How can I detect and address questionable fixed-point operations in Compare To Constant block?
This example model generates code that does not function as intended. It appears that value 15 is cast to fixdt(1,8,-5) as sfix8_E5, and quantized to zero.How can I detect and address questionable fixed-point operations in Compare To Constant block?
This example model generates code that does not function as intended. It appears that value 15 is cast to fixdt(1,8,-5) as sfix8_E5, and quantized to zero. How can I detect and address questionable fixed-point operations in Compare To Constant block?
This example model generates code that does not function as intended. It appears that value 15 is cast to fixdt(1,8,-5) as sfix8_E5, and quantized to zero. fixed point, data type rules MATLAB Answers — New Questions

​

verver ver answers version MATLAB Answers — New Questions

​

<</matlabcentral/answers/uploaded_files/108002/matlab.png>>

I mean the font size of x:5 and y:-12.38
Thanks in advance<</matlabcentral/answers/uploaded_files/108002/matlab.png>>

I mean the font size of x:5 and y:-12.38
Thanks in advance <</matlabcentral/answers/uploaded_files/108002/matlab.png>>

I mean the font size of x:5 and y:-12.38
Thanks in advance datatip, fontsize MATLAB Answers — New Questions

​

I’m designing a custom training procedure for a CNN that is different from backpropagation in that I use manual update rules for layers or sets of layers. I’m studying my gradient for two types of layers: “conv + actfun + maxpool”, and “conv + actfun + avgpool”, which are identical layers except the last action is a different pooling type.
I compared the two layer types with identical data dimension sizes to see the time differences between maxpool and avgpool, both in the forward pass and the backwards pass of the pooling layers. All other steps in calculating the gradient were exactly the same between the two layers, and showed the same time costs in the two layers. But when looking at time costs specifically of the pooling operations’ forward and backwards passes, I get significantly different times (average of 5000 runs of the gradient, each measurement is in milliseconds):
gradient step | AvgPool | MaxPool | Difference
————————–|———|———|———-
pooling (forward pass) | 0.4165 | 38.6316 | +38.2151
unpooling (backward pass) | 9.9468 | 46.1667 | +36.2199

For reference, all my data arrays are dlarrays on the GPU (gpuArrays in dlarrays), all single precision, and the pooling operations convert 32 by 32 feature maps (across 2 channels and 16384 batch size) to 16 by 16 feature maps (of same # channels and batch size), so just a 2 by 2 pooling operation.
You can see here that the maxpool forward pass (using “maxpool” function) is about 92 times slower than the avgpool forward pass (using “avgpool”), and the maxpool backward pass (using “maxunpool”) is about 4.6 times slower than the avgpool backward pass (using a custom “avgunpool” function that Anthropic’s Claude had to create for me, since matlab has no “avgunpool”).
These results are extremely suspect to me. For the forwards pass, comparing matlab’s built in "maxpool" to built in "avgpool" functions gives a 92x difference, but searching online people seem to instead claim that training max pooling is supposed to be faster than avg pooling, which contradicts the results here.
For simplicity, see the code example below that runs just "maxpool" and "avgpool" only (no other functions) and compares their times:

function analyze_pooling_timing()

% GPU setup
g = gpuDevice();
fprintf(‘GPU: %sn’, g.Name);

% Parameters matching your test
H_in = 32; W_in = 32; C_in = 3; C_out = 2;
N = 16384; % batch size. Try N = 32 small or N = 16384 big
kH = 3; kW = 3;

pool_params.pool_size = [2, 2];
pool_params.pool_stride = [2, 2];
pool_params.pool_padding = 0;

conv_params.stride = [1, 1];
conv_params.padding = ‘same’;
conv_params.dilation = [1, 1];

% Initialize data
Wj = dlarray(gpuArray(single(randn(kH, kW, C_in, C_out) * 0.01)), ‘SSCU’);
Bj = dlarray(gpuArray(single(zeros(C_out, 1))), ‘C’);
Fjmin1 = dlarray(gpuArray(single(randn(H_in, W_in, C_in, N))), ‘SSCB’);

