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I am working on a text classification problem in python using Random Forests from the scikit-learn library. I would like to try different features selection methods, such as Information Gain (IG) or Bi-Normal Separation (BNS), as described in this paper.
It seems that the only available feature selection methods available in scikit for feature selection (using the CountVectorizer class), is based on document frequency. Are other methods available in other libraries?
There is a feature-selection module with has tools to do univariate selection or recursive feature elimination: http://scikit-learn.org/dev/modules/feature_selection.html There is no information gane or BNS in scikit-learn. Document frequency is not a feature selection method.
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Is there any Hierarchical Agglomerative Clustering implementation (in Python) available that preserves the order of data points? For example, I want the output something like this.
(((seg1, seg2), (seg3, seg4)), seg5)
but not like this
(((seg1, seg5), (seg2, seg3)), seg4)
E.g., Actual output with existing implementation
Expected output (any implementation?)
Vijaya, from what I know, there is only one public library that does order preserving hierarchical clustering (ophac), but that will only return a trivial hierarchy if your data is totally ordered (which is the case with the sections of a book).
There is a theory that may offer a theoretical reply to your answer, but no industry-strength algorithms currently exist: https://arxiv.org/abs/2109.04266. I have an implementation of this theory that can deal with up to 20 elements, so if this is interesting, give me a hint, and I will share the code.
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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 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.
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I am looking for an algorithm for template matching which I can implemented in matlab or python. I have already used normalized cross correlation and sum of sqaure differences. But These are not robust for my work. Does anyone have any suggestion for me?
Any help would be appreciated.
Thank you in advanvce
Have you tried SIFT, SURF or any other feature detection algorithm?
I have a good experience with both of them in similar context and I know they have available matlab implementations. I have a good experience with VLFEAT.
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What Python libraries are recommended to complement with scikit learn (a machine learning library)?
I have .wav files that I would like to represent as feature vectors, so that I could perform audio recognition.
Is scikit.audiolab a good candidate?
It would be highly appreciated if a sample code or a reference is given, which reads a .wav file to a feature vector :).
Thanks in advance!
If I'm not mistaken, scikit.audiolab is merely for reading/writing audio files but I think in addition you'll want to look at the signal processing libraries in scipy to actually build your feature vectors.
http://docs.scipy.org/doc/scipy/reference/signal.html