Hello MATLAB friends,
I’m working on a project involving time series prediction using a Gated Recurrent Unit (GRU) model. The goal is to predict whether a stock will be a ‘winning’ or ‘losing’ stock based on historical data.
However, I’m encountering an error when trying to train the network using the trainNetwork function. The error message is:
Error using trainNetwork
Invalid training data. Predictors must be a N-by-1 cell array of sequences, where N is the number of sequences. All sequences must have the same feature dimension and at least one time step.
Here’s the relevant portion of my code:
% Initialize cell arrays to store training and testing features and labels
training_features = {};
training_labels = {};
testing_features = {};
testing_labels = {};

% Process each CSV file in the combined_stocks table
for f = 1:height(combined_stocks)
% Load data from CSV file using readtable
data = readtable(fullfile(directory_path, combined_stocks.Stock(f)), ‘VariableNamingRule’, ‘preserve’);

% Selecting the required features
features = data{:, {‘open’, ‘high’, ‘low’, ‘close’, ‘MA’, ‘MA_1’}};

% Normalizing the close prices
normalized_close = (data.close – min(data.close)) / (max(data.close) – min(data.close));

% Calculating logarithmic close prices
log_close = log(data.close);

% Combine all features into a matrix
features = [features, normalized_close, log_close];

% Determine the label for the current stock
if combined_stocks.Maximum_Profit(f) > 0
labels = ones(height(data), 1); % Winning class
else
labels = zeros(height(data), 1); % Losing class
end

% Convert labels to categorical
labels = categorical(labels);

% Generate a random permutation of indices
indices = randperm(size(features, 1));

% Split the data into 70% training and 30% testing
split_point = round(size(features, 1) * 0.7);
training_indices = indices(1:split_point);
testing_indices = indices(split_point+1:end);

% Append the training and testing features and labels to the respective arrays
training_features = [training_features; {features(training_indices, :)}];
training_labels = [training_labels; {labels(training_indices)}];
testing_features = [testing_features; {features(testing_indices, :)}];
testing_labels = [testing_labels; {labels(testing_indices)}];
end

% Define the GRU network architecture
layers = [ …
sequenceInputLayer(size(training_features{1}, 2))
gruLayer(100,’OutputMode’,’last’)
fullyConnectedLayer(2)
softmaxLayer
classificationLayer];

% Define the training options
options = trainingOptions(‘adam’, …
‘MaxEpochs’,100, …
‘MiniBatchSize’, 150, …
‘InitialLearnRate’, 0.01, …
‘GradientThreshold’, 1, …
‘ExecutionEnvironment’,’auto’,…
‘plots’,’training-progress’, …
‘Verbose’,false);

% Train the GRU network
net = trainNetwork(training_features, training_labels, layers, options);
In my code, training_features and training_labels are N-by-1 cell arrays. Each cell in training_features contains a matrix of size M-by-8, where M is the number of time steps and 8 is the number of features. Each cell in training_labels contains a categorical vector of length M, where M is the same as in the corresponding cell of training_features.
Despite this, I’m still getting the error. Any help would be greatly appreciated!Hello MATLAB friends,
I’m working on a project involving time series prediction using a Gated Recurrent Unit (GRU) model. The goal is to predict whether a stock will be a ‘winning’ or ‘losing’ stock based on historical data.
However, I’m encountering an error when trying to train the network using the trainNetwork function. The error message is:
Error using trainNetwork
Invalid training data. Predictors must be a N-by-1 cell array of sequences, where N is the number of sequences. All sequences must have the same feature dimension and at least one time step.
Here’s the relevant portion of my code:
% Initialize cell arrays to store training and testing features and labels
training_features = {};
training_labels = {};
testing_features = {};
testing_labels = {};

% Process each CSV file in the combined_stocks table
for f = 1:height(combined_stocks)
% Load data from CSV file using readtable
data = readtable(fullfile(directory_path, combined_stocks.Stock(f)), ‘VariableNamingRule’, ‘preserve’);

% Selecting the required features
features = data{:, {‘open’, ‘high’, ‘low’, ‘close’, ‘MA’, ‘MA_1’}};

% Normalizing the close prices
normalized_close = (data.close – min(data.close)) / (max(data.close) – min(data.close));

% Calculating logarithmic close prices
log_close = log(data.close);

% Combine all features into a matrix
features = [features, normalized_close, log_close];

