Enums in Go - Iota

-


 Yes, you read it right.

Like many other languages Go support the concept of enums through the IOTA function.

IOTA

According to Go documentation 

Within a constant declaration, the predeclared identifier iota represents successive untyped integer constants. Its value is the index of the respective ConstSpec in that constant declaration, starting at zero. It can be used to construct a set of related constants. 

Don't worry if you didn't get it.

Basically, Iota is a keyword in Go that helps you to give random values to your constant.

Let's say you want to define 7 constant Monday, Tuesday, Wednesday .... Sunday.

Easy way to do this is

const (
Monday = 1
Tuesday = 2
Wednesday = 3
Thursday = 4
Friday = 5
Saturday = 6
)


In this situation, values 1,2,3,4,5,6 don't make much sense. The only thing these values should follow is that they have to be unique i.e Monday should not be equal to any other value.

These are just 7 unique values, if you were to create let's say 100 such values you don't have to assign unique values to these constants, you can use iota to get unique values for all these constants.

const (
Monday = iota
Tuesday
Wednesday
Thursday
Friday
Saturday
Sunday
)

This will automatically assign unique values to all these constant variables.

By default, it starts the values from 0 and increments the values by 1 with every constant but this is not something we can rely on, so be careful while using Iota.


Thanks for reading this microblog.

Happy Learning

- The Data Singh



Comments

Post a Comment

Popular posts from this blog

Word Vectorization

-
Image
Introduction Machine Learning has become the hottest topic in Data Industry with increasing demand for professionals who can work in this domain. There is large amount of textual data present in internet and giant servers around the world. Just for some facts 1,209,600 new data producing social media users each day. 656 million tweets per day! More than 4 million hours of content uploaded to Youtube every day, with users watching 5.97 billion hours of Youtube videos each day. 67,305,600 Instagram posts uploaded each day There are over 2 billion monthly active Facebook users, compared to 1.44 billion at the start of 2015 and 1.65 at the start of 2016. Facebook has 1.32 billion daily active users on average as of June 2017 4.3 BILLION Facebook messages posted daily! 5.75 BILLION Facebook likes every day. 22 billion texts sent every day. 5.2 BILLION daily Google Searches in 2017. Need for Vectorization The amount of textual data is massive, and the problem with textual dat
Read more

Spidering the web with Python

-
Image
Introduction We will be talking about  Spidering/Scraping How to do it elegantly in python Limitations and restriction  In the previous posts, I shared some of the methods of text mining and analytics but one of the major and most important tasks before analytics is getting the data which we want to analyze. Text data is present all over in forms of blogs, articles, news, social feeds, posts etc and most of it is distributed to users in the form of API's, RSS feeds, Bulk downloads and Subscriptions. Some sites do not provide any means of pulling the data programmatically, this is where scrapping comes into the picture. Note: Scraping information from the sites which are not free or is not publically available can have serious consequences. Web Scraping is a technique of getting a web page in the form of HTML and parsing it to get the desired information. HTML is very complex in itself due to loose rules and a large number of attributes. Inform
Read more

Machine Learning -Solution or Problem

-
Image
The article will be divided into different sections as follows: Introduction to Machine Learning Types of Solutions Classification using Naive Bayes A brief about Machine Learning According to the definition by Wikipedia,  Machine learning  is the subfield of  computer science  that, according to  Arthur Samuel  in 1959, gives "computers the ability to learn without being explicitly programmed."  Machine Learning defines a set of problems that have to be evolved through the data by implying some algorithm. One factor that has to be kept in mind while defining a solution through ML is accuracy. Accuracy is very critical in case you are developing a solution in medical domain(cancer detection).There should be a threshold set for every solution which can be based on risk %age that is acceptable. A useful cheatsheet from Microsoft's site to sum up the use of different ML algorithms for the different type of problems. Types of solution Machine Lear
Read more