Java Program to Calculate Difference Between Two Time Periods
In this program, you’ll learn to calculate the difference between two time periods in Java.
Example: Calculate Difference Between Two Time Periods
public class Time{
int seconds;
int minutes;
int hours;
public Time(int hours, int minutes, int seconds){
this.hours = hours;
this.minutes = minutes;
this.seconds = seconds;
}
public static void main(String[] args){
// create objects of Time class
Time start = new Time(8, 12, 15);
Time stop = new Time(12, 34, 55);
Time diff;
// call difference method
diff = difference(start, stop);
System.out.printf("TIME DIFFERENCE: %d:%d:%d - ", start.hours, start.minutes, start.seconds);
System.out.printf("%d:%d:%d ", stop.hours, stop.minutes, stop.seconds);
System.out.printf("= %d:%d:%dn", diff.hours, diff.minutes, diff.seconds);
}
public static Time difference(Time start, Time stop){
Time diff = new Time(0, 0, 0);
// if start second is greater
// convert minute of stop into seconds
// and add seconds to stop second
if(start.seconds > stop.seconds){
--stop.minutes;
stop.seconds += 60;
}
diff.seconds = stop.seconds - start.seconds;
// if start minute is greater
// convert stop hour into minutes
// and add minutes to stop minutes
if(start.minutes > stop.minutes){
--stop.hours;
stop.minutes += 60;
}
diff.minutes = stop.minutes - start.minutes;
diff.hours = stop.hours - start.hours;
// return the difference time
return(diff);
}
}
Output
TIME DIFFERENCE: 12:34:55 - 8:12:15 = 4:22:40
In the above program, we’ve created a class named Time
with three member variables: hours, minutes, and seconds. As the name suggests, they store hours, minutes and seconds of a given time respectively.
The Time
class has a constructor that initializes the value of hours, minutes, and seconds.
We’ve also created a static function difference that takes two Time
variables as parameters, find the difference and returns it as Time
class.
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.