% Determine the label for the current stock
if combined_stocks.Maximum_Profit(f) > 0
labels = ones(height(data), 1); % Winning class
else
labels = zeros(height(data), 1); % Losing class
end

% Convert labels to categorical
labels = categorical(labels);

% Generate a random permutation of indices
indices = randperm(size(features, 1));

% Split the data into 70% training and 30% testing
split_point = round(size(features, 1) * 0.7);
training_indices = indices(1:split_point);
testing_indices = indices(split_point+1:end);

% Append the training and testing features and labels to the respective arrays
training_features = [training_features; {features(training_indices, :)}];
training_labels = [training_labels; {labels(training_indices)}];
testing_features = [testing_features; {features(testing_indices, :)}];
testing_labels = [testing_labels; {labels(testing_indices)}];
end

% Define the GRU network architecture
layers = [ …
sequenceInputLayer(size(training_features{1}, 2))
gruLayer(100,’OutputMode’,’last’)
fullyConnectedLayer(2)
softmaxLayer
classificationLayer];

% Define the training options
options = trainingOptions(‘adam’, …
‘MaxEpochs’,100, …
‘MiniBatchSize’, 150, …
‘InitialLearnRate’, 0.01, …
‘GradientThreshold’, 1, …
‘ExecutionEnvironment’,’auto’,…
‘plots’,’training-progress’, …
‘Verbose’,false);

% Train the GRU network
net = trainNetwork(training_features, training_labels, layers, options);
In my code, training_features and training_labels are N-by-1 cell arrays. Each cell in training_features contains a matrix of size M-by-8, where M is the number of time steps and 8 is the number of features. Each cell in training_labels contains a categorical vector of length M, where M is the same as in the corresponding cell of training_features.
Despite this, I’m still getting the error. Any help would be greatly appreciated! Hello MATLAB friends,
I’m working on a project involving time series prediction using a Gated Recurrent Unit (GRU) model. The goal is to predict whether a stock will be a ‘winning’ or ‘losing’ stock based on historical data.
However, I’m encountering an error when trying to train the network using the trainNetwork function. The error message is:
Error using trainNetwork
Invalid training data. Predictors must be a N-by-1 cell array of sequences, where N is the number of sequences. All sequences must have the same feature dimension and at least one time step.
Here’s the relevant portion of my code:
% Initialize cell arrays to store training and testing features and labels
training_features = {};
training_labels = {};
testing_features = {};
testing_labels = {};

% Process each CSV file in the combined_stocks table
for f = 1:height(combined_stocks)
% Load data from CSV file using readtable
data = readtable(fullfile(directory_path, combined_stocks.Stock(f)), ‘VariableNamingRule’, ‘preserve’);

% Selecting the required features
features = data{:, {‘open’, ‘high’, ‘low’, ‘close’, ‘MA’, ‘MA_1’}};

% Normalizing the close prices
normalized_close = (data.close – min(data.close)) / (max(data.close) – min(data.close));

% Calculating logarithmic close prices
log_close = log(data.close);

% Combine all features into a matrix
features = [features, normalized_close, log_close];

% Determine the label for the current stock
if combined_stocks.Maximum_Profit(f) > 0
labels = ones(height(data), 1); % Winning class
else
labels = zeros(height(data), 1); % Losing class
end

% Convert labels to categorical
labels = categorical(labels);

% Generate a random permutation of indices
indices = randperm(size(features, 1));

% Split the data into 70% training and 30% testing
split_point = round(size(features, 1) * 0.7);
training_indices = indices(1:split_point);
testing_indices = indices(split_point+1:end);

% Append the training and testing features and labels to the respective arrays
training_features = [training_features; {features(training_indices, :)}];
training_labels = [training_labels; {labels(training_indices)}];
testing_features = [testing_features; {features(testing_indices, :)}];
testing_labels = [testing_labels; {labels(testing_indices)}];
end

% Define the GRU network architecture
layers = [ …
sequenceInputLayer(size(training_features{1}, 2))
gruLayer(100,’OutputMode’,’last’)
fullyConnectedLayer(2)
softmaxLayer
classificationLayer];

% Define the training options
options = trainingOptions(‘adam’, …
‘MaxEpochs’,100, …
‘MiniBatchSize’, 150, …
‘InitialLearnRate’, 0.01, …
‘GradientThreshold’, 1, …
‘ExecutionEnvironment’,’auto’,…
‘plots’,’training-progress’, …
‘Verbose’,false);

