Necessary folders:

• Data
• Results
• Models

Generate training data

python "Dataset_Generators/generators__manifold-learning/swiss-roll.py" -n 1000 -noise 0.1 -seed 1 -output "Data/swiss-roll-1000.train"

Generate testing data

python "Dataset_Generators/generators__manifold-learning/swiss-roll.py" -n 1000 -noise 0.2 -seed 2 -output "Data/swiss-roll-1000.test"

Train the model

python train.py -input Data/swiss-roll-1000.train -model Models/swiss_roll-1000.model -bits 8 -log Models/swiss_roll-1000-train.log

Test the model

python test.py -model Models/swiss_roll-1000.model -test_file Data/swiss-roll-1000.test -compressor Compressors.vanilla -log_file_test Results/swiss_roll-1000-vanilla.log -log_file_others Results/swiss_roll-1000-vanilla-others.log

Create the 2d plot

python plot_hashcodes.py -model ./Models/4clusters_noise20_n1000_bits8.model -input ./Data/4clusters_noise20_n1000.train -compressor Compressors.pc_dominance_by_modes_order

The MNIST & SIFT Datasets

The MNIST & SIFT datasets can be generated by running the setup.sh file in their directories. These datasets are included in the Dataset_Generators directory. You'll need to have python3 and wget installed on your machine to be able to run these scripts.

Test a compressor on a given dataset. The file creates its own model with the given number of bits

python test_exact.py -data ./Data/sift.train -compressor Compressors.vanilla -bits 32 -samples 100