Hello,

I currently research the field of near-infrared spectroscopy (NIR), mainly in the analysis of the spectra of polymeric materials. I would like to make a system for classifying these spectra by machine learning. Literature indicates the APP Classification Leaner and its models to perform the training of the spectra. However, I’m having trouble inserting the spectra sheet (containing wavelength and absorbance values). Being new to Matlab, I don’t know how to organize these spreadsheets with different values, because the wavelength is constant for the different samples, but the absorbance varies for them. My idea is to classify the spectra for the different materials. How could I organize the spreadsheet and develop the training?

If anyone can help me with a tutorial or quick step by step, I would be extremely grateful.

Thanks.Hello,

I currently research the field of near-infrared spectroscopy (NIR), mainly in the analysis of the spectra of polymeric materials. I would like to make a system for classifying these spectra by machine learning. Literature indicates the APP Classification Leaner and its models to perform the training of the spectra. However, I’m having trouble inserting the spectra sheet (containing wavelength and absorbance values). Being new to Matlab, I don’t know how to organize these spreadsheets with different values, because the wavelength is constant for the different samples, but the absorbance varies for them. My idea is to classify the spectra for the different materials. How could I organize the spreadsheet and develop the training?

If anyone can help me with a tutorial or quick step by step, I would be extremely grateful.

Thanks. Hello,

I currently research the field of near-infrared spectroscopy (NIR), mainly in the analysis of the spectra of polymeric materials. I would like to make a system for classifying these spectra by machine learning. Literature indicates the APP Classification Leaner and its models to perform the training of the spectra. However, I’m having trouble inserting the spectra sheet (containing wavelength and absorbance values). Being new to Matlab, I don’t know how to organize these spreadsheets with different values, because the wavelength is constant for the different samples, but the absorbance varies for them. My idea is to classify the spectra for the different materials. How could I organize the spreadsheet and develop the training?

If anyone can help me with a tutorial or quick step by step, I would be extremely grateful.

Thanks. nir, classification learner MATLAB Answers — New Questions

​

Hi,

My main problem is this: I’m building a fiber optical communication system using simulink.

As a source, I use a bernoulli generator, then this signal runs to a low pass filter and the exit is the signal equivalent to a signal that comes off a led, so the emission part is done.

The problems begins at the transmission channel. I want to do a filter that simulates the dispersion effect in the fiber (only chromatic dispersion), but I don’t know how. I only have a transfer functions like exp(-j*alpha*f.^2) and I don’t know what to do with this. What’s the right procedure to implement this kind of t.f. in a analog filter at the simulink?

Any help is welcome.

Kind regardsHi,

My main problem is this: I’m building a fiber optical communication system using simulink.

As a source, I use a bernoulli generator, then this signal runs to a low pass filter and the exit is the signal equivalent to a signal that comes off a led, so the emission part is done.

The problems begins at the transmission channel. I want to do a filter that simulates the dispersion effect in the fiber (only chromatic dispersion), but I don’t know how. I only have a transfer functions like exp(-j*alpha*f.^2) and I don’t know what to do with this. What’s the right procedure to implement this kind of t.f. in a analog filter at the simulink?

Any help is welcome.

Kind regards Hi,

My main problem is this: I’m building a fiber optical communication system using simulink.

As a source, I use a bernoulli generator, then this signal runs to a low pass filter and the exit is the signal equivalent to a signal that comes off a led, so the emission part is done.

The problems begins at the transmission channel. I want to do a filter that simulates the dispersion effect in the fiber (only chromatic dispersion), but I don’t know how. I only have a transfer functions like exp(-j*alpha*f.^2) and I don’t know what to do with this. What’s the right procedure to implement this kind of t.f. in a analog filter at the simulink?

Any help is welcome.