% Train the GRU network
net = trainNetwork(training_features, training_labels, layers, options);
In my code, training_features and training_labels are N-by-1 cell arrays. Each cell in training_features contains a matrix of size M-by-8, where M is the number of time steps and 8 is the number of features. Each cell in training_labels contains a categorical vector of length M, where M is the same as in the corresponding cell of training_features.
Despite this, I’m still getting the error. Any help would be greatly appreciated! matlab, deep learning toolbox, gru, trainnetwork MATLAB Answers — New Questions

​

I’ll be brief.
How do I export a matlabb app so another person can edit it?
I’ve tried, but my colleague only gets a .mlapp.zip file and once he extracts it, he is not able to even open the GUI.

Thanks in advanceI’ll be brief.
How do I export a matlabb app so another person can edit it?
I’ve tried, but my colleague only gets a .mlapp.zip file and once he extracts it, he is not able to even open the GUI.

Thanks in advance I’ll be brief.
How do I export a matlabb app so another person can edit it?
I’ve tried, but my colleague only gets a .mlapp.zip file and once he extracts it, he is not able to even open the GUI.

Thanks in advance matlab gui, app designer, export MATLAB Answers — New Questions

​

Hi I have problem with plotting eddy kinetic energy (EKE)
I have a data of sea surface height (variable name: adt) with matrix [n m k]. n is latitude, m is longitude and k is time.
I want to plot EKE with equation below

here is my initial code
regionName = ‘SouthIndian’ % change as you need to ..
eval([‘load ‘ regionName]); %to evaluate the data
[n, m, k] = size(b.adt); %n =lat, m= lon, k = absolute topohraphy
adt_mean = squeeze(mean(shiftdim(b.adt,2)));
g=(9.8).^2;
f=2.*(coriolisf(b.lat)).^2;
dhx=(diff(adt_mean)./diff(b.lat)).^2;
dhy=(diff(shiftdim(adt_mean,1))./diff(b.lon)).^2;
eke=g/f*(dhx+dhy);

But it doesnt working. Plese help or any suggestion about it.Hi I have problem with plotting eddy kinetic energy (EKE)
I have a data of sea surface height (variable name: adt) with matrix [n m k]. n is latitude, m is longitude and k is time.
I want to plot EKE with equation below

here is my initial code
regionName = ‘SouthIndian’ % change as you need to ..
eval([‘load ‘ regionName]); %to evaluate the data
[n, m, k] = size(b.adt); %n =lat, m= lon, k = absolute topohraphy
adt_mean = squeeze(mean(shiftdim(b.adt,2)));
g=(9.8).^2;
f=2.*(coriolisf(b.lat)).^2;
dhx=(diff(adt_mean)./diff(b.lat)).^2;
dhy=(diff(shiftdim(adt_mean,1))./diff(b.lon)).^2;
eke=g/f*(dhx+dhy);

But it doesnt working. Plese help or any suggestion about it. Hi I have problem with plotting eddy kinetic energy (EKE)
I have a data of sea surface height (variable name: adt) with matrix [n m k]. n is latitude, m is longitude and k is time.
I want to plot EKE with equation below

here is my initial code
regionName = ‘SouthIndian’ % change as you need to ..
eval([‘load ‘ regionName]); %to evaluate the data
[n, m, k] = size(b.adt); %n =lat, m= lon, k = absolute topohraphy
adt_mean = squeeze(mean(shiftdim(b.adt,2)));
g=(9.8).^2;
f=2.*(coriolisf(b.lat)).^2;
dhx=(diff(adt_mean)./diff(b.lat)).^2;
dhy=(diff(shiftdim(adt_mean,1))./diff(b.lon)).^2;
eke=g/f*(dhx+dhy);

But it doesnt working. Plese help or any suggestion about it. ssh, derivative, spatial MATLAB Answers — New Questions

​

Mathworks has recklessly put a drawnow inside of its asynciolib for hardware read and writes:
toolboxsharedasynciolib+matlabshared+asyncio+internalStream.m Line 184: drawnow(‘limitrate’);
This will then execute callbacks in the middle of data transfer! I could remove those lines of code for every Matlab install, but if an update restores the code then I have a dangerous bug. Is there a way I can temporarily pause the event queue from executing callbacks?

