Invalid expression. When calling a function or indexing a variable, use parentheses. Otherwise, check for mismatched delimiters.
1 Ansicht (letzte 30 Tage)
Ältere Kommentare anzeigen
Nathaniel Porter
am 4 Mär. 2022
Beantwortet: Walter Roberson
am 4 Mär. 2022
Cant seem to find the issue with the parenthesis
clc; clear all; close all;
load Projectdata.mat
% Split Data Glucose
GlucoseReadings_T = GlucoseReadings';
GlucoseReadings_train = GlucoseReadings_T;
train_GlucoseReadings = GlucoseReadings_train(1:84,:);
train_GR_output = GR_output(1:17);
%Split Data Insulin
InsulinReadings_T = InsulinReadings';
InsulinReadings_train = InsulinReadings_T;
train_InsulinReadings = InsulinReadings_train(1:84,:);
train_INS_output = INS_output(1:17);
% Data Batch Glucose
GlucoseReadingsTrain=(reshape(train_GlucoseReadings', [1749,84]));
val_GlucoseReadings = GlucoseReadings_train(85:102,:);
val_GR_output = GR_output(85:102);
GlucoseReadingsVal=(reshape(val_GlucoseReadings', [1749,18]));
test_GlucoseReadings =GlucoseReadings_train(103:120,:);
test_GR_output = GR_output(103:120);
GlucoseReadingsTest=(reshape(test_GlucoseReadings', [1749,18]));
numFeatures = size(GlucoseReadings_T,2);
%Data Batch Insulin
InsulinReadingsTrain=(reshape(train_InsulinReadings', [1758,84]));
val_InsulinReadings = InsulinReadings_train(85:102,:);
val_INS_output = INS_output(85:102);
InsulinReadingsVal=(reshape(val_InsulinReadings', [1758,18]));
test_InsulinReadings = InsulinReadings_train(103:120,:);
test_INS_output = INS_output(103:120);
InsulinReadingsTest=(reshape(test_InsulinReadings', [1758,18]));
numFeatures1 = size(InsulinReadings_T,2);
% number of hidden units represent the size of the data
numHiddenUnits = 120;
%number of classes represent different patients normal,LIS,type2....
numClasses = length(categories(categorical(GR_output)));
numClasses1 = length(categories(categorical(INS_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},...
'ValidationData',{InsulinReadingsVal, val_INS_output},...
'LearnRateDropFactor',0.2,...
'LearnRateDropPeriod',5,...
'Plots','training-progress');
% Train
net = trainNetwork(GlucoseReadingsTrain,train_GR_output,layers,options);
net1 = trainNetwork(InsulinReadingsTrain,train_INS_output,layers,options);
% Test
miniBatchSize = 27;
GR_outputPred = classify(net,GlucoseReadingsTest,...
'MiniBatchSize',miniBatchSize,...
'Environment';'cpu');
INS_outputPred = classify(net1,InsulinReadingsTest,...
'MiniBatchSize',miniBatchSize;...
'Environment';'cpu');
acc = mean(GR_outputPred(:) == categorical(test_GR_output(:)))
acc1 = mean(INS_outputPred(:) == categorical(test_INS_output(:)))
figure
t = confusionchart(categorical(test_GR_output(:)),GR_outputPred(:));
figure
t1 = confusionchart(categorical(test_INS_output(:)),INS_outputPred(:));
0 Kommentare
Akzeptierte Antwort
Weitere Antworten (0)
Siehe auch
Kategorien
Mehr zu Sequence and Numeric Feature Data Workflows finden Sie in Help Center und File Exchange
Community Treasure Hunt
Find the treasures in MATLAB Central and discover how the community can help you!
Start Hunting!