% Number of iterations for averaging
num_iter = 100;
fprintf(‘Running %d iterations for each timing measurement…nn’, num_iter);

%% setup everything in forward pass before the pooling:
% Forward convolution
Sj = dlconv(Fjmin1, Wj, Bj, …
‘Stride’, conv_params.stride, …
‘Padding’, conv_params.padding, …
‘DilationFactor’, conv_params.dilation);
% activation function (and derivative)
Oj = max(Sj, 0); Fprimej = sign(Oj);

%% Time AVERAGE POOLING
fprintf(‘=== AVERAGE POOLING (conv_af_ap) ===n’);
times_ap = struct();

for iter = 1:num_iter

% Average pooling
tic;
Oj_pooled = avgpool(Oj, pool_params.pool_size, …
‘Stride’, pool_params.pool_stride, …
‘Padding’, pool_params.pool_padding);
wait(g);
times_ap.pooling(iter) = toc;

end

%% Time MAX POOLING
fprintf(‘n=== MAX POOLING (conv_af_mp) ===n’);
times_mp = struct();

for iter = 1:num_iter

% Max pooling with indices
tic;
[Oj_pooled, max_indices] = maxpool(Oj, pool_params.pool_size, …
‘Stride’, pool_params.pool_stride, …
‘Padding’, pool_params.pool_padding);
wait(g);
times_mp.pooling(iter) = toc;

end

%% Compute statistics and display results
fprintf(‘n=== TIMING RESULTS (milliseconds) ===n’);
fprintf(‘%-25s %12s %12s %12sn’, ‘Step’, ‘AvgPool’, ‘MaxPool’, ‘Difference’);
fprintf(‘%sn’, repmat(‘-‘, 1, 65));

steps_common = { ‘pooling’};

total_ap = 0;
total_mp = 0;

for i = 1:length(steps_common)
step = steps_common{i};
if isfield(times_ap, step) && isfield(times_mp, step)
mean_ap = mean(times_ap.(step)) * 1000; % times 1000 to convert seconds to milliseconds
mean_mp = mean(times_mp.(step)) * 1000; % times 1000 to convert seconds to milliseconds
total_ap = total_ap + mean_ap;
total_mp = total_mp + mean_mp;
diff = mean_mp – mean_ap;
fprintf(‘%-25s %12.4f %12.4f %+12.4fn’, step, mean_ap, mean_mp, diff);
end
end

fprintf(‘%sn’, repmat(‘-‘, 1, 65));
%fprintf(‘%-25s %12.4f %12.4f %+12.4fn’, ‘TOTAL’, total_ap, total_mp, total_mp – total_ap);
fprintf(‘%-25s %12s %12s %12.2fxn’, ‘Speedup’, ”, ”, total_mp/total_ap);

end

Now you can see one main difference between my calling of maxpool and avgpool: for avgpool I only have 1 output (the pooled values), but with maxpool I have 2 outputs (the pooled values, and the index locations of these max values).

This is important because if I replaced the call to maxpool with only requesting the pooled values (1 output), maxpool is faster as expected:

>> analyze_pooling_timing
GPU: NVIDIA GeForce RTX 5080
Running 1000 iterations for each timing measurement…

=== AVERAGE POOLING (conv_af_ap) ===

=== MAX POOLING (conv_af_mp) ===

=== TIMING RESULTS (milliseconds) ===
Step AvgPool MaxPool Difference
—————————————————————–
pooling 0.4358 0.2330 -0.2028
—————————————————————–
max/avg 0.53x
>>

However if I call maxpool like here with 2 outputs (pooled values and indices), or with 3 outputs (pooled values, indices, and inputSize), this is where maxpool is extremely slow:

>> analyze_pooling_timing
GPU: NVIDIA GeForce RTX 5080
Running 1000 iterations for each timing measurement…

=== AVERAGE POOLING (conv_af_ap) ===

=== MAX POOLING (conv_af_mp) ===

=== TIMING RESULTS (milliseconds) ===
Step AvgPool MaxPool Difference
—————————————————————–
pooling 0.4153 38.9818 +38.5665
—————————————————————–
max/avg 93.86x
>>

