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fitnetEstQE-1.m
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% refined code
close all;
clear all;
% =================== global setting ============
note = sprintf('snapshot & SNR');
id = 'VS500__5';
% ------ signal setting --------
optSig = 'VectorSensor'
% optSig = 'ULA'
isSend = 1 % disable when debug
isRadBas = 0;
isBayes = 0;
% isAM = 0
% isFM = 0
% isPS = 0
isRand = 1;
sigType = 'tone'
% pulseShaping, AM, FM
tag = strcat(id,optSig);
tagData = strcat(tag,'Data.mat');
tagRawData = strcat(tag,'RawData.mat');
tagLabel = strcat(tag,'Label.mat');
tagTargetLabel = strcat(tag,'TargetLabel.mat');
tagSampTarget = strcat('samp',tag,'Target.mat');
tagSampInput = strcat('samp',tag,'Input.mat');
theta = [58.6 26.7 26.7 36.7 48.6]./180*pi; % DoA
P = [1 1 1 1 1];
K = length(nonzeros(P)); % num of source
fc = [ 0.55 0.65 0.75 0.85 0.45 ];
% phi_c = [ 0 0 0 0 0];
phi_c = [58.6 26.7 26.7 30.0 48.6]./180*pi; % DoA
SNRdB = -5;
N = 500; % snapshot
M = 6; % num of Anntenna
if strcmp(optSig ,'ULA')
d = 0.40;
inputLen = M*(2*M+1); % ToDo related to M
elseif strcmp(optSig ,'VectorSensor')
phi = ( [360-57.4 360-69.1 13.3 106.3 360-170.8])./180*pi;
gamma = [0 1/2 3/4 1/4 1/4]*pi;
ita = [0 0 0 1/2 1/2]*pi;
dt = 0.10;
deltaT = 5 * dt;
inputLen = 78;
end
% ------ data setting --------
setN = [40 40];
Sizefull = prod(setN);
allData = zeros(Sizefull,inputLen);
rawData = zeros(Sizefull,M,N);
sampSize = round(Sizefull*0.8/100)*100;
targetID = 1;
% ------- iterator of data -----
if isRand
theta1Rnd = rand(1,180) *pi;
theta2Rnd = rand(1,180) *pi;
% theta2Rnd = theta1Rnd+ 1*pi/180; % overlap, so as mess num of source
% phi1Rnd = rand(1,180)*pi;
% phi2Rnd = rand(1,180) *pi;
else
theta1Rnd = [0:1:179]/180*pi;
theta2Rnd = [0:1:179]/180*pi;
end
% ------ neural network setting --------
hiddenLayerSize = [25 25 25];
% =================== synthesis data ============
if isempty(dir(tagData)) & isempty(dir(tagData))
tic;
i = 0;
for theta1Ind = 1:1:setN(1)
for theta2Ind = 1:1:setN(2)
i = i + 1;
% -- rewrite --
theta(1) = theta1Rnd(theta1Ind);
theta(2) = theta1Rnd(theta2Ind);
% phi(1) = phi1Rnd(theta1Ind);
% phi(2) = phi2Rnd(theta2Ind);
% theta(1) = theta1Ind;
% theta(2) = theta2Ind;
% ------- generate data -----
if strcmp(optSig ,'ULA')
x = Sig(theta, P, fc, phi_c, SNRdB, d, M, N, K);
elseif strcmp(optSig ,'VectorSensor')
x = VectorSensorSig(theta,phi,gamma,ita, P, fc,...