Kind regards simulink, optical dispersion MATLAB Answers — New Questions

​

In Excel, I can place an image into a specific cell using Insert -> Pictures -> Place in Cell; this inserts the image into the cell, not floating over the cell. Can the same thing be done from MATLAB using ActiveX? Note I already have working code to insert a floating image in any desired location. What I really need to do is to insert it into a cell of my choosing.In Excel, I can place an image into a specific cell using Insert -> Pictures -> Place in Cell; this inserts the image into the cell, not floating over the cell. Can the same thing be done from MATLAB using ActiveX? Note I already have working code to insert a floating image in any desired location. What I really need to do is to insert it into a cell of my choosing. In Excel, I can place an image into a specific cell using Insert -> Pictures -> Place in Cell; this inserts the image into the cell, not floating over the cell. Can the same thing be done from MATLAB using ActiveX? Note I already have working code to insert a floating image in any desired location. What I really need to do is to insert it into a cell of my choosing. insert image, excel MATLAB Answers — New Questions

​

Dear all,
I have been struggling for a while with the two issues that I show you below.
I have a matrix which represents the image of a pixelated neutron detector. The units of such a matrix is Count/minute. If I plot the matrix with the command surface(X, Y, Z), being Z the matrix, I get the following figure:

In this figure we can see two different trends: the x=y line (plus its width), which is what we call the "Specular Ridge", and a tiny contribution given by the white dashed line, which is what we call the "Zemann Splitting". The Zemann splitting is given by the equation y = sqrt(x^2 – (1.47e-7*H*L^2)), where H is the magnetic field and L is the wavelength of the nuetrons (5.183 AA in my case).
I would like to find the optimum H which fits best to the data inside the red circle by using the above mentioned equation (H should be something like 6-7). And later I would like to make a ROI (Region Of Interest) to count how many counts lay within such area.
I have attached the matrix (file 00607_UD_subplot.txt) and X and Y coordinates (files 00607_UD_xcoordinates.txt and 00607_UD_ycoordinates.txt).
The code to visualize the figure is the following:
A = load(‘00607_UD_subplot.txt’);
X = load(‘00607_UD_xcoordinates.txt’);
Y = load(‘00607_UD_ycoordinates.txt’);

surface(X, Y, A)
axis square
colormap(map);
caxis([5e-4 100])
set(gca,’colorscale’,’log’)
hold on
plot(X, sqrt((X.^2) – (1.47e-7*6.5*5.183^2)), ‘Color’,’white’,’LineStyle’,’–‘, ‘LineWidth’, 2)
xlabel(‘alpha_{i} [rad]’, ‘fontsize’, 18);
ylabel(‘alpha_{f} [rad]’, ‘fontsize’, 18);
cb1 = colorbar;
cb1.Label.String = ‘Neutron Intensity (CTS/MIN)’;
cb1.FontSize = 16;
cb1.Label.FontSize = 20;
cb1.LineWidth = 1.5;
Any help / suggestion would be helpful and very welcomedl!!!
Cheers,
Jose.Dear all,
I have been struggling for a while with the two issues that I show you below.
I have a matrix which represents the image of a pixelated neutron detector. The units of such a matrix is Count/minute. If I plot the matrix with the command surface(X, Y, Z), being Z the matrix, I get the following figure:

In this figure we can see two different trends: the x=y line (plus its width), which is what we call the "Specular Ridge", and a tiny contribution given by the white dashed line, which is what we call the "Zemann Splitting". The Zemann splitting is given by the equation y = sqrt(x^2 – (1.47e-7*H*L^2)), where H is the magnetic field and L is the wavelength of the nuetrons (5.183 AA in my case).
I would like to find the optimum H which fits best to the data inside the red circle by using the above mentioned equation (H should be something like 6-7). And later I would like to make a ROI (Region Of Interest) to count how many counts lay within such area.
I have attached the matrix (file 00607_UD_subplot.txt) and X and Y coordinates (files 00607_UD_xcoordinates.txt and 00607_UD_ycoordinates.txt).
The code to visualize the figure is the following:
A = load(‘00607_UD_subplot.txt’);
X = load(‘00607_UD_xcoordinates.txt’);
Y = load(‘00607_UD_ycoordinates.txt’);

surface(X, Y, A)
axis square
colormap(map);
caxis([5e-4 100])
set(gca,’colorscale’,’log’)
hold on
plot(X, sqrt((X.^2) – (1.47e-7*6.5*5.183^2)), ‘Color’,’white’,’LineStyle’,’–‘, ‘LineWidth’, 2)
xlabel(‘alpha_{i} [rad]’, ‘fontsize’, 18);
ylabel(‘alpha_{f} [rad]’, ‘fontsize’, 18);
cb1 = colorbar;
cb1.Label.String = ‘Neutron Intensity (CTS/MIN)’;
cb1.FontSize = 16;
cb1.Label.FontSize = 20;
cb1.LineWidth = 1.5;
Any help / suggestion would be helpful and very welcomedl!!!
Cheers,
Jose. Dear all,
I have been struggling for a while with the two issues that I show you below.
I have a matrix which represents the image of a pixelated neutron detector. The units of such a matrix is Count/minute. If I plot the matrix with the command surface(X, Y, Z), being Z the matrix, I get the following figure:

In this figure we can see two different trends: the x=y line (plus its width), which is what we call the "Specular Ridge", and a tiny contribution given by the white dashed line, which is what we call the "Zemann Splitting". The Zemann splitting is given by the equation y = sqrt(x^2 – (1.47e-7*H*L^2)), where H is the magnetic field and L is the wavelength of the nuetrons (5.183 AA in my case).
I would like to find the optimum H which fits best to the data inside the red circle by using the above mentioned equation (H should be something like 6-7). And later I would like to make a ROI (Region Of Interest) to count how many counts lay within such area.
I have attached the matrix (file 00607_UD_subplot.txt) and X and Y coordinates (files 00607_UD_xcoordinates.txt and 00607_UD_ycoordinates.txt).
The code to visualize the figure is the following:
A = load(‘00607_UD_subplot.txt’);
X = load(‘00607_UD_xcoordinates.txt’);
Y = load(‘00607_UD_ycoordinates.txt’);

surface(X, Y, A)
axis square
colormap(map);
caxis([5e-4 100])
set(gca,’colorscale’,’log’)
hold on
plot(X, sqrt((X.^2) – (1.47e-7*6.5*5.183^2)), ‘Color’,’white’,’LineStyle’,’–‘, ‘LineWidth’, 2)
xlabel(‘alpha_{i} [rad]’, ‘fontsize’, 18);
ylabel(‘alpha_{f} [rad]’, ‘fontsize’, 18);
cb1 = colorbar;
cb1.Label.String = ‘Neutron Intensity (CTS/MIN)’;
cb1.FontSize = 16;
cb1.Label.FontSize = 20;
cb1.LineWidth = 1.5;
Any help / suggestion would be helpful and very welcomedl!!!
Cheers,
Jose. curve fitting, region of interest MATLAB Answers — New Questions

