Metadata-Version: 2.1
Name: rmsorn
Version: 0.0.7
Summary: Reward Modulated Self-Organizing Recurrent Neural Networks
Home-page: https://github.com/Saran-nns/rm_sorn
Author: Saranraj Nambusubramaniyan
Author-email: saran_nns@hotmail.com
License: OSI Approved :: MIT License
Description: ## Reward Modulated Self-Organizing Recurrent Neural Networks 
        
        RMSORN is a subclass of neuro-inspired artificial network, Self Organizing Recurrent Neural Networks. With reward driven self-organization, this network achieves performance with networks trained with supervised learning algorithms.
        
        
        [![Build Status](https://travis-ci.org/Saran-nns/rmsorn.svg?branch=master)](https://travis-ci.org/Saran-nns/rmsorn)
        [![codecov](https://codecov.io/gh/Saran-nns/rmsorn/branch/master/graph/badge.svg)](https://codecov.io/gh/Saran-nns/rmsorn)
        [![PyPI version](https://badge.fury.io/py/rmsorn.svg)](https://badge.fury.io/py/rmsorn)
        ![PyPI - Downloads](https://img.shields.io/github/downloads/saran-nns/rmsorn/total)
        [![License](https://img.shields.io/badge/License-MIT-blue.svg)](https://img.shields.io/github/license/Saran-nns/rmsorn)
        
        #### To install the latest release:
        
        ```python
        pip install rmsorn
        ```
        
        The library is still in alpha stage, so you may also want to install the latest version from the development branch:
        
        ```python
        pip install git+https://github.com/Saran-nns/rmsorn
        ```
        #### Usage:
        ##### Update Network configurations
        
        Navigate to home/conda/envs/ENVNAME/Lib/site-packages/rmsorn
        
        or if you are unsure about the directory of rmsorn
        
        Run
        
        ```python
        import rmsorn
        
        rmsorn.__file__
        ```
        to find the location of the rmsorn package
        
        Then, update/edit the configuration.ini
        
        ```python
        from rmsorn.tasks import PatternRecognition
        
        inputs, targets = PatternRecognitionTask.generate_sequence()
        train_plast_inp_mat,X_all_inp,Y_all_inp,R_all, frac_pos_active_conn = SimulateRMSorn(phase = 'Plasticity', 
                                                                                              matrices = None,
                                                                                              inputs = np.asarray(inputs),sequence_length = 4, targets = targets,
                                                                                              reward_window_sizes = [1,5,10,20],
                                                                                              epochs = 1).train_sorn()
        ```
Keywords: Brain-Inspired Computing,Artificial Neural Networks,Neuro Informatics,Spiking Cortical Networks,Reinformcement Learning,Neural Connectomics,Neuroscience,Artificial General Intelligence,Neural Information Processing
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Description-Content-Type: text/markdown
