After deleting a model from library, I am trying to save that library, but I am coming accross a warning message as follows:

Warning: Block diagram ‘XYZ’ contains disabled library links. Use Model Advisor to find the disabled links in non-library models. The diagram has been saved but may not contain what you intended.

I can see that model advisor is able to help to resolve the Disabled library links but how can it be used to identify disabled links in non-library models.

Please suggest on this.

Regards,
Bhavnish.After deleting a model from library, I am trying to save that library, but I am coming accross a warning message as follows:

Warning: Block diagram ‘XYZ’ contains disabled library links. Use Model Advisor to find the disabled links in non-library models. The diagram has been saved but may not contain what you intended.

I can see that model advisor is able to help to resolve the Disabled library links but how can it be used to identify disabled links in non-library models.

Please suggest on this.

Regards,
Bhavnish. After deleting a model from library, I am trying to save that library, but I am coming accross a warning message as follows:

Warning: Block diagram ‘XYZ’ contains disabled library links. Use Model Advisor to find the disabled links in non-library models. The diagram has been saved but may not contain what you intended.

I can see that model advisor is able to help to resolve the Disabled library links but how can it be used to identify disabled links in non-library models.

Please suggest on this.

Regards,
Bhavnish. disabled library links MATLAB Answers — New Questions

​

Switching from r2023b to r2024 I made some changes in my Net (CNN). e.g. modified input/output and replace RegressionLayer with SoftmaxLayer, using trainnet function, etc.
I expected better performance, perspective compatibility (RegressionLayre is not more recommended) and have a vision of my Net optimization with use of Prune approach etc.
To the contrary to the previous version I am not able to involve my own Loss function (as it was done in previous version).
The (siplified) code is below, the used synthax was inspired by example:
https://www.mathworks.com/matlabcentral/answers/2100631-how-can-i-define-a-custom-loss-function-using-trainnet
The error message is:
Error using trainnet (line 46)
Error calling function during training.

Error in callMyLoss (line 55)
myTrainedNet = trainnet(Y,target,net, @(Y,target) myOwnLoss(name,Y,target),options);

Caused by:
Error using myOwnLoss
The specified superclass ‘nnet.layer.softmaxLayer’ contains a parse error, cannot be found on MATLAB’s
search
path, or is shadowed by another file with the same name.

Error in callMyLoss>@(Y,target)myOwnLoss(name,Y,target) (line 55)
myTrainedNet = trainnet(Y,target,net, @(Y,target) myOwnLoss(name,Y,target),options);

Error in nnet.internal.cnn.util.UserCodeException.fevalUserCode (line 11)
[varargout{1:nargout}] = feval(F, varargin{:});

classdef myOwnLoss < nnet.layer.softmaxLayer
% own Loss

methods
%function layer = sseClassificationLayer(name)
function layer = myOwnLoss(name)
% layer = sseClassificationLayer(name) creates a sum of squares
% error classification layer and specifies the layer name.

% Set layer name.
layer.Name = name;

% Set layer description.
layer.Description = ‘my own Loss v.2024a’;
end

function loss = forwardLoss(layer, Y, T)
%%% function loss = forwardLoss(Yo, To)
% loss = forwardLoss(layer, Y, T) returns the Tdiff loss between
% the predictions Y and the training targets T.

disp("myLoss");
aa=1;

% just something very simple
loss = sum(Y-T,’all’);
end

% original backwardLoss
function dX = backwardLoss(layer, Y, T)

numObservations = size( Y, 3);
dX = (Y – T)./numObservations;
end

end
end

%=======================eof=========================

classdef myOwnLoss < nnet.layer.softmaxLayer

% own Loss

methods
%function layer = sseClassificationLayer(name)
function layer = myOwnLoss(name)
% layer = sseClassificationLayer(name) creates a sum of squares
% error classification layer and specifies the layer name.

% Set layer name.
layer.Name = name;

% Set layer description.
layer.Description = ‘my own Loss v.2024a’;
end

function loss = forwardLoss(layer, Y, T)
%%% function loss = forwardLoss(Yo, To)
% loss = forwardLoss(layer, Y, T) returns the Tdiff loss between
% the predictions Y and the training targets T.

disp("myLoss");
aa=1;

% just something very simple
loss = sum(Y-T,’all’);
end

% original backwardLoss
function dX = backwardLoss(layer, Y, T)

numObservations = size( Y, 3);
dX = (Y – T)./numObservations;
end

end
end

%=======================eof=========================Switching from r2023b to r2024 I made some changes in my Net (CNN). e.g. modified input/output and replace RegressionLayer with SoftmaxLayer, using trainnet function, etc.
I expected better performance, perspective compatibility (RegressionLayre is not more recommended) and have a vision of my Net optimization with use of Prune approach etc.
To the contrary to the previous version I am not able to involve my own Loss function (as it was done in previous version).
The (siplified) code is below, the used synthax was inspired by example:
https://www.mathworks.com/matlabcentral/answers/2100631-how-can-i-define-a-custom-loss-function-using-trainnet
The error message is:
Error using trainnet (line 46)
Error calling function during training.