The following function CallbackTest has a 10 second main loop which should not be interrupted with the press of a button. However if TCPIP commands are executed in the loop then a hidden drawnow is invoked and the button callback will be processed.

function CallbackTest(Do_TCPIP)
% Test to see if TCPIP reads evaluate callbacks.
% There is a 10s main loop which the button callback should not interrupt.
% The main loop will not be interrupted when Do_TCPIP is 0.
% However when Do_TCPIP is 1 then it will be interrupted.

%Initialize figure and button
hFigure=uifigure();
uibutton(hFigure,ButtonPushedFcn=@ACallBack);

%Initialize TCPIP
echotcpip("on",4000);
t = tcpclient("localhost",4000);
configureTerminator(t,"CR");

waitfor(hFigure,’FigureViewReady’)

%10 second main loop which should not be interrupted because there is no drawnow, figure, pause, or waitfor command
n=0;
while n < 20

if Do_TCPIP
writeline(t,"loop");
readline(t);
end

n=n+1;
java.lang.Thread.sleep(500);

end

% Wrap up
echotcpip("off");
delete(hFigure)
fprintf(‘Done!n’);

end

function ACallBack(~,~)
fprintf(‘Main loop interrupted.n’);
endMathworks has recklessly put a drawnow inside of its asynciolib for hardware read and writes:
toolboxsharedasynciolib+matlabshared+asyncio+internalStream.m Line 184: drawnow(‘limitrate’);
This will then execute callbacks in the middle of data transfer! I could remove those lines of code for every Matlab install, but if an update restores the code then I have a dangerous bug. Is there a way I can temporarily pause the event queue from executing callbacks?

The following function CallbackTest has a 10 second main loop which should not be interrupted with the press of a button. However if TCPIP commands are executed in the loop then a hidden drawnow is invoked and the button callback will be processed.

function CallbackTest(Do_TCPIP)
% Test to see if TCPIP reads evaluate callbacks.
% There is a 10s main loop which the button callback should not interrupt.
% The main loop will not be interrupted when Do_TCPIP is 0.
% However when Do_TCPIP is 1 then it will be interrupted.

%Initialize figure and button
hFigure=uifigure();
uibutton(hFigure,ButtonPushedFcn=@ACallBack);

%Initialize TCPIP
echotcpip("on",4000);
t = tcpclient("localhost",4000);
configureTerminator(t,"CR");

waitfor(hFigure,’FigureViewReady’)

%10 second main loop which should not be interrupted because there is no drawnow, figure, pause, or waitfor command
n=0;
while n < 20

if Do_TCPIP
writeline(t,"loop");
readline(t);
end

n=n+1;
java.lang.Thread.sleep(500);

end

% Wrap up
echotcpip("off");
delete(hFigure)
fprintf(‘Done!n’);

end

function ACallBack(~,~)
fprintf(‘Main loop interrupted.n’);
end Mathworks has recklessly put a drawnow inside of its asynciolib for hardware read and writes:
toolboxsharedasynciolib+matlabshared+asyncio+internalStream.m Line 184: drawnow(‘limitrate’);
This will then execute callbacks in the middle of data transfer! I could remove those lines of code for every Matlab install, but if an update restores the code then I have a dangerous bug. Is there a way I can temporarily pause the event queue from executing callbacks?

The following function CallbackTest has a 10 second main loop which should not be interrupted with the press of a button. However if TCPIP commands are executed in the loop then a hidden drawnow is invoked and the button callback will be processed.

function CallbackTest(Do_TCPIP)
% Test to see if TCPIP reads evaluate callbacks.
% There is a 10s main loop which the button callback should not interrupt.
% The main loop will not be interrupted when Do_TCPIP is 0.
% However when Do_TCPIP is 1 then it will be interrupted.

%Initialize figure and button
hFigure=uifigure();
uibutton(hFigure,ButtonPushedFcn=@ACallBack);

%Initialize TCPIP
echotcpip("on",4000);
t = tcpclient("localhost",4000);
configureTerminator(t,"CR");

waitfor(hFigure,’FigureViewReady’)

%10 second main loop which should not be interrupted because there is no drawnow, figure, pause, or waitfor command
n=0;
while n < 20

if Do_TCPIP
writeline(t,"loop");
readline(t);
end

n=n+1;
java.lang.Thread.sleep(500);

end

% Wrap up
echotcpip("off");
delete(hFigure)
fprintf(‘Done!n’);

end

function ACallBack(~,~)
fprintf(‘Main loop interrupted.n’);
end drawnow, tcpip, hardware, callbacks MATLAB Answers — New Questions

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