1: 49
%         te = audio_feature{1,i};
%         id(i) = te(1,1);
%     end
%     for i = 1 :46
%         phqmat(pre_PHQ9(i, 1)+1, 1) = pre_PHQ9(i, 2);
%     end
%     for i = 1 :38
%         phqmat(post_PHQ9(i, 1)+1, 2) = post_PHQ9(i, 2);
%     end
%     for i = 1 :49
%         phqmat(id(i)+1, 3) = Clus1(i);
%     end
%     for i = 1 :49
%         phqmat(id(i)+1, 4) = Clus2(i);
%     end
%     for i = 1 :49
%         phqmat(id(i)+1, 5) = Clus3(i);
%     end
%     sum_1_pre = 0;
%     t =0;
%     for i = 1 : 60
%         if(phqmat(i,3) == 1 && phqmat(i, 1) > 0)
%            t = t+1;
%            sum_1_pre = sum_1_pre + phqmat(i, 1);
%            n_1_pre(t) = phqmat(i, 1);
%         end
%     end
%     sum_1_pre = sum_1_pre/t;
% 
%     t=0;
%     sum_1_post = 0;
%     for i = 1 : 60
%         if(phqmat(i,3)==1 && phqmat(i, 2)>0)
%             t = t+1;
%             sum_1_post = sum_1_post + phqmat(i, 2);
%             n_1_post(t) = phqmat(i, 2);
%         end
%     end
%     sum_1_post = sum_1_post/t;
%     
%     t=0;
%     sum_2_pre = 0;
%     for i = 1 : 60
%         if(phqmat(i,4)==1 && phqmat(i, 1)>0)
%         t = t+1;
%         sum_2_pre = sum_2_pre + phqmat(i, 1);
%         n_2_pre(t) = phqmat(i, 1);
%         end
%     end
%     sum_2_pre = sum_2_pre/t;
%     
%     t=0;
%     sum_2_post = 0;
%     for i = 1 : 60
%         if(phqmat(i,4)==1 && phqmat(i, 2)>0)
%             t = t+1;
%             sum_2_post = sum_2_post + phqmat(i, 2);
%             n_2_post(t) = phqmat(i, 2);
%         end
%     end
% sum_2_post = sum_2_post/t;
% 
%     
%     t=0;
%     sum_3_pre = 0;
%     for i = 1 : 60
%         if(phqmat(i,5)==1 && phqmat(i, 1)>0)
%         t = t+1;
%         sum_3_pre = sum_3_pre + phqmat(i, 1);
%         n_3_pre(t) = phqmat(i, 1);
%         end
%     end
%     sum_3_pre = sum_3_pre/t;
%     
%     t=0;
%     sum_3_post = 0;
%     for i = 1 : 60
%         if(phqmat(i,5)==1 && phqmat(i, 2)>0)
%             t = t+1;
%             sum_3_post = sum_3_post + phqmat(i, 2);
%             n_3_post(t) = phqmat(i, 2);
%         end
%     end
% sum_3_post = sum_3_post/t;
% 
% var_1_pre = std(n_1_pre);
% var_2_pre = std(n_2_pre);
% var_3_pre = std(n_3_pre);
% var_1_post = std(n_1_post);
% var_2_post = std(n_2_post);
% var_3_post = std(n_3_post);
% var_pre = std(pre_PHQ9(:, 2));
% var_post = std(post_PHQ9(:, 2));
% avar_1_pre = var_1_pre/var_pre ;
% avar_2_pre = var_2_pre/var_pre;
% avar_3_pre = var_3_pre/var_pre;
% avar_1_post = var_1_post/var_post;
% avar_2_post = var_2_post/var_post;
% avar_3_post = var_3_post/var_post;
% 
% sum_pre = sum(pre_PHQ9(:, 2))/46;
% sum_post = sum(post_PHQ9(:, 2))/38;
% aver_1_pre = sum_1_pre / sum_pre;%sum_1_pre = sum(where clus=1 & score >0 /total such instances)
% aver_2_pre = sum_2_pre / sum_pre;
% aver_3_pre = sum_3_pre / sum_pre;
% aver_1_post = sum_1_post / sum_post;
% aver_2_post = sum_2_post / sum_post;
% aver_3_post = sum_3_post / sum_post;
% 
% vari1 = zeros(3, 14);
% vari1(1,1) = avar_1_pre/ sum_pre; 
% vari1(1,2) = avar_1_post/ sum_post;
% vari1(2,1) = avar_2_pre/ sum_pre; 
% vari1(2,2) = avar_2_post/ sum_post;
% vari1(3,1) = avar_3_pre/ sum_pre; 
% vari1(3,2) = avar_3_post/ sum_post;
% 
% vari2 = zeros(3, 18);
% vari2(1,1) = avar_1_pre/ sum_pre; 
% vari2(1,2) = avar_1_post/ sum_post;
% vari2(2,1) = avar_2_pre/ sum_pre; 
% vari2(2,2) = avar_2_post/ sum_post;