Error in callMyLoss (line 55)
myTrainedNet = trainnet(Y,target,net, @(Y,target) myOwnLoss(name,Y,target),options);

Caused by:
Error using myOwnLoss
The specified superclass ‘nnet.layer.softmaxLayer’ contains a parse error, cannot be found on MATLAB’s
search
path, or is shadowed by another file with the same name.

Error in callMyLoss>@(Y,target)myOwnLoss(name,Y,target) (line 55)
myTrainedNet = trainnet(Y,target,net, @(Y,target) myOwnLoss(name,Y,target),options);

Error in nnet.internal.cnn.util.UserCodeException.fevalUserCode (line 11)
[varargout{1:nargout}] = feval(F, varargin{:});

classdef myOwnLoss < nnet.layer.softmaxLayer
% own Loss

methods
%function layer = sseClassificationLayer(name)
function layer = myOwnLoss(name)
% layer = sseClassificationLayer(name) creates a sum of squares
% error classification layer and specifies the layer name.

% Set layer name.
layer.Name = name;

% Set layer description.
layer.Description = ‘my own Loss v.2024a’;
end

function loss = forwardLoss(layer, Y, T)
%%% function loss = forwardLoss(Yo, To)
% loss = forwardLoss(layer, Y, T) returns the Tdiff loss between
% the predictions Y and the training targets T.

disp("myLoss");
aa=1;

% just something very simple
loss = sum(Y-T,’all’);
end

% original backwardLoss
function dX = backwardLoss(layer, Y, T)

numObservations = size( Y, 3);
dX = (Y – T)./numObservations;
end

end
end

%=======================eof=========================

classdef myOwnLoss < nnet.layer.softmaxLayer

% own Loss

methods
%function layer = sseClassificationLayer(name)
function layer = myOwnLoss(name)
% layer = sseClassificationLayer(name) creates a sum of squares
% error classification layer and specifies the layer name.

% Set layer name.
layer.Name = name;

% Set layer description.
layer.Description = ‘my own Loss v.2024a’;
end

function loss = forwardLoss(layer, Y, T)
%%% function loss = forwardLoss(Yo, To)
% loss = forwardLoss(layer, Y, T) returns the Tdiff loss between
% the predictions Y and the training targets T.

disp("myLoss");
aa=1;

% just something very simple
loss = sum(Y-T,’all’);
end

% original backwardLoss
function dX = backwardLoss(layer, Y, T)

numObservations = size( Y, 3);
dX = (Y – T)./numObservations;
end

end
end

%=======================eof========================= Switching from r2023b to r2024 I made some changes in my Net (CNN). e.g. modified input/output and replace RegressionLayer with SoftmaxLayer, using trainnet function, etc.
I expected better performance, perspective compatibility (RegressionLayre is not more recommended) and have a vision of my Net optimization with use of Prune approach etc.
To the contrary to the previous version I am not able to involve my own Loss function (as it was done in previous version).
The (siplified) code is below, the used synthax was inspired by example:
https://www.mathworks.com/matlabcentral/answers/2100631-how-can-i-define-a-custom-loss-function-using-trainnet
The error message is:
Error using trainnet (line 46)
Error calling function during training.

Error in callMyLoss (line 55)
myTrainedNet = trainnet(Y,target,net, @(Y,target) myOwnLoss(name,Y,target),options);

Caused by:
Error using myOwnLoss
The specified superclass ‘nnet.layer.softmaxLayer’ contains a parse error, cannot be found on MATLAB’s
search
path, or is shadowed by another file with the same name.

Error in callMyLoss>@(Y,target)myOwnLoss(name,Y,target) (line 55)
myTrainedNet = trainnet(Y,target,net, @(Y,target) myOwnLoss(name,Y,target),options);

Error in nnet.internal.cnn.util.UserCodeException.fevalUserCode (line 11)
[varargout{1:nargout}] = feval(F, varargin{:});

classdef myOwnLoss < nnet.layer.softmaxLayer
% own Loss

methods
%function layer = sseClassificationLayer(name)
function layer = myOwnLoss(name)
% layer = sseClassificationLayer(name) creates a sum of squares
% error classification layer and specifies the layer name.

