Comparison of the Locally Linear Embedding (LLE) and t-distributed Stochastic Neighbourhood Embedding (t-SNE).
This repository contains a project in Advanced Machine Learning, MICRO-570 at EPFL.
To run main.py
, please do the following:
- If needed, install requirements (se 'Installments')
- Make sure that you have all files belonging to the original Zip file in their original place.
- Run
main.py
from its original place - You must exit a plot-window before pressing enter in the terminal for it to work properly.
The following programs and libraries are used in this project:
- Python 3.6.1
- probably works with later versions, if not install via https://www.python.org/downloads/release/python-361/
- numpy
pip install numpy
- sklearn
pip install scikit-learn
- seaborn
pip install seaborn
- matplotlib
pip install matplotlib
- ipywidgets
pip install ipywidgets
- pickle
- A part of standard Python 3.6.1
- time
- A part of standard Python 3.6.1
- mpl_toolkits
- If compiler does not recognise module, then upgrade matplotlib with:
pip install --upgrade matplotlib
- If compiler does not recognise module, then upgrade matplotlib with:
if you do not have pip, get pip by following these instructions: * https://pip.pypa.io/en/stable/installing/
In addition, we have the following import:
from mpl_toolkits.mplot3d import Axes3D
This repository contains the following items:
- Report.pdf : The report.
main.py
- A summary of everything that is done in this project.
helpers.py
- Contains simple help functions
pickle_functions.py
- Contains functions to create or load pickles of transformations
plot_functions.py
- Contains functions used to make plots
plot_mnist.py
- Contains a function used to plot MNIST
Section_III_B-1.ipynb
Section_III_B-2.ipynb
Section_III_C.ipynb
Section_III_D.ipynb
Section_III_E.ipynb
Section_III_F.ipynb
Section_IV.ipynb
We invite the reader to explore all of the notebooks. All notebooks from section III contains interesting interactive plots. The name of the notebooks corresponds to the section in the report in which the work is described.
- SectionB
- SectionC_grid
- SectionD_grid
- SectionE_grid
- SectionF_grid
- mnist_pickles
- mnist
- Data
The first 6 contains pickles of LLE and t-SNE transformations, while the last two contains our datasets.