Pure Functions And Side Effects

In computer programming pure function is a function that satisfies two conditions:

  • It is deterministic, which means that for any given input it will always return same output.
  • It has no side effects. Which means it doesn’t change outside world. Doesn’t perform input/output operation or change global variables.

#developer #programming #javascript

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Pure Functions And Side Effects

What is “effect” or “effectful” mean in Functional Programming?

A lot of the time, when we discuss the effect, we usually talk about side-effect. However, as I study more and more into functional programming and reading more and more functional programming books, I noticed many times “Effect” or “Effectful” had been widely said in the FP community when describing abstract things.

I dig a little deeper into what an “Effect” or “Effectful” means and put that in this blog post for a note to my future self.

It is not Side Effect

Usually, what they meant for “Effect” or “Effectful” is no side effect (sometimes it does). It is Main Effect.

It has something to do with Type Category

A type category is a Math Structure to abstract out representation for all the different fields in Math. When designing a program, we can think in the properties of that program before writing code instead of the other way around. For example, a function sum can be empty (identity law), has the property of combined operation and needs to be associative. (1+2 is equal to 2+1). We can characterize them as and restrict input function to be a Monoid. This way, we can create a solution in a systematic approach that generates fewer bugs.

Within Type Category is a fancy word for a wrapper that produces an “effect” on a given type. I will quote the statement that Alvin Alexander mentioned in Functional and Reactive Domain Modeling:

  1. Option models the effects of optionality
  2. Future models latency as an effect
  3. Try abstract the consequences of failures

Those statements can be rewritten as:

  1. Option is a monad that models the effect of optionality (of being something optional)
  2. Future is a monad that models the impact of latency
  3. Try is a monad that models the impact of failures (manages exception as an effect)


  1. Reader is a monad that models the effect of composting operations based on some input.
  2. Writer is a monad that models the impact of logging
  3. State is a monad that models the impact of State
  4. Sync in Cats-effect is a monad that models the effects of synchronous lazy execution.

It is an F[A] instead of A

An effect can be said of what the monad handles.

Quoting from Rob Norris in Functional Programming with Effects — an effectual function returns F[A] rather than A.

#scala #programming #functional-programming #effect #side-effects

Vincent Lab

Vincent Lab


The Difference Between Regular Functions and Arrow Functions in JavaScript

Other then the syntactical differences. The main difference is the way the this keyword behaves? In an arrow function, the this keyword remains the same throughout the life-cycle of the function and is always bound to the value of this in the closest non-arrow parent function. Arrow functions can never be constructor functions so they can never be invoked with the new keyword. And they can never have duplicate named parameters like a regular function not using strict mode.

Here are a few code examples to show you some of the differences
this.name = "Bob";

const person = {
name: “Jon”,

<span style="color: #008000">// Regular function</span>
func1: <span style="color: #0000ff">function</span> () {
    console.log(<span style="color: #0000ff">this</span>);

<span style="color: #008000">// Arrow function</span>
func2: () =&gt; {
    console.log(<span style="color: #0000ff">this</span>);


person.func1(); // Call the Regular function
// Output: {name:“Jon”, func1:[Function: func1], func2:[Function: func2]}

person.func2(); // Call the Arrow function
// Output: {name:“Bob”}

The new keyword with an arrow function
const person = (name) => console.log("Your name is " + name);
const bob = new person("Bob");
// Uncaught TypeError: person is not a constructor

If you want to see a visual presentation on the differences, then you can see the video below:

#arrow functions #javascript #regular functions #arrow functions vs normal functions #difference between functions and arrow functions

Joseph  Murray

Joseph Murray


Pure Bliss With Pure Functions in Java

This deep dive into pure functions and collaborative development will help you see how object-oriented and functional programming can work together in Java.

Writing software is hard.

Writing good software is harder.

Writing good, simple software is the hardest.

Writing good, simple software as a team is the hardest… est.

You shouldn’t need several hours to understand what a method, class, or package does. If you need a lot of mental effort to even start programming, you will run out of energy before you can produce quality. Reduce the cognitive load of your code and you will reduce its amount of bugs.

“This is puristic nonsense!” you say. “Intellectual masturbation! Software should be modeled after real-world, mutable objects!”

I’m not saying you should go on a vision quest and return as a hardcore functional programmer. That wouldn’t be very productive. Functional and object-oriented programming can complement each other greatly.

I will show you how.

But first, let’s get to the what and why of it.

#java #tutorial #java 8 #functional programming #refactoring #clean code #team collaboration #pure functions

Tia  Gottlieb

Tia Gottlieb


Activation Functions, Optimization Techniques, and Loss Functions

Activation Functions:

A significant piece of a neural system Activation function is numerical conditions that decide the yield of a neural system. The capacity is joined to every neuron in the system and decides if it ought to be initiated (“fired”) or not, founded on whether every neuron’s info is applicable for the model’s expectation. Initiation works likewise help standardize the yield of every neuron to a range somewhere in the range of 1 and 0 or between — 1 and 1.

Progressively, neural systems use linear and non-linear activation functions, which can enable the system to learn complex information, figure and adapt practically any capacity speaking to an inquiry, and give precise forecasts.

Linear Activation Functions:

**Step-Up: **Activation functions are dynamic units of neural systems. They figure the net yield of a neural node. In this, Heaviside step work is one of the most widely recognized initiation work in neural systems. The capacity produces paired yield. That is the motivation behind why it is additionally called paired advanced capacity.

The capacity produces 1 (or valid) when info passes edge limit though it produces 0 (or bogus) when information doesn’t pass edge. That is the reason, they are extremely valuable for paired order studies. Every rationale capacity can be actualized by neural systems. In this way, step work is usually utilized in crude neural systems without concealed layer or generally referred to name as single-layer perceptions.

#machine-learning #activation-functions #loss-function #optimization-algorithms #towards-data-science #function

Julie  Donnelly

Julie Donnelly


How to Design Pure Functions

Today I am going to talk about how to design Pure Function and help you understand the benefits of pure function.

Nowadays Functional Programming is getting a lot of attention due to the advantages it offers like parallelismeasier testing_predictability _ and many others.

Pure functions is a concept mainly used in functional programming languages but it can be applied in any programming paradigm


Pure functions can be defined as

  • The function always returns the same value for the same inputs. The output is only determined by its input values.
  • A function where the return value is only determined by its input values, without observable side effects

Characteristics Of Pure Function

  • Function should always return a result.
  • Function must only depend on their inputs. That means this function should not use any other value than the function inputs to calculate the result
  • Pure functions cannot have other side effects than the computation of its return value.
  • Referentially transparent: An expression is said to be referentially transparent if
  • it can be replaced with its corresponding value without changing the
  • program’s behaviour.

#functional-programming #lambda-function #functor #kotlin #programming #functional-components #coding #coding-skills