gdbassett

### Keybase proof

I hereby claim:

  * I am gdbassett on github.
  * I am gdbassett (https://keybase.io/gdbassett) on keybase.
  * I have a public key whose fingerprint is 8F47 6E59 65B3 9C92 428C  5A8C C609 81ED D4FA 1957

To claim this, I am signing this object:

gdbassett

from scipy import stats as scistats
import numpy as np

# Implementation of Tau from http://amstat.tandfonline.com/doi/abs/10.1198/004017002188618509#.VDgKhdR4rEh
# blatently transposed R robustbase library from http://r-forge.r-project.org/scm/?group_id=59, OGK.R
def scaleTau2(x, c1 = 4.5, c2 = 3.0, consistency = True, mu_too = False, *xargs, **kargs):
	## NOTA BENE: This is *NOT* consistency corrected
	x = np.asarray(x)
	n = len(x)
	medx = np.median(x) 

gdbassett

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

"""
 AUTHOR: Gabriel Bassett
 DATE: 11-19-2014
 DEPENDENCIES: py2neo
 Copyright 2014 Gabriel Bassett

gdbassett

from sklearn.metrics.pairwise import pairwise_distances
import numpy as np

# X shoudl be a numpy matrix, very likely sparse matrix: http://docs.scipy.org/doc/scipy-0.14.0/reference/generated/scipy.sparse.csr_matrix.html#scipy.sparse.csr_matrix
# T1 > T2 for overlapping clusters
# T1 = Distance to centroid point to not include in other clusters
# T2 = Distance to centroid point to include in cluster
# T1 > T2 for overlapping clusters
# T1 < T2 will have points which reside in no clusters
# T1 == T2 will cause all points to reside in mutually exclusive clusters

gdbassett

#!/usr/bin/env python
# -*- encoding: utf-8 -*-
# based on http://scikit-learn.org/stable/auto_examples/document_clustering.html

from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.cluster import KMeans, MiniBatchKMeans
from sklearn.metrics.pairwise import pairwise_distances
import numpy as np
from time import time
from collections import defaultdict

gdbassett

> df <- df[!names(df) %in% c('root.victim.region',
+ 'root.victim.country',
+ 'root.summary',
+ 'root.summary=Source_Category',
+ 'root.victim.industry',
+ 'root.timeline.incident.year',
+ 'root.plus.dbir_year',
+ 'root.action.social.notes',
+ 'root.victim.secondary.notes',
+ 'root.action.hacking.notes',

gdbassett

#' @param df Dataframe with x and y columns.  (Hopefully in the future this can be x)
#' @param nlines The number of clusters.
#' @param ab a dataframe with a 'slopes' and 'intercepts' column and one row per initial line.  Dimensions must match nlines.
#' @param maxiter The maximum number of iterations to do
#' @export
#' @examples
linearKMeans <- function(df, ab=NULL, nlines=0, maxiter=1000) {
  # default number of lines
  nlines_default <- 5
  

gdbassett

---
title: "Test"
author: "Gabe"
date: "November 03, 2016"
output: html_document
params:
  df: data.frame()
  a: ""
  b: ""
  c: "FALSE"

gdbassett

speedrun <- XML::xmlParse("/livesplit.lss")
speedrun <- XML::xmlToList(speedrun)

chunk <- do.call(rbind, lapply(speedrun[['Segments']], function(segments) {
    
    segments.df <- do.call(rbind, lapply(segments[['SegmentHistory']], function(segment) {
        if ('RealTime' %in% names(segment))
        data.frame(`attemptID` = segment$.attrs['id'], RealTime = segment$RealTime)
    }))
    segments.df$name <- rep(segments$Name, nrow(segments.df))

gdbassett

# pick an enumeration
enum <- "action.*.variety"
# establish filter criteria (easier than a complex standard-eval  filter_ line)
df <- vcdb %>%
  dplyr::filter(plus.dbir_year == 2016, subset.2017dbir) %>%
  dplyr::filter(attribute.confidentiality.data_disclosure.Yes) %>%
  dplyr::filter(victim.industry2.92)

# establish priors from previous year
priors <- df %>%