​

Hi all, I have a question that’s either a case of "am I not understanding this right?" or a bug in eventfilters.
I’ve noticed 2 things: 1) event filters with durations seem to return the rows after the start of events, and 2) scaling the duration of events does not result in the expected scaling of the number of rows filtered.
For background, in reality I’m using this to mark the edges of TTL-pulses in datasets, but I made an example below that I think illustrates my point.
Running the following code:
% Create a 5s timetable with mock data.
test_table = timetable(seconds(linspace(1/1000, 5, 5000)).’, linspace(0, 1, 5000).’);
% Create an eventtable with 3 events at random time points and a duration
% equal to the table timestep.
test_times = seconds([1.101, 2.2, 3]);
labels = ["rising", "rising", "falling"];
delta_t = test_table.Properties.TimeStep;
lengths = repmat(1*delta_t, 1, 3);
test_events = eventtable(test_times, EventLabels = labels, EventLengths = lengths);
% Associate events with timetable.
test_table.Properties.Events = test_events;
% Create a filter to determine the time of the rising edges.
test_filter = eventfilter("rising");
test_rows = test_table(test_filter, :);
% Display number of rows returned by the filter.
disp(height(test_rows));
% Display the times of the filtered rows.
disp(test_rows.Time);
So here I’m making a mock timetable of 5 seconds at 1000 Hz, with mock data in it, and 3 time events: two rising edges, and one falling edge. Each event is given a length of 1/1000s or 1 ms, which is equal to the timestep of the table. Then, I create an eventfilter to extract the rows of the time table at these edges.
Issue 1
The filter returns 2 rows as expected. However, the row times are 1.102 and 2.201, which are the row times after the rows in which the events begin according to the event table. This seems at odds with the description in the documentation for eventtable, which says that "Interval events happen during intervals that start at event times and include all times up to, but not including, the times at the end of the events." (emphasis mine). So shouldn’t these timed events return the row times 1.101 and 2.200 seconds?
As an aside, removing the EventLengths from the eventtable constructor returns the correct row times.
Issue 2
Second, for events with lengths larger than a single TimeStep, increasing the duration by a given factor does not always increase the number of returned rows by the same amount.
In this case, if I replace
lengths = repmat(1*delta_t, 1, 3);
with
lengths = repmat(50*delta_t, 1, 3);
and run the code again:
test_events = eventtable(test_times, EventLabels = labels, EventLengths = lengths);
test_table.Properties.Events = test_events;
test_filter = eventfilter("rising");
test_rows = test_table(test_filter, :);
disp(height(test_rows));
the number of filtered rows increases from 2 to 100, as expected.
However, increasing the duration of the events to 100 TimeSteps, changes the number of filtered rows to 199 instead of 200, which is what I was expecting.
lengths = repmat(100*delta_t, 1, 3);
test_events = eventtable(test_times, EventLabels = labels, EventLengths = lengths);
test_table.Properties.Events = test_events;
test_filter = eventfilter("rising");
test_rows = test_table(test_filter, :);
disp(height(test_rows));
This seems odd, given that both 50 and 100 TimeSteps should easily fit within the time vector of the table, are exact multiples of each other, do not overlap with other events and the length was based on the TimeStep property of the table.
I’ve locally tested the script in 2024a, but the code examples above ran in 2024b.Hi all, I have a question that’s either a case of "am I not understanding this right?" or a bug in eventfilters.
I’ve noticed 2 things: 1) event filters with durations seem to return the rows after the start of events, and 2) scaling the duration of events does not result in the expected scaling of the number of rows filtered.
For background, in reality I’m using this to mark the edges of TTL-pulses in datasets, but I made an example below that I think illustrates my point.
Running the following code:
% Create a 5s timetable with mock data.
test_table = timetable(seconds(linspace(1/1000, 5, 5000)).’, linspace(0, 1, 5000).’);
% Create an eventtable with 3 events at random time points and a duration
% equal to the table timestep.
test_times = seconds([1.101, 2.2, 3]);
labels = ["rising", "rising", "falling"];
delta_t = test_table.Properties.TimeStep;
lengths = repmat(1*delta_t, 1, 3);
test_events = eventtable(test_times, EventLabels = labels, EventLengths = lengths);
% Associate events with timetable.
test_table.Properties.Events = test_events;
% Create a filter to determine the time of the rising edges.
test_filter = eventfilter("rising");
test_rows = test_table(test_filter, :);
% Display number of rows returned by the filter.
disp(height(test_rows));
% Display the times of the filtered rows.
disp(test_rows.Time);
So here I’m making a mock timetable of 5 seconds at 1000 Hz, with mock data in it, and 3 time events: two rising edges, and one falling edge. Each event is given a length of 1/1000s or 1 ms, which is equal to the timestep of the table. Then, I create an eventfilter to extract the rows of the time table at these edges.
Issue 1