% Set layer name.
layer.Name = name;

% Set layer description.
layer.Description = ‘my own Loss v.2024a’;
end

function loss = forwardLoss(layer, Y, T)
%%% function loss = forwardLoss(Yo, To)
% loss = forwardLoss(layer, Y, T) returns the Tdiff loss between
% the predictions Y and the training targets T.

disp("myLoss");
aa=1;

% just something very simple
loss = sum(Y-T,’all’);
end

% original backwardLoss
function dX = backwardLoss(layer, Y, T)

numObservations = size( Y, 3);
dX = (Y – T)./numObservations;
end

end
end

%=======================eof=========================

classdef myOwnLoss < nnet.layer.softmaxLayer

% own Loss

methods
%function layer = sseClassificationLayer(name)
function layer = myOwnLoss(name)
% layer = sseClassificationLayer(name) creates a sum of squares
% error classification layer and specifies the layer name.

% Set layer name.
layer.Name = name;

% Set layer description.
layer.Description = ‘my own Loss v.2024a’;
end

function loss = forwardLoss(layer, Y, T)
%%% function loss = forwardLoss(Yo, To)
% loss = forwardLoss(layer, Y, T) returns the Tdiff loss between
% the predictions Y and the training targets T.

disp("myLoss");
aa=1;

% just something very simple
loss = sum(Y-T,’all’);
end

% original backwardLoss
function dX = backwardLoss(layer, Y, T)

numObservations = size( Y, 3);
dX = (Y – T)./numObservations;
end

end
end

%=======================eof========================= loss function, trainnet MATLAB Answers — New Questions

​

I have this problem where i seem to have some thermal coppling of my Tank(G-TL) with my Constant Volume Chamber. Yet there shouldnt be anything that does effect the temperature in my Constant Volume Chamber i still have something definitely influencing it. When I only simulate the outflow of my Constant Volume Chamber (pink) filled with N2 with a start value of 300 bar and a starting temperature of 300 Kelvin, i get a temperature of 267 Kelvin at the point my Chamber pressure dropped down to 200 bar. But if I simulate the whole circuit (screenshot) i get a different temperature after i dropped down to 200 bar in my Constant Volume Chamber depending on the settings i set my Tank(G-TL) (pink+yellow) on. It would be very nice if someone has an idea what could cause this effect of the temperature behaviour of my Constant Volume Chamber in this Simulink modell?I have this problem where i seem to have some thermal coppling of my Tank(G-TL) with my Constant Volume Chamber. Yet there shouldnt be anything that does effect the temperature in my Constant Volume Chamber i still have something definitely influencing it. When I only simulate the outflow of my Constant Volume Chamber (pink) filled with N2 with a start value of 300 bar and a starting temperature of 300 Kelvin, i get a temperature of 267 Kelvin at the point my Chamber pressure dropped down to 200 bar. But if I simulate the whole circuit (screenshot) i get a different temperature after i dropped down to 200 bar in my Constant Volume Chamber depending on the settings i set my Tank(G-TL) (pink+yellow) on. It would be very nice if someone has an idea what could cause this effect of the temperature behaviour of my Constant Volume Chamber in this Simulink modell? I have this problem where i seem to have some thermal coppling of my Tank(G-TL) with my Constant Volume Chamber. Yet there shouldnt be anything that does effect the temperature in my Constant Volume Chamber i still have something definitely influencing it. When I only simulate the outflow of my Constant Volume Chamber (pink) filled with N2 with a start value of 300 bar and a starting temperature of 300 Kelvin, i get a temperature of 267 Kelvin at the point my Chamber pressure dropped down to 200 bar. But if I simulate the whole circuit (screenshot) i get a different temperature after i dropped down to 200 bar in my Constant Volume Chamber depending on the settings i set my Tank(G-TL) (pink+yellow) on. It would be very nice if someone has an idea what could cause this effect of the temperature behaviour of my Constant Volume Chamber in this Simulink modell? simulink, simscape MATLAB Answers — New Questions

