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I want to find area of latitude/longitude bounding box in square meters.
I need to do this quickly, that's why the solution should be offline.
I usually use Shapely for this kind of tasks.
However... this particular problem is not as easy as it looks, since you would need to transform your lat/lon data to a more appropriate projection.
You can use pyproj for that.
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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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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
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Is there a library for large precision complex numbers in Python?
Take a look at mpmath which offers real and complex floating-point arithmetic with arbitrary precision.
You can also try numpy (official website).
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I am looking for a simple Russian to English word corpus. It can be as simple as a csv that lists a russian word in the first column and the equivalent English word in the second. Any ideas where I can find such a thing? Does the NLTK toolkit have something like this?
Thanks
You can use English-Russian Müller Dictionary which is freely available in DICT format. You will need to invert it manually.