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        "author": "Pankaj Rawat",
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        "description": "# SOM\r\nThis is python implementation for Kohonen Self Organizing map using numpy and tensor\r\n\r\n## Installtion\r\n\r\n**Python 3**\r\n`pip install somlib`\r\n\r\n## Usage\r\n\r\n1. Numpy implementation\r\n\r\n```\r\n\tfrom somlib import som\r\n\ts = som.SOM(neurons=(5,5), dimentions=3, n_iter=500, learning_rate=0.1)\r\n\ts.train(samples)  # samples is a n x 3 matrix\r\n\tprint(\"Cluster centres:\", s.weights_)\r\n\tprint(\"labels:\", s.labels_)\r\n\tresult = s.predict(samples)\r\n```\r\n\r\nHere 5,5 is the dimention of neurons, 3 is the number of features. samples is numpy array with each sample a 3 dimentional vector\r\n\r\n2. Tensor implementation\r\n\r\n```\r\n\tfrom somlib import som\r\n\ts = SOM(neurons=(5,5), dimentions=3, n_iter=500, learning_rate=0.1, mode=\"tensor\")\r\n\ts.train(samples)  # samples is a n x 3 matrix\r\n\tprint(\"Cluster centres:\", s.weights_)\r\n\tprint(\"labels:\", s.labels_)\r\n\tresult = s.predict(samples)\r\n```\r\n\r\n### Display clusters\r\nTo display clusters after training use this\r\n\r\n```s.displayClusters(samples)```\r\n\r\n\r\n![clusters](https://image.ibb.co/hS4uCH/figure_3.png \"Clusters\")\r\n\r\n",
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