Vinay M rmdort

## index.html
<!DOCTYPE html>
<html>
  <head>
    <title>Twitter Search Plugin</title>
    <script src="http://code.jquery.com/jquery.js"></script>
    <script src="jquery.twitter.js"></script>
    <style type="text/css">
      .twitter-posts li       {margin-bottom: 10px; font-size: 12px; clear: both; list-style-type:none;}
      .twitter-posts li img   {float:left; width: 48px; margin:0px 10px 10px 0px;border:1px solid #c2c2c2; -moz-box-shadow: 0px 0px 4px #c2c2c2;  -webkit-box-shadow: 0px 0px 4px #c2c2c2; box-shadow: 0px 0px 4px #c2c2c2;}
      .twitter-posts li a     {text-decoration:none; color: #009;}

## demo-bouncy_balls.html
<!DOCTYPE HTML PUBLIC "-//W3C//DTD HTML 4.01//EN" "http://www.w3.org/TR/html4/strict.dtd">
<html>
  <head>
    <title>Doodle.js Demo - Bouncy Balls</title>
    <!--[if IE]>
      <script type="text/javascript" src="http://explorercanvas.googlecode.com/svn/trunk/excanvas.js"></script>
    <![endif]-->

    <script type="text/javascript" src="http://cloud.github.com/downloads/biilly/doodle.js/doodle-0.1.1.min.js"></script>

## config_bootstrap.php
<?php

/**
* Custom configuration bootsrtap file for ExpressionEngine
*
* Place config.php in your site root
* Add require(realpath(dirname(__FILE__) . '/../../config.php')); to the bottom of system/expressionengine/config/config.php
* Add require(realpath(dirname(__FILE__) . '/../../config.php')); to the bottom of system/expressionengine/config/database.php
* If you have moved your site root you'll need to update the require_once path
*

## ee-localize-mysqldump.rb
#!/bin/env ruby
if ARGV.empty?
  $stderr.puts <<-EOM

  ExpressionEngine (1.x) MYSQL DUMP Localizer

  1. Reads MYSQL DUMP of ExpressionEngine website from STDIN
  2. Replaces ALL HOSTNAME & PATH data (config) with LOCAL SETTINGS
  3. Output corrected MYSQL DYMP file.

## ios-viewport-scaling-bug-fix-original.js
// Original code from http://www.blog.highub.com/mobile-2/a-fix-for-iphone-viewport-scale-bug/

var metas = document.getElementsByTagName('meta');
var i;
if (navigator.userAgent.match(/iPhone/i)) {
	for (i=0; i<metas.length; i++) {
		if (metas[i].name == "viewport") {
			metas[i].content = "width=device-width, minimum-scale=1.0, maximum-scale=1.0";
		}
	}

## Description.md

      
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                rmdort
                / Description.md
            
            
              Created
              August 7, 2013 17:57
                — forked from Integralist/Description.md
            
          
    The BBC has a server-side image service which provides developers with multiple sized versions of any image they request. It works in a similar fashion to http://placehold.it/ but it also handles the image ratios returned (where as placehold.it doesn't).
The original BBC News process (and my re-working of the script) follows roughly these steps...

Create new instance of ImageEnhancer
Change any divs within the page (which have a class of delayed-image-load) into a transparent GIF using a Base64 encoded string.

We set the width & height HTML attributes of the image to the required size
We know what size the image needs to be because each div has custom data-attr set server-side to the size of the image
We then set a class of image-replace onto each newly created transparent image


We use a 250ms setTimeout to unblock the UI thread and which calls a function resizeImages which enhances the image-replace images so their source is now set to a URL whe


## keras_gensim_embeddings.py
from __future__ import print_function

import json
import os
import numpy as np

from gensim.models import Word2Vec
from gensim.utils import simple_preprocess
from keras.engine import Input
from keras.layers import Embedding, merge

## Attention.py
from keras.layers.core import Layer
from keras import initializers, regularizers, constraints
from keras import backend as K

class Attention(Layer):
    def __init__(self,
                 kernel_regularizer=None, bias_regularizer=None,
                 kernel_constraint=None, bias_constraint=None,
                 use_bias=True, **kwargs):
        """

## siamese_lstm.py
from keras import backend as K
from keras.layers import Input, Dense, merge, Dropout, Lambda, LSTM, Masking
from keras.models import Model, Sequential
from keras.optimizers import SGD, RMSprop, Adam, Nadam
from sys import argv
import argparse
import csv
import json
import numpy as np
import pickle

