Python Example for Beginners Python for Business Analyst Python for Citizen Data Scientist Python for Data Analyst

Learn Python By Example – Priority Queues

By on Friday, May 28, 2021

Priority Queues

Preliminaries

import heapq

Create A Priority Queue Object


/* Create a priority queue abstract base class */
class priority_queue:
    /* Initialize the instance */
    def __init__(self):
        /* Create a list to use as the queue */
        self._queue = []
        /* Create an index to use as ordering */
        self._index = 0

    /* Create a function to add a task to the queue */
    def add_task(self, item, priority):
        /* Push the arguments to the _queue using a heap */
        heapq.heappush(self._queue, (-priority, self._index, item))
        /* Add one to the index */
        self._index += 1

    /* Create a function to get the next item from the queue */
    def next_task(self):
        /* Return the next item in the queue */
        return heapq.heappop(self._queue)[-1]

/* Create a priority queue called task_list */
task_list = priority_queue()

Add Items To Queue


/* Add an item to the queue */
task_list.add_task('Clean Dishes', 1)

/* Add an item to the queue */
task_list.add_task('Wash Car', 2)

/* Add an item to the queue */
task_list.add_task('Walk Dog', 3)

Retrieve Items From Queue By Priority


/* Retrieve items from the queue */
task_list.next_task()
'Walk Dog'


/* Retrieve items from the queue */
task_list.next_task()
'Wash Car'


/* Retrieve items from the queue */
task_list.next_task()
'Clean Dishes'

 

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.

Google –> SETScholars

0
Related Posts