djspiewak

Introduction to scalaz-stream

Every application ever written can be viewed as some sort of transformation on data. Data can come from different sources, such as a network or a file or user input or the Large Hadron Collider. It can come from many sources all at once to be merged and aggregated in interesting ways, and it can be produced into many different output sinks, such as a network or files or graphical user interfaces. You might produce your output all at once, as a big data dump at the end of the world (right before your program shuts down), or you might produce it more incrementally. Every application fits into this model.

The scalaz-stream project is an attempt to make it easy to construct, test and scale programs that fit within this model (which is to say, everything). It does this by providing an abstraction around a "stream" of data, which is really just this notion of some number of data being sequentially pulled out of some unspecified data source. On top of this abstraction, sca

kixorz

"UserData": {
  "Fn::Base64": { "Fn::Join":["", [
    "#!/bin/bash -ex\n",
    "apt-get update\n",
    "apt-get -y install python-setuptools\n",
    "mkdir aws-cfn-bootstrap-latest\n",
    "curl https://s3.amazonaws.com/cloudformation-examples/aws-cfn-bootstrap-latest.tar.gz | tar xz -C aws-cfn-bootstrap-latest --strip-components 1\n",
    "easy_install aws-cfn-bootstrap-latest\n",
    "/usr/local/bin/cfn-init --stack ", { "Ref":"AWS::StackName" }, " --resource WebServer", " --region ", { "Ref": "AWS::Region" }, "\n",
    "\n",

tixxit

package net.tixxit.levenshtein

import scala.math.min

object EditDistance {
  def editDist[A](a: Iterable[A], b: Iterable[A]) =
    ((0 to b.size).toList /: a)((prev, x) =>
      (prev zip prev.tail zip b).scanLeft(prev.head + 1) {
          case (h, ((d, v), y)) => min(min(h + 1, v + 1), d + (if (x == y) 0 else 1))
        }) last

yahyaKacem

#!/usr/bin/env python
"""
  Convert camel-case to snake-case in python.
  e.g.: CamelCase  -> snake_case
  e.g.: snake_case -> CamelCase
  e.g.: CamelCase  -> dash-case
  e.g.: dash-case  -> CamelCase
  By:            Jay Taylor [@jtaylor]
  Me<modifier>:  Yahya Kacem <fuj.tyoli@gmail.com>
  Original gist: https://gist.github.com/jaytaylor/3660565

hyqneuron

import torch
import torch.nn as nn
from torch.nn import Parameter
from torch.autograd import Variable, Function
from collections import defaultdict
import graphviz

"""
This is a rather distorted implementation of graph visualization in PyTorch.

ijy

function cartesianProductOf() {
    return _.reduce(arguments, function(a, b) {
        return _.flatten(_.map(a, function(x) {
            return _.map(b, function(y) {
                return x.concat([y]);
            });
        }), true);
    }, [ [] ]);
};

louispan

# install nix (5.5 mins)
curl https://nixos.org/nix/install | sh

# open a new shell to source nix
bash

# query available haskell compilers
nix-env -qaP -A nixpkgs.haskell.compiler [QUERY]

# install ghc (2.5 mins, cached binary)

takeshy

/*
For processing data sent to Firehose by Cloudwatch Logs subscription filters.

Cloudwatch Logs sends to Firehose records that look like this:

{
  "messageType": "DATA_MESSAGE",
  "owner": "123456789012",
  "logGroup": "log_group_name",
  "logStream": "log_stream_name",

drmarshall

#! /usr/bin/env python
#
# Mixpanel, Inc. -- http://mixpanel.com/
#
# Python API client library to consume mixpanel.com analytics data.

import hashlib
import urllib
import time
try:

iMilnb

boto3 quick hands-on

This documentation aims at being a quick-straight-to-the-point-hands-on AWS resources manipulation with [boto3][0].

First of all, you'll need to install [boto3][0]. Installing it along with [awscli][1] is probably a good idea as

• [awscli][1] is boto-based
• [awscli][1] usage is really close to boto's