So it appears that this is the reason why maxpool is so much slower: requesting the maxpool function to also output the indices leads to a massive slowdown.
Unfortunately, the indices are needed to later differentiate (backwards pass) the maxpool layer… so I need the indices…
I’d assume that whenever someone wants to train a CNN in matlab using a maxpool layer, they would have to call maxpool with indices, and thus I’d expect a similar slowdown…
Can anyone here comment on this? My application needs me to train maxpool layers, so I need to both forward pass and backwards pass through them, thus I need these indices. But it appears that matlab’s version of maxpool may not be the best implementation of a maxpool operation… maybe some inefficient processes regarding obtaining the indices?I’m designing a custom training procedure for a CNN that is different from backpropagation in that I use manual update rules for layers or sets of layers. I’m studying my gradient for two types of layers: “conv + actfun + maxpool”, and “conv + actfun + avgpool”, which are identical layers except the last action is a different pooling type.
I compared the two layer types with identical data dimension sizes to see the time differences between maxpool and avgpool, both in the forward pass and the backwards pass of the pooling layers. All other steps in calculating the gradient were exactly the same between the two layers, and showed the same time costs in the two layers. But when looking at time costs specifically of the pooling operations’ forward and backwards passes, I get significantly different times (average of 5000 runs of the gradient, each measurement is in milliseconds):
gradient step | AvgPool | MaxPool | Difference
————————–|———|———|———-
pooling (forward pass) | 0.4165 | 38.6316 | +38.2151
unpooling (backward pass) | 9.9468 | 46.1667 | +36.2199

For reference, all my data arrays are dlarrays on the GPU (gpuArrays in dlarrays), all single precision, and the pooling operations convert 32 by 32 feature maps (across 2 channels and 16384 batch size) to 16 by 16 feature maps (of same # channels and batch size), so just a 2 by 2 pooling operation.
You can see here that the maxpool forward pass (using “maxpool” function) is about 92 times slower than the avgpool forward pass (using “avgpool”), and the maxpool backward pass (using “maxunpool”) is about 4.6 times slower than the avgpool backward pass (using a custom “avgunpool” function that Anthropic’s Claude had to create for me, since matlab has no “avgunpool”).
These results are extremely suspect to me. For the forwards pass, comparing matlab’s built in "maxpool" to built in "avgpool" functions gives a 92x difference, but searching online people seem to instead claim that training max pooling is supposed to be faster than avg pooling, which contradicts the results here.
For simplicity, see the code example below that runs just "maxpool" and "avgpool" only (no other functions) and compares their times:

function analyze_pooling_timing()

% GPU setup
g = gpuDevice();
fprintf(‘GPU: %sn’, g.Name);

% Parameters matching your test
H_in = 32; W_in = 32; C_in = 3; C_out = 2;
N = 16384; % batch size. Try N = 32 small or N = 16384 big
kH = 3; kW = 3;

pool_params.pool_size = [2, 2];
pool_params.pool_stride = [2, 2];
pool_params.pool_padding = 0;

conv_params.stride = [1, 1];
conv_params.padding = ‘same’;
conv_params.dilation = [1, 1];

% Initialize data
Wj = dlarray(gpuArray(single(randn(kH, kW, C_in, C_out) * 0.01)), ‘SSCU’);
Bj = dlarray(gpuArray(single(zeros(C_out, 1))), ‘C’);
Fjmin1 = dlarray(gpuArray(single(randn(H_in, W_in, C_in, N))), ‘SSCB’);

% Number of iterations for averaging
num_iter = 100;
fprintf(‘Running %d iterations for each timing measurement…nn’, num_iter);

%% setup everything in forward pass before the pooling:
% Forward convolution
Sj = dlconv(Fjmin1, Wj, Bj, …
‘Stride’, conv_params.stride, …
‘Padding’, conv_params.padding, …
‘DilationFactor’, conv_params.dilation);
% activation function (and derivative)
Oj = max(Sj, 0); Fprimej = sign(Oj);

