DSA101

# Time series forecasting based on multiple time series, including the original one
# This script is based on the following examples and discussions:
# https://gist.github.com/lukovkin/1aefa4509e066690b892
# https://groups.google.com/forum/#!topic/keras-users/9GsDwkSdqBg

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import random
import theano

scottwallacesh

# encrypt stdin
echo "secret" | openssl rsautl -encrypt -inkey ~/.ssh/id_rsa -out secret.enc

# encrypt file
openssl rsautl -encrypt -inkey ~/.ssh/id_rsa -in secret.txt -out secret.enc

# decrypt to stdout
openssl rsautl -decrypt -inkey ~/.ssh/id_rsa -in secret.enc

zeffii

bl_info = {
    "name": "FILL ME MO",
    "author": "",
    "version": (0, 1),
    "blender": (2, 7, 6),
    "category": "3D View"
}

import os
import bpy

lukovkin

# Time Series Testing 
import keras.callbacks
from keras.models import Sequential  
from keras.layers.core import Dense, Activation, Dense, Dropout
from keras.layers.recurrent import LSTM

# Call back to capture losses 
class LossHistory(keras.callbacks.Callback):
    def on_train_begin(self, logs={}):
        self.losses = []

StuartGordonReid

def approximate_entropy(self, bin_data: str, pattern_length=10):
    """
    Note that this description is taken from the NIST documentation [1]
    [1] http://csrc.nist.gov/publications/nistpubs/800-22-rev1a/SP800-22rev1a.pdf

    As with the Serial test of Section 2.11, the focus of this test is the frequency of all possible overlapping
    m-bit patterns across the entire sequence. The purpose of the test is to compare the frequency of overlapping
    blocks of two consecutive/adjacent lengths (m and m+1) against the expected result for a random sequence.

    :param bin_data: a binary string

Zulko

"""
Model of a Zombie outbreak in France, starting in Grenoble

This is a rewrite from this blog post by Max Berrgren:

http://maxberggren.github.io/2014/11/27/model-of-a-zombie-outbreak/

with a different country, a slightly different model, and different
libraries. The map of population density is taken from Wikimedia Commons

GaelVaroquaux

'''
Non-parametric computation of entropy and mutual-information

Adapted by G Varoquaux for code created by R Brette, itself
from several papers (see in the code).

This code is maintained at https://github.com/mutualinfo/mutual_info 
Please download the latest code there, to have improvements and 
bug fixes.

lmullen

SLIDES := $(patsubst %.md,%.md.slides.pdf,$(wildcard *.md))
HANDOUTS := $(patsubst %.md,%.md.handout.pdf,$(wildcard *.md))

all : $(SLIDES) $(HANDOUTS)

%.md.slides.pdf : %.md
	pandoc $^ -t beamer --slide-level 2 -o $@

%.md.handout.pdf : %.md
	pandoc $^ -t beamer --slide-level 2 -V handout -o $@ 

simono

#!/bin/zsh
#
# Highlight a given file and copy it as RTF.
#
# Simon Olofsson <[email protected]>
#

set -o errexit
set -o nounset

erogol

import numpy as np
import numpy
import theano
import theano.tensor as T
from theano import function, config, shared, sandbox
from theano import ProfileMode
from sklearn import cluster, datasets
import matplotlib.pyplot as plt

def rsom(data, cluster_num, alpha, epochs = -1, batch = 1, verbose = False):