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I am looking for an implementation of Continuous Wavelet Transform for Python that includes Haar Wavelet.
I would like to reproduce the experiment given by MathWorks for Matlab, at this link.
I tried with Pyscellania but I obtain completely different coefficients.
Is there a Python implementation of the CWT out there that includes the Haar Wavalet apart from Pyscellania?
Your request is clear.
Have you tried Pyscellania's normalised or standard Haar Wavelet?
Maybe you are just using the wrong one.
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I'm trying to get some approximation ratios for the Maximum Independent Set Problem and so I need some exact solutions !
I've found libraries written in C++ (i.e https://github.com/iPapatsoris/Maximum-Independent-Set)
but wondered if there were any directly in Python. I know of the `networkx' maximal indepedent set function but these are only approximations.
I realise it's far from the most efficient language to use but I'm only solving small Erdős–Rényi graphs (N<20).
In addition to this, are there any libraries that solve this for the weighted problem, where some nodes matter more than others?
This is the only python library I could find:
https://github.com/pchervi/Graph-Coloring/blob/master/Coloring_MWIS_heuristics.py
I haven't checked that it works correctly however.
I've been using KaMIS instead, which is a C++ implementation.
https://github.com/KarlsruheMIS/KaMIS
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I want to forecast upcoming total users on a daily basis within Python using a machine learning algorithm. Check the pattern below:
Looking at this graph, I was wondering if someone knows which forecasting method in Python I should use to predict?
Thanks!
If you have no additional data expect the user data over time which you have shown, the only thing you can do is try to find a function dependent on time which gives you a good approximation for that plot (ordinary curve fitting). I suppose that's not what you want.
To do a predection (which can be done not only by a machine learning approach), you need other data which is somehow correlated to the data you want to predict.
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I note that Matlab has a straightforward function for getting the entropy of an image. I need something similar for python. Scikit image has an entropy filter, which outputs the image using the least amount of bits needed to do so (at least, I think it does). I assume that to do this it calculates the entropy, but I can't seem to access it as a scalar value.
Before I code a function to do this manually, does anyone know if already exists and I'm somehow missing it? Or for that matter, some existing code that they'd recommend?
If you don't mind shelling out to ImageMagick you can do it like this:
convert someImage.png -format '%[entropy]' info:
0.907238
Not sure how you do it with the ImageMagick Python bindings, but it is probably possible.
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Does anyone know of any tools to do a natural neighbor interpolation in python? unless i'm being stupid, i can't seem to find it in the scipy.interpolate module
Here is a 3D discrete natural neighbor implementation:
https://github.com/innolitics/natural-neighbor-interpolation
Note that the "discrete" is important here; it is an approximation of the true geometric natural neighbor.
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I have been using the Simple Blob Detection algorithm from the OpenCV library (for Python) for a research project. I would like to reference this particular method algorithm in my paper.
Does anyone know from where this method is from and indicate me a good to reference to cite? The openCV source code does not refer to any particular literature.
Thanks
It uses the Connected-component labeling algorithm.