% vari2(3,1) = avar_3_pre/ sum_pre;  % Asma changed from vari1
% vari2(3,2) = avar_3_post/ sum_post; % Asma changed from vari1
% 
% % Asma added
% vari3 = zeros(3, 18);
% vari3(1,1) = avar_1_pre/ sum_pre; 
% vari3(1,2) = avar_1_post/ sum_post;
% vari3(2,1) = avar_2_pre/ sum_pre; 
% vari3(2,2) = avar_2_post/ sum_post;
% vari3(3,1) = avar_3_pre/ sum_pre; 
% vari3(3,2) = avar_3_post/ sum_post;
% 
% indCon_3 = find(V{1,1} ~= 0);
% indIgn_3 = find(V{1,1} == 0);
% NC = vertcat(indCon_3, indIgn_3);
% view1ReOrder = view1(:,NC);
% view1Title2 = {'Act_{still}', 'Conv_{dur}', 'Dark_{dur}', 'Dark_{cnt}', 'Conv_{cnt}', 'Act_{walk}', 'Act_{run}', 'Aud_{quiet}', 'Aud_{noisy}','Aud_{loud}','Loc_{cnt}','Loc_{dur}'};
% view1Title = {'PRE-PHQ','POST-PHQ',view1Title2{1,indCon_3},view1Title2{1,indIgn_3}};
% clc
% disp('----------------------------Ignored View 1-------------------------------------')
% view1Title2{1,indIgn_3}
% 
% indCon_3 = find(V{2,1} ~= 0);
% indIgn_3 = find(V{2,1} == 0);
% NC = vertcat(indCon_3, indIgn_3);
% view2ReOrder = view2(:,NC);
%view1Title2 = {'Act_{still}', 'Conv_{dur}', 'Dark_{dur}', 'Dark_{cnt}', 'Conv_{cnt}', 'Act_{walk}', 'Act_{run}', 'Aud_{quiet}', 'Aud_{noisy}','Aud_{loud}','Loc_{cnt}','Loc_{dur}'};
%view2Title2 = {'walk_{amp}', 'walk_{phase}','walk_{intcept}','walk{freq}','audio_{noise(amp)}', 'audio_{noise(phase)}','audio_{noise(intcept)}','audio_{noise(freq)}', 'conv_{cnt(amp)}','conv_{cnt(phase)}','conv_{cnt(intcept)}','conv_{cnt(freq)}'};
% view2Title = {'PRE-PHQ','POST-PHQ',view2Title2{1,indCon_3},view2Title2{1,indIgn_3}};
% disp('----------------------Ignored View 2--------------------------------')
% view2Title2{1,indIgn_3}
% 
% 
% 
% indCon_3 = find(V{3,1} ~= 0);
% indIgn_3 = find(V{3,1} == 0);
% NC = vertcat(indCon_3, indIgn_3);
% view3ReOrder = view3(:,NC);
% view3Title2 = {'Var','Time in K-1','Time in K-2', 'Time in K-3','Entropy','N.Entropy','Time@Home','Transition time', 'Distance travel'};
% view3Title = {'PRE-PHQ','POST-PHQ',view3Title2{1,indCon_3},view3Title2{1,indIgn_3}};
% disp('----------------------Ignored View 3--------------------------------')
% view3Title2{1,indIgn_3}
% 
% Clusmat = [Clus1, Clus2, Clus3];
% View1_perc = Clusmat' * view1ReOrder; 
% View2_perc = Clusmat' * view2ReOrder;
% 
% View3_perc = Clusmat' * view3ReOrder;
% 
% 
% View1_perc(1, :) = View1_perc(1, :) / sum(Clus1);
% View1_perc(2, :) = View1_perc(2, :) / sum(Clus2);
% View1_perc(3, :) = View1_perc(3, :) / sum(Clus3);
% View2_perc(1, :) = View2_perc(1, :) / sum(Clus1);
% View2_perc(2, :) = View2_perc(2, :) / sum(Clus2);
% View2_perc(3, :) = View2_perc(3, :) / sum(Clus3);
% View3_perc(1, :) = View3_perc(1, :) / sum(Clus1);
% View3_perc(2, :) = View3_perc(2, :) / sum(Clus2);
% View3_perc(3, :) = View3_perc(3, :) / sum(Clus3);
% 
% 
% 
% Clusall = double(Clus1|Clus2|Clus3);
% averView1 = (Clusall' * view1ReOrder) / sum(Clusall);
% averView2 = (Clusall' * view2ReOrder) /  sum(Clusall);
% averView3 = (Clusall' * view3ReOrder) /  sum(Clusall);
% 
% PHQ = [aver_1_pre,aver_1_post;aver_2_pre,aver_2_post;aver_3_pre,aver_3_post];
% for i = 1 : 3
% View1_perc(i, :) = View1_perc(i, :) ./ averView1;
% View2_perc(i, :) = View2_perc(i, :) ./ averView2;
% View3_perc(i, :) = View3_perc(i, :) ./ averView3;
% end
% TT = [PHQ, View1_perc];
% figure(1); bar(TT,1);
% 
% 
% 
% set(gca,'XTickLabel',{'Cluster1','Cluster2','Cluster3'}, 'FontSize',20)
