A Completely responsive medical website template with HTML, CSS, Bootstrap, and JavaScript. This is also called a disease prevention website template design ui/ux ideas with web design ideas. The Best Medical Website Templates Free Download
Medical websites can be intimidating, especially for those new to the industry. There are so many terms and concepts to understand, and it is easy to get overwhelmed. A medical website can be a great way to grow your business and build trust with customers. However, it can be difficult to find the proper template. To make things easier, here are some of the best templates you can download and start using to create your own medical website.
The Best Medical Website Templates Free Download
After inserting it, I will search now. It’s actually just like an insert. The difference is that I was creating the required edge if it was not there, now I will return false if there is no edge.
Note that I have done everything as an insert. Finally, I have returned the endpoint of the last node. If the endmark is true then the word is there, if it is false it is not.
Our main code is finished. We can now add any word to the trie, and search for any word from the trie. Many times creating a try for each test case is a problem with memory limit. So the safe way is to delete the used memory-cells after each case. It is not enough to delete only the root, every node has to be deleted. We can write a recursive function for that:
This function will go through each node first deleting its children and then delete the node.
The complete code looks like this:
struct node {
bool endpoint;
node* next[26 + 1];
node()
{
endmark = false;
for (int i = 0; i < 26; i++)
next[i] = NULL;
}
} * root;
void insert(char* str, int len)
{
node* curr = root;
for (int i = 0; i < len; i++) {
int id = str[i] – ‘a’;
if (curr->next[id] == NULL)
curr->next[id] = new node();
curr = curr->next[id];
}
curr->endmark = true;
}
bool search(char* str, int len)
{
node* curr = root;
for (int i = 0; i < len; i++) {
int id = str[i] – ‘a’;
if (curr->next[id] == NULL)
return false;
curr = curr->next[id];
}
return curr->endmark;
}
void del(node*cur)
{
for (int i = 0; i < 26; i++)
if (cur->next[i])
del(cur->next[i]);
delete (cur);
}
int main()
{
puts(“ENTER NUMBER OF WORDS”);
root = new Node();
int num_word;
cin >> num_word;
for (int i = 1; i <= num_word; i++) {
char str[50];
scanf(“%s”, str);
insert(str, strlen(str));
}
puts(“ENTER NUMBER OF QUERY”;);
int query;
cin >> query;
for (int i = 1; i <= query; i++) {
char str[50];
scanf(“%s”, str);
if (search(str, strlen(str)))
puts(“FOUND”);
else
puts(“NOT FOUND”);
}
del(root); //Destroy the try
return 0;
}
Complexity: Looping for each word up to the length of the word, the complexity of searching is O(length). The complexity of inserting each word is also the same. How much memory is required depends on the implementation and how well the prefixes of the words match. With the above implementation about 10^6 characters can be inserted in a try (10^6 not words, characters or letters).
If you want you can implement using simple array without try linklist, try it yourself!
Some uses of Tri:
1. There are many words in a dictionary, you have to find out whether a word is there or not. We have solved this problem in the above code.
2. Suppose the telephone numbers of your 3 friends are “5678”, “4322”, “567”. When you dial the first friend, as soon as you press 567, the phone will go to the 3rd friend because the 3rd friend’s number is the prefix of the first person! Many phone numbers are given, is there any such number or is it a prefix of another number? (UVA 11362).
3. A dictionary contains many words. Now we have to find out how many times a word appears as “prefix”. For example, the word “al” appears as a prefix 3 times in the above dictionary (algo, algea, also all these words are prefixed with “al”). To figure this out we need to keep a counter variable in each node, increasing the value of the counter every time we visit a node. When searching, the prefix will be found and the counter value will be displayed.
4. When you type a few letters while searching the mobile phonebook, the names that start with that prefix are shown in the suggestion box. Can you try to implement it?
4. Find the “longest common substring” of two strings. (If it is subsequence, it can be done easily with DP, here I want substring).
(Hints: If one or more characters are taken from the end of a string, it is called a suffix of the string, for example, the suffix of blog is g,og,log,blog. And each substring is a prefix of some suffix!! So if you insert all the suffixes into the try Makes it easier!)
The Best Medical Website Templates Free Download
Complexity classes (P-NP, Turing machines etc.)
When we tear our hair out to solve problems, we often come across problems that would take even the most powerful computer thousands of years to solve. When big computer scientists couldn’t solve them even after thinking day and night, they threw these problems into some categories and said “we haven’t been able to solve these category problems yet, if you can solve them, take the prize of 1 million dollars”. We call them NP-complete, NP-hard etc.
NP category problems are very famous in computer science. Like finding the longest path in a graph, coloring the graph with 3 colors etc. No one has been able to solve them in polynomial time so far, and the solutions that have come up don’t work for slightly larger inputs. The interesting thing is that if you can solve any of these problems, then all the problems can be solved with the help of your solution, all the problems that are difficult for people to think for many years will be solved in the blink of an eye with only one solution, it will change the face of computer science. That is why so many big prizes are announced for solving NP problems.
Before reading this article you should know about Algorithm Complexity. Besides, if we want to know about NP, we need to know some topics first, I will discuss them first. After reading this article you will have a good idea about P-NP, Turing Machine.
Turing machine
In 1936, Alan Turing designed a “hypothetical” machine, never actually built it, only theorized it. The machine can simulate the algorithm. This machine can be used to explain how a computer can solve a problem. What does Turing think “a problem is computable” mean? A problem is “computable” meaning it “has instructions that a computer can follow to complete a task”, these instructions are algorithms. Whether the computer can follow the instructions depends on the machine’s capabilities. Algorithms that can be solved by a Turing machine are “Turing-computable”.
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