Skip to main content

A python package for sampling from determinantal point processes

Project description

=====
pyDPP
=====

A python package for sampling from determinantal point processes. Below are instances of sampling from a bicluster and from a random set of points using pyDPP. Refer to examples and references for more information.


.. raw:: html

<img src="https://raw.githubusercontent.com/satwik77/pyDPP/master/example/dpp_selection_k12.png?token=AKhAbS05A3CBgKfXR9P7i4adhlM7Q-whks5b0bhYwA%3D%3D" height="220px">



Usage
-----

Usage example:

::

>>> from pydpp.dpp import DPP
>>> import numpy as np
>>> X = np.random.random((10,10))
>>> dpp = DPP(X)
>>> dpp.compute_kernel(kernel_type = 'rbf', sigma= 0.4) # use 'cos-sim' for cosine similarity
>>> samples = dpp.samples() # samples := [1,7,2,5]
>>> ksamlpes = dpp.sample_k(3) # ksamples := [5,8,0]

Installation
------------

To get the project's source code, clone the github repository:

::

$ git clone https://github.com/satwik77/pyDPP.git
$ cd pyDPP

Create a virtual environment and activate it. [optional]

::

$ [sudo] pip install virtualenv
$ virtualenv -p python3 venv
$ source venv/bin/activate
(venv)$

Next, install all the dependencies in the environment.

::

(venv)$ pip install -r requirements.txt


Install the package into the virtual environment.

::

(venv)$ python setup.py install

Requirements
^^^^^^^^^^^^
- Numpy
- Scipy

To run the example jupyter notebook you need install jupyter notebook, sklearn and matplotlib.

Compatibility
^^^^^^^^^^^^^
The package has been test with python 2.7 and python 3.5.2


Reference
^^^^^^^^^^

- Kulesza, A. and Taskar, B., 2011. k-DPPs: Fixed-size determinantal point processes. In Proceedings of the 28th International Conference on Machine Learning (ICML-11) (pp. 1193-1200). [`paper <https://homes.cs.washington.edu/~taskar/pubs/kdpps_icml11.pdf>`__]

- Kulesza, A. and Taskar, B., 2012. Determinantal point processes for machine learning. Foundations and Trends® in Machine Learning, 5(2–3), pp.123-286. [`paper <http://www.alexkulesza.com/pubs/dpps_fnt12.pdf>`__]




Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

pydpp-0.2.1.tar.gz (4.0 kB view hashes)

Uploaded source

Built Distribution

pydpp-0.2.1-py3-none-any.whl (4.7 kB view hashes)

Uploaded py3

Supported by

AWS AWS Cloud computing Datadog Datadog Monitoring Facebook / Instagram Facebook / Instagram PSF Sponsor Fastly Fastly CDN Google Google Object Storage and Download Analytics Huawei Huawei PSF Sponsor Microsoft Microsoft PSF Sponsor NVIDIA NVIDIA PSF Sponsor Pingdom Pingdom Monitoring Salesforce Salesforce PSF Sponsor Sentry Sentry Error logging StatusPage StatusPage Status page