Metadata-Version: 2.1
Name: somlib
Version: 0.0.4
Summary: This is python implementation for Kohonen Self Organizing map using numpy and tensor
Home-page: https://github.com/pankajr141/SOM
Author: Pankaj Rawat
Author-email: pankajr141@gmail.com
License: UNKNOWN
Description: # SOM
        This is python implementation for Kohonen Self Organizing map using numpy and tensor
        
        ## Installtion
        
        **Python 3**
        `pip install somlib`
        
        ## Usage
        
        1. Numpy implementation
        
        ```
        	from somlib import som
        	s = som.SOM(neurons=(5,5), dimentions=3, n_iter=500, learning_rate=0.1)
        	s.train(samples)  # samples is a n x 3 matrix
        	print("Cluster centres:", s.weights_)
        	print("labels:", s.labels_)
        	result = s.predict(samples)
        ```
        
        Here 5,5 is the dimention of neurons, 3 is the number of features. samples is numpy array with each sample a 3 dimentional vector
        
        2. Tensor implementation
        
        ```
        	from somlib import som
        	s = SOM(neurons=(5,5), dimentions=3, n_iter=500, learning_rate=0.1, mode="tensor")
        	s.train(samples)  # samples is a n x 3 matrix
        	print("Cluster centres:", s.weights_)
        	print("labels:", s.labels_)
        	result = s.predict(samples)
        ```
        
        ### Display clusters
        To display clusters after training use this
        
        ```s.displayClusters(samples)```
        
        
        ![clusters](https://image.ibb.co/hS4uCH/figure_3.png "Clusters")
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Description-Content-Type: text/markdown