%% Time AVERAGE POOLING
fprintf(‘=== AVERAGE POOLING (conv_af_ap) ===n’);
times_ap = struct();

for iter = 1:num_iter

% Average pooling
tic;
Oj_pooled = avgpool(Oj, pool_params.pool_size, …
‘Stride’, pool_params.pool_stride, …
‘Padding’, pool_params.pool_padding);
wait(g);
times_ap.pooling(iter) = toc;

end

%% Time MAX POOLING
fprintf(‘n=== MAX POOLING (conv_af_mp) ===n’);
times_mp = struct();

for iter = 1:num_iter

% Max pooling with indices
tic;
[Oj_pooled, max_indices] = maxpool(Oj, pool_params.pool_size, …
‘Stride’, pool_params.pool_stride, …
‘Padding’, pool_params.pool_padding);
wait(g);
times_mp.pooling(iter) = toc;

end

%% Compute statistics and display results
fprintf(‘n=== TIMING RESULTS (milliseconds) ===n’);
fprintf(‘%-25s %12s %12s %12sn’, ‘Step’, ‘AvgPool’, ‘MaxPool’, ‘Difference’);
fprintf(‘%sn’, repmat(‘-‘, 1, 65));

steps_common = { ‘pooling’};

total_ap = 0;
total_mp = 0;

for i = 1:length(steps_common)
step = steps_common{i};
if isfield(times_ap, step) && isfield(times_mp, step)
mean_ap = mean(times_ap.(step)) * 1000; % times 1000 to convert seconds to milliseconds
mean_mp = mean(times_mp.(step)) * 1000; % times 1000 to convert seconds to milliseconds
total_ap = total_ap + mean_ap;
total_mp = total_mp + mean_mp;
diff = mean_mp – mean_ap;
fprintf(‘%-25s %12.4f %12.4f %+12.4fn’, step, mean_ap, mean_mp, diff);
end
end

fprintf(‘%sn’, repmat(‘-‘, 1, 65));
%fprintf(‘%-25s %12.4f %12.4f %+12.4fn’, ‘TOTAL’, total_ap, total_mp, total_mp – total_ap);
fprintf(‘%-25s %12s %12s %12.2fxn’, ‘Speedup’, ”, ”, total_mp/total_ap);

end

Now you can see one main difference between my calling of maxpool and avgpool: for avgpool I only have 1 output (the pooled values), but with maxpool I have 2 outputs (the pooled values, and the index locations of these max values).

This is important because if I replaced the call to maxpool with only requesting the pooled values (1 output), maxpool is faster as expected:

>> analyze_pooling_timing
GPU: NVIDIA GeForce RTX 5080
Running 1000 iterations for each timing measurement…

=== AVERAGE POOLING (conv_af_ap) ===

=== MAX POOLING (conv_af_mp) ===

=== TIMING RESULTS (milliseconds) ===
Step AvgPool MaxPool Difference
—————————————————————–
pooling 0.4358 0.2330 -0.2028
—————————————————————–
max/avg 0.53x
>>

However if I call maxpool like here with 2 outputs (pooled values and indices), or with 3 outputs (pooled values, indices, and inputSize), this is where maxpool is extremely slow:

>> analyze_pooling_timing
GPU: NVIDIA GeForce RTX 5080
Running 1000 iterations for each timing measurement…

=== AVERAGE POOLING (conv_af_ap) ===

=== MAX POOLING (conv_af_mp) ===

=== TIMING RESULTS (milliseconds) ===
Step AvgPool MaxPool Difference
—————————————————————–
pooling 0.4153 38.9818 +38.5665
—————————————————————–
max/avg 93.86x
>>