​

I’m using R2020b, and I want to set up a master table for appending new data to – and as part of this I want to identify any duplicate rows in the new, incoming table to filter them out before appending. Ideally, the master table will live in a related directory in a .mat file, and the new data will be read in directly from a set-name, set-location .csv using e.g.
fullname = fullfile(‘relativepath’,’newdata.csv’);
% grab column headers from input sheet
opts = detectImportOptions(fullname);
% set all variable types to categorical
opts.VariableTypes(:) = {‘categorical’};
% read in new data
T = readtable(fullname,opts);
% make any modifications to new data headers to match old data
T = renamevars(T,"NewLabel","OldLabel");
% clean new table headers to match originally-wizard-imported headers (I’d ask why these exhibit different behaviour, but that’s a separate tragedy, and this current fix works – I think)
T.Properties.VariableNames = regexprep(T.Properties.VariableNames, ‘ ‘, ”);
T.Properties.VariableNames = regexprep(T.Properties.VariableNames, ‘(‘, ”);
T.Properties.VariableNames = regexprep(T.Properties.VariableNames, ‘)’, ”);
T.Properties.VariableNames = regexprep(T.Properties.VariableNames, ‘_’, ”);
I found the solution suggested here: https://au.mathworks.com/matlabcentral/answers/514921-finding-identical-rows-in-2-tables, but having done a quick test via:
foo = T(str2double(string(T.Year))<1943,:); % not my actual query, but structurally the same; this gave me ~40% of my original data
bar = T(str2double(string(T.Year))>1941,:); % similar, gave me ~70% of the original data
baz = ismember(foo,bar); % similar, gives the overlap for 1 particular year (should be about 14% of my original data)
blah = T(str2double(string(T.Year))==1942,:); % to directly extract the number of rows I am looking for
sum(baz) % What I expect here is the number of rows in the overlap
ans =
0
I found that ismember was not finding any duplicates (which were there by construction).
Note: due to categorical data I actually used T(str2double(string(T.Year))…)
Replacing
baz = ismember(foo,bar,’rows’);
sum(baz)
ans =
0
results in the same not finding any duplicates. Using double quotes "rows" does not change the behaviour.
On the other hand, using the function to assess single variables gives the expected behaviour (to some degree):
testest = ismember(foo.var1,bar.var1)
sum(testest)
The sum is now non-zero, and (because single variables are repeated more often than their combinations) gives more like 30% of the original data, which seems reasonable (the number of unique entries in the original set in that variable was about 40% of the total).
I guess I could create a logical index based on the product of multiple calls of this kind, but that seems rather… inefficient… and sensitive to the exact construction of the table/variables used in the filter. I’d rather have a generic solution for full table rows that will be robust if the overall table changes over the long term (or if/when I functionalise the code and use it for other work). Whilst most of the time, a couple of key variables can be used to identify unique rows, occasionally more information is required to distinguish pathological cases. I will probably use this approach if a more elegant solution doesn’t appear, though, and put some thought into which groups of variables are 100% correlated (and therefore useless for this distinction) to cut down the Boolean product.
I could also throw good coding practice to the winds and just write two nested loops (one for rows, one for variables) and exhaustively test every combination, but I suspect that would be even less efficient (although I wonder whether the scaling order would be the same given the nature of the comparisons required).
If it is pertinent, I imported all (>25) data columns from a .csv file as categorical variables. The original data before that were a mix of number and general columns from an Excel sheet; I could have used any or all of {double,string,categorical,datetime} to store the various variables, but there are some data which are best stored as categorical to avoid character trimming and consequent data cleaning / returning to original state steps.
Digging further, I also found this: https://au.mathworks.com/matlabcentral/answers/1775400-how-do-i-find-all-indexes-of-duplicate-names-in-a-table-column-then-compare-the-row-values-for-each which appears to imply that ismember should have the functionality I need here.
Similarly, methods using unique (see e.g. https://au.mathworks.com/matlabcentral/answers/1999193-find-duplicated-rows-in-matlab-without-for-loop or https://au.mathworks.com/matlabcentral/answers/1571588-table-find-duplicate-rows-double-char-datetime or https://au.mathworks.com/matlabcentral/answers/305987-identify-duplicate-rows-in-a-matrix) give:
size(unique([foo;bar],’rows’),1) == size(foo,1)+size(bar,1)
ans =
logical
1
instead of the expected 0 due to the lower amount of actual full-row matches. (Same for "rows" again.)