The filter returns 2 rows as expected. However, the row times are 1.102 and 2.201, which are the row times after the rows in which the events begin according to the event table. This seems at odds with the description in the documentation for eventtable, which says that "Interval events happen during intervals that start at event times and include all times up to, but not including, the times at the end of the events." (emphasis mine). So shouldn’t these timed events return the row times 1.101 and 2.200 seconds?
As an aside, removing the EventLengths from the eventtable constructor returns the correct row times.
Issue 2
Second, for events with lengths larger than a single TimeStep, increasing the duration by a given factor does not always increase the number of returned rows by the same amount.
In this case, if I replace
lengths = repmat(1*delta_t, 1, 3);
with
lengths = repmat(50*delta_t, 1, 3);
and run the code again:
test_events = eventtable(test_times, EventLabels = labels, EventLengths = lengths);
test_table.Properties.Events = test_events;
test_filter = eventfilter("rising");
test_rows = test_table(test_filter, :);
disp(height(test_rows));
the number of filtered rows increases from 2 to 100, as expected.
However, increasing the duration of the events to 100 TimeSteps, changes the number of filtered rows to 199 instead of 200, which is what I was expecting.
lengths = repmat(100*delta_t, 1, 3);
test_events = eventtable(test_times, EventLabels = labels, EventLengths = lengths);
test_table.Properties.Events = test_events;
test_filter = eventfilter("rising");
test_rows = test_table(test_filter, :);
disp(height(test_rows));
This seems odd, given that both 50 and 100 TimeSteps should easily fit within the time vector of the table, are exact multiples of each other, do not overlap with other events and the length was based on the TimeStep property of the table.
I’ve locally tested the script in 2024a, but the code examples above ran in 2024b. Hi all, I have a question that’s either a case of "am I not understanding this right?" or a bug in eventfilters.
I’ve noticed 2 things: 1) event filters with durations seem to return the rows after the start of events, and 2) scaling the duration of events does not result in the expected scaling of the number of rows filtered.
For background, in reality I’m using this to mark the edges of TTL-pulses in datasets, but I made an example below that I think illustrates my point.
Running the following code:
% Create a 5s timetable with mock data.
test_table = timetable(seconds(linspace(1/1000, 5, 5000)).’, linspace(0, 1, 5000).’);
% Create an eventtable with 3 events at random time points and a duration
% equal to the table timestep.
test_times = seconds([1.101, 2.2, 3]);
labels = ["rising", "rising", "falling"];
delta_t = test_table.Properties.TimeStep;
lengths = repmat(1*delta_t, 1, 3);
test_events = eventtable(test_times, EventLabels = labels, EventLengths = lengths);
% Associate events with timetable.
test_table.Properties.Events = test_events;
% Create a filter to determine the time of the rising edges.
test_filter = eventfilter("rising");
test_rows = test_table(test_filter, :);
% Display number of rows returned by the filter.
disp(height(test_rows));
% Display the times of the filtered rows.
disp(test_rows.Time);
So here I’m making a mock timetable of 5 seconds at 1000 Hz, with mock data in it, and 3 time events: two rising edges, and one falling edge. Each event is given a length of 1/1000s or 1 ms, which is equal to the timestep of the table. Then, I create an eventfilter to extract the rows of the time table at these edges.
Issue 1
The filter returns 2 rows as expected. However, the row times are 1.102 and 2.201, which are the row times after the rows in which the events begin according to the event table. This seems at odds with the description in the documentation for eventtable, which says that "Interval events happen during intervals that start at event times and include all times up to, but not including, the times at the end of the events." (emphasis mine). So shouldn’t these timed events return the row times 1.101 and 2.200 seconds?
As an aside, removing the EventLengths from the eventtable constructor returns the correct row times.
Issue 2
Second, for events with lengths larger than a single TimeStep, increasing the duration by a given factor does not always increase the number of returned rows by the same amount.
In this case, if I replace
lengths = repmat(1*delta_t, 1, 3);
with
lengths = repmat(50*delta_t, 1, 3);
and run the code again:
test_events = eventtable(test_times, EventLabels = labels, EventLengths = lengths);
test_table.Properties.Events = test_events;
test_filter = eventfilter("rising");
test_rows = test_table(test_filter, :);
disp(height(test_rows));
the number of filtered rows increases from 2 to 100, as expected.
However, increasing the duration of the events to 100 TimeSteps, changes the number of filtered rows to 199 instead of 200, which is what I was expecting.
lengths = repmat(100*delta_t, 1, 3);
test_events = eventtable(test_times, EventLabels = labels, EventLengths = lengths);
test_table.Properties.Events = test_events;
test_filter = eventfilter("rising");
test_rows = test_table(test_filter, :);
disp(height(test_rows));
This seems odd, given that both 50 and 100 TimeSteps should easily fit within the time vector of the table, are exact multiples of each other, do not overlap with other events and the length was based on the TimeStep property of the table.
I’ve locally tested the script in 2024a, but the code examples above ran in 2024b. time series, eventtable, timetable, matlab, eventfilter MATLAB Answers — New Questions