So it appears that this is the reason why maxpool is so much slower: requesting the maxpool function to also output the indices leads to a massive slowdown.
Unfortunately, the indices are needed to later differentiate (backwards pass) the maxpool layer… so I need the indices…
I’d assume that whenever someone wants to train a CNN in matlab using a maxpool layer, they would have to call maxpool with indices, and thus I’d expect a similar slowdown…
Can anyone here comment on this? My application needs me to train maxpool layers, so I need to both forward pass and backwards pass through them, thus I need these indices. But it appears that matlab’s version of maxpool may not be the best implementation of a maxpool operation… maybe some inefficient processes regarding obtaining the indices? I’m designing a custom training procedure for a CNN that is different from backpropagation in that I use manual update rules for layers or sets of layers. I’m studying my gradient for two types of layers: “conv + actfun + maxpool”, and “conv + actfun + avgpool”, which are identical layers except the last action is a different pooling type.
I compared the two layer types with identical data dimension sizes to see the time differences between maxpool and avgpool, both in the forward pass and the backwards pass of the pooling layers. All other steps in calculating the gradient were exactly the same between the two layers, and showed the same time costs in the two layers. But when looking at time costs specifically of the pooling operations’ forward and backwards passes, I get significantly different times (average of 5000 runs of the gradient, each measurement is in milliseconds):
gradient step | AvgPool | MaxPool | Difference
————————–|———|———|———-
pooling (forward pass) | 0.4165 | 38.6316 | +38.2151
unpooling (backward pass) | 9.9468 | 46.1667 | +36.2199

For reference, all my data arrays are dlarrays on the GPU (gpuArrays in dlarrays), all single precision, and the pooling operations convert 32 by 32 feature maps (across 2 channels and 16384 batch size) to 16 by 16 feature maps (of same # channels and batch size), so just a 2 by 2 pooling operation.
You can see here that the maxpool forward pass (using “maxpool” function) is about 92 times slower than the avgpool forward pass (using “avgpool”), and the maxpool backward pass (using “maxunpool”) is about 4.6 times slower than the avgpool backward pass (using a custom “avgunpool” function that Anthropic’s Claude had to create for me, since matlab has no “avgunpool”).
These results are extremely suspect to me. For the forwards pass, comparing matlab’s built in "maxpool" to built in "avgpool" functions gives a 92x difference, but searching online people seem to instead claim that training max pooling is supposed to be faster than avg pooling, which contradicts the results here.
For simplicity, see the code example below that runs just "maxpool" and "avgpool" only (no other functions) and compares their times:

function analyze_pooling_timing()

% GPU setup
g = gpuDevice();
fprintf(‘GPU: %sn’, g.Name);

% Parameters matching your test
H_in = 32; W_in = 32; C_in = 3; C_out = 2;
N = 16384; % batch size. Try N = 32 small or N = 16384 big
kH = 3; kW = 3;

pool_params.pool_size = [2, 2];
pool_params.pool_stride = [2, 2];
pool_params.pool_padding = 0;

conv_params.stride = [1, 1];
conv_params.padding = ‘same’;
conv_params.dilation = [1, 1];

% Initialize data
Wj = dlarray(gpuArray(single(randn(kH, kW, C_in, C_out) * 0.01)), ‘SSCU’);
Bj = dlarray(gpuArray(single(zeros(C_out, 1))), ‘C’);
Fjmin1 = dlarray(gpuArray(single(randn(H_in, W_in, C_in, N))), ‘SSCB’);

% Number of iterations for averaging
num_iter = 100;
fprintf(‘Running %d iterations for each timing measurement…nn’, num_iter);

%% setup everything in forward pass before the pooling:
% Forward convolution
Sj = dlconv(Fjmin1, Wj, Bj, …
‘Stride’, conv_params.stride, …
‘Padding’, conv_params.padding, …
‘DilationFactor’, conv_params.dilation);
% activation function (and derivative)
Oj = max(Sj, 0); Fprimej = sign(Oj);

%% Time AVERAGE POOLING
fprintf(‘=== AVERAGE POOLING (conv_af_ap) ===n’);
times_ap = struct();

for iter = 1:num_iter

% Average pooling
tic;
Oj_pooled = avgpool(Oj, pool_params.pool_size, …
‘Stride’, pool_params.pool_stride, …
‘Padding’, pool_params.pool_padding);
wait(g);
times_ap.pooling(iter) = toc;

end

%% Time MAX POOLING
fprintf(‘n=== MAX POOLING (conv_af_mp) ===n’);
times_mp = struct();

for iter = 1:num_iter

% Max pooling with indices
tic;
[Oj_pooled, max_indices] = maxpool(Oj, pool_params.pool_size, …
‘Stride’, pool_params.pool_stride, …
‘Padding’, pool_params.pool_padding);
wait(g);
times_mp.pooling(iter) = toc;

