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right now I'm looking for an sklearn method that does something like:
arr = [13,15,41,45,90,100]
print(KMeans.num_clusters(arr))
Outputs 3
You can use mean-shift clustering. It does not require number of clusters beforehand. However, the drawback of mean shift is that it is not very efficient compared to the k-means. Since your example array is only 1 dimensional it should not be a problem. If you are going to use mean-shift with 2 or more dimensional data, be careful with the curse of dimensionality.
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I am looking for a python package that supports Monte Carlo Cross Validation (Repeated random sub-sampling validation). SkLearn has k-fold, but this will not allow me to specify the ratio of training/testing.
I have seen a package in R that will supposedly achieve this (Caret), but is there an equivalent for python?
The package you're after is in fact available in Scikit learn, but is called ShuffleSplit.
Check also the user guide here, where the function is referred to as Random permutations cross-validation.
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Often on the Internet I saw such images and now I am interested in the implementation of this algorithm. The input is a template and a set of words, on the basis of which the image is drawn, the more often the word is used the more space it takes. As far as I know there is already a library for python that allows you to generate such images. Could you tell me which one?
Word cloud allows you to do this, and using its masks features should allow you to shape them: https://github.com/amueller/word_cloud would give you more information on how this can be done.
The above image is constructed using wordCloud!. Here you can find a tutorial on python.
I hope this helps...
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I would like to make a numpy-like multi-dimensional array of non-numerical objects in python. I believe Numpy arrays only allow numerical values. List-of-lists are much less convenient to index- for example, I'd like to be able to ask for myarray[1,:,2] which requires much more complicated calls from lists of lists.
Is there a good tool for this?
NumPy arrays actually do allow non-numerical contents, so you can just use NumPy.
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I have this optimization problem and I wonder any function in any python library can solve it? Say I want to minimize f(x) by gradient descent. x is a vector of say 3 dimensions, x=(x1,x2,x3). The constraint is x1>0, x2>0, x3>0, and x1+x2+x3=1. Any function can solve this constrained gradient descent? Thank you.
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I am trying to implement the newton method for maximization in higher dimensions and I was wondering if there exists any solvers for this in Python? In Scipy there is a solver for the 1-dimensional case, but I do not see one for the multi-dimensional case. I suppose that it is possible to implement it using the Hessian and Gradient solvers in Numdifftools
EDIT:
It looks like scipy.optimize.minimize does this. I was looking under the multi-dimensional heading and it wasn't there, that's why I missed it. It was under the general-purpose heading
scipy.optimize.minimize does this.