henrik

tmux cheatsheet

As configured in my dotfiles.

start new:

tmux

start new with session name:

IanColdwater

Mute these words in your settings here: https://twitter.com/settings/muted_keywords

ActivityTweet
generic_activity_highlights
generic_activity_momentsbreaking
RankedOrganicTweet
suggest_activity
suggest_activity_feed
suggest_activity_highlights
suggest_activity_tweet

baojie

import multiprocessing

# split a list into evenly sized chunks
def chunks(l, n):
    return [l[i:i+n] for i in range(0, len(l), n)]


def do_job(job_id, data_slice):
    for item in data_slice:
        print "job", job_id, item

jbochi

abresler

library(dplyr)
library(tidyr)
library(magrittr)
library(ggplot2)
"http://academic.udayton.edu/kissock/http/Weather/gsod95-current/NYNEWYOR.txt" %>%
	read.table() %>% data.frame %>% tbl_df -> data
names(data) <- c("month", "day", "year", "temp")

data %>%
	group_by(year, month) %>%

zacstewart

import os
import numpy
from pandas import DataFrame
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.naive_bayes import MultinomialNB
from sklearn.pipeline import Pipeline
from sklearn.cross_validation import KFold
from sklearn.metrics import confusion_matrix, f1_score

NEWLINE = '\n'

tomhopper

#' @title PRESS
#' @author Thomas Hopper
#' @description Returns the PRESS statistic (predictive residual sum of squares).
#'              Useful for evaluating predictive power of regression models.
#' @param linear.model A linear regression model (class 'lm'). Required.
#' 
PRESS <- function(linear.model) {
  #' calculate the predictive residuals
  pr <- residuals(linear.model)/(1-lm.influence(linear.model)$hat)
  #' calculate the PRESS

dsal1951

def calc_lift(x,y,clf,bins=10):
	"""
	Takes input arrays and trained SkLearn Classifier and returns a Pandas
	DataFrame with the average lift generated by the model in each bin
	
	Parameters
	-------------------
	x:    Numpy array or Pandas Dataframe with shape = [n_samples, n_features]

	y:    A 1-d Numpy array or Pandas Series with shape = [n_samples]

h3

# -*- coding: utf-8 -*-

from __future__ import unicode_literals

import re
import sys


def c(i):
    """

dstufft

We couldn’t find that file to show.