I’ve also looked into outerjoin/join/innerjoin, but those don’t seem to remove duplicates like I need.I’m using R2020b, and I want to set up a master table for appending new data to – and as part of this I want to identify any duplicate rows in the new, incoming table to filter them out before appending. Ideally, the master table will live in a related directory in a .mat file, and the new data will be read in directly from a set-name, set-location .csv using e.g.
fullname = fullfile(‘relativepath’,’newdata.csv’);
% grab column headers from input sheet
opts = detectImportOptions(fullname);
% set all variable types to categorical
opts.VariableTypes(:) = {‘categorical’};
% read in new data
T = readtable(fullname,opts);
% make any modifications to new data headers to match old data
T = renamevars(T,"NewLabel","OldLabel");
% clean new table headers to match originally-wizard-imported headers (I’d ask why these exhibit different behaviour, but that’s a separate tragedy, and this current fix works – I think)
T.Properties.VariableNames = regexprep(T.Properties.VariableNames, ‘ ‘, ”);
T.Properties.VariableNames = regexprep(T.Properties.VariableNames, ‘(‘, ”);
T.Properties.VariableNames = regexprep(T.Properties.VariableNames, ‘)’, ”);
T.Properties.VariableNames = regexprep(T.Properties.VariableNames, ‘_’, ”);
I found the solution suggested here: https://au.mathworks.com/matlabcentral/answers/514921-finding-identical-rows-in-2-tables, but having done a quick test via:
foo = T(str2double(string(T.Year))<1943,:); % not my actual query, but structurally the same; this gave me ~40% of my original data
bar = T(str2double(string(T.Year))>1941,:); % similar, gave me ~70% of the original data
baz = ismember(foo,bar); % similar, gives the overlap for 1 particular year (should be about 14% of my original data)
blah = T(str2double(string(T.Year))==1942,:); % to directly extract the number of rows I am looking for
sum(baz) % What I expect here is the number of rows in the overlap
ans =
0
I found that ismember was not finding any duplicates (which were there by construction).
Note: due to categorical data I actually used T(str2double(string(T.Year))…)
Replacing
baz = ismember(foo,bar,’rows’);
sum(baz)
ans =
0
results in the same not finding any duplicates. Using double quotes "rows" does not change the behaviour.
On the other hand, using the function to assess single variables gives the expected behaviour (to some degree):
testest = ismember(foo.var1,bar.var1)
sum(testest)
The sum is now non-zero, and (because single variables are repeated more often than their combinations) gives more like 30% of the original data, which seems reasonable (the number of unique entries in the original set in that variable was about 40% of the total).
I guess I could create a logical index based on the product of multiple calls of this kind, but that seems rather… inefficient… and sensitive to the exact construction of the table/variables used in the filter. I’d rather have a generic solution for full table rows that will be robust if the overall table changes over the long term (or if/when I functionalise the code and use it for other work). Whilst most of the time, a couple of key variables can be used to identify unique rows, occasionally more information is required to distinguish pathological cases. I will probably use this approach if a more elegant solution doesn’t appear, though, and put some thought into which groups of variables are 100% correlated (and therefore useless for this distinction) to cut down the Boolean product.
I could also throw good coding practice to the winds and just write two nested loops (one for rows, one for variables) and exhaustively test every combination, but I suspect that would be even less efficient (although I wonder whether the scaling order would be the same given the nature of the comparisons required).
If it is pertinent, I imported all (>25) data columns from a .csv file as categorical variables. The original data before that were a mix of number and general columns from an Excel sheet; I could have used any or all of {double,string,categorical,datetime} to store the various variables, but there are some data which are best stored as categorical to avoid character trimming and consequent data cleaning / returning to original state steps.
Digging further, I also found this: https://au.mathworks.com/matlabcentral/answers/1775400-how-do-i-find-all-indexes-of-duplicate-names-in-a-table-column-then-compare-the-row-values-for-each which appears to imply that ismember should have the functionality I need here.
Similarly, methods using unique (see e.g. https://au.mathworks.com/matlabcentral/answers/1999193-find-duplicated-rows-in-matlab-without-for-loop or https://au.mathworks.com/matlabcentral/answers/1571588-table-find-duplicate-rows-double-char-datetime or https://au.mathworks.com/matlabcentral/answers/305987-identify-duplicate-rows-in-a-matrix) give:
size(unique([foo;bar],’rows’),1) == size(foo,1)+size(bar,1)
ans =
logical
1
instead of the expected 0 due to the lower amount of actual full-row matches. (Same for "rows" again.)
