PyIBP is an Indian Buffet Process package, developed by the Cloud Computing Research Team in [University of Maryland, College Park] (http://www.umd.edu).
Please download the latest version from our GitHub repository.
Please send any bugs of problems to Ke Zhai (kzhai@umd.edu).
This package depends on many external python libraries, such as numpy, scipy, matplotlib and nltk.
Assume the PyIBP package is downloaded under directory $PROJECT_SPACE/src/, i.e.,
$PROJECT_SPACE/src/PyIBP
To prepare the example dataset,
tar zxvf cambridge-bars.tar.gz
To launch PyIBP, first redirect to the directory of PyIBP source code,
cd $PROJECT_SPACE/src/PyIBP
and run the following command on example dataset,
python -m launch_train --input_directory=./cambridge-bars --output_directory=./ --training_iterations=100
The generic argument to run PyIBP is
python -m launch_train --input_directory=$INPUT_DIRECTORY/$DATASET_NAME --output_directory=$OUTPUT_DIRECTORY --training_iterations=$NUMBER_OF_ITERATIONS
You should be able to find the output at directory $OUTPUT_DIRECTORY/$DATASET_NAME.
Under any circumstances, you may also get help information and usage hints by running the following command
python -m launch_train --help