The Hierarchical Temporal Memory (HTM) algorithm is based on the well understood principles and core building blocks of the Thousand Brains Theory, which is the core-model based, sensory-motor framework of intelligence putting together the neuroscience research developed at Numenta founded by Jeff Hawkings. It aims to reflect the functioning of the human cortex. It focuses on three main properties: sequence learning, continual learning, and sparse distributed representations.
This project aims to explore the applications of HTM, specifically its Anomaly Detection technique, in the AIOps domain.
├── LICENSE
├── Makefile <- Makefile with commands like `make data` or `make train`
├── Pipfile <- Pipfile stating package configuration as used by Pipenv.
├── Pipfile.lock <- Pipfile.lock stating a pinned down software stack with as used by Pipenv.
├── README.md <- The top-level README for developers using this project.
├── data
│ ├── external <- Data from third party sources.
│ ├── interim <- Intermediate data that has been transformed.
│ ├── processed <- The final, canonical data sets for modeling.
│ └── raw <- The original, immutable data dump.
│
├── docs <- A default Sphinx project; see sphinx-doc.org for details
│
├── models <- Trained and serialized models, model predictions, or model summaries
│
├── notebooks <- Jupyter notebooks. Naming convention is a number (for ordering),
│ the creator's initials, and a short `-` delimited description, e.g.
│ `1.0-jqp-initial-data-exploration`.
│
├── references <- Data dictionaries, manuals, and all other explanatory materials.
│
├── reports <- Generated analysis as HTML, PDF, LaTeX, etc.
│ └── figures <- Generated graphics and figures to be used in reporting
│
├── requirements.txt <- The requirements file stating direct dependencies if a library
│ is developed.
│
├── setup.py <- makes project pip installable (pip install -e .) so src can be imported
├── src <- Source code for use in this project.
│ ├── __init__.py <- Makes src a Python module
│ │
│ ├── data <- Scripts to download or generate data
│ │ └── make_dataset.py
│ │
│ ├── features <- Scripts to turn raw data into features for modeling
│ │ └── build_features.py
│ │
│ ├── models <- Scripts to train models and then use trained models to make
│ │ │ predictions
│ │ ├── predict_model.py
│ │ └── train_model.py
│ │
│ └── visualization <- Scripts to create exploratory and results oriented visualizations
│ └── visualize.py
│
├── .thoth.yaml <- Thoth's configuration file
├── .aicoe-ci.yaml <- AICoE CI configuration file (https://github.com/AICoE/aicoe-ci)
│
├── .github <- GitHub configuration folder
│ ├── PULL_REQUEST_TEMPLATE <- GitHub templates for pull requests
│ │
│ ├── ISSUE_TEMPLATE <- GitHub templates for issues
| ├── {major|minor|patch}_release.md <- If Khebut GitHub App Bot (https://github.com/apps/khebhut) is installed, the issue will trigger a major|minor|patch release.
│ | The bot will open a Pull Request to update the CHANGELOG and fix the opened issue.
│ | NOTE: only users that are allowed to release (a.k.a. maintainers specified in the .thoth.yaml file) should open the issue, otherwise bot will
│ | reject them, commenting and closing the issue.
│ | If AICoE CI GitHub App (https://github.com/apps/aicoe-ci) is installed, once the pull request is merged a new tag is created by the bot
│ | and the pipeline to build and push image starts.
│ |
| └── redeliver_container_image.md <- If the tag exists and AICoE CI GitHub App (https://github.com/apps/aicoe-ci) is installed, the issue will retrigger the pipeline to build and
│ push image container image. NOTE: It should be used if the pipeline triggered with the {major|minor|patch}_release failed for any reason.
|
└── tox.ini <- tox file with settings for running tox; see tox.readthedocs.ioProject based on the cookiecutter data science project template. #cookiecutterdatascience