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I have a lot of latitude and longitude information that belong to different countries. How do I map each of these lat-long to the country it belongs to in an efficient manner?
I came across googleapis, but that would be very inefficient and time consuming if I had to query this api for every lat-long pair that I have. I thought of grouping all the lat-long pairs that are closer and then figure out a way to find the country that each of these groups belongs to.
Could you please point me to the right algorithms that I could use for this purpose? Or are there better ways to do this?
import reverse_geocode
coordinates = (-37.81, 144.96), (31.76, 35.21)
reverse_geocode.search(coordinates)
Can find the locations offline like this using the python reverse_geocode library
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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 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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Can anyone guide me towards any dataset which consists of questions/survey based on psychology which when answered in full extent can tell you the gender if the person taking the test?
I need it to create a tool through which we can detect the patterns of fake profiles on the social platforms.
I know a few groups which are gender-specific (e.g. for mothers, for women private talk) but the opposite gender tries to trash it getting into the group pretending to be female.
I know it sounds silly for now, but anyone who wants to join these group can go through the questionnaire and the AI can detect it's gender.
Thank you in advance.
There is a dataset that I came across on kaggle. It does not have question-answer pairs from surveys, but the project was mainly about attempting to predict gender based on users' tweets. Not sure if you need your dataset in questionnaire format, but if not then you can check this out:
Twitter User Gender Classification
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I have a list of binary strings and two center strings which are not in the list.
I would like to classify that list around the center strings in order to create two clusters. A string of the list will be assigned to the cluster whose center is nearest to that string (hamming distance as metric).
I've seen that there are alghoritms like Neighbours Classifier, k-medoids, Affinity propagation, but all these procedure calculate centroids on their own; I have to use my center strings instead.
Any suggestion?
Perform 1-nearest neighbor classification using your centers only.
No need for anything special here. Just assign to the nearest center.