MATLAB Answers

Constrained Global Optimization Problem with MultiStart, GA and HybridFunction using Parallel Processing whithout Step

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David Hastana
David Hastana on 21 Feb 2020
Answered: David Hastana on 24 Feb 2020
Hello,
I am interested in using a genetic algorithm approach to fitting measurement data to a function of 3 variables with 4 unknown coefficients.
I am not concerned with the computational time required for the genetic approach, as, for now, I am just trying to develop a methodology for fitting complex, non-linear/non-smooth, functions using the various features available in Matlab's Global Optimization toolbox. The complexity and non-linearity of my function (presented below) will greatly increase in the near future.
The function which I am trying to fit my data to is defined as following:
VALUE=a1*x+a2*ln(y)+a3*abs(z)+a4
With: a1,a2,a3,a4 being the regression model's unknown coefficients
The following optimization constraints need to be imposed on the coefficients of the function:
a3<0; a4>0; a1*a2<0;
The data to which I am trying to fit my function to is presented in the attached SampleData.csv file. The file contains column labels which define the measured value and x,y,z parameters.
My initial guesses for the values of a1,a2,a3,a4 are defined as a row vector: [0,0,0,0].
I would like to learn how to set this problem up using MultiStart and parallel processing. I would like to use parallel processing for the MultiStart process, as well as to include a Hybrid Function, if such an operation is possible.
The work flow which I would like to achieve should be similar to:
1. Define Function (referring to x,y,z data contained in columns of SampleData.csv file) :
function [ Value ] = FittingFunction(x,data)
Value=x(1).*data(:,2)+...
x(2).*log(data(:,3))+...
x(3).*abs(data(:,4))+...
x(4);
end
I would prefer to not use an anonymous function as the complexity of the model to which I will be fitting my future data to will only increase and an anonymous function would be messy to adjust as the regression model matures.
2.Define Initial Guess Row Vector x=[0,0,0,0]
3.Define optimization problem (referring to value data & x,y,z data contained in columns of SampleData.csv file) as a minimization problem trying to minimize a cost function defined as:
Cost=sum((FittingFunction(x,data)-data(:,1)).^2);
4. Pass optimization problem to a Genetic Algorithm routine with a hybrid function included using fminunc.
5. Create a MultiStart Optimization Object and Start a pool of workers.
lowerBounds for MultiStart =[-Inf,-Inf,-Inf,-Inf]; upperBounds for MultiStart =[Inf,Inf,Inf,Inf];
Number of MultiStart Iterations= 5.
5. Run optimization in parallel.
I realize that the problem which I defined is quite complex in its definition. I am not an expert user of Matlab and may not realize that my approach to solving this problem and the requirements (using the genetic algorithm, parallel processing, constraining coefficient ranges, and doing all of this using a MultiStart approach) for the fitting process may be unachievable.
I am using the serial port to read numbers from the arduino encoder. I did drawing a chart in app designer but in the X axis I only have the number of the next displayed number. I would like to see the time after which the number appeared in the X axis. The first number is e.g. 5 and in the x axis its time is 0, the next number 6 in the x axis appeared after 120ms and wants this time on the X axis.
delete(instrfind({'Port'},{app.PORT}))
app.x=serial(app.PORT,'BAUD',9600);
flushinput(app.x);
fopen(app.x); % otwarcie portu szeregowego
for i=1:app.Zakres.Value
drawnow
if app.Z == 1
break
end
h = str2num(fscanf(app.x));
app.aPolozenie.Value = h;
s = [s,h];
plot(app.UIAxes,s);
app.UIAxes.YLim = [ app.Od.Value app.Do.Value];
app.UIAxes.XLim = [0 app.Zakres.Value];
pause(.0001);
app.Zatrzymaj
end
end
This is my current status:
  • I do not know how to set up the fitting routine to use the Genetic Algorithm; my code is currently using lsqcurvefit, because this was the only way that I could figure out how to instantiate the matlab optimization problem object using the createOptimProblem() constructor.
  • I understand that lsqcurvefit cannot perform constrained optimization, but the Genetic Algorithm approach can do that; any help with setting up this problem using the genetic approach would be greatly appreciated, even if it is without the use of MultiStart for now.
  • As I am currently not using the Genetic Algorithm, I am not forcing the optimization solver to use the 'HybridFunction','on' option.
  • I am aware of the fact that the Global Optimization Toolbox has two options for finding global minima: GlobalSearch and MultiStart. I have decided to use MultiStart because according to the following article: http://www.mathworks.com/help/gads/how-globalsearch-and-multistart-work.html#bsc9eec MultiStart Can be setup in parallel on a multicore processor, while GlobalSearch cannot.
  • I am not sure if MultiStart or GlobalSearch can be setup to run with a Genetic Algorithm...
the MinGW Support Package is still shown as installed.
Why does MATLAB not detect the MinGW compiler anymore after a restart?
When installing the MinGW compiler, the environment variable MW_MINGW64_LOC is set with the path to the MinGW installation folder. On some systems where users have limited privileges, there may be policies in place preventing environment variables from being set. If this environment variable is not staying set following the installation, or after restarting MATLAB, the behavior described above may occur.
Here is my current code (requires the data.mat file attached to my posting):
clc
clear variables
%%Loading Measurement Data File
load('data.mat')
%%Defining function to which the data will be fit to
% I would like to move away from using the anonymous function,
% and move to using the function file defined in the directory but I don't
% know how to pass a function file to the createOptimProblem function call.
fitfcn1 = @(x,data)x(1).*data.x+x(2).*log(data.y)+abs(data.z).*x(3)+x(4);
%%Defining lower and upper bounds for MultiStart procedure
lb1 = [-Inf,-Inf,-Inf,-Inf];
ub1 = [Inf,Inf,Inf,Inf];
%%Creating row vector for intial guess to optimization routine
p01 = 0*ones(1,4);
%%Defining optimization problem object
% Currently running lsqcurvefit, but I need to move to the genetic
% algorythm approach. I do not understand how to setup a genetic alorythm
% data fitting routine in matlab using the GA functionality built into the
% global optimization toolbox.
problem1 = createOptimProblem('lsqcurvefit','x0',p01,'objective',fitfcn1,...
'lb',lb1,'ub',ub1,'xdata',data,'ydata',data.value);
%%Creating live fitting progress plot
ms1 = MultiStart('PlotFcns',@gsplotbestf);
%%Running MultiStart Optimization Routine
[xmulti1,errormulti1] = run(ms1,problem1,5)
This is my current status:
  • I do not know how to set up the fitting routine to use the Genetic Algorithm; my code is currently using lsqcurvefit, because this was the only way that I could figure out how to instantiate the matlab optimization problem object using the createOptimProblem() constructor.
  • I understand that lsqcurvefit cannot perform constrained optimization, but the Genetic Algorithm approach can do that; any help with setting up this problem using the genetic approach would be greatly appreciated, even if it is without the use of MultiStart for now.
  • As I am currently not using the Genetic Algorithm, I am not forcing the optimization solver to use the 'HybridFunction','on' option.
  • I am aware of the fact that the Global Optimization Toolbox has two options for finding global minima: GlobalSearch and MultiStart. I have decided to use MultiStart because according to the following article: http://www.mathworks.com/help/gads/how-globalsearch-and-multistart-work.html#bsc9eec MultiStart Can be setup in parallel on a multicore processor, while GlobalSearch cannot.
  • I am not sure if MultiStart or GlobalSearch can be setup to run with a Genetic Algorithm...
Can someone help on this? I'm sure it is straight forward but getting stumped. I would simply like to add two matrices together, element-wise, ignoring the NaNs. I realize one solution is to replace NaNs with zeros but I have some reported zeros that I want to be able to later identify. A solution to that is to set any real zeros to extremely small numbers. Rather than doing the above I am wondering if there is any other way to ignoring the NaNs -- I know of sum(...,'omitnan') and nansum() but they work on one matrix or if I concatenate the two matrices together then they aggregate either all rows or all columns depending on the dimension I select.
For example: A = ones(10,3); B = ones(10,3);
% inserting some NaNs and zeros A([1 6 10],1) = NaN; A([3 7],[2 3]) = NaN; B([3 7],2) = 0;
C = A + B;
I would like C = [1 2 2; 2 2 2; 2 0 1; 2 2 2; 2 2 2; 1 2 2; 2 0 1; 2 2 2; 2 2 2; 1 2 2]
I realize that the problem which I defined is quite complex in its definition. I am not an expert user of Matlab and may not realize that my approach to solving this problem and the requirements (using the genetic algorithm, parallel processing, constraining coefficient ranges, and doing all of this using a MultiStart approach) for the fitting process may be unachievable.
After installing the "MATLAB Support for MinGW-w64 C/C++ Compiler" support package, the command
>> mex -setup
reports that the compiler is correctly installed. However, when I restart MATLAB, the same command does not detect it as installed anymore.
When I check the Add-On Explorer, or run the command
>> matlabshared.supportpkg.getInstalled
the MinGW Support Package is still shown as installed.
Why does MATLAB not detect the MinGW compiler anymore after a restart?
When installing the MinGW compiler, the environment variable MW_MINGW64_LOC is set with the path to the MinGW installation folder. On some systems where users have limited privileges, there may be policies in place preventing environment variables from being set. If this environment variable is not staying set following the installation, or after restarting MATLAB, the behavior described above may occur.
Running the 'mex -setup' command in verbose mode
>> mex -setup -v
will display whether the MW_MINGW64_LOC environment variable is set. If this is not the case, the output will look as shown below:
... Looking for compiler 'MinGW64 Compiler (C)' ...
... Looking for environment variable 'MW_MINGW64_LOC' ...No.
The solution is to have an administrator set the environment variable. On Windows 10, you find this at:
Control Panel > System and Security > System > Advanced system settings > Environment Variables... > System variables: New...
The environment variable should have the following name and value:
Variable Name: MW_MINGW64_LOC
Variable Value: <SupportPackageRoot>\3P.instrset\mingw_w64.instrset
where <SupportPackage Root> is the output received for the command:
>> matlabshared.supportpkg.getSupportPackageRoot
Before the loop:
start_time = datetime('now');
rel_times = [];
In the loop, after
s = [s,h];
add
dt = milliseconds(datetime('now') - start_time);
rel_times = [rel_times, dt];
and change
plot(app.UIAxes,s);
to
plot(app.UIAxes, rel_times, s);
Note that performance of your application will get worse and worse over time, due to the way that you keep extending the s and rel_time arrays. Consider using a fixed-size buffer. Consider using animatedline() especially with a maximum number of points.
The Fixed-Point Tool collects ranges and proposes data types by data type groups rather than individual blocks.
A Data type group is a collection of block paths that need to have the same data type. If they don’t, Simulink will throw a data type mismatch error.
For instance, the output of the gain and the input to the subsystem need to have the same data type (they belong to the same data type group). Otherwise Simulink will throw a data type mismatch error. You can try this by changing the data type of the gain in the attached model to single.
