I'd like to ask you question about some matlab code I wrote, But first of all tell you my problem. I have 2 vectors, Test Labels and Predicted labels and I want to compare them for classification reasons. Further more I want to allocate some labels new. so I have something like this 111121111 = 1 or 1122222222 =2 which I want to achieve. My code is
y = [];
for k = 10:length(predictedLabel)-10
y = [y; newlabel(predictedLabel, k, 5)];
end
and the function newlabel is
function [nl] = newlabel(x, n, L)
numClasses = 3;
x1 = x(n-L:n+L);
c = zeros(numClasses, 1);
for k=1:length(x1)
c(x1(k)) = c(x1(k))+1;
end
[~,nl]=max(c);
end
My problem is now that I result following vector lengths
predictedLabel = 4996*1;
k=4986*1;
y=4977*1;
and I can't see my mistake
Any help is appreciated even new ideas with probability estimations
Thanks
loop
1- when using the length function on a scalar value it gives 1 then the value inside the for loop becomes from 10:1-10 which is meaningless. (If you want to use decreasing step you should provide something like this 10:-1:1).
2- if you want to make a vector with the size of 4996x1 it would be like this : zeros(4996,1) or ones(4996,1) or sth like this.
3- tell us about your desired result and your error.... the description is not obvious to me.
Related
Can you please tell me what's wrong with the following code?
function [n]=calculate_n(p,delta)
e = 1.6*power(10,-19);
k = 1.38*power(10,-23);
T = 298;
co = 3.25*power(10,13)*e*power(10,4);
er=12.5;
eo=1.0;
Nv=3*power(10,13);
us = log((p*e)/sqrt(2*k*T*er*eo*Nv))*2*k*T;
tmp = delta+(e*e*p)/co+us;
n = 1/(exp((tmp))+1);
end
I am getting matrix dimension error while calculating n. Please help me.
Caller:
e = 1.6*power(10,-19);
x = logspace(13,18);
y=calculate_n(x,0.2*e);
semilogx(x,y,'-s');
grid on;
Just replace n = 1/(exp((tmp))+1); with n = 1./(exp(tmp)+1);. But beware, tmp is so small for these values that exp(tmp) will always be 1. Also, there is a surplus bracket around tmp, you might want to check if you put them correctly.
Edit:
The reason is that A/B tries to solve the system of linear equations A*x = B for x which is not what you wanted. It threw an error because it requires both variables to have the same number of columns. A./B performs element-wise matrix division which is what you wanted. However, if A and B are singular A/B = A./B. See the documentation for more info.
having a problem with my "new love", matlab: I wrote a function to calculate an integral using the trapz-method: `
function [L]=bogenlaenge_innen(schwingungen)
R = 1500; %Ablegeradius
OA = 1; %Amplitude
S = schwingungen; %Schwingungszahl
B = 3.175; %Tapebreite
phi = 0:2.*pi./10000:2.*pi;
BL = sqrt((R-B).^2+2.*(R-B).*OA.*sin(S.*phi)+OA.^2.*(sin(S.*phi)).^2+OA.^2.*S.^2.*(cos(S.*phi)).^2);
L = trapz(phi,BL)`
this works fine when i start it with one specific number out of the command-window. Now I want to plot the values of "L" for several S.
I did the following in a new *.m-file:
W = (0:1:1500);
T = bogenlaenge_innen(W);
plot(W,T)
And there it is:
Error using .*
Matrix dimensions must agree.
What is wrong? Is it just a dot somewhere? I am using matlab for the second day now, so please be patient.... ;) Thank you so much in advance!
PS: just ignore the german part of the code, it does not really matter :)
In your code, the arrays S and phi in the expression sin(S.*phi) should have same size or one of them should be a constant in order the code works
The error is most likely because you have made it so that the number of elements in schwingungen, i.e. W in your code, must be equal to the number of elements in phi. Since size(W) gives you a different result from size(0:2.*pi./10000:2.*pi), you get the error.
The way .* works is that is multiplies each corresponding elements of two matrices provided that they either have the same dimensions or that one of them is a scalar. So your code will work when schwingungen is a scalar, but not when it's a vector as chances are it has a different number of elements from the way you hard coded phi.
The simplest course of action (not necessarily the most Matlabesque though) for you is to loop through the different values of S:
W = (0:1:1500);
T = zeros(size(W); %Preallocate for speed)
for ii = 1:length(W)
T(ii) = bogenlaenge_innen(W(ii));
end
plot(W,T)
In your function you define phi as a vector of 10001 elements.
