I've got a convolution where the final result is
y=(-t/2)+5t=6
Is there any chance to check this in matlab but not through convolution, I have programmed that part. What I am wondering is it possible to plot the signal using this equation and compare it with the one that I got with coding convolution.
You can plot functions easily in matlab: look at the examles from here.
For example using this code:
t = 0:.1:10
plot(t,(-t/2)+5*t)
will plot you your function between the values x = [0, 10].
Related
I need to plot exactly this graph in MATLAB:
I am struggling with writing hermite vector functions as the vectors showed like they correspond to just last 2 rows of the matrixs in figure.
In MATLAB I wrote equations as below, but I didn't understand why it shows only the last two.
equation1 = (1/3)*t^3+(1/2)*t^2;
equation2 = (1/3)*t^3-(1/2)*t^2;
equation3 = t^4+5*t^3+t^2+t;
equation4 = t^4+5*t^3+t^2+t;
Thanks!
I have a vector of x and y coordinates drawn from two separate unknown Gaussian distributions. I would like to fit these points to a three dimensional Gauss function and evaluate this function at any x and y.
So far the only manner I've found of doing this is using a Gaussian Mixture model with a maximum of 1 component (see code below) and going into the handle of ezcontour to take the X, Y, and Z data out.
The problems with this method is firstly that its a very ugly roundabout manner of getting this done and secondly the ezcontour command only gives me a grid of 60x60 but I need a much higher resolution.
Does anyone know a more elegant and useful method that will allow me to find the underlying Gauss function and extract its value at any x and y?
Code:
GaussDistribution = fitgmdist([varX varY],1); %Not exactly the intention of fitgmdist, but it gets the job done.
h = ezcontour(#(x,y)pdf(GaussDistributions,[x y]),[-500 -400], [-40 40]);
Gaussian Distribution in general form is like this:
I am not allowed to upload picture but the Formula of gaussian is:
1/((2*pi)^(D/2)*sqrt(det(Sigma)))*exp(-1/2*(x-Mu)*Sigma^-1*(x-Mu)');
where D is the data dimension (for you is 2);
Sigma is covariance matrix;
and Mu is mean of each data vector.
here is an example. In this example a guassian is fitted into two vectors of randomly generated samples from normal distributions with parameters N1(4,7) and N2(-2,4):
Data = [random('norm',4,7,30,1),random('norm',-2,4,30,1)];
X = -25:.2:25;
Y = -25:.2:25;
D = length(Data(1,:));
Mu = mean(Data);
Sigma = cov(Data);
P_Gaussian = zeros(length(X),length(Y));
for i=1:length(X)
for j=1:length(Y)
x = [X(i),Y(j)];
P_Gaussian(i,j) = 1/((2*pi)^(D/2)*sqrt(det(Sigma)))...
*exp(-1/2*(x-Mu)*Sigma^-1*(x-Mu)');
end
end
mesh(P_Gaussian)
run the code in matlab. For the sake of clarity I wrote the code like this it can be written more more efficient from programming point of view.
The figure shown above is the plot of cumulative distribution function (cdf) plot for relative error (attached together the code used to generate the plot). The relative error is defined as abs(measured-predicted)/(measured). May I know the possible error/interpretation as the plot is supposed to be a smooth curve.
X = load('measured.txt');
Xhat = load('predicted.txt');
idx = find(X>0);
x = X(idx);
xhat = Xhat(idx);
relativeError = abs(x-xhat)./(x);
cdfplot(relativeError);
The input data file is a 4x4 matrix with zeros on the diagonal and some unmeasured entries (represent with 0). Appreciate for your kind help. Thanks!
The plot should be a discontinuous one because you are using discrete data. You are not plotting an analytic function which has an explicit (or implicit) function that maps, say, x to y. Instead, all you have is at most 16 points that relates x and y.
The CDF only "grows" when new samples are counted; otherwise its value remains steady, just because there isn't any satisfying sample that could increase the "frequency".
You can check the example in Mathworks' `cdfplot1 documentation to understand the concept of "empirical cdf". Again, only when you observe a sample can you increase the cdf.
If you really want to "get" a smooth curve, either 1) add more points so that the discontinuous line looks smoother, or 2) find any statistical model of whatever you are working on, and plot the analytic function instead.
I am using matlab and I have a certain set of x and y data,
x=[0,1.25,1.88,2.5,5,6.25,6.88,7.19,7.5,10,12.5,15,20];
y=[-85.93,-78.82,-56.95,-34.56,-33.57,-39.64,-41.96,-49.28,-66.6,-66.61,-59.16,-48.78,-41.53];
I want to use the curve fitting toolbox which has the spline function to generate a graph, so i did this,
cftool
It would bring me to the toolbox which i can then choose the spline fit. I was thinking if its possible that i extract the data points from the spline graph generated. Which means i probably would have more x and y data points than those that i input in, since the spline graph is sort of a continuous graph. Anyone could give me some advice on this? Thanks!
You can perform the equivalent of the spline fit performed with cftool with fit, see for instance here, here, or here:
% perform spline fit without cftool
ft = fittype('cubicspline');
coeff=fit(x,y,ft);
% use plot to display the interpolating polynomial
% (relying on internal plot settings)
figure
h=plot(coeff,x,y,'o');
% extract the *interpolated* curve from the figure
xi=get(h,'XData');
yi=get(h,'YData');
Replot it just to show that we can:
But if you just want to interpolate do as Fraukje explained here. Define a finer grid on x and use the interp1 function, as in the following example (same x,y input data as before):
% interpolate
Ni = 100;
xi = linspace(x(1),x(end),Ni);
yi = interp1(x,y,xi,'spline');
Now xi,yi is the interpolated data:
i am plotting a convolution in matlab for that purpose i create 2 arrays representing the values of the functions in various points.
x=[1:1000];
c=[1:1000];
t = linspace(-18,18,1000);
for k=1:1000, x(k)=input(t(k));
c(k)=h(t(k));
end;
plot(conv(c,x));
the thing is that it plots the conv against the place of the answer in the array.
i want to plot the conv against the 'n' that will give the value.
plotting against t,c or x from the example above does not give the righ answer. also the plot here is of length 1999.
creating a linspace of length 1999 will plot but it wont give the right answer.
any suggestions?