For more details on the data type group, please refer to the documentation link below:
All the shared ranges are an aggregation of the data type group ranges. In this case, shared simulation range is the aggregate of the simulation ranges of the data type group and similarly the shared design range is an aggregate of the design ranges of the group.
Fixed Point Tool proposes a data type that can accommodate all these shared ranges for the group.You can infact click the link at the bottom of the Result Details pane to highlight all the members of the data type group.
I am not concerned with the computational time required for the genetic approach, as, for now, I am just trying to develop a methodology for fitting complex, non-linear/non-smooth, functions using the various features available in Matlab's Global Optimization toolbox. The complexity and non-linearity of my function (presented below) will greatly increase in the near future.
The function which I am trying to fit my data to is defined as following:
VALUE=a1*x+a2*ln(y)+a3*abs(z)+a4
With: a1,a2,a3,a4 being the regression model's unknown coefficients
The following optimization constraints need to be imposed on the coefficients of the function:
a3<0; a4>0; a1*a2<0;
The data to which I am trying to fit my function to is presented in the attached SampleData.csv file. The file contains column labels which define the measured value and x,y,z parameters.
My initial guesses for the values of a1,a2,a3,a4 are defined as a row vector: [0,0,0,0].
I would like to learn how to set this problem up using MultiStart and parallel processing. I would like to use parallel processing for the MultiStart process, as well as to include a Hybrid Function, if such an operation is possible.
The work flow which I would like to achieve should be similar to:
1. Define Function (referring to x,y,z data contained in columns of SampleData.csv file) :
function [ Value ] = FittingFunction(x,data)
Value=x(1).*data(:,2)+...
x(2).*log(data(:,3))+...
x(3).*abs(data(:,4))+...
x(4);
end
I would prefer to not use an anonymous function as the complexity of the model to which I will be fitting my future data to will only increase and an anonymous function would be messy to adjust as the regression model matures.
2.Define Initial Guess Row Vector x=[0,0,0,0]
3.Define optimization problem (referring to value data & x,y,z data contained in columns of SampleData.csv file) as a minimization problem trying to minimize a cost function defined as:
Cost=sum((FittingFunction(x,data)-data(:,1)).^2);
4. Pass optimization problem to a Genetic Algorithm routine with a hybrid function included using fminunc.
5. Create a MultiStart Optimization Object and Start a pool of workers.
lowerBounds for MultiStart =[-Inf,-Inf,-Inf,-Inf]; upperBounds for MultiStart =[Inf,Inf,Inf,Inf];
Number of MultiStart Iterations= 5.
5. Run optimization in parallel.
I realize that the problem which I defined is quite complex in its definition. I am not an expert user of Matlab and may not realize that my approach to solving this problem and the requirements (using the genetic algorithm, parallel processing, constraining coefficient ranges, and doing all of this using a MultiStart approach) for the fitting process may be unachievable.
I am using the serial port to read numbers from the arduino encoder. I did drawing a chart in app designer but in the X axis I only have the number of the next displayed number. I would like to see the time after which the number appeared in the X axis. The first number is e.g. 5 and in the x axis its time is 0, the next number 6 in the x axis appeared after 120ms and wants this time on the X axis.
delete(instrfind({'Port'},{app.PORT}))
app.x=serial(app.PORT,'BAUD',9600);
flushinput(app.x);
fopen(app.x); % otwarcie portu szeregowego
for i=1:app.Zakres.Value
drawnow
if app.Z == 1
break
end
h = str2num(fscanf(app.x));
app.aPolozenie.Value = h;
s = [s,h];
plot(app.UIAxes,s);
app.UIAxes.YLim = [ app.Od.Value app.Do.Value];
app.UIAxes.XLim = [0 app.Zakres.Value];
pause(.0001);
app.Zatrzymaj
end
end
This is my current status:
  • I do not know how to set up the fitting routine to use the Genetic Algorithm; my code is currently using lsqcurvefit, because this was the only way that I could figure out how to instantiate the matlab optimization problem object using the createOptimProblem() constructor.
  • I understand that lsqcurvefit cannot perform constrained optimization, but the Genetic Algorithm approach can do that; any help with setting up this problem using the genetic approach would be greatly appreciated, even if it is without the use of MultiStart for now.
  • As I am currently not using the Genetic Algorithm, I am not forcing the optimization solver to use the 'HybridFunction','on' option.
  • I am aware of the fact that the Global Optimization Toolbox has two options for finding global minima: GlobalSearch and MultiStart. I have decided to use MultiStart because according to the following article: http://www.mathworks.com/help/gads/how-globalsearch-and-multistart-work.html#bsc9eec MultiStart Can be setup in parallel on a multicore processor, while GlobalSearch cannot.
  • I am not sure if MultiStart or GlobalSearch can be setup to run with a Genetic Algorithm...
Here is my current code (requires the data.mat file attached to my posting):
clc
clear variables
%%Loading Measurement Data File
load('data.mat')
%%Defining function to which the data will be fit to
% I would like to move away from using the anonymous function,
% and move to using the function file defined in the directory but I don't
% know how to pass a function file to the createOptimProblem function call.
fitfcn1 = @(x,data)x(1).*data.x+x(2).*log(data.y)+abs(data.z).*x(3)+x(4);
%%Defining lower and upper bounds for MultiStart procedure
lb1 = [-Inf,-Inf,-Inf,-Inf];
ub1 = [Inf,Inf,Inf,Inf];
%%Creating row vector for intial guess to optimization routine
p01 = 0*ones(1,4);
%%Defining optimization problem object
% Currently running lsqcurvefit, but I need to move to the genetic
% algorythm approach. I do not understand how to setup a genetic alorythm
% data fitting routine in matlab using the GA functionality built into the
% global optimization toolbox.
problem1 = createOptimProblem('lsqcurvefit','x0',p01,'objective',fitfcn1,...
'lb',lb1,'ub',ub1,'xdata',data,'ydata',data.value);
%%Creating live fitting progress plot
ms1 = MultiStart('PlotFcns',@gsplotbestf);
%%Running MultiStart Optimization Routine
[xmulti1,errormulti1] = run(ms1,problem1,5)
This is my current status:
  • I do not know how to set up the fitting routine to use the Genetic Algorithm; my code is currently using lsqcurvefit, because this was the only way that I could figure out how to instantiate the matlab optimization problem object using the createOptimProblem() constructor.
  • I understand that lsqcurvefit cannot perform constrained optimization, but the Genetic Algorithm approach can do that; any help with setting up this problem using the genetic approach would be greatly appreciated, even if it is without the use of MultiStart for now.
  • As I am currently not using the Genetic Algorithm, I am not forcing the optimization solver to use the 'HybridFunction','on' option.
  • I am aware of the fact that the Global Optimization Toolbox has two options for finding global minima: GlobalSearch and MultiStart. I have decided to use MultiStart because according to the following article: http://www.mathworks.com/help/gads/how-globalsearch-and-multistart-work.html#bsc9eec MultiStart Can be setup in parallel on a multicore processor, while GlobalSearch cannot.
  • I am not sure if MultiStart or GlobalSearch can be setup to run with a Genetic Algorithm...
Can someone help on this? I'm sure it is straight forward but getting stumped. I would simply like to add two matrices together, element-wise, ignoring the NaNs. I realize one solution is to replace NaNs with zeros but I have some reported zeros that I want to be able to later identify. A solution to that is to set any real zeros to extremely small numbers. Rather than doing the above I am wondering if there is any other way to ignoring the NaNs -- I know of sum(...,'omitnan') and nansum() but they work on one matrix or if I concatenate the two matrices together then they aggregate either all rows or all columns depending on the dimension I select.
For example: A = ones(10,3); B = ones(10,3);
% inserting some NaNs and zeros A([1 6 10],1) = NaN; A([3 7],[2 3]) = NaN; B([3 7],2) = 0;
C = A + B;
I would like C = [1 2 2; 2 2 2; 2 0 1; 2 2 2; 2 2 2; 1 2 2; 2 0 1; 2 2 2; 2 2 2; 1 2 2]
I realize that the problem which I defined is quite complex in its definition. I am not an expert user of Matlab and may not realize that my approach to solving this problem and the requirements (using the genetic algorithm, parallel processing, constraining coefficient ranges, and doing all of this using a MultiStart approach) for the fitting process may be unachievable.
After installing the "MATLAB Support for MinGW-w64 C/C++ Compiler" support package, the command
>> mex -setup
reports that the compiler is correctly installed. However, when I restart MATLAB, the same command does not detect it as installed anymore.
When I check the Add-On Explorer, or run the command
>> matlabshared.supportpkg.getInstalled
the MinGW Support Package is still shown as installed.
Why does MATLAB not detect the MinGW compiler anymore after a restart?
When installing the MinGW compiler, the environment variable MW_MINGW64_LOC is set with the path to the MinGW installation folder. On some systems where users have limited privileges, there may be policies in place preventing environment variables from being set. If this environment variable is not staying set following the installation, or after restarting MATLAB, the behavior described above may occur.
Running the 'mex -setup' command in verbose mode
>> mex -setup -v
will display whether the MW_MINGW64_LOC environment variable is set. If this is not the case, the output will look as shown below:
... Looking for compiler 'MinGW64 Compiler (C)' ...
... Looking for environment variable 'MW_MINGW64_LOC' ...No.
The solution is to have an administrator set the environment variable. On Windows 10, you find this at:
Control Panel > System and Security > System > Advanced system settings > Environment Variables... > System variables: New...
The environment variable should have the following name and value:
Variable Name: MW_MINGW64_LOC
Variable Value: <SupportPackageRoot>\3P.instrset\mingw_w64.instrset
where <SupportPackage Root> is the output received for the command:
>> matlabshared.supportpkg.getSupportPackageRoot
Before the loop:
start_time = datetime('now');
rel_times = [];
In the loop, after
s = [s,h];
add
dt = milliseconds(datetime('now') - start_time);
rel_times = [rel_times, dt];
and change
plot(app.UIAxes,s);
to
plot(app.UIAxes, rel_times, s);
Note that performance of your application will get worse and worse over time, due to the way that you keep extending the s and rel_time arrays. Consider using a fixed-size buffer. Consider using animatedline() especially with a maximum number of points.
The Fixed-Point Tool collects ranges and proposes data types by data type groups rather than individual blocks.
A Data type group is a collection of block paths that need to have the same data type. If they don’t, Simulink will throw a data type mismatch error.
For instance, the output of the gain and the input to the subsystem need to have the same data type (they belong to the same data type group). Otherwise Simulink will throw a data type mismatch error. You can try this by changing the data type of the gain in the attached model to single.
For more details on the data type group, please refer to the documentation link below:
All the shared ranges are an aggregation of the data type group ranges. In this case, shared simulation range is the aggregate of the simulation ranges of the data type group and similarly the shared design range is an aggregate of the design ranges of the group.
