.py files in the base folder contain working code for TM (data processing, SDR creation, training, inference, visualization, etc.). The notebooks/ folder contains experimental notebooks for training, validation of data, and SDR creation. Use these notebooks with caution. The old/ folder contains old code that validated old experiments. Some of this code might be deprecated.
Please run python run.py -e <experiment name>. Experiment configs can be found in experiments/ -- please edit these configs as needed to run any custom experiments.
The run.py script will fail if the processed dataset (curvature and location SDRs, etc.) is not found. If it is not found, then follow the script's instructions
to generate this dataset (with the help of scripts like process_data.py, coordinate_encoder.py, curvature_encoder.py, and/or cluster.py).
Test to see if the conda installation is successful by checking to see if the following import works:
python
from nupic.bindings.math import SparseMatrixConnections
Ensure that trimesh is the correct version: pip install trimesh==3.10.8. Later versions of trimesh don't always produce the correct results for these experiments.
Flags:
sdr_p: where to save the dataset
ycb_p: root directory of the YCB path (most likely ~/tbp/data/habitat/objects/ycb)
-r: hash radius in Cartesian Coordinate space (ultimately determines how much one SDR overlaps with another)
-d1, -d2: range of objects to save
-n: either the total number of bits in the SDR (in the case of the curvature_encoder.py and coordinate_encoder.py) or the total number of training points to generate (in the case of cluster.py)
-w: total number of ON bits in the SDR
coord: wheteher to cluster datapoints by coordinates -- n most commonly occurring clusters are chosen for training
curve: whether to cluster datapoints by curvatures -- n most commonly occurring clusters are chosen for training
Pre-process the data:
Update <YOU> in the following lines to reflect your user ID. The following are single-line commands.
python ~/tbp/tbp.monty/projects/temporal_memory/process_data.py -sdr_p /Users/<YOU>/tbp/tbp.monty/projects/temporal_memory/tm_dataset -ycb_p ~/tbp/data/habitat/objects/ycb
python ~/tbp/tbp.monty/projects/temporal_memory/curvature_encoder.py -sdr_p /Users/<YOU>/tbp/tbp.monty/projects/temporal_memory/tm_dataset -r 5 -d1 0 -d2 10 -n 1024 -w 11
python ~/tbp/tbp.monty/projects/temporal_memory/coordinate_encoder.py -sdr_p /Users/<YOU>/tbp/tbp.monty/projects/temporal_memory/tm_dataset -r 5 -d1 0 -d2 10 -n 2048 -w 30
python ~/tbp/tbp.monty/projects/temporal_memory/cluster.py -sdr_p /Users/<YOU>/tbp/tbp.monty/projects/temporal_memory/tm_dataset -n 50 -coord True -curve True
python run.py -e <CONFIG NAME>. For example, you can run python run.py -e occlusion_cluster_by_coord_curve.