end

%% Compute statistics and display results
fprintf(‘n=== TIMING RESULTS (milliseconds) ===n’);
fprintf(‘%-25s %12s %12s %12sn’, ‘Step’, ‘AvgPool’, ‘MaxPool’, ‘Difference’);
fprintf(‘%sn’, repmat(‘-‘, 1, 65));

steps_common = { ‘pooling’};

total_ap = 0;
total_mp = 0;

for i = 1:length(steps_common)
step = steps_common{i};
if isfield(times_ap, step) && isfield(times_mp, step)
mean_ap = mean(times_ap.(step)) * 1000; % times 1000 to convert seconds to milliseconds
mean_mp = mean(times_mp.(step)) * 1000; % times 1000 to convert seconds to milliseconds
total_ap = total_ap + mean_ap;
total_mp = total_mp + mean_mp;
diff = mean_mp – mean_ap;
fprintf(‘%-25s %12.4f %12.4f %+12.4fn’, step, mean_ap, mean_mp, diff);
end
end

fprintf(‘%sn’, repmat(‘-‘, 1, 65));
%fprintf(‘%-25s %12.4f %12.4f %+12.4fn’, ‘TOTAL’, total_ap, total_mp, total_mp – total_ap);
fprintf(‘%-25s %12s %12s %12.2fxn’, ‘Speedup’, ”, ”, total_mp/total_ap);

end

Now you can see one main difference between my calling of maxpool and avgpool: for avgpool I only have 1 output (the pooled values), but with maxpool I have 2 outputs (the pooled values, and the index locations of these max values).

This is important because if I replaced the call to maxpool with only requesting the pooled values (1 output), maxpool is faster as expected:

>> analyze_pooling_timing
GPU: NVIDIA GeForce RTX 5080
Running 1000 iterations for each timing measurement…

=== AVERAGE POOLING (conv_af_ap) ===

=== MAX POOLING (conv_af_mp) ===

=== TIMING RESULTS (milliseconds) ===
Step AvgPool MaxPool Difference
—————————————————————–
pooling 0.4358 0.2330 -0.2028
—————————————————————–
max/avg 0.53x
>>

However if I call maxpool like here with 2 outputs (pooled values and indices), or with 3 outputs (pooled values, indices, and inputSize), this is where maxpool is extremely slow:

>> analyze_pooling_timing
GPU: NVIDIA GeForce RTX 5080
Running 1000 iterations for each timing measurement…

=== AVERAGE POOLING (conv_af_ap) ===

=== MAX POOLING (conv_af_mp) ===

=== TIMING RESULTS (milliseconds) ===
Step AvgPool MaxPool Difference
—————————————————————–
pooling 0.4153 38.9818 +38.5665
—————————————————————–
max/avg 93.86x
>>

So it appears that this is the reason why maxpool is so much slower: requesting the maxpool function to also output the indices leads to a massive slowdown.
Unfortunately, the indices are needed to later differentiate (backwards pass) the maxpool layer… so I need the indices…
I’d assume that whenever someone wants to train a CNN in matlab using a maxpool layer, they would have to call maxpool with indices, and thus I’d expect a similar slowdown…
Can anyone here comment on this? My application needs me to train maxpool layers, so I need to both forward pass and backwards pass through them, thus I need these indices. But it appears that matlab’s version of maxpool may not be the best implementation of a maxpool operation… maybe some inefficient processes regarding obtaining the indices? deep learning, maxpool, avgpool, indexing MATLAB Answers — New Questions

​

I have some data that I am displaying in my script and have outputted it in 28 different tables. My script is quite long so I am just posting a screenshot for now.

Where is says var 2 I want to change to the storey number. So for table 1 I want storey 1, for table 2, storey 2 etc…

If you can see for the main output i am running a loop and using the matrix called "Int_force". I also have made the row headers as [Axial Force, Shear Force, Bending Moment. Thus, I am hoping someone can help me so that I can change the column headers to my desired wish? I am not sure how I would change the loop to accomplish this.
Many thanks,
ScottI have some data that I am displaying in my script and have outputted it in 28 different tables. My script is quite long so I am just posting a screenshot for now.