In this same function you do S.*phi, so if S is not the same length as phi, you will get the "dimensions must agree" error.
In your call to the function you are doing it with a vector of length 1501, so there is your error.
Regards
I’m currently a Physics student and for several weeks have been compiling data related to ‘Quantum Entanglement’. I’ve now got to a point where I have to plot my data (which should resemble a cos² graph - and does) to a sort of “best fit” cos² graph. The lab script says the following:
A more precise determination of the visibility V (this is basically how 'clean' the data is) follows from the best fit to the measured data using the function:
f(b) = A/2[1-Vsin(b-b(center)/P)]
Granted this probably doesn’t mean much out of context, but essentially A is the amplitude, b is an angle and P is the periodicity. Hence this is also a “wave” like the experimental data I have found.
From this I understand, as previously mentioned, I am making a “best fit” curve. However, I have been told that this isn’t possible with Excel and that the best approach is Matlab.
I know intermediate JavaScript but do not know Matlab and was hoping for some direction.
Is there a tutorial I can read for this? Is it possible for someone to go through it with me? I really have no idea what it entails, so any feed back would be greatly appreciated.
Thanks a lot!
Initial steps
I guess we should begin by getting a representation in Matlab of the function that you're trying to model. A direct translation of your formula looks like this:
function y = targetfunction(A,V,P,bc,b)
y = (A/2) * (1 - V * sin((b-bc) / P));
end
Getting hold of the data
My next step is going to be to generate some data to work with (you'll use your own data, naturally). So here's a function that generates some noisy data. Notice that I've supplied some values for the parameters.
function [y b] = generateData(npoints,noise)
A = 2;
V = 1;
P = 0.7;
bc = 0;
b = 2 * pi * rand(npoints,1);
y = targetfunction(A,V,P,bc,b) + noise * randn(npoints,1);
end
The function rand generates random points on the interval [0,1], and I multiplied those by 2*pi to get points randomly on the interval [0, 2*pi]. I then applied the target function at those points, and added a bit of noise (the function randn generates normally distributed random variables).
Fitting parameters
The most complicated function is the one that fits a model to your data. For this I use the function fminunc, which does unconstrained minimization. The routine looks like this:
function [A V P bc] = bestfit(y,b)
x0(1) = 1; %# A
x0(2) = 1; %# V
x0(3) = 0.5; %# P
x0(4) = 0; %# bc
f = #(x) norm(y - targetfunction(x(1),x(2),x(3),x(4),b));
x = fminunc(f,x0);
A = x(1);
V = x(2);
P = x(3);
bc = x(4);
end
Let's go through line by line. First, I define the function f that I want to minimize. This isn't too hard. To minimize a function in Matlab, it needs to take a single vector as a parameter. Therefore we have to pack our four parameters into a vector, which I do in the first four lines. I used values that are close, but not the same, as the ones that I used to generate the data.
Then I define the function I want to minimize. It takes a single argument x, which it unpacks and feeds to the targetfunction, along with the points b in our dataset. Hopefully these are close to y. We measure how far they are from y by subtracting from y and applying the function norm, which squares every component, adds them up and takes the square root (i.e. it computes the root mean square error).
Then I call fminunc with our function to be minimized, and the initial guess for the parameters. This uses an internal routine to find the closest match for each of the parameters, and returns them in the vector x.
Finally, I unpack the parameters from the vector x.
Putting it all together
We now have all the components we need, so we just want one final function to tie them together. Here it is:
function master
%# Generate some data (you should read in your own data here)
[f b] = generateData(1000,1);
%# Find the best fitting parameters
[A V P bc] = bestfit(f,b);
%# Print them to the screen
fprintf('A = %f\n',A)
fprintf('V = %f\n',V)
fprintf('P = %f\n',P)
fprintf('bc = %f\n',bc)
%# Make plots of the data and the function we have fitted
plot(b,f,'.');
hold on
plot(sort(b),targetfunction(A,V,P,bc,sort(b)),'r','LineWidth',2)
end
If I run this function, I see this being printed to the screen:
>> master
Local minimum found.
Optimization completed because the size of the gradient is less than
the default value of the function tolerance.
A = 1.991727
V = 0.979819
P = 0.695265
bc = 0.067431
And the following plot appears:
That fit looks good enough to me. Let me know if you have any questions about anything I've done here.
