Naive Bayes Classifier in C++11

This is a pure C++11 implementation of a Naive Bayesian classifier using Gaussian priors.

Features:

1. Pure C++11 implementation, no other pre-requisite library.
2. Data container with fast search without modifying the input data or creating new copies.
3. Easily extensible for other conditional priors.

Brief Description

The naive bayes classifier treats each attribute as independent of each other. If a gaussian prior is assumed i.e. the class conditional probabilities are drawn from N(mu, sigma) then the posterior class given a data sample is computed as:

where Z is a normalization function obtained by:

Here p(C|x) is the posterior probability, p(Ci) is the apriori class probability and p(x|Ci) is the class conditional probability.

Installation and Usage

The code compiles into a single nb binary that requires the training and test data. You can use the provided preprocess.py script for splitting the data into appropriate train/test split. To build the code:

git clone https://github.com/aicaffeinelife/naiveBayes
mkdir build && cd build 
cmake ..
make

If everything compiles, you should be able to run the binary with your training and test files. Included in the repo is the shuttle landing dataset[1] which predicts whether to land a space shuttle in manual or auto mode.

[1]: Dataset found here, Cestnik et al.