Fixed Point Tool proposes a data type that can accommodate all these shared ranges for the group.You can infact click the link at the bottom of the Result Details pane to highlight all the members of the data type group.
I am not concerned with the computational time required for the genetic approach, as, for now, I am just trying to develop a methodology for fitting complex, non-linear/non-smooth, functions using the various features available in Matlab's Global Optimization toolbox. The complexity and non-linearity of my function (presented below) will greatly increase in the near future.
The function which I am trying to fit my data to is defined as following:
VALUE=a1*x+a2*ln(y)+a3*abs(z)+a4
With: a1,a2,a3,a4 being the regression model's unknown coefficients
The following optimization constraints need to be imposed on the coefficients of the function:
a3<0; a4>0; a1*a2<0;
The data to which I am trying to fit my function to is presented in the attached SampleData.csv file. The file contains column labels which define the measured value and x,y,z parameters.
My initial guesses for the values of a1,a2,a3,a4 are defined as a row vector: [0,0,0,0].
I would like to learn how to set this problem up using MultiStart and parallel processing. I would like to use parallel processing for the MultiStart process, as well as to include a Hybrid Function, if such an operation is possible.
The work flow which I would like to achieve should be similar to:
1. Define Function (referring to x,y,z data contained in columns of SampleData.csv file) :
function [ Value ] = FittingFunction(x,data)
Value=x(1).*data(:,2)+...
x(2).*log(data(:,3))+...
x(3).*abs(data(:,4))+...
x(4);
end
I would prefer to not use an anonymous function as the complexity of the model to which I will be fitting my future data to will only increase and an anonymous function would be messy to adjust as the regression model matures.
2.Define Initial Guess Row Vector x=[0,0,0,0]
3.Define optimization problem (referring to value data & x,y,z data contained in columns of SampleData.csv file) as a minimization problem trying to minimize a cost function defined as:
Cost=sum((FittingFunction(x,data)-data(:,1)).^2);
4. Pass optimization problem to a Genetic Algorithm routine with a hybrid function included using fminunc.
5. Create a MultiStart Optimization Object and Start a pool of workers.
lowerBounds for MultiStart =[-Inf,-Inf,-Inf,-Inf]; upperBounds for MultiStart =[Inf,Inf,Inf,Inf];
Number of MultiStart Iterations= 5.
5. Run optimization in parallel.
I realize that the problem which I defined is quite complex in its definition. I am not an expert user of Matlab and may not realize that my approach to solving this problem and the requirements (using the genetic algorithm, parallel processing, constraining coefficient ranges, and doing all of this using a MultiStart approach) for the fitting process may be unachievable.
I am using the serial port to read numbers from the arduino encoder. I did drawing a chart in app designer but in the X axis I only have the number of the next displayed number. I would like to see the time after which the number appeared in the X axis. The first number is e.g. 5 and in the x axis its time is 0, the next number 6 in the x axis appeared after 120ms and wants this time on the X axis.
delete(instrfind({'Port'},{app.PORT}))
app.x=serial(app.PORT,'BAUD',9600);
flushinput(app.x);
fopen(app.x); % otwarcie portu szeregowego
for i=1:app.Zakres.Value
drawnow
if app.Z == 1
break
end
h = str2num(fscanf(app.x));
app.aPolozenie.Value = h;
s = [s,h];
plot(app.UIAxes,s);
app.UIAxes.YLim = [ app.Od.Value app.Do.Value];
app.UIAxes.XLim = [0 app.Zakres.Value];
pause(.0001);
app.Zatrzymaj
end
end
This is my current status:
  • I do not know how to set up the fitting routine to use the Genetic Algorithm; my code is currently using lsqcurvefit, because this was the only way that I could figure out how to instantiate the matlab optimization problem object using the createOptimProblem() constructor.
  • I understand that lsqcurvefit cannot perform constrained optimization, but the Genetic Algorithm approach can do that; any help with setting up this problem using the genetic approach would be greatly appreciated, even if it is without the use of MultiStart for now.
  • As I am currently not using the Genetic Algorithm, I am not forcing the optimization solver to use the 'HybridFunction','on' option.
  • I am aware of the fact that the Global Optimization Toolbox has two options for finding global minima: GlobalSearch and MultiStart. I have decided to use MultiStart because according to the following article: http://www.mathworks.com/help/gads/how-globalsearch-and-multistart-work.html#bsc9eec MultiStart Can be setup in parallel on a multicore processor, while GlobalSearch cannot.
  • I am not sure if MultiStart or GlobalSearch can be setup to run with a Genetic Algorithm...
Here is my current code (requires the data.mat file attached to my posting):
clc
clear variables
%%Loading Measurement Data File
load('data.mat')
%%Defining function to which the data will be fit to
% I would like to move away from using the anonymous function,
% and move to using the function file defined in the directory but I don't
% know how to pass a function file to the createOptimProblem function call.
fitfcn1 = @(x,data)x(1).*data.x+x(2).*log(data.y)+abs(data.z).*x(3)+x(4);
%%Defining lower and upper bounds for MultiStart procedure
lb1 = [-Inf,-Inf,-Inf,-Inf];
ub1 = [Inf,Inf,Inf,Inf];
%%Creating row vector for intial guess to optimization routine
p01 = 0*ones(1,4);
%%Defining optimization problem object
% Currently running lsqcurvefit, but I need to move to the genetic
% algorythm approach. I do not understand how to setup a genetic alorythm
% data fitting routine in matlab using the GA functionality built into the
% global optimization toolbox.
problem1 = createOptimProblem('lsqcurvefit','x0',p01,'objective',fitfcn1,...
'lb',lb1,'ub',ub1,'xdata',data,'ydata',data.value);
%%Creating live fitting progress plot
ms1 = MultiStart('PlotFcns',@gsplotbestf);
%%Running MultiStart Optimization Routine
[xmulti1,errormulti1] = run(ms1,problem1,5)
This is my current status:
  • I do not know how to set up the fitting routine to use the Genetic Algorithm; my code is currently using lsqcurvefit, because this was the only way that I could figure out how to instantiate the matlab optimization problem object using the createOptimProblem() constructor.
  • I understand that lsqcurvefit cannot perform constrained optimization, but the Genetic Algorithm approach can do that; any help with setting up this problem using the genetic approach would be greatly appreciated, even if it is without the use of MultiStart for now.
  • As I am currently not using the Genetic Algorithm, I am not forcing the optimization solver to use the 'HybridFunction','on' option.
  • I am aware of the fact that the Global Optimization Toolbox has two options for finding global minima: GlobalSearch and MultiStart. I have decided to use MultiStart because according to the following article: http://www.mathworks.com/help/gads/how-globalsearch-and-multistart-work.html#bsc9eec MultiStart Can be setup in parallel on a multicore processor, while GlobalSearch cannot.
  • I am not sure if MultiStart or GlobalSearch can be setup to run with a Genetic Algorithm...
Can someone help on this? I'm sure it is straight forward but getting stumped. I would simply like to add two matrices together, element-wise, ignoring the NaNs. I realize one solution is to replace NaNs with zeros but I have some reported zeros that I want to be able to later identify. A solution to that is to set any real zeros to extremely small numbers. Rather than doing the above I am wondering if there is any other way to ignoring the NaNs -- I know of sum(...,'omitnan') and nansum() but they work on one matrix or if I concatenate the two matrices together then they aggregate either all rows or all columns depending on the dimension I select.
For example: A = ones(10,3); B = ones(10,3);
% inserting some NaNs and zeros A([1 6 10],1) = NaN; A([3 7],[2 3]) = NaN; B([3 7],2) = 0;
C = A + B;
I would like C = [1 2 2; 2 2 2; 2 0 1; 2 2 2; 2 2 2; 1 2 2; 2 0 1; 2 2 2; 2 2 2; 1 2 2]
I realize that the problem which I defined is quite complex in its definition. I am not an expert user of Matlab and may not realize that my approach to solving this problem and the requirements (using the genetic algorithm, parallel processing, constraining coefficient ranges, and doing all of this using a MultiStart approach) for the fitting process may be unachievable.
After installing the "MATLAB Support for MinGW-w64 C/C++ Compiler" support package, the command
>> mex -setup
reports that the compiler is correctly installed. However, when I restart MATLAB, the same command does not detect it as installed anymore.
When I check the Add-On Explorer, or run the command
>> matlabshared.supportpkg.getInstalled
the MinGW Support Package is still shown as installed.
Why does MATLAB not detect the MinGW compiler anymore after a restart?
When installing the MinGW compiler, the environment variable MW_MINGW64_LOC is set with the path to the MinGW installation folder. On some systems where users have limited privileges, there may be policies in place preventing environment variables from being set. If this environment variable is not staying set following the installation, or after restarting MATLAB, the behavior described above may occur.
Running the 'mex -setup' command in verbose mode
>> mex -setup -v
will display whether the MW_MINGW64_LOC environment variable is set. If this is not the case, the output will look as shown below:
... Looking for compiler 'MinGW64 Compiler (C)' ...
... Looking for environment variable 'MW_MINGW64_LOC' ...No.
The solution is to have an administrator set the environment variable. On Windows 10, you find this at:
Control Panel > System and Security > System > Advanced system settings > Environment Variables... > System variables: New...
The environment variable should have the following name and value:
Variable Name: MW_MINGW64_LOC
Variable Value: <SupportPackageRoot>\3P.instrset\mingw_w64.instrset
where <SupportPackage Root> is the output received for the command:
>> matlabshared.supportpkg.getSupportPackageRoot
Before the loop:
start_time = datetime('now');
rel_times = [];
In the loop, after
s = [s,h];
add
dt = milliseconds(datetime('now') - start_time);
rel_times = [rel_times, dt];
and change
plot(app.UIAxes,s);
to
plot(app.UIAxes, rel_times, s);
Note that performance of your application will get worse and worse over time, due to the way that you keep extending the s and rel_time arrays. Consider using a fixed-size buffer. Consider using animatedline() especially with a maximum number of points.
The Fixed-Point Tool collects ranges and proposes data types by data type groups rather than individual blocks.
A Data type group is a collection of block paths that need to have the same data type. If they don’t, Simulink will throw a data type mismatch error.
For instance, the output of the gain and the input to the subsystem need to have the same data type (they belong to the same data type group). Otherwise Simulink will throw a data type mismatch error. You can try this by changing the data type of the gain in the attached model to single.
For more details on the data type group, please refer to the documentation link below:
All the shared ranges are an aggregation of the data type group ranges. In this case, shared simulation range is the aggregate of the simulation ranges of the data type group and similarly the shared design range is an aggregate of the design ranges of the group.
Fixed Point Tool proposes a data type that can accommodate all these shared ranges for the group.You can infact click the link at the bottom of the Result Details pane to highlight all the members of the data type group.
I am not concerned with the computational time required for the genetic approach, as, for now, I am just trying to develop a methodology for fitting complex, non-linear/non-smooth, functions using the various features available in Matlab's Global Optimization toolbox. The complexity and non-linearity of my function (presented below) will greatly increase in the near future.
