kalepy 0.1.3

Kernel density estimation and sampling.

# kalepy: Kernel Density Estimation and Sampling

[](https://travis-ci.org/lzkelley/kalepy) [](https://codecov.io/gh/lzkelley/kalepy)

dev: [](https://travis-ci.org/lzkelley/kalepy)

This package performs KDE operations on multidimensional data to: 1) calculate estimated PDFs (probability distribution functions), and 2) to resample new data from those PDFs.

## Installation

#### from source

`bash git clone https://github.com/lzkelley/kalepy.git pip install -e kalepy/ `

## Examples

### Use ‘reflecting’ boundary conditions to improve PDF reconstruction at boundaries

Without reflection, the KDE (red line) noticeably underestimates the edges of this uniform distribution (grey histogram). When resampling from the KDE, the new samples (red carpet and histogram) are drawn outside of the original distribution edges. Reflecting boundary conditions better estimate the PDF, and constrain new samples to be within bounds.

`python import kalepy as kale # here `data` has shape (N,) for N data points kde = kale.KDE(data) grid = np.linspace(-0.5, 2.5, 1000) # choose reflection boundaries boundaries = [0.0, 2.0] pdf = kde.pdf(grid, reflect=boundaries) samples = kde.resample(100, reflect=boundaries) `


This also works in multiple dimensions. In each dimension, reflecting boundaries can be applied either on both sides (e.g. x-axis), or only on one side (e.g. y-axis).

`python import kalepy as kale # here `data` has shape (2,N) 2-parameters and N data points kde = kale.KDE(data) xc, yc = np.meshgrid([np.linspace(-0.5, 2.5, 100), np.linspace(-3.0, 2.5, 200)]) grid = np.vstack([xc.ravel(), yc.ravel()]) # choose reflection boundaries in each parameter boundaries = [[0.0, 2.0], [None, 2.0]] pdf = kde.pdf(grid, reflect=boundaries) samples = kde.resample(1000, reflect=boundaries) `


### Comparison of Different Histogram Parameters and Different Kernel

The choice in bin-widths and bin-origins makes a significant difference in how a histogram appears. In general, both parameters are chosen arbitrarily. KDE also have freedom in what kernel is used, and the bandwidth (amount of smoothing), but there are heuristics for optimizing these parameters. In particular, for general data, the Parabola/”Epanechnikov” kernel is optimal in reducing bias, and the bandwidth can be estimated using Scott’s method.


### Calculate projected / marginalized PDF across target parameters

`python # 2-parameter data, shaped (2,N) for N data-points kde = kale.KDE(data) # Create bins in each dimension edges = [np.linspace(-7.5, 10.5, 100), np.linspace(-3, 9, 100)] xe, ye = np.meshgrid(*edges) # Grid of test points grid = np.vstack([xe.ravel(), ye.ravel()]) # Calculate 2D PDF pdf_2d = kde.pdf(grid) # Calculate each 1D PDF pdf_x = kde.pdf(edges[0], param=0) pdf_y = kde.pdf(edges[1], param=1) `


### KDE Refinement with increasing data points