Metadata-Version: 1.1
Name: pydpp
Version: 0.1.4
Summary: A python package for sampling from determinantal point processes
Home-page: https://github.com/satwik77/pyDPP
Author: Satwik Bhattamishra
Author-email: satwik55@gmail.com
License: MIT
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>`__]
        
        
        
Platform: UNKNOWN
Classifier: Development Status :: 2 - Pre-Alpha
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 2
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.4
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