The function which I am trying to fit my data to is defined as following:
VALUE=a1*x+a2*ln(y)+a3*abs(z)+a4
With: a1,a2,a3,a4 being the regression model's unknown coefficients
The following optimization constraints need to be imposed on the coefficients of the function:
a3<0; a4>0; a1*a2<0;
The data to which I am trying to fit my function to is presented in the attached SampleData.csv file. The file contains column labels which define the measured value and x,y,z parameters.
My initial guesses for the values of a1,a2,a3,a4 are defined as a row vector: [0,0,0,0].
I would like to learn how to set this problem up using MultiStart and parallel processing. I would like to use parallel processing for the MultiStart process, as well as to include a Hybrid Function, if such an operation is possible.
The work flow which I would like to achieve should be similar to:
1. Define Function (referring to x,y,z data contained in columns of SampleData.csv file) :
function [ Value ] = FittingFunction(x,data)
Value=x(1).*data(:,2)+...
x(2).*log(data(:,3))+...
x(3).*abs(data(:,4))+...
x(4);
end
I would prefer to not use an anonymous function as the complexity of the model to which I will be fitting my future data to will only increase and an anonymous function would be messy to adjust as the regression model matures.
2.Define Initial Guess Row Vector x=[0,0,0,0]
3.Define optimization problem (referring to value data & x,y,z data contained in columns of SampleData.csv file) as a minimization problem trying to minimize a cost function defined as:
Cost=sum((FittingFunction(x,data)-data(:,1)).^2);
4. Pass optimization problem to a Genetic Algorithm routine with a hybrid function included using fminunc.
5. Create a MultiStart Optimization Object and Start a pool of workers.
lowerBounds for MultiStart =[-Inf,-Inf,-Inf,-Inf]; upperBounds for MultiStart =[Inf,Inf,Inf,Inf];
Number of MultiStart Iterations= 5.
5. Run optimization in parallel.
I realize that the problem which I defined is quite complex in its definition. I am not an expert user of Matlab and may not realize that my approach to solving this problem and the requirements (using the genetic algorithm, parallel processing, constraining coefficient ranges, and doing all of this using a MultiStart approach) for the fitting process may be unachievable.
I am using the serial port to read numbers from the arduino encoder. I did drawing a chart in app designer but in the X axis I only have the number of the next displayed number. I would like to see the time after which the number appeared in the X axis. The first number is e.g. 5 and in the x axis its time is 0, the next number 6 in the x axis appeared after 120ms and wants this time on the X axis.
delete(instrfind({'Port'},{app.PORT}))
app.x=serial(app.PORT,'BAUD',9600);
flushinput(app.x);
fopen(app.x); % otwarcie portu szeregowego
for i=1:app.Zakres.Value
drawnow
if app.Z == 1
break
end
h = str2num(fscanf(app.x));
app.aPolozenie.Value = h;
s = [s,h];
plot(app.UIAxes,s);
app.UIAxes.YLim = [ app.Od.Value app.Do.Value];
app.UIAxes.XLim = [0 app.Zakres.Value];
pause(.0001);
app.Zatrzymaj
end
end
This is my current status:
  • I do not know how to set up the fitting routine to use the Genetic Algorithm; my code is currently using lsqcurvefit, because this was the only way that I could figure out how to instantiate the matlab optimization problem object using the createOptimProblem() constructor.
  • I understand that lsqcurvefit cannot perform constrained optimization, but the Genetic Algorithm approach can do that; any help with setting up this problem using the genetic approach would be greatly appreciated, even if it is without the use of MultiStart for now.
  • As I am currently not using the Genetic Algorithm, I am not forcing the optimization solver to use the 'HybridFunction','on' option.
  • I am aware of the fact that the Global Optimization Toolbox has two options for finding global minima: GlobalSearch and MultiStart. I have decided to use MultiStart because according to the following article: http://www.mathworks.com/help/gads/how-globalsearch-and-multistart-work.html#bsc9eec MultiStart Can be setup in parallel on a multicore processor, while GlobalSearch cannot.
  • I am not sure if MultiStart or GlobalSearch can be setup to run with a Genetic Algorithm...
Here is my current code (requires the data.mat file attached to my posting):
clc
clear variables
%%Loading Measurement Data File
load('data.mat')
%%Defining function to which the data will be fit to
% I would like to move away from using the anonymous function,
% and move to using the function file defined in the directory but I don't
% know how to pass a function file to the createOptimProblem function call.
fitfcn1 = @(x,data)x(1).*data.x+x(2).*log(data.y)+abs(data.z).*x(3)+x(4);
%%Defining lower and upper bounds for MultiStart procedure
lb1 = [-Inf,-Inf,-Inf,-Inf];
ub1 = [Inf,Inf,Inf,Inf];
%%Creating row vector for intial guess to optimization routine
p01 = 0*ones(1,4);
%%Defining optimization problem object
% Currently running lsqcurvefit, but I need to move to the genetic
% algorythm approach. I do not understand how to setup a genetic alorythm
% data fitting routine in matlab using the GA functionality built into the
% global optimization toolbox.
problem1 = createOptimProblem('lsqcurvefit','x0',p01,'objective',fitfcn1,...
'lb',lb1,'ub',ub1,'xdata',data,'ydata',data.value);
%%Creating live fitting progress plot
ms1 = MultiStart('PlotFcns',@gsplotbestf);
%%Running MultiStart Optimization Routine
[xmulti1,errormulti1] = run(ms1,problem1,5)
This is my current status:
  • I do not know how to set up the fitting routine to use the Genetic Algorithm; my code is currently using lsqcurvefit, because this was the only way that I could figure out how to instantiate the matlab optimization problem object using the createOptimProblem() constructor.
  • I understand that lsqcurvefit cannot perform constrained optimization, but the Genetic Algorithm approach can do that; any help with setting up this problem using the genetic approach would be greatly appreciated, even if it is without the use of MultiStart for now.
  • As I am currently not using the Genetic Algorithm, I am not forcing the optimization solver to use the 'HybridFunction','on' option.
  • I am aware of the fact that the Global Optimization Toolbox has two options for finding global minima: GlobalSearch and MultiStart. I have decided to use MultiStart because according to the following article: http://www.mathworks.com/help/gads/how-globalsearch-and-multistart-work.html#bsc9eec MultiStart Can be setup in parallel on a multicore processor, while GlobalSearch cannot.
  • I am not sure if MultiStart or GlobalSearch can be setup to run with a Genetic Algorithm...
Can someone help on this? I'm sure it is straight forward but getting stumped. I would simply like to add two matrices together, element-wise, ignoring the NaNs. I realize one solution is to replace NaNs with zeros but I have some reported zeros that I want to be able to later identify. A solution to that is to set any real zeros to extremely small numbers. Rather than doing the above I am wondering if there is any other way to ignoring the NaNs -- I know of sum(...,'omitnan') and nansum() but they work on one matrix or if I concatenate the two matrices together then they aggregate either all rows or all columns depending on the dimension I select.
For example: A = ones(10,3); B = ones(10,3);
% inserting some NaNs and zeros A([1 6 10],1) = NaN; A([3 7],[2 3]) = NaN; B([3 7],2) = 0;
C = A + B;
I would like C = [1 2 2; 2 2 2; 2 0 1; 2 2 2; 2 2 2; 1 2 2; 2 0 1; 2 2 2; 2 2 2; 1 2 2]
I realize that the problem which I defined is quite complex in its definition. I am not an expert user of Matlab and may not realize that my approach to solving this problem and the requirements (using the genetic algorithm, parallel processing, constraining coefficient ranges, and doing all of this using a MultiStart approach) for the fitting process may be unachievable.
After installing the "MATLAB Support for MinGW-w64 C/C++ Compiler" support package, the command
>> mex -setup
reports that the compiler is correctly installed. However, when I restart MATLAB, the same command does not detect it as installed anymore.
When I check the Add-On Explorer, or run the command
>> matlabshared.supportpkg.getInstalled
the MinGW Support Package is still shown as installed.
Why does MATLAB not detect the MinGW compiler anymore after a restart?
When installing the MinGW compiler, the environment variable MW_MINGW64_LOC is set with the path to the MinGW installation folder. On some systems where users have limited privileges, there may be policies in place preventing environment variables from being set. If this environment variable is not staying set following the installation, or after restarting MATLAB, the behavior described above may occur.
Running the 'mex -setup' command in verbose mode
>> mex -setup -v
will display whether the MW_MINGW64_LOC environment variable is set. If this is not the case, the output will look as shown below:
... Looking for compiler 'MinGW64 Compiler (C)' ...
... Looking for environment variable 'MW_MINGW64_LOC' ...No.
The solution is to have an administrator set the environment variable. On Windows 10, you find this at:
Control Panel > System and Security > System > Advanced system settings > Environment Variables... > System variables: New...
rel_times = [rel_times, dt];
and change
plot(app.UIAxes,s);
to
plot(app.UIAxes, rel_times, s);
Note that performance of your application will get worse and worse over time, due to the way that you keep extending.
the MinGW Support Package is still shown as installed.
Why does MATLAB not detect the MinGW compiler anymore after a restart?
When installing the MinGW compiler, the environment variable MW_MINGW64_LOC is set with the path to the MinGW installation folder. On some systems where users have limited privileges, there may be policies in place preventing environment variables from being set. If this environment variable is not staying set following the installation, or after restarting MATLAB, the behavior described above may occur.
the MinGW Support Package is still shown as installed.
Why does MATLAB not detect the MinGW compiler anymore after a restart?
When installing the MinGW compiler, the environment variable MW_MINGW64_LOC is set with the path to the MinGW installation folder. On some systems where users have limited privileges, there may be policies in place preventing environment variables from being set. If this environment variable is not staying set following the installation, or after restarting MATLAB, the behavior described above may occur.
h = str2num(fscanf(app.x));
app.aPolozenie.Value = h;
s = [s,h];
plot(app.UIAxes,s);
app.UIAxes.YLim = [ app.Od.Value app.Do.Value];
app.UIAxes.XLim = [0 app.Zakres.Value];
pause(.0001);
app.Zatrzymaj
end

  1 Comment

Matt J
Matt J on 21 Feb 2020
The original question, before it was edited away by the OP, was how to "minimize" the code below (reproduced to the best of my recollection).
A=input('Enter Matrix A: ')
B=input('Enter Matrix B: ')
C=input('Enter Matrix C: ')
n=length(A);
Con=B;
Obs=C;
for x = 1: n-1
Con =[Con, A^(x)*B];
Obs =[Obs, C*A^(x)];
end
[a,b]=size(Con);
[c,d]=size(Obs);
if a==b
if det(Con)~=0
input('The systeme is controlable')
else
input('The systeme is not controlable')
end
elseif a~=b
if rank(Con)==n
input('The systeme is controlable')
else
input('The systeme is not controlable')
end
end
if c==d
if det(Obs)~=0
input('The systeme is observable')
else
input('The systeme is not observable')
end
elseif c~=d
if rank(Obs)==n
input('The systeme is observable')
else
input('The systeme is not observable')
end
end

Sign in to comment.

