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?
Related
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].
I need to generate some random 2D points (for example 30 points) near the y=x line, insert them in a matrix, plot it and then calculate the SVD of the matrix. But since I'm new to MATLAB I don't know how can I generate my desired matrix.
Since this looks like homework I'll just post some general ideas here.
randi can be used to get semi-random integers. Using that you can create a 2D matrix by duplicating the array and putting them together. Thus: generate a 30x1 column and duplicate it to a 30x2 column. All rows will have the same two entries, i.e. x=y.
Noise can be added to this by creating a 30x2 matrix of random numbers, use rand for that and simply add that to the previously created matrix.
Check the documentation on svd to see how the singular-value decomposition works, it's fairly straight-forward if you know your linear algebra.
Finally for plotting you can use various tools such as image, imagesc, plot, surf and scatter, try them and see which works best for you.
Here is a quick example I made: https://saturnapi.com/fullstack/2d-points-randomly-near-line
%// Welcome to Saturn's MATLAB-Octave API.
%// Delete the sample code below these comments and write your own!'
x = 13 + 6.*rand(20,1);
y = x*0.7 + 0.5*rand(20,1);
[X,Y] = meshgrid(x,y)
figure(1);
plot(x,y,'.');
%// Print plot as PNG with resultion of 60 pixels per inch
print("MyPNG.png", "-dpng", "-r60");
I've been using interp1 to plot curves to follow sets of datapoints, and for most of the datapoints it's been working:
But when I try it with another set of datapoints it doesn't follow them at all:
For both interpolations the code I'm using is just:
curve = interp1(x, y, 'pchip');
Where x is just a set of numbers that correspond to the x axis of each datapoint, and y is the values themselves.
I can't tell what is different about the second dataset that is causing the interp1 function to not follow the data.
So with thanks to #m.s. for providing his code, it turns out the issue is that with the second graph I was interpolating with x= -90:10:90, whereas if I interpolate with 1:19, in a similar manner to the first graph, then the problem is fixed.
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.
Starting from the plot of one curve, it is possible to obtain the parametric equation of that curve?
In particular, say x={1 2 3 4 5 6....} the x axis, and y = {a b c d e f....} the corresponding y axis. I have the plot(x,y).
Now, how i can obtain the equation that describe the plotted curve? it is possible to display the parametric equation starting from the spline interpolation?
Thank you
If you want to display a polynomial fit function alongside your graph, the following example should help:
x=-3:.1:3;
y=4*x.^3-5*x.^2-7.*x+2+10*rand(1,61);
p=polyfit(x,y,3); %# third order polynomial fit, p=[a,b,c,d] of ax^3+bx^2+cx+d
yfit=polyval(p,x); %# evaluate the curve fit over x
plot(x,y,'.')
hold on
plot(x,yfit,'-g')
equation=sprintf('y=%2.2gx^3+%2.2gx^2+%2.2gx+%2.2g',p); %# format string for equation
equation=strrep(equation,'+-','-'); %# replace any redundant signs
text(-1,-80,equation) %# place equation string on graph
legend('Data','Fit','Location','northwest')
Last year, I wrote up a set of three blogs for Loren, on the topic of modeling/interpolationg a curve. They may cover some of your questions, although I never did find the time to add another 3 blogs to finish the topic to my satisfaction. Perhaps one day I will get that done.
The problem is to recognize there are infinitely many curves that will interpolate a set of data points. A spline is a nice choice, because it can be made well behaved. However, that spline has no simple "equation" to write down. Instead, it has many polynomial segments, pieced together to be well behaved.
You're asking for the function/mapping between two data sets. Knowing the physics involved, the function can be derived by modeling the system. Write down the differential equations and solve it.
Left alone with just two data series, an input and an output with a 'black box' in between you may approximate the series with an arbitrary function. You may start with a polynomial function
y = a*x^2 + b*x + c
Given your input vector x and your output vector y, parameters a,b,c must be determined applying a fitness function.
There is an example of Polynomial Curve Fitting in the MathWorks documentation.
Curve Fitting Tool provides a flexible graphical user interfacewhere you can interactively fit curves and surfaces to data and viewplots. You can:
Create, plot, and compare multiple fits
Use linear or nonlinear regression, interpolation,local smoothing regression, or custom equations
View goodness-of-fit statistics, display confidenceintervals and residuals, remove outliers and assess fits with validationdata
Automatically generate code for fitting and plottingsurfaces, or export fits to workspace for further analysis