I’ve also looked into outerjoin/join/innerjoin, but those don’t seem to remove duplicates like I need. I’m using R2020b, and I want to set up a master table for appending new data to – and as part of this I want to identify any duplicate rows in the new, incoming table to filter them out before appending. Ideally, the master table will live in a related directory in a .mat file, and the new data will be read in directly from a set-name, set-location .csv using e.g.
fullname = fullfile(‘relativepath’,’newdata.csv’);
% grab column headers from input sheet
opts = detectImportOptions(fullname);
% set all variable types to categorical
opts.VariableTypes(:) = {‘categorical’};
% read in new data
T = readtable(fullname,opts);
% make any modifications to new data headers to match old data
T = renamevars(T,"NewLabel","OldLabel");
% clean new table headers to match originally-wizard-imported headers (I’d ask why these exhibit different behaviour, but that’s a separate tragedy, and this current fix works – I think)
T.Properties.VariableNames = regexprep(T.Properties.VariableNames, ‘ ‘, ”);
T.Properties.VariableNames = regexprep(T.Properties.VariableNames, ‘(‘, ”);
T.Properties.VariableNames = regexprep(T.Properties.VariableNames, ‘)’, ”);
T.Properties.VariableNames = regexprep(T.Properties.VariableNames, ‘_’, ”);
I found the solution suggested here: https://au.mathworks.com/matlabcentral/answers/514921-finding-identical-rows-in-2-tables, but having done a quick test via:
foo = T(str2double(string(T.Year))<1943,:); % not my actual query, but structurally the same; this gave me ~40% of my original data
bar = T(str2double(string(T.Year))>1941,:); % similar, gave me ~70% of the original data
baz = ismember(foo,bar); % similar, gives the overlap for 1 particular year (should be about 14% of my original data)
blah = T(str2double(string(T.Year))==1942,:); % to directly extract the number of rows I am looking for
sum(baz) % What I expect here is the number of rows in the overlap
ans =
0
I found that ismember was not finding any duplicates (which were there by construction).
Note: due to categorical data I actually used T(str2double(string(T.Year))…)
Replacing
baz = ismember(foo,bar,’rows’);
sum(baz)
ans =
0
results in the same not finding any duplicates. Using double quotes "rows" does not change the behaviour.
On the other hand, using the function to assess single variables gives the expected behaviour (to some degree):
testest = ismember(foo.var1,bar.var1)
sum(testest)
The sum is now non-zero, and (because single variables are repeated more often than their combinations) gives more like 30% of the original data, which seems reasonable (the number of unique entries in the original set in that variable was about 40% of the total).
I guess I could create a logical index based on the product of multiple calls of this kind, but that seems rather… inefficient… and sensitive to the exact construction of the table/variables used in the filter. I’d rather have a generic solution for full table rows that will be robust if the overall table changes over the long term (or if/when I functionalise the code and use it for other work). Whilst most of the time, a couple of key variables can be used to identify unique rows, occasionally more information is required to distinguish pathological cases. I will probably use this approach if a more elegant solution doesn’t appear, though, and put some thought into which groups of variables are 100% correlated (and therefore useless for this distinction) to cut down the Boolean product.
I could also throw good coding practice to the winds and just write two nested loops (one for rows, one for variables) and exhaustively test every combination, but I suspect that would be even less efficient (although I wonder whether the scaling order would be the same given the nature of the comparisons required).
If it is pertinent, I imported all (>25) data columns from a .csv file as categorical variables. The original data before that were a mix of number and general columns from an Excel sheet; I could have used any or all of {double,string,categorical,datetime} to store the various variables, but there are some data which are best stored as categorical to avoid character trimming and consequent data cleaning / returning to original state steps.
Digging further, I also found this: https://au.mathworks.com/matlabcentral/answers/1775400-how-do-i-find-all-indexes-of-duplicate-names-in-a-table-column-then-compare-the-row-values-for-each which appears to imply that ismember should have the functionality I need here.
Similarly, methods using unique (see e.g. https://au.mathworks.com/matlabcentral/answers/1999193-find-duplicated-rows-in-matlab-without-for-loop or https://au.mathworks.com/matlabcentral/answers/1571588-table-find-duplicate-rows-double-char-datetime or https://au.mathworks.com/matlabcentral/answers/305987-identify-duplicate-rows-in-a-matrix) give:
size(unique([foo;bar],’rows’),1) == size(foo,1)+size(bar,1)
ans =
logical
1
instead of the expected 0 due to the lower amount of actual full-row matches. (Same for "rows" again.)
I’ve also looked into outerjoin/join/innerjoin, but those don’t seem to remove duplicates like I need. table, ismember, rows, duplicate MATLAB Answers — New Questions

