Locally Linear Regression for graph-linked data

Python package implementing a compression based version for fitting and predicting responses using Locally Linear Regression(LLR) on graph-linked data.

Getting Started

Following steps discribe the installation process, as well as few examples using LLR.

Prerequisites

The packege requires you to have Python version 3.3 +. To check your Python version please follow the directions below.

Checking the Python version:

python --version

Installation

pip

Documentation

Class

LLR()

Locally Linear Regression model.

Methods

LLR.fit(X, y, mu, v, perm_size = 50, var=None, Graph=None):

Full fit of the model.

Parameters:

     X: array_like      An n by p array with n observations and p features.

     y: array_like      1-d array of response variable.

     mu: int      Tuning parameter affecting the y-intercept .

     v: int      Tuning parameter affecting the regression coefficients.

     perm_size: int      Number of data points to keep in compression.

     var: double      Variance of Gaussian Kernel, needed if Graph parameter is None.

     Graph: array_like      Graph of data points passed as an adjacency matrix.

LLR.predict(X_new):

Returns the predicted values of the response variable as a 1-d array.

Parameters:

     X_new: array_like      New array of data points, with m observations and p features.

     Graph: array_like      Graph of the data points in X_new passed as an adjacency matrix.

Running the examples

The examples in the /examples folder describe ways to fit the LLR model to the data, predict responses and choose tuning parameters using cross validation.

License

This project is licensed under the MIT License - see the LICENSE.md file for details.