jayelm’s gists

jayelm / transactions

Last active August 29, 2015 13:57

Bitcoin Transactions History CSV

	date,transactions
	2009-01-03,1
	2009-01-04,0
	2009-01-05,0
	2009-01-06,0
	2009-01-07,0
	2009-01-08,0
	2009-01-09,14
	2009-01-10,31
	2009-01-11,106

jayelm / bitcoin

Created March 24, 2014 06:44

bitcoin_transactions

	date,transactions,symbol
	01/03/2009,1,SPY
	01/04/2009,0,SPY
	01/05/2009,0,SPY
	01/06/2009,0,SPY
	01/07/2009,0,SPY
	01/08/2009,0,SPY
	01/09/2009,14,SPY
	01/10/2009,31,SPY
	01/11/2009,106,SPY

jayelm / bitcoin_transactions.csv

Created March 24, 2014 06:48

bitcoin_transactions

date	symbol	transactions
1/3/2009	SPY	1
1/4/2009	SPY	0
1/5/2009	SPY	0
1/6/2009	SPY	0
1/7/2009	SPY	0
1/8/2009	SPY	0
1/9/2009	SPY	14
1/10/2009	SPY	31
1/11/2009	SPY	106

jayelm / buttondown.css

Created October 13, 2015 02:33 — forked from ryangray/buttondown.css

A clean, minimal CSS stylesheet for Markdown, Pandoc and MultiMarkdown HTML output.

	/*
	Buttondown
	A Markdown/MultiMarkdown/Pandoc HTML output CSS stylesheet
	Author: Ryan Gray
	Date: 15 Feb 2011
	Revised: 21 Feb 2012

	General style is clean, with minimal re-definition of the defaults or
	overrides of user font settings. The body text and header styles are
	left alone except title, author and date classes are centered. A Pandoc TOC

jayelm / vmeasure.R

Last active May 16, 2018 15:47

V-Measure (Rosenberg, 2007) Implementation in R. This almost exactly follows scikit-learn's implementation (http://scikit-learn.org/stable/modules/generated/sklearn.metrics.homogeneity_completeness_v_measure.html)

	library(infotheo)

	v.measure <- function(a, b) {
	mi <- mutinformation(a, b)
	entropy.a <- entropy(a)
	entropy.b <- entropy(b)
	if (entropy.a == 0.0) {
	homogeneity <- 1.0
	} else {
	homogeneity <- mi / entropy.a

jayelm / clustercounter.py

Last active October 11, 2016 14:50

Convenient Cluster Counter

	"""
	Neat wrapper around a autoincrementing defaultdict. Most useful for assigning
	unique numbers to unseen examples while clustering, but probably has other uses
	as well.

	Example usage

	In [1]: cc = ClusterCounter()

	In [2]: cc['setosa']

jayelm / explanations.CONTACT.1.embs.tsv

Created February 4, 2019 22:53

We can't make this file beautiful and searchable because it's too large.

-0.077709	0.149783	0.256109	0.224514	0.093126	0.261685	-0.007795	-0.047023	0.084129	0.337066	0.172014	-0.063369	-0.015219	0.053277	-0.055675	-0.139981	0.129056	0.273224	0.228618	-0.174408	-0.213103	-0.034061	0.162580	0.266921	0.212209	-0.020353	0.066753	0.087186	0.046093	0.158467	-0.238940	0.246970	0.071708	0.044725	-0.094469	-0.028163	-0.306692	0.150777	0.083379	-0.000724	0.175186	-0.338446	-0.206074	-0.014390	0.122420	-0.125712	-0.144950	0.097734	0.323000	-0.282750	0.213719	0.032570	0.146699	0.172559	0.124033	-0.272105	0.316080	-0.003835	-0.088711	-0.419845	0.247369	0.133099	0.153687	0.092116	-0.242148	0.435301	0.154582	-0.127496	0.050410	-0.008653	0.120244	-0.242726	0.321186	-0.489628	0.434109	0.071279	-0.058971	-0.040261	0.045671	0.017411	-0.265096	-0.284714	0.092979	0.170229	0.016413	0.011602	0.173791	0.222625	0.174836	0.156307	0.132448	-0.294564	0.252246	-0.189660	-0.197140	-0.135401	-0.039114	0.100365	-0.231006	0.295340	-0.062513	0.205744	0.265017	0.055648	-0.075210	-0.068220	-0.073566	0.149855	-0.1483

jayelm / explanations.tsv

Last active February 4, 2019 23:16

	explanation	is_heldout
	from [email protected]	False
	from [email protected]	False
	from [email protected]	False
	from [email protected]	False
	from [email protected]	False
	! is used	False
	coffee is used	False
	festival is used	False
	meet up is used	False

jayelm / explanations.untrained.CONTACT.1.embs.tsv

Created February 4, 2019 23:06

We can't make this file beautiful and searchable because it's too large.

0.164774	0.187791	-0.131299	0.993267	-0.416966	0.033960	0.419105	0.187504	0.066782	0.665488	0.025184	-0.008034	0.029332	0.176970	-0.044142	-0.093475	0.225013	0.575576	-0.227436	-0.411197	0.297845	0.181617	0.039377	0.121431	-0.367486	-0.116069	0.482964	-0.026578	0.032461	-0.089207	0.128192	0.234638	-0.206866	0.069460	-0.119355	0.221972	-0.019061	-0.020051	0.304974	0.074571	0.125557	0.456757	0.013005	0.045541	-0.372146	0.163087	-0.289012	0.156688	-0.086266	-0.313922	-0.101044	0.371096	-0.190387	-0.142513	-0.281456	0.054664	0.212146	0.493715	0.309555	-0.026499	0.338488	0.335129	0.046711	-0.026531	0.786381	0.440620	-0.274265	-0.275056	0.271499	-0.086399	-0.285385	0.247016	-0.273882	0.186570	-0.095521	-0.728028	0.494695	-0.161679	0.083163	-0.214479	0.685633	0.074415	0.553963	-0.202995	0.256899	-0.271948	0.211317	-0.578751	-0.221827	-0.428904	0.034738	-0.203083	0.093217	0.094973	-0.320172	-0.081616	0.074687	-0.460855	-0.165996	0.440688	0.060450	-0.104874	-0.039443	-0.281879	-0.125332	-0.285776	0.259326	0.633879	0.5

jayelm / explanation_embeddings_config.json

Last active February 4, 2019 23:16

	{
	"embeddings": [
	{
	"tensorName": "explanations_trained",
	"tensorShape": [182, 300],
	"tensorPath": "https://gist.githubusercontent.com/jayelm/17a3408ebd8476c074038ff45f417866/raw/60850b33f29215f8c60d3f756a7fe72847312288/explanations.CONTACT.1.embs.tsv",
	"metadataPath": "https://gist.githubusercontent.com/jayelm/e1090d69997657bc50a80c9e8b62faad/raw/402da61f7506534b2c6098f26b6e0bd4c8e92621/explanations.tsv"
	},
	{
	"tensorName": "explanations_untrained",

Jesse Mu jayelm