Where is says var 2 I want to change to the storey number. So for table 1 I want storey 1, for table 2, storey 2 etc…

If you can see for the main output i am running a loop and using the matrix called "Int_force". I also have made the row headers as [Axial Force, Shear Force, Bending Moment. Thus, I am hoping someone can help me so that I can change the column headers to my desired wish? I am not sure how I would change the loop to accomplish this.
Many thanks,
Scott I have some data that I am displaying in my script and have outputted it in 28 different tables. My script is quite long so I am just posting a screenshot for now.

Where is says var 2 I want to change to the storey number. So for table 1 I want storey 1, for table 2, storey 2 etc…

If you can see for the main output i am running a loop and using the matrix called "Int_force". I also have made the row headers as [Axial Force, Shear Force, Bending Moment. Thus, I am hoping someone can help me so that I can change the column headers to my desired wish? I am not sure how I would change the loop to accomplish this.
Many thanks,
Scott tables, for loop, matrices MATLAB Answers — New Questions

​

Is there any way to change the width of the edit controls in a livescript, either globally or on a per-control basis?
The edit box is a bit small when using it to enter Matlab code etc. and I would like to have a bit of control over it’s width.
The screenshot below shows the limited width of the edit box,

It would be nice if it was longer.Is there any way to change the width of the edit controls in a livescript, either globally or on a per-control basis?
The edit box is a bit small when using it to enter Matlab code etc. and I would like to have a bit of control over it’s width.
The screenshot below shows the limited width of the edit box,

It would be nice if it was longer. Is there any way to change the width of the edit controls in a livescript, either globally or on a per-control basis?
The edit box is a bit small when using it to enter Matlab code etc. and I would like to have a bit of control over it’s width.
The screenshot below shows the limited width of the edit box,

It would be nice if it was longer. livescript, mlx MATLAB Answers — New Questions

​

I was trying to run the polyspace qualification kit but ihave an issue on a ReportGeneratorQual.pm module
The module can’t be found (i have also installed perl) and i can’t find online anything about this module
How can i install/find this module?I was trying to run the polyspace qualification kit but ihave an issue on a ReportGeneratorQual.pm module
The module can’t be found (i have also installed perl) and i can’t find online anything about this module
How can i install/find this module? I was trying to run the polyspace qualification kit but ihave an issue on a ReportGeneratorQual.pm module
The module can’t be found (i have also installed perl) and i can’t find online anything about this module
How can i install/find this module? polyspace, do, qualification, qual MATLAB Answers — New Questions

​

A very basic question, but I am just getting back into it and I’m struggling to find the right answer on the forums (or I very bad at using Google?).
I have an array which is 100×50 and want to find the mode of each row. When I run the below function, it provides a 98% correct solution. Two of the rows have a mix of values and it is not as easy to tell the mode, whereas the other 98 rows have a distint value. Is it possible to set a threshold so that out of the row, if the value is only less than 30% of the total value then ignore it?
So if row 99 has a value of 1 as the mode, but 1 only occurs 20% (10/50) of the time in the total row then ignore it.
mode(a(:,:));A very basic question, but I am just getting back into it and I’m struggling to find the right answer on the forums (or I very bad at using Google?).
I have an array which is 100×50 and want to find the mode of each row. When I run the below function, it provides a 98% correct solution. Two of the rows have a mix of values and it is not as easy to tell the mode, whereas the other 98 rows have a distint value. Is it possible to set a threshold so that out of the row, if the value is only less than 30% of the total value then ignore it?
So if row 99 has a value of 1 as the mode, but 1 only occurs 20% (10/50) of the time in the total row then ignore it.
mode(a(:,:)); A very basic question, but I am just getting back into it and I’m struggling to find the right answer on the forums (or I very bad at using Google?).
I have an array which is 100×50 and want to find the mode of each row. When I run the below function, it provides a 98% correct solution. Two of the rows have a mix of values and it is not as easy to tell the mode, whereas the other 98 rows have a distint value. Is it possible to set a threshold so that out of the row, if the value is only less than 30% of the total value then ignore it?
So if row 99 has a value of 1 as the mode, but 1 only occurs 20% (10/50) of the time in the total row then ignore it.
mode(a(:,:)); mode, frequency, array MATLAB Answers — New Questions