Answers (2)

Matt J
Matt J on 21 Feb 2020
Edited: Matt J on 21 Feb 2020
A=input('Enter Matrix A: ')
B=input('Enter Matrix B: ')
C=input('Enter Matrix C: ')
n=length(A);
Con=cell(1,n); Obs=Con.';
Con{1}=B; Obs{1}=C;
for m = 2: n
Con{m} = A*Con{m-1};
Obs{m} = Obs{m-1}*A;
end
Con=cell2mat(Con); Obs=cell2mat(Obs);
analyzeIt(Con,"controllable",n)
analyzeIt(Obs,"observable",n)
function analyzeIt(Q,msg,n)
[a,b]=size(Q);
threshold=1e-8; %non-singularity tolerance
if (a==b && rcond(Q)>threshold) || (a~=b && rank(Q)==n)
disp("The system is "+msg)
else
disp("The system is not "+msg)
end
end

  0 Comments

Sign in to comment.


David Hastana
David Hastana on 24 Feb 2020
The function which I am trying to fit my data to is defined as following:
VALUE=a1*x+a2*ln(y)+a3*abs(z)+a4
With: a1,a2,a3,a4 being the regression model's unknown coefficients
The following optimization constraints need to be imposed on the coefficients of the function:
a3<0; a4>0; a1*a2<0;
The data to which I am trying to fit my function to is presented in the attached SampleData.csv file. The file contains column labels which define the measured value and x,y,z parameters.
My initial guesses for the values of a1,a2,a3,a4 are defined as a row vector: [0,0,0,0].
I would like to learn how to set this problem up using MultiStart and parallel processing. I would like to use parallel processing for the MultiStart process, as well as to include a Hybrid Function, if such an operation is possible.
The work flow which I would like to achieve should be similar to:
1. Define Function (referring to x,y,z data contained in columns of SampleData.csv file) :
function [ Value ] = FittingFunction(x,data)
Value=x(1).*data(:,2)+...
x(2).*log(data(:,3))+...
x(3).*abs(data(:,4))+...
x(4);
end
I would prefer to not use an anonymous function as the complexity of the model to which I will be fitting my future data to will only increase and an anonymous function would be messy to adjust as the regression model matures.
2.Define Initial Guess Row Vector x=[0,0,0,0]
3.Define optimization problem (referring to value data & x,y,z data contained in columns of SampleData.csv file) as a minimization problem trying to minimize a cost function defined as:
Cost=sum((FittingFunction(x,data)-data(:,1)).^2);
4. Pass optimization problem to a Genetic Algorithm routine with a hybrid function included using fminunc.
5. Create a MultiStart Optimization Object and Start a pool of workers.
lowerBounds for MultiStart =[-Inf,-Inf,-Inf,-Inf]; upperBounds for MultiStart =[Inf,Inf,Inf,Inf];
Number of MultiStart Iterations= 5.
5. Run optimization in parallel.
I realize that the problem which I defined is quite complex in its definition. I am not an expert user of Matlab and may not realize that my approach to solving this problem and the requirements (using the genetic algorithm, parallel processing, constraining coefficient ranges, and doing all of this using a MultiStart approach) for the fitting process may be unachievable.
I am using the serial port to read numbers from the arduino encoder. I did drawing a chart in app designer but in the X axis I only have the number of the next displayed number. I would like to see the time after which the number appeared in the X axis. The first number is e.g. 5 and in the x axis its time is 0, the next number 6 in the x axis appeared after 120ms and wants this time on the X axis.
delete(instrfind({'Port'},{app.PORT}))
app.x=serial(app.PORT,'BAUD',9600);
flushinput(app.x);
fopen(app.x); % otwarcie portu szeregowego
for i=1:app.Zakres.Value
drawnow
if app.Z == 1
break
end
h = str2num(fscanf(app.x));
app.aPolozenie.Value = h;
s = [s,h];
plot(app.UIAxes,s);
app.UIAxes.YLim = [ app.Od.Value app.Do.Value];
app.UIAxes.XLim = [0 app.Zakres.Value];
pause(.0001);
app.Zatrzymaj
end
end
This is my current status:
  • I do not know how to set up the fitting routine to use the Genetic Algorithm; my code is currently using lsqcurvefit, because this was the only way that I could figure out how to instantiate the matlab optimization problem object using the createOptimProblem() constructor.
  • I understand that lsqcurvefit cannot perform constrained optimization, but the Genetic Algorithm approach can do that; any help with setting up this problem using the genetic approach would be greatly appreciated, even if it is without the use of MultiStart for now.
  • As I am currently not using the Genetic Algorithm, I am not forcing the optimization solver to use the 'HybridFunction','on' option.
  • I am aware of the fact that the Global Optimization Toolbox has two options for finding global minima: GlobalSearch and MultiStart. I have decided to use MultiStart because according to the following article: http://www.mathworks.com/help/gads/how-globalsearch-and-multistart-work.html#bsc9eec MultiStart Can be setup in parallel on a multicore processor, while GlobalSearch cannot.
  • I am not sure if MultiStart or GlobalSearch can be setup to run with a Genetic Algorithm...
the MinGW Support Package is still shown as installed.
Why does MATLAB not detect the MinGW compiler anymore after a restart?
When installing the MinGW compiler, the environment variable MW_MINGW64_LOC is set with the path to the MinGW installation folder. On some systems where users have limited privileges, there may be policies in place preventing environment variables from being set. If this environment variable is not staying set following the installation, or after restarting MATLAB, the behavior described above may occur.
Here is my current code (requires the data.mat file attached to my posting):
clc
clear variables
%%Loading Measurement Data File
load('data.mat')
%%Defining function to which the data will be fit to
% I would like to move away from using the anonymous function,
% and move to using the function file defined in the directory but I don't
% know how to pass a function file to the createOptimProblem function call.
fitfcn1 = @(x,data)x(1).*data.x+x(2).*log(data.y)+abs(data.z).*x(3)+x(4);
%%Defining lower and upper bounds for MultiStart procedure
lb1 = [-Inf,-Inf,-Inf,-Inf];
ub1 = [Inf,Inf,Inf,Inf];
%%Creating row vector for intial guess to optimization routine
p01 = 0*ones(1,4);
%%Defining optimization problem object
% Currently running lsqcurvefit, but I need to move to the genetic
% algorythm approach. I do not understand how to setup a genetic alorythm
% data fitting routine in matlab using the GA functionality built into the
% global optimization toolbox.
problem1 = createOptimProblem('lsqcurvefit','x0',p01,'objective',fitfcn1,...
'lb',lb1,'ub',ub1,'xdata',data,'ydata',data.value);
%%Creating live fitting progress plot
ms1 = MultiStart('PlotFcns',@gsplotbestf);
%%Running MultiStart Optimization Routine
[xmulti1,errormulti1] = run(ms1,problem1,5)
This is my current status:
  • I do not know how to set up the fitting routine to use the Genetic Algorithm; my code is currently using lsqcurvefit, because this was the only way that I could figure out how to instantiate the matlab optimization problem object using the createOptimProblem() constructor.
  • I understand that lsqcurvefit cannot perform constrained optimization, but the Genetic Algorithm approach can do that; any help with setting up this problem using the genetic approach would be greatly appreciated, even if it is without the use of MultiStart for now.
  • As I am currently not using the Genetic Algorithm, I am not forcing the optimization solver to use the 'HybridFunction','on' option.
  • I am aware of the fact that the Global Optimization Toolbox has two options for finding global minima: GlobalSearch and MultiStart. I have decided to use MultiStart because according to the following article: http://www.mathworks.com/help/gads/how-globalsearch-and-multistart-work.html#bsc9eec MultiStart Can be setup in parallel on a multicore processor, while GlobalSearch cannot.
  • I am not sure if MultiStart or GlobalSearch can be setup to run with a Genetic Algorithm...
Can someone help on this? I'm sure it is straight forward but getting stumped. I would simply like to add two matrices together, element-wise, ignoring the NaNs. I realize one solution is to replace NaNs with zeros but I have some reported zeros that I want to be able to later identify. A solution to that is to set any real zeros to extremely small numbers. Rather than doing the above I am wondering if there is any other way to ignoring the NaNs -- I know of sum(...,'omitnan') and nansum() but they work on one matrix or if I concatenate the two matrices together then they aggregate either all rows or all columns depending on the dimension I select.
For example: A = ones(10,3); B = ones(10,3);
% inserting some NaNs and zeros A([1 6 10],1) = NaN; A([3 7],[2 3]) = NaN; B([3 7],2) = 0;
C = A + B;
I would like C = [1 2 2; 2 2 2; 2 0 1; 2 2 2; 2 2 2; 1 2 2; 2 0 1; 2 2 2; 2 2 2; 1 2 2]
I realize that the problem which I defined is quite complex in its definition. I am not an expert user of Matlab and may not realize that my approach to solving this problem and the requirements (using the genetic algorithm, parallel processing, constraining coefficient ranges, and doing all of this using a MultiStart approach) for the fitting process may be unachievable.
After installing the "MATLAB Support for MinGW-w64 C/C++ Compiler" support package, the command
>> mex -setup
reports that the compiler is correctly installed. However, when I restart MATLAB, the same command does not detect it as installed anymore.
When I check the Add-On Explorer, or run the command
>> matlabshared.supportpkg.getInstalled
the MinGW Support Package is still shown as installed.
Why does MATLAB not detect the MinGW compiler anymore after a restart?
When installing the MinGW compiler, the environment variable MW_MINGW64_LOC is set with the path to the MinGW installation folder. On some systems where users have limited privileges, there may be policies in place preventing environment variables from being set. If this environment variable is not staying set following the installation, or after restarting MATLAB, the behavior described above may occur.
Running the 'mex -setup' command in verbose mode
>> mex -setup -v
will display whether the MW_MINGW64_LOC environment variable is set. If this is not the case, the output will look as shown below:
... Looking for compiler 'MinGW64 Compiler (C)' ...
... Looking for environment variable 'MW_MINGW64_LOC' ...No.
The solution is to have an administrator set the environment variable. On Windows 10, you find this at:
Control Panel > System and Security > System > Advanced system settings > Environment Variables... > System variables: New...
The environment variable should have the following name and value:
Variable Name: MW_MINGW64_LOC
Variable Value: <SupportPackageRoot>\3P.instrset\mingw_w64.instrset
where <SupportPackage Root> is the output received for the command:
>> matlabshared.supportpkg.getSupportPackageRoot
Before the loop:
start_time = datetime('now');
rel_times = [];
In the loop, after
s = [s,h];
add
dt = milliseconds(datetime('now') - start_time);
rel_times = [rel_times, dt];
and change
plot(app.UIAxes,s);
to
plot(app.UIAxes, rel_times, s);
Note that performance of your application will get worse and worse over time, due to the way that you keep extending the s and rel_time arrays. Consider using a fixed-size buffer. Consider using animatedline() especially with a maximum number of points.
