sauln

Quick List of Resources for Topological Data Analysis with Emphasis on Machine Learning

This is just a quick list of resourses on TDA that I put together for @rickasaurus after he was asking for links to papers, books, etc on Twitter and is by no means an exhaustive list.

Survey Papers

Both Carlsson's and Ghrist's survey papers offer a very good introduction to the subject

• Topology and Data by Gunnar Carlsson
• Barcodes: The Persistent Topology of Data by Robert Ghrist

Other Papers and Web Resources

• Extracting insights from the shape of complex data using topology A good introductory paper in Nature on the Mapper algorithm.

sauln

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import IPython\n",
    "import html\n",

sauln

import json
import click

@click.command()
@click.argument('infile')
@click.argument('outfile')
def parse_barcode(infile, outfile):
    """ This function will parse the result of the Ripser (https://github.com/Ripser/ripser) barcodes 
        and output the barcodes in json format.
        

sauln

"""
Minimal character-level Vanilla RNN model. Written by Andrej Karpathy (@karpathy)
BSD License

Updated by Nathaniel Saul 2/8/2016 - Runs in Python3.4
"""
import numpy as np

# data I/O
data = open('input.txt', 'r').read() # should be simple plain text file