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

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):