Kotlin example for Beginners – Kotlin Program to Reverse a Sentence Using Recursion

Kotlin Program to Reverse a Sentence Using Recursion

In this program, you’ll learn to reverse a given sentence using a recursive loop in Kotlin.

Example: Reverse a Sentence Using Recursion


fun main(args: Array<String>) {
    val sentence = "Go work"
    val reversed = reverse(sentence)
    println("The reversed sentence is: $reversed")
}

fun reverse(sentence: String): String {
    if (sentence.isEmpty())
        return sentence

    return reverse(sentence.substring(1)) + sentence[0]
}

When you run the program, the output will be:

The reversed sentence is: krow oG

In the above program, we’ve a recursive function reverse().

On each iteration, we add (concatenate) the result of next reverse() function to the first character of sentence using charAt(0).

The recursive call must be before the charAt(), because that way the last characters will start adding to the left hand side. If you reverse the order, you’ll end up with the original sentence.

In the end, we end up with an empty sentence and reverse() returns the reversed sentence.

Execution steps
Iteration reverse() substring() reversedString
1 reverse(“Go work”) “o Work” result + “G”
2 reverse(“o Work” ” Work” result + “o” + “G”
3 reverse(” Work”) “Work” result + ” ” + “o” + “G”
4 reverse(“Work”) “ork” result + “W” + ” ” + “o” + “G”
5 reverse(“ork”) “rk” result + “o” + “W” + ” ” + “o” + “G”
6 reverse(“rk”) “k” result + “r” + “o” + “W” + ” ” + “o” + “G”
7 reverse(“k”) “” result + “k” + “r” + “o” + “W” + ” ” + “o” + “G”
Final reverse(“”) “” + “k” + “r” + “o” + “W” + ” ” + “o” + “G” = “kroW oG”

 

 

Python Example for Beginners

Two Machine Learning Fields

There are two sides to machine learning:

  • Practical Machine Learning:This is about querying databases, cleaning data, writing scripts to transform data and gluing algorithm and libraries together and writing custom code to squeeze reliable answers from data to satisfy difficult and ill defined questions. It’s the mess of reality.
  • Theoretical Machine Learning: This is about math and abstraction and idealized scenarios and limits and beauty and informing what is possible. It is a whole lot neater and cleaner and removed from the mess of reality.

Data Science Resources: Data Science Recipes and Applied Machine Learning Recipes

Introduction to Applied Machine Learning & Data Science for Beginners, Business Analysts, Students, Researchers and Freelancers with Python & R Codes @ Western Australian Center for Applied Machine Learning & Data Science (WACAMLDS) !!!

Latest end-to-end Learn by Coding Recipes in Project-Based Learning:

Applied Statistics with R for Beginners and Business Professionals

Data Science and Machine Learning Projects in Python: Tabular Data Analytics

Data Science and Machine Learning Projects in R: Tabular Data Analytics

Python Machine Learning & Data Science Recipes: Learn by Coding

R Machine Learning & Data Science Recipes: Learn by Coding

Comparing Different Machine Learning Algorithms in Python for Classification (FREE)

Disclaimer: The information and code presented within this recipe/tutorial is only for educational and coaching purposes for beginners and developers. Anyone can practice and apply the recipe/tutorial presented here, but the reader is taking full responsibility for his/her actions. The author (content curator) of this recipe (code / program) has made every effort to ensure the accuracy of the information was correct at time of publication. The author (content curator) does not assume and hereby disclaims any liability to any party for any loss, damage, or disruption caused by errors or omissions, whether such errors or omissions result from accident, negligence, or any other cause. The information presented here could also be found in public knowledge domains.