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i am converted rgb color image to im2bw format and gray scale image.
after i am extracted the boundary then i want to stored the the colored image.
please tell how to manage the color then recover or apply color for gray scale image and im2bw imagei am converted rgb color image to im2bw format and gray scale image.
after i am extracted the boundary then i want to stored the the colored image.
please tell how to manage the color then recover or apply color for gray scale image and im2bw image i am converted rgb color image to im2bw format and gray scale image.
after i am extracted the boundary then i want to stored the the colored image.
please tell how to manage the color then recover or apply color for gray scale image and im2bw image image processing, digital image processing, image, image segmentation, image analysis MATLAB Answers — New Questions

​

I am using the pdf function (https://nl.mathworks.com/help/stats/prob.normaldistribution.pdf.html) to just visuilize the pdf of the t statistic for teaching purposes. I am wondering how MATLAB generates this continous distrubtion. That is, this is the limiting distrbituion, so MATLAB should be taking many many random draws from the t distrubtion so that the frequency distribtuon converges to the pdf as the limiting distribtuon and looks all smooth, i.e. it does not look at all like a frequency distribution. But then how come that this happens so fast that I get the pdf immedaitly when I call the pdf function? Or is this not how MATLAB generates the pdf?I am using the pdf function (https://nl.mathworks.com/help/stats/prob.normaldistribution.pdf.html) to just visuilize the pdf of the t statistic for teaching purposes. I am wondering how MATLAB generates this continous distrubtion. That is, this is the limiting distrbituion, so MATLAB should be taking many many random draws from the t distrubtion so that the frequency distribtuon converges to the pdf as the limiting distribtuon and looks all smooth, i.e. it does not look at all like a frequency distribution. But then how come that this happens so fast that I get the pdf immedaitly when I call the pdf function? Or is this not how MATLAB generates the pdf? I am using the pdf function (https://nl.mathworks.com/help/stats/prob.normaldistribution.pdf.html) to just visuilize the pdf of the t statistic for teaching purposes. I am wondering how MATLAB generates this continous distrubtion. That is, this is the limiting distrbituion, so MATLAB should be taking many many random draws from the t distrubtion so that the frequency distribtuon converges to the pdf as the limiting distribtuon and looks all smooth, i.e. it does not look at all like a frequency distribution. But then how come that this happens so fast that I get the pdf immedaitly when I call the pdf function? Or is this not how MATLAB generates the pdf? pdf, function MATLAB Answers — New Questions

​

Greetings,

I use MATLAB 2013a on a Windows 7 platform and am having trouble reading GRiB files with the NCTOOLBOX package (downloaded from https://github.com/nctoolbox/nctoolbox). The package reads netcdf fine….but gives a whole slew of errors for GRiB (yes I verified the GRiB was good).

My question is……is anyone having trouble reading GRiB on Windows platforms? If not, what are you using to read the data?Greetings,

I use MATLAB 2013a on a Windows 7 platform and am having trouble reading GRiB files with the NCTOOLBOX package (downloaded from https://github.com/nctoolbox/nctoolbox). The package reads netcdf fine….but gives a whole slew of errors for GRiB (yes I verified the GRiB was good).

My question is……is anyone having trouble reading GRiB on Windows platforms? If not, what are you using to read the data? Greetings,

I use MATLAB 2013a on a Windows 7 platform and am having trouble reading GRiB files with the NCTOOLBOX package (downloaded from https://github.com/nctoolbox/nctoolbox). The package reads netcdf fine….but gives a whole slew of errors for GRiB (yes I verified the GRiB was good).

My question is……is anyone having trouble reading GRiB on Windows platforms? If not, what are you using to read the data? read grib, windows MATLAB Answers — New Questions

​

Hi everyone,
I am currently running a reinforcement learning model, integrated with simevents blocks in simulink. I have both a reinforcement learning script and the RL agent present in the simulink. Currently my reward function works based on a matlab function block that is connected to the reward input of RL agent block. I am facing an issue of constant reward generated throughout the entire episodes of RL iteration. Any ideas why? Because I try to assign the reward function (code below) as simple as possible, extract values from the entities of simevents, to generate values that are supposed to be different with each iteration.
function r = w(u1, u2, u3) %#codegen
% Extract Entities
FH = u1 + 1;
Cost = u2 + 1;
Downtime = u3 + 1;

% Reward calculation based on values
r = (Downtime/Cost) * FH;
end
There seems to be a problem as well because this reward area is red, eventhough the simulation runs normally.

