The k-nearest neighbor algorithm implemented in Python. To test our model for different distance metrics the MNIST dataset is used. The run and main files are meant to run the model on either the small dataset included in the repository or the large dataset. The results of these runs are stored in the results directory.
The notebooks contain the evaluation of the model and our choice of pre-processing and PCA. In plots notebook the different distance metrics and their results are plotted as well as the performance on processed data. The choice of Max-pooling and PCA is also implemented in the notebooks.