5. Higher Order Functions and Functional Programming | Programming Kotlin

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Programming Kotlin

By : Stephen Samuel, Stefan Bocutiu

3.4 (7)

Programming Kotlin

3.4 (7)

By: Stephen Samuel, Stefan Bocutiu

Overview of this book

Quickly learn the fundamentals of the Kotlin language and see it in action on the web. Easy to follow and covering the full set of programming features, this book will get you fluent in Kotlin for Android.

Preface

Preface

What this book covers

What you need for this book

Who this book is for

Conventions

Reader feedback

Customer support

Free Chapter

1. Getting Started with Kotlin

1. Getting Started with Kotlin

Using the command line to compile and run Kotlin code

Kotlin runtime

The REPL

Kotlin for scripting

Kotlin with Gradle

Kotlin with Maven

IntelliJ and Kotlin

Eclipse and Kotlin

Mixing Kotlin and Java in a project

Summary

2. Kotlin Basics

2. Kotlin Basics

Vals and vars

Type inference

Basic types

Comments

Packages

Imports

String templates

Ranges

Loops

Exception handling

Instantiating classes

Referential equality and structural equality

This expression

Visibility modifiers

Control flow as expressions

Null syntax

When expression

Type hierarchy

Summary

3. Object-Oriented Programming in Kotlin

3. Object-Oriented Programming in Kotlin

Classes

Interfaces

Inheritance

Visibility modifiers

Abstract classes

Interface or abstract class

Polymorphism

Overriding rules

Inheritance versus composition

Class delegation

Sealed classes

Summary

4. Functions in Kotlin

4. Functions in Kotlin

Defining functions

Single expression functions

Member functions

Local functions

Top-level functions

Named parameters

Default parameters

Extension functions

Operators

Function literals

Tail recursive functions

Varargs

Standard library functions

Generic functions

Pure functions

Java from Kotlin

Kotlin from Java

Summary

5. Higher Order Functions and Functional Programming

5. Higher Order Functions and Functional Programming

Higher order functions

Closures

Anonymous functions

Function references

Function-literal receivers

Functions in the JVM

Function composition

Inline functions

Currying and partial application

Memoization

Type alias

Either

Custom DSLs

Validation and error accumulation

Summary

6. Properties

6. Properties

Why use properties?

Syntax and variations

Visibility

Late initialization

Delegated properties

Lazy initializations

Lateinit versus lazy

Observables

A non-null property delegate

Properties or methods?

Summary

7. Null Safety, Reflection, and Annotations

7. Null Safety, Reflection, and Annotations

Nullable types

Smart cast

Safe null access

Elvis operator

Safe casting

Optionals

Reflection

Constructors

Objects and companions

Useful KClass properties

Reflective functions and properties

Annotations

Standard annotations

Runtime annotation discovery

Summary

8. Generics

8. Generics

Parameterised functions

Parameterized types

Bounded polymorphism

Type variance

Nothing type

Type projection

Type erasure

Type reification

Recursive type bounds

Algebraic data types

Summary

9. Data Classes

9. Data Classes

Automatic creation of getters and setters

The copy method

toString out of the box

hashCode and equals methods generated for you

Destructed declarations

Destructing types

Data class definition rules

Limitations

Summary

10. Collections

10. Collections

Class hierarchy

Arrays

Lists

Maps

Sets

Read-only views

Indexed access

Sequences

Summary

11. Testing in Kotlin

11. Testing in Kotlin

Getting started

Choosing a spec

Matchers

Inspectors

Interceptors

Project config

Property testing

Table-driven testing

Tags, conditions, and config

Summary

12. Microservices with Kotlin

12. Microservices with Kotlin

Definition

Drawbacks

Why microservices?

Lagom

Defining services

Implementing a Lagom service

Summary

13. Concurrency

13. Concurrency

Threads

Deadlocks and livelocks

Executors

Race conditions

Summary

Memoization

Memoization is a technique for speeding up function calls by caching and reusing the output instead of recomputing for a given set of inputs. This technique offers a trade-off between memory and speed. The typical applications are for computationally expensive functions or for recursive functions, which branch out calling the recursive function many times with the same values, such as Fibonacci.

Let's use the latter to explore the effects of memoization. Fibonacci itself can be implemented recursively in the following manner:

    fun fib(k: Int): Long = when (k) { 
      0 -> 1 
      1 -> 1 
      else -> fib(k - 1) + fib(k - 2) 
    }

Note that when we invoke fib(k), we need to invoke fib(k-1) and fib(k-2). However, fib(k-1) will itself invoke fib(k-2) and fib(k-3), and so on. The result is that we are making many duplicated calls with the same value. For example, for fib(5) we invoke fib(1) five separate times.

This diagram shows how the number...

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