The Fixed-Point Tool collects ranges and proposes data types by data type groups rather than individual blocks.
A Data type group is a collection of block paths that need to have the same data type. If they don’t, Simulink will throw a data type mismatch error.
For instance, the output of the gain and the input to the subsystem need to have the same data type (they belong to the same data type group). Otherwise Simulink will throw a data type mismatch error. You can try this by changing the data type of the gain in the attached model to single.
For more details on the data type group, please refer to the documentation link below:
All the shared ranges are an aggregation of the data type group ranges. In this case, shared simulation range is the aggregate of the simulation ranges of the data type group and similarly the shared design range is an aggregate of the design ranges of the group.
Fixed Point Tool proposes a data type that can accommodate all these shared ranges for the group.You can infact click the link at the bottom of the Result Details pane to highlight all the members of the data type group.
I am not concerned with the computational time required for the genetic approach, as, for now, I am just trying to develop a methodology for fitting complex, non-linear/non-smooth, functions using the various features available in Matlab's Global Optimization toolbox. The complexity and non-linearity of my function (presented below) will greatly increase in the near future.
The function which I am trying to fit my data to is defined as following:
VALUE=a1*x+a2*ln(y)+a3*abs(z)+a4
With: a1,a2,a3,a4 being the regression model's unknown coefficients
The following optimization constraints need to be imposed on the coefficients of the function:
a3<0; a4>0; a1*a2<0;
The data to which I am trying to fit my function to is presented in the attached SampleData.csv file. The file contains column labels which define the measured value and x,y,z parameters.
My initial guesses for the values of a1,a2,a3,a4 are defined as a row vector: [0,0,0,0].
I would like to learn how to set this problem up using MultiStart and parallel processing. I would like to use parallel processing for the MultiStart process, as well as to include a Hybrid Function, if such an operation is possible.
The work flow which I would like to achieve should be similar to:
1. Define Function (referring to x,y,z data contained in columns of SampleData.csv file) :
function [ Value ] = FittingFunction(x,data)
Value=x(1).*data(:,2)+...
x(2).*log(data(:,3))+...
x(3).*abs(data(:,4))+...
x(4);
end
I would prefer to not use an anonymous function as the complexity of the model to which I will be fitting my future data to will only increase and an anonymous function would be messy to adjust as the regression model matures.
2.Define Initial Guess Row Vector x=[0,0,0,0]
3.Define optimization problem (referring to value data & x,y,z data contained in columns of SampleData.csv file) as a minimization problem trying to minimize a cost function defined as:
Cost=sum((FittingFunction(x,data)-data(:,1)).^2);
4. Pass optimization problem to a Genetic Algorithm routine with a hybrid function included using fminunc.
5. Create a MultiStart Optimization Object and Start a pool of workers.
lowerBounds for MultiStart =[-Inf,-Inf,-Inf,-Inf]; upperBounds for MultiStart =[Inf,Inf,Inf,Inf];
Number of MultiStart Iterations= 5.
5. Run optimization in parallel.
I realize that the problem which I defined is quite complex in its definition. I am not an expert user of Matlab and may not realize that my approach to solving this problem and the requirements (using the genetic algorithm, parallel processing, constraining coefficient ranges, and doing all of this using a MultiStart approach) for the fitting process may be unachievable.
I am using the serial port to read numbers from the arduino encoder. I did drawing a chart in app designer but in the X axis I only have the number of the next displayed number. I would like to see the time after which the number appeared in the X axis. The first number is e.g. 5 and in the x axis its time is 0, the next number 6 in the x axis appeared after 120ms and wants this time on the X axis.
delete(instrfind({'Port'},{app.PORT}))
app.x=serial(app.PORT,'BAUD',9600);
flushinput(app.x);
fopen(app.x); % otwarcie portu szeregowego
for i=1:app.Zakres.Value
drawnow
if app.Z == 1
break
end
h = str2num(fscanf(app.x));
app.aPolozenie.Value = h;
s = [s,h];
plot(app.UIAxes,s);
app.UIAxes.YLim = [ app.Od.Value app.Do.Value];
app.UIAxes.XLim = [0 app.Zakres.Value];
pause(.0001);
app.Zatrzymaj
end
end
This is my current status:
  • I do not know how to set up the fitting routine to use the Genetic Algorithm; my code is currently using lsqcurvefit, because this was the only way that I could figure out how to instantiate the matlab optimization problem object using the createOptimProblem() constructor.
  • I understand that lsqcurvefit cannot perform constrained optimization, but the Genetic Algorithm approach can do that; any help with setting up this problem using the genetic approach would be greatly appreciated, even if it is without the use of MultiStart for now.
  • As I am currently not using the Genetic Algorithm, I am not forcing the optimization solver to use the 'HybridFunction','on' option.
  • I am aware of the fact that the Global Optimization Toolbox has two options for finding global minima: GlobalSearch and MultiStart. I have decided to use MultiStart because according to the following article: http://www.mathworks.com/help/gads/how-globalsearch-and-multistart-work.html#bsc9eec MultiStart Can be setup in parallel on a multicore processor, while GlobalSearch cannot.
  • I am not sure if MultiStart or GlobalSearch can be setup to run with a Genetic Algorithm...
Here is my current code (requires the data.mat file attached to my posting):
clc
clear variables
%%Loading Measurement Data File
load('data.mat')
%%Defining function to which the data will be fit to
% I would like to move away from using the anonymous function,
% and move to using the function file defined in the directory but I don't
% know how to pass a function file to the createOptimProblem function call.
fitfcn1 = @(x,data)x(1).*data.x+x(2).*log(data.y)+abs(data.z).*x(3)+x(4);
%%Defining lower and upper bounds for MultiStart procedure
lb1 = [-Inf,-Inf,-Inf,-Inf];
ub1 = [Inf,Inf,Inf,Inf];
%%Creating row vector for intial guess to optimization routine
p01 = 0*ones(1,4);
%%Defining optimization problem object
% Currently running lsqcurvefit, but I need to move to the genetic
% algorythm approach. I do not understand how to setup a genetic alorythm
% data fitting routine in matlab using the GA functionality built into the
% global optimization toolbox.
problem1 = createOptimProblem('lsqcurvefit','x0',p01,'objective',fitfcn1,...
'lb',lb1,'ub',ub1,'xdata',data,'ydata',data.value);
%%Creating live fitting progress plot
ms1 = MultiStart('PlotFcns',@gsplotbestf);
%%Running MultiStart Optimization Routine
[xmulti1,errormulti1] = run(ms1,problem1,5)
This is my current status:
  • I do not know how to set up the fitting routine to use the Genetic Algorithm; my code is currently using lsqcurvefit, because this was the only way that I could figure out how to instantiate the matlab optimization problem object using the createOptimProblem() constructor.
  • I understand that lsqcurvefit cannot perform constrained optimization, but the Genetic Algorithm approach can do that; any help with setting up this problem using the genetic approach would be greatly appreciated, even if it is without the use of MultiStart for now.
  • As I am currently not using the Genetic Algorithm, I am not forcing the optimization solver to use the 'HybridFunction','on' option.
  • I am aware of the fact that the Global Optimization Toolbox has two options for finding global minima: GlobalSearch and MultiStart. I have decided to use MultiStart because according to the following article: http://www.mathworks.com/help/gads/how-globalsearch-and-multistart-work.html#bsc9eec MultiStart Can be setup in parallel on a multicore processor, while GlobalSearch cannot.
  • I am not sure if MultiStart or GlobalSearch can be setup to run with a Genetic Algorithm...
Can someone help on this? I'm sure it is straight forward but getting stumped. I would simply like to add two matrices together, element-wise, ignoring the NaNs. I realize one solution is to replace NaNs with zeros but I have some reported zeros that I want to be able to later identify. A solution to that is to set any real zeros to extremely small numbers. Rather than doing the above I am wondering if there is any other way to ignoring the NaNs -- I know of sum(...,'omitnan') and nansum() but they work on one matrix or if I concatenate the two matrices together then they aggregate either all rows or all columns depending on the dimension I select.
For example: A = ones(10,3); B = ones(10,3);
% inserting some NaNs and zeros A([1 6 10],1) = NaN; A([3 7],[2 3]) = NaN; B([3 7],2) = 0;
C = A + B;
I would like C = [1 2 2; 2 2 2; 2 0 1; 2 2 2; 2 2 2; 1 2 2; 2 0 1; 2 2 2; 2 2 2; 1 2 2]
I realize that the problem which I defined is quite complex in its definition. I am not an expert user of Matlab and may not realize that my approach to solving this problem and the requirements (using the genetic algorithm, parallel processing, constraining coefficient ranges, and doing all of this using a MultiStart approach) for the fitting process may be unachievable.
After installing the "MATLAB Support for MinGW-w64 C/C++ Compiler" support package, the command
>> mex -setup
reports that the compiler is correctly installed. However, when I restart MATLAB, the same command does not detect it as installed anymore.
When I check the Add-On Explorer, or run the command
>> matlabshared.supportpkg.getInstalled
the MinGW Support Package is still shown as installed.
Why does MATLAB not detect the MinGW compiler anymore after a restart?
When installing the MinGW compiler, the environment variable MW_MINGW64_LOC is set with the path to the MinGW installation folder. On some systems where users have limited privileges, there may be policies in place preventing environment variables from being set. If this environment variable is not staying set following the installation, or after restarting MATLAB, the behavior described above may occur.
Running the 'mex -setup' command in verbose mode
>> mex -setup -v
will display whether the MW_MINGW64_LOC environment variable is set. If this is not the case, the output will look as shown below:
... Looking for compiler 'MinGW64 Compiler (C)' ...
... Looking for environment variable 'MW_MINGW64_LOC' ...No.
The solution is to have an administrator set the environment variable. On Windows 10, you find this at:
Control Panel > System and Security > System > Advanced system settings > Environment Variables... > System variables: New...
The environment variable should have the following name and value:
Variable Name: MW_MINGW64_LOC
Variable Value: <SupportPackageRoot>\3P.instrset\mingw_w64.instrset
where <SupportPackage Root> is the output received for the command:
>> matlabshared.supportpkg.getSupportPackageRoot
Before the loop:
start_time = datetime('now');
rel_times = [];
In the loop, after
s = [s,h];
add
dt = milliseconds(datetime('now') - start_time);
rel_times = [rel_times, dt];
and change
plot(app.UIAxes,s);
to
plot(app.UIAxes, rel_times, s);
Note that performance of your application will get worse and worse over time, due to the way that you keep extending the s and rel_time arrays. Consider using a fixed-size buffer. Consider using animatedline() especially with a maximum number of points.
