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README.md

OgmaNeoDemos

Join the chat at https://gitter.im/ogmaneo/Lobby

Examples and experiments using the OgmaNeo C++ library.

Demos

Currently released demos include:

  • Video prediction
  • Simple anomaly detection
  • Level generation
  • Sinusoidal prediction
  • Runner
  • Ball physics

The Ogma YouTube channel contains videos associated with certain demos, https://www.youtube.com/ogmaai.

Video Prediction

An OgmaNeo Predictor class is used to build an online predictive hierarchy. The video file is streamed through the hierarchy numIter (currently 32) times. Each video frame is split into three colour channels, and an associated corrupted frame (based on a blend of noise and the previous hierarchy prediction), are passed into the hierarchy. Progress information is shown in the main window as this occurs as well as in the terminal, and a graph of the errors for each predicted frame is shown in the window.

Once the video has been presented a few times, the original video is ignored and the hierarchy is fed with predictions from the hierarchy. These predictions are then shown in the main window.

Two sample video files are provided in resources, and suggested settings for each are shown below. A useBullFinchMovie boolean can be toggled to determine which video to use:

Setting Bullfinch192.mp4 Tesseract.wmv Notes
netScale 192 128 Input layer has netScale * netScale units
chunkLayers 3 1 Number of Chunk Encoder bottom layers
nLayers 6 4 Total number of Encoder Layers
- 128x128 (x3), 64x64 (x3) 64x64 (x4) Units in the Encoder layers

An optional debug window can be displayed that shows various images from within the hierarchy as it is show each frame of the video. This debug window can be enabled using the enableDebugWindow boolean.

This demo uses:
OpenCV (Open Source Computer Vision, version 3.x) library to load a video file.
SFML (Simple and Fast Multimedia Library, version 2.4.x) to create a Window and handle keyboard entry.

Note for OpenCV if you have CUDA 8 or higher (latest version), there is currently an error in OpenCV 3.1 as explained here. Essentially, you need to edit opencv-3.1.0/modules/cudalegacy/src/graphcuts.cpp and change the line:

#if !defined (HAVE_CUDA) || defined (CUDA_DISABLER)

to:

#if !defined (HAVE_CUDA) || defined (CUDA_DISABLER)  || (CUDART_VERSION >= 8000)

Makefile target for build this demo: make Video_Prediction

MNIST Anomaly Detection

The MNIST database of handwritten digits is used as the dataset for this demo.

The train-images-idx3-ubyte.gz and train-labels-idx1-ubyte.gz training set files must be downloaded from http://yann.lecun.com/exdb/mnist/ and extracted into the resources directory.

A predictor learns to predict the pixels of the digits as they move by, and when there is a large enough discrepancy between the prediction at (t) and the input at (t + 1), then an anomaly has been detected.

Initially only a selection of the digit 3 training images are presented to the hierarchy. This can be seen in the left half of the main window. After a short amount of time (a few minutes), the R key can be pressed to change from learning mode into detection mode. The right half of the window has a graph that shows peaks when a non-3 digit occurs. A red bar will also appear when the system detects an anomaly. Further, a bar at the top left of the window displays digits as the come, with the center of the bar indicating the current digit.

This demo uses:
SFML (Simple and Fast Multimedia Library, version 2.4.x).

Makefile target for this demo: make MNIST_Anomaly_Detection

Level Gen

An image of a game level is presented to the hierarchy and scrolls to the left. An infinite level is then generated through recall with noise.

This demo uses:
SFML (Simple and Fast Multimedia Library, version 2.4.x).

Makefile target for this demo: make Level_Gen

Sinusoidal Prediction

Multiple sine functions are combined, plotted (red line), and presented to a hierarchy. The predicted next value is plotted to the main window (blue line). An ogmaneo::ScalarEncoder is used to encode the combined sine functions for presenting to the hierarchy, and decoding predictions from the hierarchy.

The space key is used to pause the hierarchy and plotting. The c key is used to continue from a paused state.

An optional debug window can be displayed that shows various images from within the hierarchy. This debug window can be enabled using the enableDebugWindow boolean.

This demo uses:
SFML (Simple and Fast Multimedia Library, version 2.4.x).

Makefile target for this demo: make Wavy_Test

Runner

A running quadruped robot that uses online reinforcement learning to learn to run to the left or to the right. An ogmaneo::ScalarEncoder is used to encode limb angles, contact sensors, and body angles into a SDR.

A swarming hierarchical reinforcement learning agent (ogmaneo::Agent) optimizes the distance traveled per unit time in the specified direction.

Press the t key to toggle speed mode (past real-time simulation) and k to toggle the target direction (left to right is the default).

This demo uses:
SFML (Simple and Fast Multimedia Library, version 2.4.x).
Box2D (Box2D, version 2.3.1).

Makefile target for this demo: make Runner

Ball Physics

A test to see how well the hierarchy can approximate the physics of a bouncing 2D ball. A hierarchy is trained on several instances of bouncing balls with random initial starting velocities.

Once trained, the hierarchy receives a few of the starting frames of the ball to determine its trajectory, after which it loops on its own predictions to complete the physical interactions.

Press g to switch between train/test modes. Press k to show the SDRs.

This demo uses:
SFML (Simple and Fast Multimedia Library, version 2.4.x).
Box2D (Box2D, version 2.3.1).

Makefile target for this demo: make Ball_Physics

Building

All the demos have a reliance on the main OgmaNeo C++ library, and therefore have the same requirements -

  • A C++1x compiler,
  • CMake,
  • FlatBuffers package (version 1.4.0),
  • An OpenCL 1.2 SDK, and the Khronos Group cl2.hpp file.

Refer to the main OgmaNeo README.md file for further details.

Each demo has further dependencies outlined in the previous demo description sections that are required before building the OgmaNeoDemos.

CMake is used to build all the demos and discover each demo's additional dependencies. To build all the demos you can use the following (on Linux):

cmake .
make

make on its own builds all the demos within the repository. Demos can be built individually. Refer to the previous sections for the appropriate make target name.

The CMake/FindOgmaNeo.cmake file tries to find an installation of the OgmaNeo library. If it does not exist on your system, a custom Makefile target will download and build the OgmaNeo library. Other CMake package discovery modules are used to find additional demo dependencies.

If you do have the OgmaNeo library installed on your system (via a make install, or an INSTALL project, of the OgmaNeo library), the FindOgmaNeo.cmake module will try to find the library. This discovery process can be overridden by setting the OGMANEO_INCLUDE_DIR and OGMANEO_LIBRARY options when running cmake with the OgmaNeoDemos CMakeLists.txt file.

Contributions

Refer to the CONTRIBUTING.md file for information on making contributions to OgmaNeoDemos.

License and Copyright

Creative Commons License
The work in this repository is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. See the OGMANEODEMOS_LICENSE.md and LICENSE.md file for further information.

Contact Ogma licenses@ogmacorp.com to discuss commercial use and licensing options.

OgmaNeoDemos Copyright (c) 2016 Ogma Intelligent Systems Corp. All rights reserved.