% xlabel('View-Average','FontSize',20),ylabel('Relative distance to the average','FontSize',20)
% legend(view1Title,-1, 'FontSize',14)%'ConvDur', 'DarkCnt', 'DarkDur','AudLd','PhlckDur','ActNoMvt','ConvCnt', 'ActWalk', 'ActRun', 'AudQte', 'AudNoisy', 'PhLckCnt', -1)
% grid on
% grid minor
% % legend('PRE-PHQ','POST-PHQ','Act_1', 'Conv_t', 'Dark_n', 'Dark_t', 'Conv_n', 'Act_2', 'Act_3', 'Aud_1', 'Aud_2','Aud_3','Loc_n','Loc_t',-1)
% % axis([0,4 ,0.6, 1.8 ]);
% axis([0,4 ,0,3 ]);        
% hold on;
% numgroups = size(View1_perc, 1); 
% numbars = size(View1_perc, 2)+2; 
% groupwidth = min(0.8, numbars/(numbars+1.5));
%     for u = 1:numbars
%       % Based on barweb.m by Bolu Ajiboye from MATLAB File Exchange
%       x = (1:numgroups) - groupwidth/2 + (2*u-1) * groupwidth / (2*numbars);  % Aligning error bar with individual bar
%       errorbar(x, TT(:, u), vari1(:,u), 'k', 'linestyle', 'none');
%     end
% 
% TT = [PHQ, View2_perc];
% figure(2); bar(TT,1);
% set(gca,'XTickLabel',{'Cluster1','Cluster2','Cluster3'}, 'FontSize',20)
% xlabel('View-Variance','FontSize',20),ylabel('Relative distance to the average','FontSize',20)
% legend(view2Title,-1, 'FontSize',14)%'act_1(amp)', 'act_2(phase)','act_4(Freq)','audio_4(Freq)','conv_4(Freq)', 'act_3(intcpt)','audio_1(amp)', 'audio_2(phase)','audio_3(intcpt)','conv_1(amp)','conv_2(phase)','conv_3(intcpt)', -1)
% grid on
% grid minor
% % amp, phase, intercept and freq
% % legend('PRE-PHQ','POST-PHQ','act_1', 'act_2','act_3','act_4','audio_1', 'audio_2','audio_3','audio_4','conv_1','conv_2','conv_3','conv_4', 'lock_1','lock_2','lock_3','lock_4',-1)
% % axis([0,4 ,0,3 ]);
% axis([0,4 ,-1,3 ]);        
% hold on;
% numgroups = size(View2_perc, 1); 
% numbars = size(View2_perc, 2)+2; 
% groupwidth = min(0.8, numbars/(numbars+1.5));
%     for u = 1:numbars
%       % Based on barweb.m by Bolu Ajiboye from MATLAB File Exchange
%       x = (1:numgroups) - groupwidth/2 + (2*u-1) * groupwidth / (2*numbars);  % Aligning error bar with individual bar
%       errorbar(x, TT(:, u), vari2(:,u), 'k', 'linestyle', 'none');
%     end
%     
% TT = [PHQ, View3_perc];
% figure(3); bar(TT,1);
% set(gca,'XTickLabel',{'Cluster1','Cluster2','Cluster3'},'FontSize',20)
% xlabel('GPS Features','FontSize',20),ylabel('Relative distance to the average','FontSize',20)
% legend(view3Title,-1, 'FontSize',14)%'Var','Time in K-1','Ent','N.Ent','Time@Home','Transition time', 'Time in K-2', 'Time in K-3', 'Distance travel',-1)
% grid on
% grid minor
% axis([0,4 ,0,3 ]);    
% % axis([0,4 ,-1,3 ]);        
% hold on;
% numgroups = size(View3_perc, 1); 
% numbars = size(View3_perc, 2)+2; 
% groupwidth = min(0.8, numbars/(numbars+1.5));
%     for u = 1:numbars
%       % Based on barweb.m by Bolu Ajiboye from MATLAB File Exchange
%       x = (1:numgroups) - groupwidth/2 + (2*u-1) * groupwidth / (2*numbars);  % Aligning error bar with individual bar
%       errorbar(x, TT(:, u), vari3(:,u), 'k', 'linestyle', 'none');
%     end
% %% View 3 have the GPS features that include: Location variance[1 ], Time spent in each cluster (K = 3)[2,3,4], 
% % Entropy [5], Normalized entropy [6], percentage of time @ home[7], Transition time[8], and distance traveled in km [9].
% % Time spent in first cluster, Entropy [5], Normalized entropy [6], percentage of time @ home[7], Transition time[8]