#COUNTING SORTY FULL#
#COUNTING SORTY CODE#

The third and fourth loops iterate through vect once, so both loop n times. The second loop iterates through countVect once and we can call this k. The first loop iterated through vect once and we can call this number n. At first the counting algorithm seems like a strange way to sort a vector of integers, but because Counting Sort never compares numbers in vect to the other numbers in vect, it doesn’t need any nested for loops. Hopefully you now understand how Counting Sort works and have an appreciation of the fact that it does not make as many comparisons as the other algorithms. I made a video showing exactly what happens in this loop because it is hard to understand, and I attached it bellow. The same process is repeated for -3 and so on until the loop has gone through every number in vect.

#COUNTING SORTY CODE#

Then the second line of code in the loop subtracts 1 from the same index it just visited in countVect so that the next number is never placed at the same index in outputVect. We remember from before that vect is referring to 5 in this iteration so 5 is placed at index 7 of outpurVect. The number at index 10 of countVect is 8 so then the line of code simplifies to outputVect = vect and then outputVect = vect.

This results in countVect - min - 1] equaling countVect. Then vect - min will equal 10 because min = -5. So, vect will = 5 because 5 is at the last index in vect. First, vect will start by retrieving the last number in vect because i is equal to vect.size() - 1 at the beginning of the for loop and works its way towards 0. The first line of code is the most complex because of the layers within the outputVect brackets. The third loop is the most confusing loop because there is a lot going on within each iteration. I will go into greater detail on these loops later in the article because it is a hard sorting algorithm to understand. This sorting algorithm does not have to do multiple comparisons like the others algorithms listed and instead can order the integers from smallest to largest by looping over the unsorted vector a couple times. It seems like a very unorthodox way to sort a vector, but if used correctly, this sorting algorithm can be faster than all of the other sorting algorithms talked about above.

#COUNTING SORTY FULL#

A common misconception about Counting Sort is that it can only sort positive integers, but this is not true considering my example sorts a vector full of positive and negative integers. This sorting algorithm sorts data by counting the frequency of the values within a dataset which is where the sorting algorithm gets its name. However, a strong sorting algorithm like merge sort still has its limitations because it does not work with arrays, instead it mainly works with linked lists.Ĭounting Sort is a sorting algorithm invented by Harold H. For larger data sets merge sort or quick sort would be better to use because they handle the data in a more efficient way. If a simple sort is needed on a small data size then an algorithm like selection sort might be used because it is easier to implement and understand. Like tools in a tool shop, different sorting algorithms should be used on different data sets, based on their size and contents. This causes a demand for new sorting algorithms to work faster because time is money and sorting algorithms can waste a lot of time if they are unreliable or used improperly. Even though sorting algorithms can seem like they work instantaneously at times, as the datasets increase the time needed to sort the dataset increases as well. Sorting algorithms are the engines that manipulate the data and turn it into something that is more readable and easier to understand. One of these steps is that the data needs to be sorted before anyone can see trends within the numbers and values of the dataset.ĭata needs to be sorted before anyone can see trends by the numbers and values within the dataset. Information is what is produced from data, but there are different steps that need to be taken before data can be turned into information. By itself, data is worthless, but like all raw materials, data can be turned into something that people can use and benefit from. These days, data turns into knowledge and knowledge turns into money which is what all companies desire.

Counting sorty