The Fixed-Point Tool collects ranges and proposes data types by data type groups rather than individual blocks.
A Data type group is a collection of block paths that need to have the same data type. If they don’t, Simulink will throw a data type mismatch error.
For instance, the output of the gain and the input to the subsystem need to have the same data type (they belong to the same data type group). Otherwise Simulink will throw a data type mismatch error. You can try this by changing the data type of the gain in the attached model to single.
For more details on the data type group, please refer to the documentation link below:
All the shared ranges are an aggregation of the data type group ranges. In this case, shared simulation range is the aggregate of the simulation ranges of the data type group and similarly the shared design range is an aggregate of the design ranges of the group.
Fixed Point Tool proposes a data type that can accommodate all these shared ranges for the group.You can infact click the link at the bottom of the Result Details pane to highlight all the members of the data type group.
I am not concerned with the computational time required for the genetic approach, as, for now, I am just trying to develop a methodology for fitting complex, non-linear/non-smooth, functions using the various features available in Matlab's Global Optimization toolbox. The complexity and non-linearity of my function (presented below) will greatly increase in the near future.
The function which I am trying to fit my data to is defined as following:
VALUE=a1*x+a2*ln(y)+a3*abs(z)+a4
With: a1,a2,a3,a4 being the regression model's unknown coefficients
The following optimization constraints need to be imposed on the coefficients of the function:
a3<0; a4>0; a1*a2<0;
The data to which I am trying to fit my function to is presented in the attached SampleData.csv file. The file contains column labels which define the measured value and x,y,z parameters.
My initial guesses for the values of a1,a2,a3,a4 are defined as a row vector: [0,0,0,0].
I would like to learn how to set this problem up using MultiStart and parallel processing. I would like to use parallel processing for the MultiStart process, as well as to include a Hybrid Function, if such an operation is possible.
The work flow which I would like to achieve should be similar to:
1. Define Function (referring to x,y,z data contained in columns of SampleData.csv file) :
function [ Value ] = FittingFunction(x,data)
Value=x(1).*data(:,2)+...
x(2).*log(data(:,3))+...
x(3).*abs(data(:,4))+...
x(4);
end
I would prefer to not use an anonymous function as the complexity of the model to which I will be fitting my future data to will only increase and an anonymous function would be messy to adjust as the regression model matures.
2.Define Initial Guess Row Vector x=[0,0,0,0]
3.Define optimization problem (referring to value data & x,y,z data contained in columns of SampleData.csv file) as a minimization problem trying to minimize a cost function defined as:
Cost=sum((FittingFunction(x,data)-data(:,1)).^2);
4. Pass optimization problem to a Genetic Algorithm routine with a hybrid function included using fminunc.
5. Create a MultiStart Optimization Object and Start a pool of workers.
lowerBounds for MultiStart =[-Inf,-Inf,-Inf,-Inf]; upperBounds for MultiStart =[Inf,Inf,Inf,Inf];
Number of MultiStart Iterations= 5.
5. Run optimization in parallel.
I realize that the problem which I defined is quite complex in its definition. I am not an expert user of Matlab and may not realize that my approach to solving this problem and the requirements (using the genetic algorithm, parallel processing, constraining coefficient ranges, and doing all of this using a MultiStart approach) for the fitting process may be unachievable.
I am using the serial port to read numbers from the arduino encoder. I did drawing a chart in app designer but in the X axis I only have the number of the next displayed number. I would like to see the time after which the number appeared in the X axis. The first number is e.g. 5 and in the x axis its time is 0, the next number 6 in the x axis appeared after 120ms and wants this time on the X axis.
delete(instrfind({'Port'},{app.PORT}))
app.x=serial(app.PORT,'BAUD',9600);
flushinput(app.x);
fopen(app.x); % otwarcie portu szeregowego
for i=1:app.Zakres.Value
drawnow
if app.Z == 1
break
end
h = str2num(fscanf(app.x));
app.aPolozenie.Value = h;
s = [s,h];
plot(app.UIAxes,s);
app.UIAxes.YLim = [ app.Od.Value app.Do.Value];
app.UIAxes.XLim = [0 app.Zakres.Value];
pause(.0001);
app.Zatrzymaj
end
end
This is my current status:
  • I do not know how to set up the fitting routine to use the Genetic Algorithm; my code is currently using lsqcurvefit, because this was the only way that I could figure out how to instantiate the matlab optimization problem object using the createOptimProblem() constructor.
  • I understand that lsqcurvefit cannot perform constrained optimization, but the Genetic Algorithm approach can do that; any help with setting up this problem using the genetic approach would be greatly appreciated, even if it is without the use of MultiStart for now.
  • As I am currently not using the Genetic Algorithm, I am not forcing the optimization solver to use the 'HybridFunction','on' option.
  • I am aware of the fact that the Global Optimization Toolbox has two options for finding global minima: GlobalSearch and MultiStart. I have decided to use MultiStart because according to the following article: http://www.mathworks.com/help/gads/how-globalsearch-and-multistart-work.html#bsc9eec MultiStart Can be setup in parallel on a multicore processor, while GlobalSearch cannot.
  • I am not sure if MultiStart or GlobalSearch can be setup to run with a Genetic Algorithm...
Here is my current code (requires the data.mat file attached to my posting):
clc
clear variables
%%Loading Measurement Data File
load('data.mat')
%%Defining function to which the data will be fit to
% I would like to move away from using the anonymous function,
% and move to using the function file defined in the directory but I don't
% know how to pass a function file to the createOptimProblem function call.
fitfcn1 = @(x,data)x(1).*data.x+x(2).*log(data.y)+abs(data.z).*x(3)+x(4);
%%Defining lower and upper bounds for MultiStart procedure
lb1 = [-Inf,-Inf,-Inf,-Inf];
ub1 = [Inf,Inf,Inf,Inf];
%%Creating row vector for intial guess to optimization routine
p01 = 0*ones(1,4);
%%Defining optimization problem object
% Currently running lsqcurvefit, but I need to move to the genetic
% algorythm approach. I do not understand how to setup a genetic alorythm
% data fitting routine in matlab using the GA functionality built into the
% global optimization toolbox.
problem1 = createOptimProblem('lsqcurvefit','x0',p01,'objective',fitfcn1,...
'lb',lb1,'ub',ub1,'xdata',data,'ydata',data.value);
%%Creating live fitting progress plot
ms1 = MultiStart('PlotFcns',@gsplotbestf);
%%Running MultiStart Optimization Routine
[xmulti1,errormulti1] = run(ms1,problem1,5)
This is my current status:
  • I do not know how to set up the fitting routine to use the Genetic Algorithm; my code is currently using lsqcurvefit, because this was the only way that I could figure out how to instantiate the matlab optimization problem object using the createOptimProblem() constructor.
  • I understand that lsqcurvefit cannot perform constrained optimization, but the Genetic Algorithm approach can do that; any help with setting up this problem using the genetic approach would be greatly appreciated, even if it is without the use of MultiStart for now.
  • As I am currently not using the Genetic Algorithm, I am not forcing the optimization solver to use the 'HybridFunction','on' option.
  • I am aware of the fact that the Global Optimization Toolbox has two options for finding global minima: GlobalSearch and MultiStart. I have decided to use MultiStart because according to the following article: http://www.mathworks.com/help/gads/how-globalsearch-and-multistart-work.html#bsc9eec MultiStart Can be setup in parallel on a multicore processor, while GlobalSearch cannot.
  • I am not sure if MultiStart or GlobalSearch can be setup to run with a Genetic Algorithm...
Can someone help on this? I'm sure it is straight forward but getting stumped. I would simply like to add two matrices together, element-wise, ignoring the NaNs. I realize one solution is to replace NaNs with zeros but I have some reported zeros that I want to be able to later identify. A solution to that is to set any real zeros to extremely small numbers. Rather than doing the above I am wondering if there is any other way to ignoring the NaNs -- I know of sum(...,'omitnan') and nansum() but they work on one matrix or if I concatenate the two matrices together then they aggregate either all rows or all columns depending on the dimension I select.
For example: A = ones(10,3); B = ones(10,3);
% inserting some NaNs and zeros A([1 6 10],1) = NaN; A([3 7],[2 3]) = NaN; B([3 7],2) = 0;
C = A + B;
I would like C = [1 2 2; 2 2 2; 2 0 1; 2 2 2; 2 2 2; 1 2 2; 2 0 1; 2 2 2; 2 2 2; 1 2 2]
I realize that the problem which I defined is quite complex in its definition. I am not an expert user of Matlab and may not realize that my approach to solving this problem and the requirements (using the genetic algorithm, parallel processing, constraining coefficient ranges, and doing all of this using a MultiStart approach) for the fitting process may be unachievable.
After installing the "MATLAB Support for MinGW-w64 C/C++ Compiler" support package, the command
>> mex -setup
reports that the compiler is correctly installed. However, when I restart MATLAB, the same command does not detect it as installed anymore.
When I check the Add-On Explorer, or run the command
>> matlabshared.supportpkg.getInstalled
the MinGW Support Package is still shown as installed.
Why does MATLAB not detect the MinGW compiler anymore after a restart?
When installing the MinGW compiler, the environment variable MW_MINGW64_LOC is set with the path to the MinGW installation folder. On some systems where users have limited privileges, there may be policies in place preventing environment variables from being set. If this environment variable is not staying set following the installation, or after restarting MATLAB, the behavior described above may occur.
Running the 'mex -setup' command in verbose mode
>> mex -setup -v
will display whether the MW_MINGW64_LOC environment variable is set. If this is not the case, the output will look as shown below:
... Looking for compiler 'MinGW64 Compiler (C)' ...
... Looking for environment variable 'MW_MINGW64_LOC' ...No.
The solution is to have an administrator set the environment variable. On Windows 10, you find this at:
Control Panel > System and Security > System > Advanced system settings > Environment Variables... > System variables: New...
The environment variable should have the following name and value:
Variable Name: MW_MINGW64_LOC
Variable Value: <SupportPackageRoot>\3P.instrset\mingw_w64.instrset
where <SupportPackage Root> is the output received for the command:
>> matlabshared.supportpkg.getSupportPackageRoot
Before the loop:
start_time = datetime('now');
rel_times = [];
In the loop, after
s = [s,h];
add
dt = milliseconds(datetime('now') - start_time);
rel_times = [rel_times, dt];
and change
plot(app.UIAxes,s);
to
plot(app.UIAxes, rel_times, s);
Note that performance of your application will get worse and worse over time, due to the way that you keep extending the s and rel_time arrays. Consider using a fixed-size buffer. Consider using animatedline() especially with a maximum number of points.
The Fixed-Point Tool collects ranges and proposes data types by data type groups rather than individual blocks.
A Data type group is a collection of block paths that need to have the same data type. If they don’t, Simulink will throw a data type mismatch error.
