Index in position 1 exceeds array bounds. Index must not exceed 24. Error in RNN_CW2 (line 20) GlucoseReadings_T = GlucoseReadings_T(ind, :);

clc; clear all; close all;
load GlucoseReadings.mat
rand('seed', 0)
GlucoseReadings_T = GlucoseReadings';
GR_outputC1 = categorical(GR_output);
CS = categories(GR_outputC1);
train_index = []; val_index = []; test_index = [];
for i = 1 : length(CS)
    indi = find(GR_outputC1==CS{i});
    % Shuffling data
    indi = indi(randperm(length(indi)));    
    % 2/3---train, 1/6---val, 1/6---test
    index1 = round(length(indi)*2/3);
    index2 = round(length(indi)*(2/3+1/6));
    train_index = [train_index indi(1:index1)];
    val_index = [val_index indi(1+index1:index2)];
    test_index = [test_index indi(1+index2:end)];
end
ind = [train_index val_index test_index];
GlucoseReadings_T = GlucoseReadings_T(ind, :);

Index in position 1 exceeds array bounds. Index must not exceed 24.

GR_output = categorical(GR_output(ind));
% Split Data
GlucoseReadings_train = GlucoseReadings_T;
train_GlucoseReadings = GlucoseReadings_train(1:17,:);
train_GR_output = GR_output(1:17);
% Data Batch
GlucoseReadingsTrain=(reshape(train_GlucoseReadings', [1438,17]));
val_GlucoseReadings = GlucoseReadings_train(18:21,:);
val_GR_output = GR_output(18:21);
GlucoseReadingsVal=(reshape(val_GlucoseReadings', [1438,4]));
test_GlucoseReadings =GlucoseReadings_train(18:21,:);
test_GR_output = Gr_output(22:24);
GlucoseReadingsTest=(reshape(test_GlucoseReadings', [1438,3]));
numFeatures = size(GlucoseReadings_T,2);
% number of hidden units represent the size of the data
numHiddenUnits = 24;
%number of classes represent different patients normal,LIS,type2....
numClasses = length(categories(categorical(GR_output)));
layers = [ ...
    sequenceInputLayer(numFeatures) 
    %dropoutLayer(0.5)
    instanceNormalizationLayer
    bilstmLayer(round(numHiddenUnits/2),'OutputMode','sequence')
    fullyConnectedLayer(numClasses)
    instanceNormalizationLayer
    softmaxLayer
    classificationLayer];
options = trainingOptions('adam', ...
    'MaxEpochs',100, ...
    'GradientThreshold',1, ...
    'Verbose',false, ...
    'ValidationData',{GlucoseReadingsVal, val_GR_output},...
    'LearnRateDropFactor',0.2,...
    'LearnRateDropPeriod',5,...
    'Plots','training-progress');
% Train
net = trainNetwork(GlucoseReadingsTrain,train_GR_output,layers,options);
% Test
miniBatchSize = 27;
GR_outputPred = classify(net,GlucoseReadingsTest, ...
    'MiniBatchSize',miniBatchSize,...
    'ExecutionEnvironment', 'cpu');
acc = mean(GR_outputPred(:) == categorical(test_GR_output(:)))
figure
t = confusionchart(categorical(test_GR_output(:)),GR_outputPred(:));

Answer 1

[yanqi liu]

clc; clear all; close all;
load GlucoseReadings.mat
rand('seed', 0)
GlucoseReadings_T = GlucoseReadings';
GR_outputC1 = categorical(GR_output);
len = min(length(GR_outputC1),size(GlucoseReadings_T,1));
GlucoseReadings_T = GlucoseReadings_T(1:len,:);
GR_outputC1 = GR_outputC1(1:len);
CS = categories(GR_outputC1);
train_index = []; val_index = []; test_index = [];
for i = 1 : length(CS)
    indi = find(GR_outputC1==CS{i});
    % Shuffling data
    indi = indi(randperm(length(indi)));    
    % 2/3---train, 1/6---val, 1/6---test
    index1 = round(length(indi)*2/3);
    index2 = round(length(indi)*(2/3+1/6));
    train_index = [train_index indi(1:index1)];
    val_index = [val_index indi(1+index1:index2)];
    test_index = [test_index indi(1+index2:end)];
end
ind = [train_index val_index test_index];
GlucoseReadings_T = GlucoseReadings_T(ind, :);
GR_output = categorical(GR_output(ind));
% Split Data
GlucoseReadings_train = GlucoseReadings_T;
train_GlucoseReadings = GlucoseReadings_train(1:17,:);
train_GR_output = GR_output(1:17);
% Data Batch
GlucoseReadingsTrain=(reshape(train_GlucoseReadings', [1438,size(train_GlucoseReadings,1)]));
val_GlucoseReadings = GlucoseReadings_train(18:21,:);
val_GR_output = GR_output(18:21);
GlucoseReadingsVal=(reshape(val_GlucoseReadings', [1438,size(val_GlucoseReadings,1)]));
test_GlucoseReadings =GlucoseReadings_train(22:24,:);
test_GR_output = GR_output(22:24);
GlucoseReadingsTest=(reshape(test_GlucoseReadings', [1438,size(test_GlucoseReadings,1)]));
numFeatures = size(GlucoseReadings_T,2);
% number of hidden units represent the size of the data
numHiddenUnits = 24;
%number of classes represent different patients normal,LIS,type2....
numClasses = length(categories(categorical(GR_output)));
layers = [ ...
    sequenceInputLayer(numFeatures) 
    %dropoutLayer(0.5)
    instanceNormalizationLayer
    bilstmLayer(round(numHiddenUnits/2),'OutputMode','sequence')
    fullyConnectedLayer(numClasses)
    instanceNormalizationLayer
    softmaxLayer
    classificationLayer];
options = trainingOptions('adam', ...
    'MaxEpochs',100, ...
    'GradientThreshold',1, ...
    'Verbose',false, ...
    'ValidationData',{GlucoseReadingsVal, val_GR_output},...
    'LearnRateDropFactor',0.2,...
    'LearnRateDropPeriod',5,...
    'Plots','training-progress');
% Train
net = trainNetwork(GlucoseReadingsTrain,train_GR_output,layers,options);
% Test
miniBatchSize = 27;
GR_outputPred = classify(net,GlucoseReadingsTest, ...
    'MiniBatchSize',miniBatchSize,...
    'ExecutionEnvironment', 'cpu');
acc = mean(GR_outputPred(:) == categorical(test_GR_output(:)))
figure
t = confusionchart(categorical(test_GR_output(:)),GR_outputPred(:));