​

Hello,
I’ve installed MATLAB R2024a on a Ubuntu Server 24.04.3 LTS machine. My goal is to run it headlessly (without a GUI), so I try launching it with:
matlab -nodesktop -nojvm -nosplash
However, when doing this, MATLAB attempts to use a graphical interface and triggers errors related to missing graphical libraries (libXrandr, libgdk_pixbuf, libXinerama, etc.).
The error messages appear to come from a temporary ServiceHost installer located at:
~/.MathWorks/ServiceHost/<machine_name>/_tmp_MSHI_xxxxx/mci/_tempinstaller_glnxa64/bin/glnxa64/InstallMathWorksServiceHost
It seems that the ServiceHost installer is trying to use GUI components, which doesn’t work in this headless server environment.
My questions are:
Is there a way to disable or bypass the ServiceHost installation when running MATLAB in headless mode?
Alternatively, is there a way to install ServiceHost in a non-GUI/headless manner so MATLAB won’t attempt to launch the graphical installer?Hello,
I’ve installed MATLAB R2024a on a Ubuntu Server 24.04.3 LTS machine. My goal is to run it headlessly (without a GUI), so I try launching it with:
matlab -nodesktop -nojvm -nosplash
However, when doing this, MATLAB attempts to use a graphical interface and triggers errors related to missing graphical libraries (libXrandr, libgdk_pixbuf, libXinerama, etc.).
The error messages appear to come from a temporary ServiceHost installer located at:
~/.MathWorks/ServiceHost/<machine_name>/_tmp_MSHI_xxxxx/mci/_tempinstaller_glnxa64/bin/glnxa64/InstallMathWorksServiceHost
It seems that the ServiceHost installer is trying to use GUI components, which doesn’t work in this headless server environment.
My questions are:
Is there a way to disable or bypass the ServiceHost installation when running MATLAB in headless mode?
Alternatively, is there a way to install ServiceHost in a non-GUI/headless manner so MATLAB won’t attempt to launch the graphical installer? Hello,
I’ve installed MATLAB R2024a on a Ubuntu Server 24.04.3 LTS machine. My goal is to run it headlessly (without a GUI), so I try launching it with:
matlab -nodesktop -nojvm -nosplash
However, when doing this, MATLAB attempts to use a graphical interface and triggers errors related to missing graphical libraries (libXrandr, libgdk_pixbuf, libXinerama, etc.).
The error messages appear to come from a temporary ServiceHost installer located at:
~/.MathWorks/ServiceHost/<machine_name>/_tmp_MSHI_xxxxx/mci/_tempinstaller_glnxa64/bin/glnxa64/InstallMathWorksServiceHost
It seems that the ServiceHost installer is trying to use GUI components, which doesn’t work in this headless server environment.
My questions are:
Is there a way to disable or bypass the ServiceHost installation when running MATLAB in headless mode?
Alternatively, is there a way to install ServiceHost in a non-GUI/headless manner so MATLAB won’t attempt to launch the graphical installer? installation, servicehost, linux, ubuntu, headless MATLAB Answers — New Questions

​

Hi there,
I am using ode15s to resolve a DAE system. Since there are 300 variables x1(t)~x300(t) in my case, so I feel it is a little bit inconvenient to define them by writing
syms x1(t) … x300(t)
one by one. Are there any efficient codes to define them?

Many thanks!Hi there,
I am using ode15s to resolve a DAE system. Since there are 300 variables x1(t)~x300(t) in my case, so I feel it is a little bit inconvenient to define them by writing
syms x1(t) … x300(t)
one by one. Are there any efficient codes to define them?

Many thanks! Hi there,
I am using ode15s to resolve a DAE system. Since there are 300 variables x1(t)~x300(t) in my case, so I feel it is a little bit inconvenient to define them by writing
syms x1(t) … x300(t)
one by one. Are there any efficient codes to define them?

Many thanks! function calls, ode, differential equations MATLAB Answers — New Questions

​