Dr. Jens Preußner jenzopr

## basset_sfc_setup.py
#install keras from https://github.com/kundajelab/keras/tree/keras_1
from __future__ import print_function
import keras
import numpy as np
np.random.seed(1)

#build a model
model = keras.models.Sequential()
model.add(keras.layers.convolutional.Convolution1D(input_shape=(1000,4),
                                                   nb_filter=300,

## cumsum_with_reset.R
testvector <- c(.5, .1, .2, .9, .9, .2, .5)

# group rows based on cumsum with reset
cumsum_group <- function(x, threshold) {
  cumsum <- 0
  group <- 1
  result <- numeric()

  for (i in 1:length(x)) {
    cumsum <- cumsum + x[i]

## revcomp_weightsharing_setup.py
#install keras from https://github.com/kundajelab/keras/tree/keras_1
from __future__ import print_function
import keras
import numpy as np
np.random.seed(1)

#build a sample model
model = keras.models.Sequential()
model.add(keras.layers.convolutional.RevCompConv1D(input_shape=(100,4),
                                                   nb_filter=10,

## jupyter.service
# After Ubuntu 16.04, Systemd becomes the default.
# It is simpler than https://gist.github.com/Doowon/38910829898a6624ce4ed554f082c4dd

[Unit]
Description=Jupyter Notebook

[Service]
Type=simple
PIDFile=/run/jupyter.pid
ExecStart=/home/phil/Enthought/Canopy_64bit/User/bin/jupyter-notebook --config=/home/phil/.jupyter/jupyter_notebook_config.py

## Cassandra Schemas
  create KEYSPACE spark_demo WITH replication = {'class': 'SimpleStrategy', 'replication_factor': '1'};
  create table spark_demo.raw_files (filename text,line int, contents text, PRIMARY KEY(filename,line));
  create table spark_demo.users (id int PRIMARY KEY );
  create table spark_demo.movies (id int PRIMARY KEY, name text, year int);
  create table spark_demo.ratings (id int PRIMARY KEY, user_id int, movie_id int, rating float );

## mygene.html
<!DOCTYPE HTML>
<html>

<head>

<meta http-equiv="Content-Type" content="text/html; charset=utf-8">

<script src="//ajax.googleapis.com/ajax/libs/jquery/1.11.1/jquery.min.js"></script>
<script type="text/javascript" src="js/typeahead.bundle.min.js"></script>

## 95-lolcat
#!/bin/bash
# see: http://blog.tomtung.com/2009/11/cowsay-fortune
#      http://www.commandlinefu.com/commands/view/3584/remove-color-codes-special-characters-with-sed
#      https://github.com/busyloop/lolcat
#      https://github.com/dorentus/mruby-lolcat-bin
#
#      requires `fortune`, `cowsay`,
#        and ruby gem `lolcat` or its mruby version equivalent

export LANG="en_US.UTF-8"

## gist:1421185
#' Randomly subsample a matrix or data frame.
#'
#' To randomly subsample, we are using the parametric option in the boot function
#' within the boot package. The 'parametric' option requires the specification of
#' a 'ran.gen' function to generate observations based on the original data and a list of
#' maximum likelihood estimators (MLE). We utilize this method, but instead of the
#' MLE, we instead pass the subsample_size.
#'
#' As noted in the boot documentation: Use of sim = "parametric" with a suitable ran.gen
#' allows the user to implement any types of nonparametric resampling which are not
	#install keras from https://github.com/kundajelab/keras/tree/keras_1
	from __future__ import print_function
	import keras
	import numpy as np
	np.random.seed(1)

	#build a model
	model = keras.models.Sequential()
	model.add(keras.layers.convolutional.Convolution1D(input_shape=(1000,4),
	nb_filter=300,
	testvector <- c(.5, .1, .2, .9, .9, .2, .5)

	# group rows based on cumsum with reset
	cumsum_group <- function(x, threshold) {
	cumsum <- 0
	group <- 1
	result <- numeric()

	for (i in 1:length(x)) {
	cumsum <- cumsum + x[i]
	# After Ubuntu 16.04, Systemd becomes the default.
	# It is simpler than https://gist.github.com/Doowon/38910829898a6624ce4ed554f082c4dd

	[Unit]
	Description=Jupyter Notebook

	[Service]
	Type=simple
	PIDFile=/run/jupyter.pid
	ExecStart=/home/phil/Enthought/Canopy_64bit/User/bin/jupyter-notebook --config=/home/phil/.jupyter/jupyter_notebook_config.py
	create KEYSPACE spark_demo WITH replication = {'class': 'SimpleStrategy', 'replication_factor': '1'};
	create table spark_demo.raw_files (filename text,line int, contents text, PRIMARY KEY(filename,line));
	create table spark_demo.users (id int PRIMARY KEY );
	create table spark_demo.movies (id int PRIMARY KEY, name text, year int);
	create table spark_demo.ratings (id int PRIMARY KEY, user_id int, movie_id int, rating float );
	<!DOCTYPE HTML>
	<html>

	<head>

	<meta http-equiv="Content-Type" content="text/html; charset=utf-8">

	<script src="//ajax.googleapis.com/ajax/libs/jquery/1.11.1/jquery.min.js"></script>
	<script type="text/javascript" src="js/typeahead.bundle.min.js"></script>
	#!/bin/bash
	# see: http://blog.tomtung.com/2009/11/cowsay-fortune
	# http://www.commandlinefu.com/commands/view/3584/remove-color-codes-special-characters-with-sed
	# https://github.com/busyloop/lolcat
	# https://github.com/dorentus/mruby-lolcat-bin
	#
	# requires `fortune`, `cowsay`,
	# and ruby gem `lolcat` or its mruby version equivalent

	export LANG="en_US.UTF-8"
	#' Randomly subsample a matrix or data frame.
	#'
	#' To randomly subsample, we are using the parametric option in the boot function
	#' within the boot package. The 'parametric' option requires the specification of
	#' a 'ran.gen' function to generate observations based on the original data and a list of
	#' maximum likelihood estimators (MLE). We utilize this method, but instead of the
	#' MLE, we instead pass the subsample_size.
	#'
	#' As noted in the boot documentation: Use of sim = "parametric" with a suitable ran.gen
	#' allows the user to implement any types of nonparametric resampling which are not