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I have a system which is configurable. It has 3 parameters
Parameter1 - can vary between [0, 2^30]
Parameter2 - can vary between [0, 2^30]
Parameter3 - can vary between [0, 2^12]
I have a python code which when given a set of valid numbers corresponding to (parameter1, parameter2, parameter3) can configure the system and after few mins return a number say a score for the configuration.
Suppose the aim to maximize the score. Is there a python library to generate the parameters with constraints intelligently.
Thanks in advance
This is a bounded optimization problem. Depending on how the "system" is structured different optimization strategies will behave differently well.
My suggestions is to either look at scipy.optimize.minimize or a genetic algorithm. I know there are multiple GA implementations in Python, but I haven't tried them.
Since a single evaluation takes on the order of minutes, you are looking at long optimization times though.
If you can compute the gradient to your score function, and if it is well-behaved (i.e. convex) then the scipy.optimize.minimize is probably your best bet. Otherwise you might have better luck with a GA.
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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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In MATLAB, binofit returns the maximum likelihood estimate of the success of binomial probability distribution and confidence intervals.
statsmodels.stats.proportion.proportion_confint returns confidence intervals as well, but couldn't find a function for maximum likelihood estimate of the binomial probability distribution. Is there any function that you can suggest as a binofit function in MATLAB for python?
I think the function you suggested is good enough. I ran some test comparing Matlab binofit and Python statsmodels.stats.proportion.proportion_confint. The test was empyrical like testin 100K experiments like [phat,pci] = binofit(x,n,alpha) with min_conf,max_conf = proportion_confint(x,n,alpha=alpha,method='beta').
The RMSE bewteen confidence interval limits from Matlab and Python are below 5e-6 for values values of x and n between 0 and 10000. Tested with alpha=0.05 and 0.01.
I know this is not strict demonstrations but for my project I decided to consider the two estimates of confidence intervals as equivalent.
Try using one of these two libraries: statsmodels or scipy.
I do not know if it is exactly what you're looking for, but I hope you find it useful still.
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I'm planning to write a script that reads in text input data. This would consist of certain terms e.g "red car".
What machine learning tools for python should I use if I wanted to identify potential matches to a term in my text input data within a database of terms and sentences.
For example, I would want similarly spelled terms (e.g mis-spelled terms) like "redd car" to be identified and listed in the output of my script.
Edit 1: I have a method of identifying string similarity using FuzzyWuzzy to return a number representation of two strings's similarity to each other. My question would be now how to divide the words in the database into "similar" and "not similar" using machine learning approaches.
Without knowing much of your setup I would recommend using scikit-learn packages for your project. It has support for almost every aspect of machine learning including but not limited to:
Classification
Regression
Clustering
Dimensionality reduction
Model selection
Preprocessing