Maybe it’s because it’s reminiscent of my early days. Maybe it’s because I don’t have to worry so much about logic, and can focus more on the user experience.
Or maybe it’s because I just love the experience of watching my creation come to life, and when It comes to life from my words, it’s exactly how I imagined it in my head. When it’s visual, there’s always some compromise I end up having to make, and it no longer feels like 100% “mine”.
My inner-voice is very narrative. I don’t know if it’s the same for everyone, but when I think, It’s mostly words in my head - with some imagery if I’m thinking of something particularly vivid. But most of the time, I don’t imagine scenery. When I remember an interaction with someone, I remember words more than faces. What I’m trying to say is, your mileage may vary. Take my words with a grain of salt (as I hope you always do as a programmer). But I digress…
One of the best learning experiences I’ve had to date was a 2-day project I just finished in Python. I’m brand new to Python, just having started learning the basics 2 days before this project.
#games #adventure #programming #learning-to-code #python
Hello, there fellow learner! Today we are going to make a fun text-based adventure game from scratch. First, let’s understand what a text-based game and then we will implement the same in the python programming language.
Table of Contents
A text-based game is a completely text-based input-output simple game. In such type of game, users have options to handle various situations as they arrive with choices taken by the user in the form of inputs.
The figure below displays the small story we will be building in python in this tutorial. You can expand or change the story according to your own preferences.
Text Based Story Game
#python programming #python programming examples #game #a text-based adventure game #text based adventure #how to create a text-based adventure game in python?
Text Processing mainly requires Natural Language Processing( NLP), which is processing the data in a useful way so that the machine can understand the Human Language with the help of an application or product. Using NLP we can derive some information from the textual data such as sentiment, polarity, etc. which are useful in creating text processing based applications.
Python provides different open-source libraries or modules which are built on top of NLTK and helps in text processing using NLP functions. Different libraries have different functionalities that are used on data to gain meaningful results. One such Library is Pattern.
Pattern is an open-source python library and performs different NLP tasks. It is mostly used for text processing due to various functionalities it provides. Other than text processing Pattern is used for Data Mining i.e we can extract data from various sources such as Twitter, Google, etc. using the data mining functions provided by Pattern.
In this article, we will try and cover the following points:
#developers corner #data mining #text analysis #text analytics #text classification #text dataset #text-based algorithm
Here, I will show you how to create full text search in laravel app. You just follow the below easy steps and create full text search with mysql db in laravel.
Let’s start laravel full-text search implementation in laravel 7, 6 versions:
#laravel full text search mysql #laravel full text search query #mysql full text search in laravel #full text search in laravel 6 #full text search in laravel 7 #using full text search in laravel
Full-Text Search refers to techniques for searching text content within a document or a collection of documents that hold textual content. A Full-Text search engine examines all the textual content within documents as it tries to match a single search term or several terms, text analysis being a pivotal component.
You’ve probably heard of the most well-known Full-Text Search engine: Lucene with Elasticsearch built on top of it. Couchbase’s Full-Text Search (FTS) Engine is powered by Bleve, and this article will showcase the various ways to analyze text within this engine.
Bleve is an open-sourced text indexing and search library implemented in Go, developed in-house at Couchbase.
Couchbase’s FTS engine supports indexes that subscribe to data residing within a Couchbase Server and indexes data that it ingests from the server. It’s a distributed system – meaning it can partition data across multiple nodes in a cluster and searches involve scattering the request and gathering responses from across all nodes within the cluster before responding to the application.
The FTS engine distributes documents ingested for an index across a configurable number of partitions and these partitions could reside across multiple nodes within a cluster. Each partition follows the same set of rules that the FTS index is configured with – to analyze and index text into the full-text search database.
The text analysis component of a Full-Text search engine is responsible for breaking down the raw text into a list of words – which we’ll refer to as tokens. These tokens are more suitable for indexing in the database and searching.
Couchbase’s FTS Engine handles text indexing for JSON documents. It builds an index for the content that is analyzed and stores into the database – the index along with all the relevant metadata needed to link the tokens generated to the original documents within which they reside.
An Inverted index is the data structure chosen to index the tokens generated from text, to make search queries faster. This index links every token generated to documents that contain the token.
For example, take the following documents …
The inverted index for the tokens generated from the 2 documents above would resemble this…
Here’s a diagram highlighting the components of the full-text search engine …
The components of a text analyzer can broadly be classified into 2 categories:
Couchbase’s engine further categorizes filters into:
Before we dive into the function of each of these components, here’s an overview of a text analyzer …
A tokenizer is the first component to which the documents are subjected to. As the name suggests, it breaks the raw text into a list of tokens. This conversion will depend on a rule-set defined for the tokenizer.
Take this sample text for an example: “_this is my email ID: firstname.lastname@example.org”
A couple of configurable tokenizers…
#json #couchbase #search #go #text analysis #full-text search #bleve #full-text #full-text-indexing
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