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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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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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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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Is there a data structure in Python(considering only built-in libraries with version 3.6x) that could meet with both of the following reqs?
1) allocate memory space sequentially, as the array structure in C provides (Or resembles C#'s LayoutKind.Sequential, Pack= 1). and
2) store custom types.(the elements in the structure would be guaranteed by design to be of the same custom type)
Things I have thought of but doesn't work:
meets 1) but fails 2): array.array
satisfies 2) but fails to meet 1): list... etc.
EDIT :
As Graipher pointed out in the comment below, and the link he provides. List structure is actually an "array of pointers to Py_Objects". While that technically meets 1) that the memory is sequentially allocated, the data structure I am looking for should sequentially allocate objects, instead of object pointers.
Did I miss any handy data structure provided? Or what tricks out there could provide me with the features I want? Thanks in advance.
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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.
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I need python library that can construct suffix trees and especially generalised suffix trees. Could you suggest me some libraries. Thanks.
See the following libraries.
suffixtree
Python-Suffix-Tree
SuffixTree
SuffixTree (same name different project, supports generalized suffix trees)
pysuffix (This is suffix arrays)