linear search and binary search, Class lecture of Data Structure and Algorithms and Python.
Stack, Queue, Tree, Python, Python Code, Computer Science, Data, Data Analysis, Machine Learning, Artificial Intellegence, Deep Learning, Programming, Information Technology, Psuedocide, Tree, pseudocode, Binary Tree, Binary Search Tree, implementation, Binary search, linear search, Binary search operation, real-life example of binary search, linear search operation, real-life example of linear search, example bubble sort, sorting, insertion sort example, stack implementation, queue implementation, binary tree implementation, priority queue, binary heap, binary heap implementation, object-oriented programming, def, in BST, Binary search tree, Red-Black tree, Splay Tree, Problem-solving using Binary tree, problem-solving using BST, inorder, preorder, postorder
linear search and binary search, Class lecture of Data Structure and Algorithms and Python.
Stack, Queue, Tree, Python, Python Code, Computer Science, Data, Data Analysis, Machine Learning, Artificial Intellegence, Deep Learning, Programming, Information Technology, Psuedocide, Tree, pseudocode, Binary Tree, Binary Search Tree, implementation, Binary search, linear search, Binary search operation, real-life example of binary search, linear search operation, real-life example of linear search, example bubble sort, sorting, insertion sort example, stack implementation, queue implementation, binary tree implementation, priority queue, binary heap, binary heap implementation, object-oriented programming, def, in BST, Binary search tree, Red-Black tree, Splay Tree, Problem-solving using Binary tree, problem-solving using BST, inorder, preorder, postorder
Content of slide
Tree
Binary tree Implementation
Binary Search Tree
BST Operations
Traversal
Insertion
Deletion
Types of BST
Complexity in BST
Applications of BST
this is a briefer overview about the Big O Notation. Big O Notaion are useful to check the Effeciency of an algorithm and to check its limitation at higher value. with big o notation some examples are also shown about its cases and some functions in c++ are also described.
Content of slide
Tree
Binary tree Implementation
Binary Search Tree
BST Operations
Traversal
Insertion
Deletion
Types of BST
Complexity in BST
Applications of BST
this is a briefer overview about the Big O Notation. Big O Notaion are useful to check the Effeciency of an algorithm and to check its limitation at higher value. with big o notation some examples are also shown about its cases and some functions in c++ are also described.
Searching is an extremely fascinating and useful computer science technique. It helps to find the desired object with its location and number of occurrences. The presentation includes the basic principles, algorithms and c-language implementation.
Please Please Please Read the instructions and do everything li.docxtienmixon
Please Please Please Read the instructions and do everything listed there. if you no going to read the instructions and I received bad Credit I will do Bad Rating.
Lab #11:
Write a program that will do the following:
In main, declare an array of size 20 and name it "randomArray." Use the function in step 2 to fill the array. Use the function in step 3 to print the array.
Create a function that generates 20 random integers with a range of 1 to 10 and places them into an array. Re-cycle the functions from Lab 10 where appropriate.
Make this a function.
There will be two arguments in the parameter list of this function: an array and the size of the array.
Within the function and the function prototype name the array: intArray.
Within the function and the function prototype name the size of the array: size.
The data type of the function will be void since the array will be sent back through the parameter list.
Bring in the function that generates and returns a random number that you created from the previous module
.
Call that function
from this within the loop that adds random numbers to the array.
Display the contents of the array. Re-cycle the function that prints out the contents of an integer array from Lab 10.
Make this a function.
There will be two arguments in the parameter list of this function: an array and the size of the array.
Within the function and the function prototype name the array: intArray.
Within the function and the function prototype name the size of the array: size.
The data type of the function will be void since the array will be sent back through the parameter list.
From main, generate one more random number (also from 1 to 10) from the random number function. Do not put this in an array. This is a stand alone variable that contains one random number.
Search though the array and count how many times the extra random number occurs. It is possible that the extra random number may occur more than once or not at all.
Output:
• Display the entire array.
• Display the extra random number.
• Depending upon the result of your search, display one of the following:
– How many times the random number occurs.
– That the random number did not occur at all.
Also include:
Use a sentinel driven outer While Loop to repeat the task
Ask the User if they wish to generate a new set of random numbers
Clear the previous list of numbers from the output screen before displaying the new set.
NOTE 1: Other than the prompt to repeat the task, there is no input from the User in this program.
NOTE 2: This program will have 3 functions:
The function that fills the array with random numbers.
The function that generates one random number at a time (re-use the one from Lab 10).
The function that prints out an array of integers (re-use the one from Lab 10).
======================= Please Read ======================
Searching Through Arrays
Now t.
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1. LINEAR SEARCH ALGORITHM
Algorithm involves checking all the elements
of the array(or any other structure) one by
one and in sequence until the desired result
is found.
2. Daily life example
If you are asked to find the name of the person having
phone number say “1234” with the help of a telephone
directory .
Since telephone directory is sorted by name not by
numbers,we have to go through each and every number
of the directory
3. Best case
● If the first number in the directory is the number you
were searching for ,then lucky you!!.
● Since you have found it on the very first page,now its
not important for you that how many pages are there in
the directory.
● Whether if it is of 1000 pages or 2000 pages it will take
u same time to find you the number ,if it is at the very
beginning .
● So it does not depends on no. on elements in the
directory.Hence constant time .
InBig O notation : 0(1)
4. Worst Case
It may happen that the number you are searching for is the last
number of directory or if it is not in the directory at all.
In that case you have to search the whole directory.
Now number of elements will matter to you.if there are 500 pages
,you have to search 500;if it has 1000 you have to search 1000.
Your search time is proportional to number of elements in the
directory. In big O notation O(n)
No of elements
No of comparisons to be done
15
15
600
600
6. C++ code [Iterative]
void iterSearch(const double data [ ],int n,double key) //const for safety ,we want to keep array unchanged
{
for(int i=0;i<=n-1;i++)
//looping through all elements of the array
{
if(data[i]==key)
{
cout<<key<<" found at index "<<i<<" of the array"<<endl;
break;
//if element is found,come out of the loop
}
if(i==n-1)
//searched through the array,still not found
cout<<"n Element not found n";
}
}
7. Recursive Linear Search
bool recursiveSearch (const double data[ ],int n,double key)
{
static int i=0;
//static will prevent i being initialised to 0 every time we enter the function
if(data[i]==key)
{
cout<<key<<" found at index "<<i<<" of the array"<<endl;
return true; //this will end recursion which is desired as the element has been found
}
else
{ ++i;
if(i==n)
{
cout<<"nElemnent not foundn";
}
recursiveSearch(data,n,key);
}
}
i=0;
return false; //i=0 to reset i for next search.
8. Discussions
1.Sorted array is not needed.
2.Works fine for small number of elements .Search time
increases with number of elements.
3.Elements with higher probability of being searched
should be kept in the beginning.
4.Trick: Get completely rid of the end-check, namely
putting a sentinel value after the end of the last valid
array element.