smartinsightsfromdata

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.

smartinsightsfromdata

# coding: utf-8
import sys
import re
import datetime
import MeCab
import random
import numpy as np
# random.seed(1234)
# np.random.seed(1234)

smartinsightsfromdata

# required tensorflow 0.12
# required gensim 0.13.3+ for new api model.wv.index2word or just use model.index2word

from gensim.models import Word2Vec
import tensorflow as tf
from tensorflow.contrib.tensorboard.plugins import projector

# loading your gensim
model = Word2Vec.load("YOUR-MODEL")

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# Using airquality dataset
data <- airquality
data[4:10,3] <- rep(NA,7)
data[1:5,4] <- NA

# Removing categorical variables
data <- airquality[-c(5,6)]
summary(data)

#-------------------------------------------------------------------------------

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###Sessionization using dplyr

library(dplyr)

#Open a localhost connection to Postgres
#Use table 'single_col_timestamp'
#group by uid and sort by timestamp for window function
#Do minutes calculation, working around missing support for extract(epoch from timestamp)
#Calculate event boundary and unique id via cumulative sum window function
sessions <-  

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SankeyR <- function(inputs, losses, unit, labels, format="plot"){
########################
# SankeyR version 1.01 (updated August 10, 2010)
# is a function for creating Sankey Diagrams in R.
# See http://www.sankey-diagrams.com for excellent examples of Sankey Diagrams.
# 
# OPTIONS:
# 'inputs' is a vector of input values
# 'losses' is a vector of loss values
# 'unit' is a string of the unit

smartinsightsfromdata

from lxml import etree
from elasticsearch.helpers import scan
from elasticsearch import Elasticsearch
from multiprocessing import Pool
import bz2
import gensim
import itertools
import logging
import nltk
import os

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##VGG19 model for Keras

This is the Keras model of the 19-layer network used by the VGG team in the ILSVRC-2014 competition.

It has been obtained by directly converting the Caffe model provived by the authors.

Details about the network architecture can be found in the following arXiv paper:

Very Deep Convolutional Networks for Large-Scale Image Recognition

K. Simonyan, A. Zisserman

smartinsightsfromdata

#!/usr/bin/env python
# -*- coding: utf-8 -*-

'''

This script just show the basic workflow to compute TF-IDF similarity matrix with Gensim 


OUTPUT :