| { | |
| "require": { | |
| "mfacenet/hello-world": "v1.*" | |
| } | |
| } |
| /** | |
| * Pre Select a dropdown choice/option in Gravity Forms | |
| * | |
| * Create a dynamic list of dropdown choices from a custom post type | |
| * Based on the current page, pre-select a choice in the drop down | |
| * | |
| * @var howto_dynamic_field = the dynamic field set in Gravity Forms on the field group under the Advanced tab | |
| * @var howto_post_type = Your custom post type you are listing in the drop down | |
| * | |
| */ |
| /** | |
| * Creates an equalateral triangle inside a given SVG parent node. | |
| * @Author Julien Etienne - 2015 | |
| * @param {Number} sideLength - Length of side | |
| * @param {Array} centerPosition - central position the of triangle. | |
| * @param {Object} parentNode - The parentNode of the new triangle. | |
| * @return {Object} - The polygon element. | |
| */ | |
| function equilateral(sideLength, centerPosition, parentNode) { | |
| var points, |
A lot of important government documents are created and saved in Microsoft Word (*.docx). But Microsoft Word is a proprietary format, and it's not really useful for presenting documents on the web. So, I wanted to find a way to convert a .docx file into markdown.
On a mac you can use homebrew by running the command brew install pandoc.
| /* bling.js */ | |
| window.$ = document.querySelector.bind(document); | |
| window.$$ = document.querySelectorAll.bind(document); | |
| Node.prototype.on = window.on = function(name, fn) { this.addEventListener(name, fn); }; | |
| NodeList.prototype.__proto__ = Array.prototype; | |
| NodeList.prototype.on = function(name, fn) { this.forEach((elem) => elem.on(name, fn)); }; |
To remove a submodule you need to:
(by @andrestaltz)
If you prefer to watch video tutorials with live-coding, then check out this series I recorded with the same contents as in this article: Egghead.io - Introduction to Reactive Programming.
| function Promise(fn) { | |
| this.promise = $.Deferred(function(dfd) { | |
| fn(dfd.resolve, dfd.fail); | |
| }).promise(); | |
| } | |
| Promise.prototype.then = function(done, fail) { | |
| this.promise.then(done, fail); | |
| }; |
| <!DOCTYPE html> | |
| <html> | |
| <pre id="page" style="font-family: monospace; white-space: pre-wrap;"> | |
| <h3>A Sinus Activated Multi-Stochastic-Descending/Ascending (MSD/A) Feed Forward Artificial Neural Network (ANN) Computed by GPU via JavaScript and WebGL GLSL (GATO 2014)</h3>This web page attempts to outline an implementation for solving non-linearly-separable (NLS) classification and function approximation problems using a sinus activated feed forward neural network, trained via multi-stochastic-descension/ascension (MSD/A), and evaluated using the GPU via JavaScript and WebGL GLSL source code. | |
| In order to overcome NLS using MSD/A, a sinus activation function: <b>sin(x)</b>, has been used in place of sigmoid: <b>1 / (1 + exp(-x))</b>, hyper-tangent: <b>htan(x)</b>, and/or averaging: <b>sum / count</b>, activation functions. | |
| Although ANNs capable of overcoming NLS problems are said to be capable of entering any computationally complete state, actually finding and entering a specific state required to solve a real |
| window.onerror = function (msg, url, lineno) { | |
| alert(url + '(' + lineno + '): ' + msg); | |
| } | |
| function createShader (str, type) { | |
| var shader = gl.createShader(type); | |
| gl.shaderSource(shader, str); | |
| gl.compileShader(shader); | |
| if (!gl.getShaderParameter(shader, gl.COMPILE_STATUS)) | |
| throw gl.getShaderInfoLog(shader); |
