links-own [ Variable ]
Hello I am trying to find a way to make a histogram based on links-own variable and the number of links as the frequency.
I used the following logic in plot and it's not showing anything
histogram [Variable] of links
Related
How can we calculate the percentage of similarities between two pattern of Histogram?
For example, I have a histogram of template which I called HistA, and I have another histogram which is HistB where I want to check the similarities percentage of HistB with HistA.
I tried check out some of method such as histogram equalization, histogram matching but none of them works with my problem.
As image below, I create a multiple histogram between HistA and HistB. The value of the frequencies were actually value from a 1D data.
I saw the pattern of HistA and HistB almost the same, so I want to know how to calculate the percentage of the similarities of this two histogram.
Measure Bhattacharya co-efficient between the two normalized histograms and as
where N is the number of bins in the histograms.
Note the normalization.
For more information, see Bhattacharya distance|Wikipedia or On a measure of divergence between two statistical populations defined by their probability distributions.
I wish to distribute turtles according to Multimodal distribution(positive and negative) in netlogo. The turtles should be distributed along positive and negative but only along negative y axis. But NetLogo seems to provide no methods for doing this like random-normal or random-poission.
Depends on the type of multimodal distribution you want. Multimodal just means there is more than one peak in the density curve. Just add together multiple distributions or choose between multiple distributions. The only thing you need to be careful about is that total probability remains 1. Here's one option that will give you a mode at +1 and another at -1 with equal variance around the two.
[ set num ifelse-value (random-float 1 < 0.5)
[ random-normal -1 0.2 ]
[ random-normal 1 0.2 ]
]
I have about 10000 floating point data, and have read them into a single row matrix.
Now I would like to plot them and show their distribution, would there be some simple functions to do that?
plot() actually plots value with respect to data number...which is not what I want
bar() is similar to what I want, but actually I would like to lower the sample rate and merge neighbor bars which are close enough (e.g. one bar for 0.50-0.55, and one bar for 0.55-0.60, etc) instead of having one single bar for every single data sample.
would there be a function to calculate this distribution by dividing the range into small steps, and outputting the prob density in each step?
Thank you!
hist() would be best. It plots a histogram, with a lot of options which you can see by doc hist, or by checking the Matlab website. Options include a specified number of bins, or a range of bins. This will plot a histogram of 1000 normally random points, with 50 bins.
hist(randn(1000,1),50)
I am a new to MATLAB. I have 6 histograms which are created from subdividing the image into patches. How I can merge these patches to make one histogram (i.e. concatenating the 6 histograms)?
I have tried this code:
subplot(3,4,1)
imhist(Patch1)
subplot(3,4,2)
imhist(Patch2)
subplot(3,4,3)
imhist(Patch3)
subplot(3,4,4)
imhist(Patch4)
You can concatenate all of the patches into a single vector, then call imhist on this vector. You mentioned that you have six histograms when your code is only showing four. As such, I will assume that the other histograms come from Patch5 and Patch6. Knowing this, the code would simply be:
patch = [Patch1(:); Patch2(:); Patch3(:); Patch4(:); Patch5(:); Patch6(:)];
imhist(patch);
Histograms are agnostic with the dimensions of the image you're looking at. It simply counts how many pixels you encounter per intensity level of the data you are using. As such, we can simply put all of these pixels for each patch into a single vector and do a histogram of that vector. This will work nicely as this will disregard the dimensions of each patch so we don't have to construct a new image and do a histogram of the newly constructed image.
My problem is as follows; I have two vectors u and v. I have computed a table of cross-ratios like so:
[ u1/v1, u1/v2, u1/v3, u1/v4, ... ]
[ u2/v1, u2/v2, u1/v3, u2/v4, ... ]
[ u3/v1, u3/v2, u1/v3, u3/v4, ... ]
[ u4/v1, u4/v2, u1/v3, u4/v4, ... ]
[ ...
My task now is to compute a histogram of these cross ratios. However, it is clear that using linear histogram bins would not make sense - any ratios below 1 would have a far lower sample resolution than the ratios above 1, and the long-tailed nature of the ratio distribution means that my choice of bins would be skewed heavily by large values.
So, my question is: is there a 'correct', or at least better, choice of histogram bins (or equivalently, a transformation to apply to the data) for this situation? I can see that the Cauchy distribution might be relevant although I'm quite sure how.
Many thanks in advance.
Try plotting the histogram of the log of the ratios. This works as long as your values are strictly positive, and has the nice property that log(u1/v1) = -log(v1/u1).
You may calibrate the histogram's bins manually using histc.