Number of observations in X and Y disagree.

clc; clear all; close all;

%Import/Upload data

load generated_data.mat

%transposing glucose data

X1_T = X1';

%transposing insulin data

X2_T = X2';

%Separating data in training, validation and testing data

X1_train = X1_T;

%Partioning data for training

train_X1 = X1_train(1:120,:);

train_Y1 = Y1(1:120);

%DataParts = zeros(size(Train_inputX1,1), size(Train_inputX1,2),1,2); %(4500,400,1,2)

%DataParts(:,:,:,1) = real(cell2mat(Train_inputX1));

%DataParts(:,:,:,2) = imag(cell2mat(Train_inputX1)) ;

XTrain=(reshape(train_X1, [120,1,1,2289])); %Train data

%Separating and partioning for validation data

val_X1 = X1_train(121:150,:);

val_Y1 = Y1(121:150);

XVal=(reshape(val_X1, [30,1,1,2289])); %Train data

%Separating and partioning for test data

test_X1 = X1_train(151:180,:);

test_Y1 = Y1(151:180);

XTest=(reshape(test_X1, [30,1,1,2289])); %Train data

%Xtest=(reshape(test_X1, [120,1,1,2289])); %Train data

%Separating data in training, validation and testing data

%X2_train = X2_T;

%Partioning data for training

%train_X2 = X2_train(1:120,:);

%Separating and partioning for validation data

%val_X2 = X2_train(121:150,:);

%Separating and partioning for test data

%test_X2 = X2_train(151:180,:);

%The number of features chosen to be two representing both glucose and

%insulin

%% NETWORK ARCHITECTURE

layers = [imageInputLayer([120 1 1]) % Creating the image layer

convolution2dLayer([102 1],3,'Stride',1)

maxPooling2dLayer(2,'Stride',2,'Padding',[0 0 0 1])

% convolution2dLayer([24 1],10,'Stride',1)

% maxPooling2dLayer(2,'Stride',2,'Padding',[0 0 0 1])

% convolution2dLayer([11 1],10,'Stride',1)

% maxPooling2dLayer(2,'Stride',2,'Padding',[0 0 0 1])

% convolution2dLayer([9 1],10,'Stride',1)

% maxPooling2dLayer(2,'Stride',2,'Padding',[0 0 0 1])

fullyConnectedLayer(6)

%fullyConnectedLayer(6)

%fullyConnectedLayer(6)

softmaxLayer

classificationLayer];

% Specify training options.

opts = trainingOptions('adam', ...

'MaxEpochs',50, ...

'Shuffle','every-epoch', ...

'Plots','training-progress', ...

'Verbose',false, ...

'ValidationData',{XVal,val_Y1},...

'ValidationPatience',Inf);

%% Train network

%net = trainNetwork(XTrain,Trainoutfinal,layers,opts);

net1 = trainNetwork(XTrain,categorical(train_Y1),layers,opts);

%% Compare against testing Data

miniBatchSize =27;

YPred = classify(net1,test_X1, ...

MiniBatchSize=miniBatchSize, ...

SequencePaddingDirection="left");

acc = mean(YPred == categorical(test_Y1));

figure

t = confusionchart(categorical(test_Y1),YPred);