I am a bit surprised as you mention f(a) and your function does not contain an a, but in general, suppose you want to plot f(x) = cos(x)^2
First determine for which values of x you want to make a plot, for example
xmin = 0;
stepsize = 1/100;
xmax = 6.5;
x = xmin:stepsize:xmax;
y = cos(x).^2;
plot(x,y)
However, note that this approach works just as well in excel, you just have to do some work to get your x values and function in the right cells.
Let's say I want to take the sin of 1 through 100 (in degrees).
I come from a C background so my instinct is to loop 1 through 100 in a for loop (something I can do in Matlab). In a matrix/vector/array I would store sin(x) where x is the counter of the for loop.
I cannot figure out how to do this in Matlab. Do I create a array like
x = [1 .. 100];
And then do
x[offset] = numberHere;
I know the "correct" way. For operations like addition you use .+ instead of + and with a function like sin I'm pretty sure you just do
resultArray = sin(x);
I just want to know that I could do it the C way in case that ever came up, thus my question here on SO. :)
% vectorized
x = sin((1:100)*pi/180);
or
% nonvectorized
x=[];
for i = 1:100
x(i) = sin(i*pi/180);
end
I beleive this can actually be done as a one liner in MatLab:
x = sind(1:100);
Note that you use sind() instead of sin(). Sin() takes radians as arguments.
As others have already pointed out there are for-loops in MATLAB as well.
help for
should give you everything you need about how it works. The difference from C is that the loop can go over objects and not only an integer:
objects = struct('Name', {'obj1', 'obj2'}, 'Field1', {'Value1','Value2'});
for x = objects
disp(sprintf('Object %s Field1 = %d', x.Name, x.Field1))
end
That example will output:
Object obj1 Field1 = Value1
Object obj2 field1 = Value2
This could have been done as
for i=1:length(objects)
x = objects(i);
disp(sprintf('Object %s Field1 = %d', x.Name, x.Field1))
end
And now to what I really wanted to say: If you ever write a for loop in MATLAB, stop and think!. For most tasks you can vectorize the code so that it uses matrix operations and builtin functions instead of looping over the data. This usually gives a huge speed gain. It is not uncommon that vectorized code executes 100x faster than looping code. Recent versions of MATLAB has JIT compilation which makes it less dramatic than before, but still: Always vectorize if you can.
#Daniel Fath
I think you'll need the final line to read
resultArray(i) = sin(x(i)) (rather than x(1))
I think you can also do:
for i = x
...
though that will behave differently if x is not a simple 1-100 vector
Hmm, if understand correctly you want a loop like structure
resultArray = zeros(1,length(x)) %% initialization aint necessary I just forgot how you dynamically add members :x
for i = 1:length(x) %% starts with 1 instead of zero
resultArray(i) = sin(x(i))
end
Warning I didn't test this but it should be about right.
I have been unable to find a function in matlab or octave to do what I want.
I have a matrix m of two columns (x and y values). I know that I can extract the column by doing m(:,1) or m(:,2). I want to split it into smaller matricies of [potentially] equal size and and plot the mean of these matricies. In other words, I want to put the values into bins based on the x values, then find means of the bins. I feel like the hist function should help me, but it doesn't seem to.
Does anyone know of a built-in function to do something like this?
edit
I had intended to mention that I looked at hist and couldn't get it to do what I wanted, but it must have slipped my mind.
Example: Let's say I have the following (I'm trying this in octave, but afaik it works in matlab):
x=1:20;
y=[1:10,10:1];
m=[x, y];
If I want 10 bins, I would like m to be split into:
m1=[1:2, 1:2]
...
m5=[9:10, 9:10]
m6=[10:11, 10:-1:9]
...
m10=[19:20, 2:-1:1]
and then get the mean of each bin.
Update: I have posted a follow-up question here. I would greatly appreciate responses.
I have answered this in video form on my blog:
http://blogs.mathworks.com/videos/2009/01/07/binning-data-in-matlab/
Here is the code:
m = rand(10,2); %Generate data
x = m(:,1); %split into x and y
y = m(:,2);
topEdge = 1; % define limits
botEdge = 0; % define limits
numBins = 2; % define number of bins
binEdges = linspace(botEdge, topEdge, numBins+1);
[h,whichBin] = histc(x, binEdges);
for i = 1:numBins
flagBinMembers = (whichBin == i);
binMembers = y(flagBinMembers);
binMean(i) = mean(binMembers);
end