For instance, the output of the gain and the input to the subsystem need to have the same data type (they belong to the same data type group). Otherwise Simulink will throw a data type mismatch error. You can try this by changing the data type of the gain in the attached model to single.
For more details on the data type group, please refer to the documentation link below:
All the shared ranges are an aggregation of the data type group ranges. In this case, shared simulation range is the aggregate of the simulation ranges of the data type group and similarly the shared design range is an aggregate of the design ranges of the group.
Fixed Point Tool proposes a data type that can accommodate all these shared ranges for the group.You can infact click the link at the bottom of the Result Details pane to highlight all the members of the data type group.
I am not concerned with the computational time required for the genetic approach, as, for now, I am just trying to develop a methodology for fitting complex, non-linear/non-smooth, functions using the various features available in Matlab's Global Optimization toolbox. The complexity and non-linearity of my function (presented below) will greatly increase in the near future.
The function which I am trying to fit my data to is defined as following:
VALUE=a1*x+a2*ln(y)+a3*abs(z)+a4
With: a1,a2,a3,a4 being the regression model's unknown coefficients
The following optimization constraints need to be imposed on the coefficients of the function:
a3<0; a4>0; a1*a2<0;
The data to which I am trying to fit my function to is presented in the attached SampleData.csv file. The file contains column labels which define the measured value and x,y,z parameters.
My initial guesses for the values of a1,a2,a3,a4 are defined as a row vector: [0,0,0,0].
I would like to learn how to set this problem up using MultiStart and parallel processing. I would like to use parallel processing for the MultiStart process, as well as to include a Hybrid Function, if such an operation is possible.
The work flow which I would like to achieve should be similar to:
1. Define Function (referring to x,y,z data contained in columns of SampleData.csv file) :
function [ Value ] = FittingFunction(x,data)
Value=x(1).*data(:,2)+...
x(2).*log(data(:,3))+...
x(3).*abs(data(:,4))+...
x(4);
end
I would prefer to not use an anonymous function as the complexity of the model to which I will be fitting my future data to will only increase and an anonymous function would be messy to adjust as the regression model matures.
2.Define Initial Guess Row Vector x=[0,0,0,0]
3.Define optimization problem (referring to value data & x,y,z data contained in columns of SampleData.csv file) as a minimization problem trying to minimize a cost function defined as:
Cost=sum((FittingFunction(x,data)-data(:,1)).^2);
4. Pass optimization problem to a Genetic Algorithm routine with a hybrid function included using fminunc.
5. Create a MultiStart Optimization Object and Start a pool of workers.
lowerBounds for MultiStart =[-Inf,-Inf,-Inf,-Inf]; upperBounds for MultiStart =[Inf,Inf,Inf,Inf];
Number of MultiStart Iterations= 5.
5. Run optimization in parallel.
I realize that the problem which I defined is quite complex in its definition. I am not an expert user of Matlab and may not realize that my approach to solving this problem and the requirements (using the genetic algorithm, parallel processing, constraining coefficient ranges, and doing all of this using a MultiStart approach) for the fitting process may be unachievable.
I am using the serial port to read numbers from the arduino encoder. I did drawing a chart in app designer but in the X axis I only have the number of the next displayed number. I would like to see the time after which the number appeared in the X axis. The first number is e.g. 5 and in the x axis its time is 0, the next number 6 in the x axis appeared after 120ms and wants this time on the X axis.
delete(instrfind({'Port'},{app.PORT}))
app.x=serial(app.PORT,'BAUD',9600);
flushinput(app.x);
fopen(app.x); % otwarcie portu szeregowego
for i=1:app.Zakres.Value
drawnow
if app.Z == 1
break
end
h = str2num(fscanf(app.x));
app.aPolozenie.Value = h;
s = [s,h];
plot(app.UIAxes,s);
app.UIAxes.YLim = [ app.Od.Value app.Do.Value];
app.UIAxes.XLim = [0 app.Zakres.Value];
pause(.0001);
app.Zatrzymaj
end
end
This is my current status:
  • I do not know how to set up the fitting routine to use the Genetic Algorithm; my code is currently using lsqcurvefit, because this was the only way that I could figure out how to instantiate the matlab optimization problem object using the createOptimProblem() constructor.
  • I understand that lsqcurvefit cannot perform constrained optimization, but the Genetic Algorithm approach can do that; any help with setting up this problem using the genetic approach would be greatly appreciated, even if it is without the use of MultiStart for now.
  • As I am currently not using the Genetic Algorithm, I am not forcing the optimization solver to use the 'HybridFunction','on' option.
  • I am aware of the fact that the Global Optimization Toolbox has two options for finding global minima: GlobalSearch and MultiStart. I have decided to use MultiStart because according to the following article: http://www.mathworks.com/help/gads/how-globalsearch-and-multistart-work.html#bsc9eec MultiStart Can be setup in parallel on a multicore processor, while GlobalSearch cannot.
  • I am not sure if MultiStart or GlobalSearch can be setup to run with a Genetic Algorithm...
Here is my current code (requires the data.mat file attached to my posting):
clc
clear variables
%%Loading Measurement Data File
load('data.mat')
%%Defining function to which the data will be fit to
% I would like to move away from using the anonymous function,
% and move to using the function file defined in the directory but I don't
% know how to pass a function file to the createOptimProblem function call.
fitfcn1 = @(x,data)x(1).*data.x+x(2).*log(data.y)+abs(data.z).*x(3)+x(4);
%%Defining lower and upper bounds for MultiStart procedure
lb1 = [-Inf,-Inf,-Inf,-Inf];
ub1 = [Inf,Inf,Inf,Inf];
%%Creating row vector for intial guess to optimization routine
p01 = 0*ones(1,4);
%%Defining optimization problem object
% Currently running lsqcurvefit, but I need to move to the genetic
% algorythm approach. I do not understand how to setup a genetic alorythm
% data fitting routine in matlab using the GA functionality built into the
% global optimization toolbox.
problem1 = createOptimProblem('lsqcurvefit','x0',p01,'objective',fitfcn1,...
'lb',lb1,'ub',ub1,'xdata',data,'ydata',data.value);
%%Creating live fitting progress plot
ms1 = MultiStart('PlotFcns',@gsplotbestf);
%%Running MultiStart Optimization Routine
[xmulti1,errormulti1] = run(ms1,problem1,5)
This is my current status:
  • I do not know how to set up the fitting routine to use the Genetic Algorithm; my code is currently using lsqcurvefit, because this was the only way that I could figure out how to instantiate the matlab optimization problem object using the createOptimProblem() constructor.
  • I understand that lsqcurvefit cannot perform constrained optimization, but the Genetic Algorithm approach can do that; any help with setting up this problem using the genetic approach would be greatly appreciated, even if it is without the use of MultiStart for now.
  • As I am currently not using the Genetic Algorithm, I am not forcing the optimization solver to use the 'HybridFunction','on' option.
  • I am aware of the fact that the Global Optimization Toolbox has two options for finding global minima: GlobalSearch and MultiStart. I have decided to use MultiStart because according to the following article: http://www.mathworks.com/help/gads/how-globalsearch-and-multistart-work.html#bsc9eec MultiStart Can be setup in parallel on a multicore processor, while GlobalSearch cannot.
  • I am not sure if MultiStart or GlobalSearch can be setup to run with a Genetic Algorithm...
Can someone help on this? I'm sure it is straight forward but getting stumped. I would simply like to add two matrices together, element-wise, ignoring the NaNs. I realize one solution is to replace NaNs with zeros but I have some reported zeros that I want to be able to later identify. A solution to that is to set any real zeros to extremely small numbers. Rather than doing the above I am wondering if there is any other way to ignoring the NaNs -- I know of sum(...,'omitnan') and nansum() but they work on one matrix or if I concatenate the two matrices together then they aggregate either all rows or all columns depending on the dimension I select.
For example: A = ones(10,3); B = ones(10,3);
% inserting some NaNs and zeros A([1 6 10],1) = NaN; A([3 7],[2 3]) = NaN; B([3 7],2) = 0;
C = A + B;
I would like C = [1 2 2; 2 2 2; 2 0 1; 2 2 2; 2 2 2; 1 2 2; 2 0 1; 2 2 2; 2 2 2; 1 2 2]
I realize that the problem which I defined is quite complex in its definition. I am not an expert user of Matlab and may not realize that my approach to solving this problem and the requirements (using the genetic algorithm, parallel processing, constraining coefficient ranges, and doing all of this using a MultiStart approach) for the fitting process may be unachievable.
After installing the "MATLAB Support for MinGW-w64 C/C++ Compiler" support package, the command
>> mex -setup
reports that the compiler is correctly installed. However, when I restart MATLAB, the same command does not detect it as installed anymore.
When I check the Add-On Explorer, or run the command
>> matlabshared.supportpkg.getInstalled
the MinGW Support Package is still shown as installed.
Why does MATLAB not detect the MinGW compiler anymore after a restart?
When installing the MinGW compiler, the environment variable MW_MINGW64_LOC is set with the path to the MinGW installation folder. On some systems where users have limited privileges, there may be policies in place preventing environment variables from being set. If this environment variable is not staying set following the installation, or after restarting MATLAB, the behavior described above may occur.
Running the 'mex -setup' command in verbose mode
>> mex -setup -v
will display whether the MW_MINGW64_LOC environment variable is set. If this is not the case, the output will look as shown below:
... Looking for compiler 'MinGW64 Compiler (C)' ...
... Looking for environment variable 'MW_MINGW64_LOC' ...No.
The solution is to have an administrator set the environment variable. On Windows 10, you find this at:
Control Panel > System and Security > System > Advanced system settings > Environment Variables... > System variables: New...
rel_times = [rel_times, dt];
and change
plot(app.UIAxes,s);
to
plot(app.UIAxes, rel_times, s);
Note that performance of your application will get worse and worse over time, due to the way that you keep extending.
the MinGW Support Package is still shown as installed.
Why does MATLAB not detect the MinGW compiler anymore after a restart?
When installing the MinGW compiler, the environment variable MW_MINGW64_LOC is set with the path to the MinGW installation folder. On some systems where users have limited privileges, there may be policies in place preventing environment variables from being set. If this environment variable is not staying set following the installation, or after restarting MATLAB, the behavior described above may occur.
the MinGW Support Package is still shown as installed.
Why does MATLAB not detect the MinGW compiler anymore after a restart?
When installing the MinGW compiler, the environment variable MW_MINGW64_LOC is set with the path to the MinGW installation folder. On some systems where users have limited privileges, there may be policies in place preventing environment variables from being set. If this environment variable is not staying set following the installation, or after restarting MATLAB, the behavior described above may occur.
h = str2num(fscanf(app.x));
app.aPolozenie.Value = h;
s = [s,h];
plot(app.UIAxes,s);
app.UIAxes.YLim = [ app.Od.Value app.Do.Value];
app.UIAxes.XLim = [0 app.Zakres.Value];
pause(.0001);
app.Zatrzymaj
end

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