## AttentionWithContext.py
class AttentionWithContext(Layer):
    """
        Attention operation, with a context/query vector, for temporal data.
        Supports Masking.
        Follows the work of Yang et al. [https://www.cs.cmu.edu/~diyiy/docs/naacl16.pdf]
        "Hierarchical Attention Networks for Document Classification"
        by using a context vector to assist the attention
        # Input shape
            3D tensor with shape: `(samples, steps, features)`.
        # Output shape
	<!DOCTYPE html>
	<html>
	<head>
	<title>Twitter Search Plugin</title>
	<script src="http://code.jquery.com/jquery.js"></script>
	<script src="jquery.twitter.js"></script>
	<style type="text/css">
	.twitter-posts li {margin-bottom: 10px; font-size: 12px; clear: both; list-style-type:none;}
	.twitter-posts li img {float:left; width: 48px; margin:0px 10px 10px 0px;border:1px solid #c2c2c2; -moz-box-shadow: 0px 0px 4px #c2c2c2; -webkit-box-shadow: 0px 0px 4px #c2c2c2; box-shadow: 0px 0px 4px #c2c2c2;}
	.twitter-posts li a {text-decoration:none; color: #009;}
	<!DOCTYPE HTML PUBLIC "-//W3C//DTD HTML 4.01//EN" "http://www.w3.org/TR/html4/strict.dtd">
	<html>
	<head>
	<title>Doodle.js Demo - Bouncy Balls</title>
	<!--[if IE]>
	<script type="text/javascript" src="http://explorercanvas.googlecode.com/svn/trunk/excanvas.js"></script>
	<![endif]-->

	<script type="text/javascript" src="http://cloud.github.com/downloads/biilly/doodle.js/doodle-0.1.1.min.js"></script>
	<?php

	/**
	* Custom configuration bootsrtap file for ExpressionEngine
	*
	* Place config.php in your site root
	* Add require(realpath(dirname(__FILE__) . '/../../config.php')); to the bottom of system/expressionengine/config/config.php
	* Add require(realpath(dirname(__FILE__) . '/../../config.php')); to the bottom of system/expressionengine/config/database.php
	* If you have moved your site root you'll need to update the require_once path
	*
	#!/bin/env ruby
	if ARGV.empty?
	$stderr.puts <<-EOM

	ExpressionEngine (1.x) MYSQL DUMP Localizer

	1. Reads MYSQL DUMP of ExpressionEngine website from STDIN
	2. Replaces ALL HOSTNAME & PATH data (config) with LOCAL SETTINGS
	3. Output corrected MYSQL DYMP file.
	// Original code from http://www.blog.highub.com/mobile-2/a-fix-for-iphone-viewport-scale-bug/

	var metas = document.getElementsByTagName('meta');
	var i;
	if (navigator.userAgent.match(/iPhone/i)) {
	for (i=0; i<metas.length; i++) {
	if (metas[i].name == "viewport") {
	metas[i].content = "width=device-width, minimum-scale=1.0, maximum-scale=1.0";
	}
	}
	from __future__ import print_function

	import json
	import os
	import numpy as np

	from gensim.models import Word2Vec
	from gensim.utils import simple_preprocess
	from keras.engine import Input
	from keras.layers import Embedding, merge
	from keras.layers.core import Layer
	from keras import initializers, regularizers, constraints
	from keras import backend as K

	class Attention(Layer):
	def __init__(self,
	kernel_regularizer=None, bias_regularizer=None,
	kernel_constraint=None, bias_constraint=None,
	use_bias=True, **kwargs):
	"""
	from keras import backend as K
	from keras.layers import Input, Dense, merge, Dropout, Lambda, LSTM, Masking
	from keras.models import Model, Sequential
	from keras.optimizers import SGD, RMSprop, Adam, Nadam
	from sys import argv
	import argparse
	import csv
	import json
	import numpy as np
	import pickle
	class AttentionWithContext(Layer):
	"""
	Attention operation, with a context/query vector, for temporal data.
	Supports Masking.
	Follows the work of Yang et al. [https://www.cs.cmu.edu/~diyiy/docs/naacl16.pdf]
	"Hierarchical Attention Networks for Document Classification"
	by using a context vector to assist the attention
	# Input shape
	3D tensor with shape: `(samples, steps, features)`.
	# Output shape