I uploaded my model and a screenshot of the RL training result of the reward. If you are interested to replicate my results here are the steps:
Run script A.mlx to generate random number set A
Run script B.mlx to generate random number set B
Run MainScript.mlx to run the simulation
Thank you so much in advance! Let me know should you require any further information.
Best,
Aaron.Hi everyone,
I am currently running a reinforcement learning model, integrated with simevents blocks in simulink. I have both a reinforcement learning script and the RL agent present in the simulink. Currently my reward function works based on a matlab function block that is connected to the reward input of RL agent block. I am facing an issue of constant reward generated throughout the entire episodes of RL iteration. Any ideas why? Because I try to assign the reward function (code below) as simple as possible, extract values from the entities of simevents, to generate values that are supposed to be different with each iteration.
function r = w(u1, u2, u3) %#codegen
% Extract Entities
FH = u1 + 1;
Cost = u2 + 1;
Downtime = u3 + 1;

% Reward calculation based on values
r = (Downtime/Cost) * FH;
end
There seems to be a problem as well because this reward area is red, eventhough the simulation runs normally.

I uploaded my model and a screenshot of the RL training result of the reward. If you are interested to replicate my results here are the steps:
Run script A.mlx to generate random number set A
Run script B.mlx to generate random number set B
Run MainScript.mlx to run the simulation
Thank you so much in advance! Let me know should you require any further information.
Best,
Aaron. Hi everyone,
I am currently running a reinforcement learning model, integrated with simevents blocks in simulink. I have both a reinforcement learning script and the RL agent present in the simulink. Currently my reward function works based on a matlab function block that is connected to the reward input of RL agent block. I am facing an issue of constant reward generated throughout the entire episodes of RL iteration. Any ideas why? Because I try to assign the reward function (code below) as simple as possible, extract values from the entities of simevents, to generate values that are supposed to be different with each iteration.
function r = w(u1, u2, u3) %#codegen
% Extract Entities
FH = u1 + 1;
Cost = u2 + 1;
Downtime = u3 + 1;

% Reward calculation based on values
r = (Downtime/Cost) * FH;
end
There seems to be a problem as well because this reward area is red, eventhough the simulation runs normally.

I uploaded my model and a screenshot of the RL training result of the reward. If you are interested to replicate my results here are the steps:
Run script A.mlx to generate random number set A
Run script B.mlx to generate random number set B
Run MainScript.mlx to run the simulation
Thank you so much in advance! Let me know should you require any further information.
Best,
Aaron. matlab, simulink, matlab function MATLAB Answers — New Questions

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Is there any way how to effectively generalize movmedian function to work with variable window length or local variable k-point median values, where k is vector with the same length as length of input vector (lenght(x) = lenght(k))?
Example:
x = 1:6
k = 2,3,3,5,3,2
M = movmedian_vk(x,k)
M = 1, 2, 3, 4, 5, 5.5
My naive solution looks like:
function M = movmedian_vk(x,k)

if length(k) ~= length(x)
error(‘Incomaptible input data’)
end

M = zeros(size(x));
[uk,~,ck] = unique(k);
for i = 1:length(uk)
M_i = movmedian(x,uk(i));
I_i = (ck == i);
M(I_i) = M_i(I_i);
end
endIs there any way how to effectively generalize movmedian function to work with variable window length or local variable k-point median values, where k is vector with the same length as length of input vector (lenght(x) = lenght(k))?
Example:
x = 1:6
k = 2,3,3,5,3,2
M = movmedian_vk(x,k)
M = 1, 2, 3, 4, 5, 5.5
My naive solution looks like:
function M = movmedian_vk(x,k)

if length(k) ~= length(x)
error(‘Incomaptible input data’)
end

M = zeros(size(x));
[uk,~,ck] = unique(k);
for i = 1:length(uk)
M_i = movmedian(x,uk(i));
I_i = (ck == i);
M(I_i) = M_i(I_i);
end
end Is there any way how to effectively generalize movmedian function to work with variable window length or local variable k-point median values, where k is vector with the same length as length of input vector (lenght(x) = lenght(k))?
Example:
x = 1:6
k = 2,3,3,5,3,2
M = movmedian_vk(x,k)
M = 1, 2, 3, 4, 5, 5.5
My naive solution looks like:
function M = movmedian_vk(x,k)

if length(k) ~= length(x)
error(‘Incomaptible input data’)
end

M = zeros(size(x));
[uk,~,ck] = unique(k);
for i = 1:length(uk)
M_i = movmedian(x,uk(i));
I_i = (ck == i);
M(I_i) = M_i(I_i);
end
end moving, median MATLAB Answers — New Questions

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