KDEpy
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tommyod
Kernel Density Estimation in Python
The current version of the library only supports Python 3. Using some future imports and removing some type annotations, this make the library backwards compatible with Python 2.
Currently, the bandwidth in `d` dimensions is `bw * np.eye(d)`---the covariance matrix is a multiple of the identity. As a result, the KDE works best if anisotropic data is shifted,...
Hi, First: thanks you for the great package, it works really well! However I experience something that I don't quite understand. I'm fitting a pretty large 4D KDE and it...
Hi, I'm having some trouble installing KDEpy on my M1 mac. I've included the install log below. I've seen on pip that you don't provide wheels for M1 ARM processors,...
Hi all, Thanks for the package, it has been a great help! I am using version 1.1.0. Whenever I try to save a NaiveKDE object with pickle, e.g. ```python import...
`silvermans_rule` algorithm is essentially the same, but with some handling for multidimensional data thrown in there. Also wrote some tests for checking the shape of the output.
Hello everyone, I am getting the following FutureWarning: ``` ...lib\site-packages\scipy\optimize\_zeros_py.py:809: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure...
There is a rule of thumb which should, in theory, perform better than Silverman's rule. Here is the relevant paper: https://www.hindawi.com/journals/jps/2015/242683/ And here's my simple Python implementation for one-dimensional data:...
Hello, I'm trying to display several distributions in the same plot. For that I need to estimate the kde of those distributions, and then evaluate each one of them in...
Hi, Thank you for this very useful package. I'm wondering if it's possible to estimate the kde on a unit sphere data, i.e. the custom grid is defined by the...
