It has been quite a while since my last post; things have been pretty busy in preparation for the launch of our new product VoodooDreams. Now that we have shipped our baby, I am super eager to try out Kotlin - a new programming language from JetBrains targeting the JVM. One interesting aspect about Kotlin (similar to Scala and other JVM-based languages) is that it treats functions as first class citizens (IMO Functional Interfaces just patch the gap). In order to try out this language we will create a Decision Tree Learning algorithm which is one of the most used and practical methods for learning inductive inference.
One of the advantages of using TypeScript is that it augments ES6 with type information and annotations. When using JSON, data might not be represented using camelCase notation and hence one cannot simply typecast a JSON object directly onto a TypeScript "typed" object. Traditionally one would solve this problem by creating custom mappers for all the data objects. In this post we will create a generic custom mapper which automates this process by using declarative annotations on Typescript objects. Finally we will package this custom mapper in a class which can be used directly from Angular to handle conversion of JSON objects to "typed" objects.
AngularJS is a great framework to consider when developing Single Page Applications however, as the displayed datasets grow in size, the application response times deteriorate quickly. In this post I will go through a number of techniques which can be used to tackle these performance problems and I will suggest a new technique to solve this problem. So let's get started.
In our previous post we have described the general process of training an N-Gram language detector. If you have not yet read the post describing the general technique I suggest that you have a look at that first. In this post we will go through the implementation details and creation of a simple language detector using Node. To keep this post concise I will set the language detection parameters - top n-grams to select and n-gram length - manually. In a future post I will show you how to optimise the selection of these parameters by splitting the dataset into three parts; training, validation and test.
At SuprNation we have been using Docker for quite a while now. Docker has been an amazing tool in our arsenal - it has enabled us to package and deploy microservices without having to worry about inconsistencies (library, servers, OS) between live, staging and dev environments. In this post we will create a simple Java/Spring application, container-ise it using Docker, and deploy it on Google Cloud Platform using Kubernetes.
Modules are normally divided into two parts: i) Authoring which loosely refers to how one can export and import modules and ii) Loading which defines how a module is loaded under each environment (e.g. Node vs Browser). Initially ES6 Harmony included both aspects within the specification but in 2014 the loader specification was removed from the spec. WHATWG, has filled in this gap by creating a loader spec API. But why should we care about this today? - because of SystemJS. SystemJS is a universal module loader which is based on the WHATWG loader spec (through the ES6 Loader polyfill). Using SystemJS in our application relieves us from having to choose between today's popular module formats (AMD, CommonJS and globals) and allows us to load multiple module formats within the same application. In this post we will create global modules from scratch and basic CommonJS and AMD loaders. In a future post we will see how these module formats can be universally loaded through SystemJS.
At SuprNation we are using Slack as our primary communication tool. Slack is great, it has great search capabilities, allows rich messaging and most of all is able to integrate with other software components e.g. integrate Jenkins to post build updates to a slack channel. In this post we will integrate Slack with Hubot. Hubot is a virtual bot which will login to slack and provide some awesome and fun features e.g. automate deployment, language translation, integration with Google Maps, react to comments by posting an image from Imgr and so on. There are various scripts which you can add-on to hubot but the fun really starts when you create your own scripts to automate some of your own processes. Apart from our Slack integration with Hubot we will also create a plugin which integrates with GitHub to illustrate this idea.
In the previous post we made use of higher order functions to generalise the summation function. Using HOFs we managed to implement a solution that was much simpler - as defined by Rich Hickey over here - than its iterative (and less evolved) cousin. In this post we are going to apply the same technique on arrays and apply this on a dataset of house prices. Who knows maybe this techniques will help you find your dream home!