phi_c, SNRdB, N, dt,K,sigType);
end
% ------- compress data -----
S = myCompress(x);
allData(i,:) = S;
rawData(i,:,:) = x; % for MUSIC
Label(i).K = K;
Label(i).theta = theta;
Label(i).fc = fc;
Label(i).a = P;
Label(i).N = N;
Label(i).SNRdB = SNRdB;
if strcmp(optSig,'VectorSensor')
Label(i).phi = phi;
Label(i).gamma = gamma;
Label(i).ita = ita;
Label(i).dt = dt;
Label(i).deltaT = deltaT;
end
if mod(i,1000) == 0
processMsg = sprintf('data generating %.2f %%', i*100.0/Sizefull);
disp(processMsg);
toc;
end
end
end
toc;
else
disp("data found, load data");
load(tagData);
load(tagRawData);
load(tagLabel);
load(tagTargetLabel);
load(tagSampInput);
load(tagSampTarget);
end
% -------extract Label -----
temp = reshape([Label.theta],[5,Sizefull]);
temp = temp';
TargetLabel = temp(:,targetID); % 1st sig
% ------- sample data -----
Inputs = allData(1:sampSize,:)';
Targets = TargetLabel(1:sampSize,:)';
save(tagData,'allData');
save(tagRawData,'rawData');
save(tagLabel,'Label');
save(tagTargetLabel,'TargetLabel');
save(tagSampInput,'Inputs');
save(tagSampTarget,'Targets');
% =================== train ============
net = fitnet(hiddenLayerSize);
if isRadBas && ~isBayes
numLayer = length(hiddenLayerSize);
for i = 1:numLayer
net.layers{i}.transferFcn = 'radbas';
end
[net, tr] = train(net, Inputs, Targets);
elseif ~isRadBas && isBayes
net.trainFcn = 'trainbr';
[net, tr] = train(net, Inputs, Targets);
elseif isRadBas && isBayes
numLayer = length(hiddenLayerSize);
for i = 1:numLayer
net.layers{i}.transferFcn = 'radbas';
end
net.trainFcn = 'trainbr';
[net, tr] = train(net, Inputs, Targets);
else
[net, tr] = train(net, Inputs, Targets,'useGPU','yes');
end
% =================== test ============
tInd = tr.testInd;
predict = net(Inputs(:, tInd));
ground = Targets(tInd);
% =================== eval ============
tstPerform = perform(net, ground, predict);
errors = abs(gsubtract(ground, predict));
mse = immse(ground, predict);
errM = mean(errors);
% MAE
eerMdeg = errM*180/pi
% RMSE
% RMSE = sqrt(mean((y - yhat).^2));
RMSE = sqrt(mean((errors).^2))
errVar = var(errors);
save(strcat(id,'errors.mat'),'errors');
save(strcat(id,'ground.mat'),'ground');
save(strcat(id,'predict.mat'),'predict');
% ======== save fig & send email =====s=======
figure;
histogram(errors);
clear title xlabel ylabel;
title('estimate error histogram');
xlabel('estimate error (rad)');
ylabel('count');
grid on;
saveas(gcf,'hist.png');
% cdf of error
figure;
[hy, hx] = ecdf(errors);
plot(hx,hy);
clear title xlabel ylabel;
title('CDF of error');
xlabel('estimate error (rad)');
ylabel('P(X < x)');
grid on;
saveas(gcf,'cdf.png');
save(strcat(id,'hx.mat'),'hx');
save(strcat(id,'hy.mat'),'hy');
figure;
scatter(ground,predict);
clear title xlabel ylabel;
grid on;
title('ground vs ML estimate')
xlabel('estimate ML');
ylabel('ground');
saveas(gcf,'scatter.png');
% neural network topoloy
jframe = view(net);
hFig = figure('Menubar','none', 'Position',[100 100 1200 166]);
jpanel = get(jframe,'ContentPane');
[~,h] = javacomponent(jpanel);
set(h, 'units','normalized', 'position',[0 0 1 1])
jframe.setVisible(false);
jframe.dispose();
set(hFig, 'PaperPositionMode', 'auto')
saveas(hFig, 'nn.png')
close(hFig)
figure;
plotperform(tr);
saveas(gcf,'perf.png');
figure;
plottrainstate(tr);
saveas(gcf,'trainState.png');
figure;
ploterrhist(errors);
saveas(gcf,'hist2.png');
figure;
plotregression(ground, predict);
saveas(gcf,'regression.png');
title = strcat('(Dell) ',optSig, ' ',note, sprintf(' mean error %.4f deg, mse %.4f deg',eerMdeg,mse));
setting = sprintf('SNRdB=%.2f; P1=%.2f, P2=%.2f, P3=%.2f, P4=%.2f, P5=%.2f, ',...
SNRdB,P(1),P(2),P(3),P(4),P(5));
nnSetting = sprintf('TrainSize = %d, time = %.2f, trainFcn = %s, isRadBas = %d, isBayes = %d ,%s', ...
sampSize, tr.time(end),tr.trainFcn,isRadBas, isBayes,sigType);
content = strcat( setting,newline,nnSetting);
attachment = {'hist.png','cdf.png','scatter.png','nn.png',...
'perf.png','trainState.png','hist2.png',...
'regression.png',strcat(id,'errors.mat'),strcat(id,'ground.mat'),strcat(id,'predict.mat'),...
strcat(id,'hx.mat'),strcat(id,'hy.mat'),mfilename,'Sig.m'};
if isSend
sendEmail(title,content,attachment);
end