​

x=1:10;
y=exp(.3).*x.^(1); % equation 1
loglog(x,y);
grid on

I want to change the grid line of y axis with slope of equation 1.x=1:10;
y=exp(.3).*x.^(1); % equation 1
loglog(x,y);
grid on

I want to change the grid line of y axis with slope of equation 1. x=1:10;
y=exp(.3).*x.^(1); % equation 1
loglog(x,y);
grid on

I want to change the grid line of y axis with slope of equation 1. grid line, slope, logarithmic MATLAB Answers — New Questions

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Hi, I encountered a problem when I resized a figure: The aim of resizing is to manually drag the top/bottom boundary to adjust the panel size, in order to hide the label and only show the buttons. However, the reality is that no matter where I drag, it is the top space hidden, but the space between the label and bottom remains constant. I can only hide the buttons or hide the buttons and label together, but I cannot find a way to resize to keep the bottom. Is there a way to reverse the direction of resizing?

fig = figure(‘Name’,’GUI with Buttons and Labels’,’NumberTitle’,’off’,’Position’,[100 100 400 300]);

% Create buttons

btn1 = uicontrol(fig,’Style’,’pushbutton’,’String’,’Button 1′,’Position’,[50 200 100 30]);

btn2 = uicontrol(fig,’Style’,’pushbutton’,’String’,’Button 2′,’Position’,[150 200 100 30]);

btn3 = uicontrol(fig,’Style’,’pushbutton’,’String’,’Button 3′,’Position’,[250 200 100 30]);

% Create labels

lbl1 = uicontrol(fig,’Style’,’text’,’String’,’Label 1′,’Position’,[50 150 100 30]);

lbl2 = uicontrol(fig,’Style’,’text’,’String’,’Label 2′,’Position’,[150 150 100 30]);

lbl3 = uicontrol(fig,’Style’,’text’,’String’,’Label 3′,’Position’,[250 150 100 30]);Hi, I encountered a problem when I resized a figure: The aim of resizing is to manually drag the top/bottom boundary to adjust the panel size, in order to hide the label and only show the buttons. However, the reality is that no matter where I drag, it is the top space hidden, but the space between the label and bottom remains constant. I can only hide the buttons or hide the buttons and label together, but I cannot find a way to resize to keep the bottom. Is there a way to reverse the direction of resizing?

fig = figure(‘Name’,’GUI with Buttons and Labels’,’NumberTitle’,’off’,’Position’,[100 100 400 300]);

% Create buttons

btn1 = uicontrol(fig,’Style’,’pushbutton’,’String’,’Button 1′,’Position’,[50 200 100 30]);

btn2 = uicontrol(fig,’Style’,’pushbutton’,’String’,’Button 2′,’Position’,[150 200 100 30]);

btn3 = uicontrol(fig,’Style’,’pushbutton’,’String’,’Button 3′,’Position’,[250 200 100 30]);

% Create labels

lbl1 = uicontrol(fig,’Style’,’text’,’String’,’Label 1′,’Position’,[50 150 100 30]);

lbl2 = uicontrol(fig,’Style’,’text’,’String’,’Label 2′,’Position’,[150 150 100 30]);

lbl3 = uicontrol(fig,’Style’,’text’,’String’,’Label 3′,’Position’,[250 150 100 30]); Hi, I encountered a problem when I resized a figure: The aim of resizing is to manually drag the top/bottom boundary to adjust the panel size, in order to hide the label and only show the buttons. However, the reality is that no matter where I drag, it is the top space hidden, but the space between the label and bottom remains constant. I can only hide the buttons or hide the buttons and label together, but I cannot find a way to resize to keep the bottom. Is there a way to reverse the direction of resizing?

fig = figure(‘Name’,’GUI with Buttons and Labels’,’NumberTitle’,’off’,’Position’,[100 100 400 300]);

% Create buttons

btn1 = uicontrol(fig,’Style’,’pushbutton’,’String’,’Button 1′,’Position’,[50 200 100 30]);

btn2 = uicontrol(fig,’Style’,’pushbutton’,’String’,’Button 2′,’Position’,[150 200 100 30]);

btn3 = uicontrol(fig,’Style’,’pushbutton’,’String’,’Button 3′,’Position’,[250 200 100 30]);

% Create labels

lbl1 = uicontrol(fig,’Style’,’text’,’String’,’Label 1′,’Position’,[50 150 100 30]);

lbl2 = uicontrol(fig,’Style’,’text’,’String’,’Label 2′,’Position’,[150 150 100 30]);

lbl3 = uicontrol(fig,’Style’,’text’,’String’,’Label 3′,’Position’,[250 150 100 30]); figure, resize MATLAB Answers — New Questions

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The circuit is a systhetic circuit usually used for circuit breaker. I add a half sine current on the breaker, and then put a very high voltage Oscillating voltage on it. The half sine current (20kA) meets zero at the 0.01s, and the extern signal for breaker is 0.009s, which means that it will open at the 0.01s. And it actually did. The Oscillating volatge source is added at 0.011s. However, when I reduce the half sine current to about 5kA, something magical happened. The breaker closes again and there is another current that flows through the breaker. And this problem still existed even when I changed the time stamp to put a very high voltage Oscillating voltage to 0.015s as I thought it needed enough time to open the breaker.
I really want to know why this happens and how to solve the problem that the breaker doesn’t opens as i think.
Here is the current that flows through breaker.

Here is my breaker configurations.The circuit is a systhetic circuit usually used for circuit breaker. I add a half sine current on the breaker, and then put a very high voltage Oscillating voltage on it. The half sine current (20kA) meets zero at the 0.01s, and the extern signal for breaker is 0.009s, which means that it will open at the 0.01s. And it actually did. The Oscillating volatge source is added at 0.011s. However, when I reduce the half sine current to about 5kA, something magical happened. The breaker closes again and there is another current that flows through the breaker. And this problem still existed even when I changed the time stamp to put a very high voltage Oscillating voltage to 0.015s as I thought it needed enough time to open the breaker.
I really want to know why this happens and how to solve the problem that the breaker doesn’t opens as i think.
Here is the current that flows through breaker.

