A tool for NuPIC to run CLA models and visually examine the output of the Spatial Pooler. Click here for a video introduction and tutorial.
- A DVR for the CLA algorithm
- A visual debugging tool for the CLA
- A dataset generator
- A local web app
- Another client of the OPF
- A tool for finding mutants
To install (with Homebrew for Mac OS X):
brew install libevent
brew install mongodb
pip install -r requirements.txt
To launch:
mongod &
python cerebro.py <portnum>
In a browser, navigate to:
http://localhost:<portnum>
- Need a description.py file as input
- Can load a base and sub description.py file
- Path to data file should be in the description.py file
- Write a function to procedurally generate datasets
- Python
- Provides a number of useful utilities for generating datasets
- Can save to CSV
- Function written in python. Don’t need to include def funcname()
- Dictionary fields should be filled with data fields. Fields can be strings or scalars
- Utility variable history passed to function. Used to create sequential patterns. history[-1][‘foo’] retrieves the field value of ‘foo’ from 1 timestep ago
- Several useful libraries are imported on your behalf:
- Random
- Numpy
- String
- Math
- Provides some syntactic sugar for adding randomness to datasets
- If a field is set to a list-like, values are randomly sampled from set.�Ex. fields[‘f’] = (‘A’, ‘B’, ‘C’, ‘D’) => Randomly sample ‘A’, ‘B’, ‘C’, D’�Ex. Fields[‘foo’] = list(string.uppercase) => randomly sample from uppercase letters
- If a field is set to a dict-like, values are randomly sampled according to likelihood ratios.�Ex. fields[‘f’] = {‘A’: 1.0, ‘B’:2.0} => Randomly sample ‘A’, ‘B’, where ‘B’ is twice as likely.
- Show’s predicted vs. actual graphs
- If present, also shows anomaly score graph
- On right, shows predicted and active columns
- Shows encoder representations on the bottom
- At the very bottom, shows a whole bunch of textual output.
- Verbose output: captures everything from stdout while CLA processes a single record