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Kernel density estimation with Mixture of Gaussians

Basics

Main code + steps : main.ipynb else : report.pdf

To run the code You should be able to get started if you have a basic Python3.5+ environment with Jupyter, Numpy, Matplotlib and Pandas (used for reporting and tracking results). Alternatively install via requirements.txt.

Installation

Follow these steps if you don't have a suitable environment.

  1. Install Python 3.5+
  2. Install Dependencies
pip install -r requirements.txt
  1. Launch
jupyter lab main.ipynb
  1. Set paths to extracted MNIST and CIFAR100 in the code: CIFAR_100_DIR and MNIST_PICKLE
  2. Set other variables such as NUM_PROCS to control number of processes spawned.

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KDE with MoG

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