richmarr

/*
    I found it handy to reduce the amount of manual require() lines in a big 
    project by grouping small modules together like below.

    If you put this code into "index.js" then it'll pick up any other
    JS modules in that directory and expose them as sub-modules.
    
    - Any exports in *this* module would be myPackage.exportName
    - Any exports in other modules would be myPackage.moduleName.exportName

richmarr

import java.util.concurrent.Callable
import java.util.concurrent.Executors

def THREADS = 2
def pool = Executors.newFixedThreadPool(THREADS)
def done = 0
	
def defer = { closure -> 
	pool.submit( closure as Callable )
}

richmarr

int lightPin0 = 0;
int lightPin1 = 1;
int relayPin = 2;
int night = 1000;
int sunny = 8000;
float filter = 0.0;

void setup() {
  pinMode(relayPin,OUTPUT);
  Serial.begin(9600);

richmarr

/**
 	 
 */
var routes = require('../routes'),
	assert = require('assert');

describe('routes', function(){
	
	it("should exist", function(done){
		assert(routes);

richmarr

var crypto = require('crypto'),
	algos = crypto.getCiphers(),
	testString = 'something to match, something to match, something to match, something to match, something to match '+algos.join("#"),
	password = '12345asdfgh',
	ciphertextEncoding = 'binary',
	cleartextEncoding = 'utf8',
	iterations = 1000;

algos.forEach(function( algorithm ){

richmarr

Here are the areas I've been researching, some things I've read and some open source packages...

Nearly all text processing starts by transforming text into vectors: http://en.wikipedia.org/wiki/Vector_space_model

Often it uses transforms such as TFIDF to normalise the data and control for outliers (words that are too frequent or too rare confuse the algorithms): http://en.wikipedia.org/wiki/Tf%E2%80%93idf

Collocations is a technique to detect when two or more words occur more commonly together than separately (e.g. "wishy-washy" in English) - I use this to group words into n-gram tokens because many NLP techniques consider each word as if it's independent of all the others in a document, ignoring order: http://matpalm.com/blog/2011/10/22/collocations_1/

richmarr

#!/usr/bin/env python

from numpy import asmatrix, asarray, ones, zeros, mean, sum, arange, prod, dot, loadtxt
from numpy.random import random, randint
import pickle

MISSING_VALUE = -1 # a constant I will use to denote missing integer values

def impute_hidden_node(E, I, theta, sample_hidden):

richmarr

package richmarr.xml.example
import org.xml.sax.InputSource
import nu.xom.*
import nux.xom.io.*
import nux.xom.xquery.*

// this would normally be pulled from a database
def config = [
	item : "/xpath/to/item/elements",
	fields : [

richmarr

$.fn.parsley.defaults = {
  // basic data-api overridable properties here..
  inputs: 'input, textarea, select'           // Default supported inputs.
  , excluded: 'input[type=hidden], :disabled' // Do not validate input[type=hidden] & :disabled.
  , trigger: false                            // $.Event() that will trigger validation. eg: keyup, change..
  , animate: true                             // fade in / fade out error messages
  , animateDuration: 300                      // fadein/fadout ms time
  , focus: 'first'                            // 'fist'|'last'|'none' which error field would have focus first on form validation
  , validationMinlength: 3                    // If trigger validation specified, only if value.length > validationMinlength
  , successClass: 'has-success'           // Class name on each valid input