Here is my breaker configurations. The circuit is a systhetic circuit usually used for circuit breaker. I add a half sine current on the breaker, and then put a very high voltage Oscillating voltage on it. The half sine current (20kA) meets zero at the 0.01s, and the extern signal for breaker is 0.009s, which means that it will open at the 0.01s. And it actually did. The Oscillating volatge source is added at 0.011s. However, when I reduce the half sine current to about 5kA, something magical happened. The breaker closes again and there is another current that flows through the breaker. And this problem still existed even when I changed the time stamp to put a very high voltage Oscillating voltage to 0.015s as I thought it needed enough time to open the breaker.
I really want to know why this happens and how to solve the problem that the breaker doesn’t opens as i think.
Here is the current that flows through breaker.

Here is my breaker configurations. breaker MATLAB Answers — New Questions

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Hi everyone,
I have a cell array containing multiple gait cycles of an SEMG signal. Each cycle represents the push phase and recovery phase of a wheelchair. I want to get the average of the SEMG signal of all the cycles however, each cycle contains a different amount of data points. How do I normalise the cycles to contain a certain amount of data points without disrupting the signals? I also have a cell array containg the times of propulsion.

If anyone could point me in the right direction that would be great!

Thanks,Hi everyone,
I have a cell array containing multiple gait cycles of an SEMG signal. Each cycle represents the push phase and recovery phase of a wheelchair. I want to get the average of the SEMG signal of all the cycles however, each cycle contains a different amount of data points. How do I normalise the cycles to contain a certain amount of data points without disrupting the signals? I also have a cell array containg the times of propulsion.

If anyone could point me in the right direction that would be great!

Thanks, Hi everyone,
I have a cell array containing multiple gait cycles of an SEMG signal. Each cycle represents the push phase and recovery phase of a wheelchair. I want to get the average of the SEMG signal of all the cycles however, each cycle contains a different amount of data points. How do I normalise the cycles to contain a certain amount of data points without disrupting the signals? I also have a cell array containg the times of propulsion.

If anyone could point me in the right direction that would be great!

Thanks, signal processing, semg MATLAB Answers — New Questions

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A farm manufacturers three products P1, P2 and P3 using two machines M1 and M2. The product yield a contribution of sh.3 sh.2 and sh.4 respectively. Machine M1 and M2 have 2000 and 2500 machine hours respectively. There is an agreement with trading association to manufacture at least 100 units of P1, 200 units of P2 and 50 units of P3 but not more than 150 units of P1. The table below shows the processing time in hours for each machine on each product.
products
machines P1 P2 P3
M1 4 3 5
M2 2 2 4

required:
i. The production plan that maximizes contributionA farm manufacturers three products P1, P2 and P3 using two machines M1 and M2. The product yield a contribution of sh.3 sh.2 and sh.4 respectively. Machine M1 and M2 have 2000 and 2500 machine hours respectively. There is an agreement with trading association to manufacture at least 100 units of P1, 200 units of P2 and 50 units of P3 but not more than 150 units of P1. The table below shows the processing time in hours for each machine on each product.
products
machines P1 P2 P3
M1 4 3 5
M2 2 2 4

required:
i. The production plan that maximizes contribution A farm manufacturers three products P1, P2 and P3 using two machines M1 and M2. The product yield a contribution of sh.3 sh.2 and sh.4 respectively. Machine M1 and M2 have 2000 and 2500 machine hours respectively. There is an agreement with trading association to manufacture at least 100 units of P1, 200 units of P2 and 50 units of P3 but not more than 150 units of P1. The table below shows the processing time in hours for each machine on each product.
products
machines P1 P2 P3
M1 4 3 5
M2 2 2 4

required:
i. The production plan that maximizes contribution #operarions research MATLAB Answers — New Questions

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I am using One Class Support Vector Machines for anomaly detection. Here is the anomaly scores histogram (attached) for the model trained with 274 samples and tested with 31 samples. How do I determine the true/false prediction rates from the anomaly scores histogram.
Thank YouI am using One Class Support Vector Machines for anomaly detection. Here is the anomaly scores histogram (attached) for the model trained with 274 samples and tested with 31 samples. How do I determine the true/false prediction rates from the anomaly scores histogram.
Thank You I am using One Class Support Vector Machines for anomaly detection. Here is the anomaly scores histogram (attached) for the model trained with 274 samples and tested with 31 samples. How do I determine the true/false prediction rates from the anomaly scores histogram.
Thank You anomaly, one class svm, scores MATLAB Answers — New Questions

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