added trie concept

docs/day-others/_category_.json

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+{
+  "label": "Day others",
+  "position": 30,
+  "link": {
+    "type": "generated-index"
+  }
+}

docs/day-others/trie-data-structure.md

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+---
+sidebar_position: 1
+title: "Trie Data Structure in C++"
+description: "In this tutorial, we will learn about Trie data structure."
+sidebar_label: "Trie"
+slug: Trie-in-CPP
+---
+
+## Trie Data Structure:
+
+This repository presents a C++ implementation of the Trie data structure, a powerful tool for efficiently storing and retrieving strings, particularly when dealing with common prefixes.
+
+**What is a Trie?**
+
+A Trie, also known as a prefix tree or radix tree, is a specialized tree-like data structure specifically designed for storing and retrieving strings. It achieves this by representing each node as a character, where the path from the root to a node signifies a prefix of a string.
+
+**Key Features:**
+
+* **Efficient Prefix Search:**  Tries excel at searching for strings based on their prefixes. Imagine a search engine – you can quickly find websites containing specific keywords by only typing a few initial letters. Tries mirror this efficiency.
+* **Space Optimization:**  When storing a vast collection of strings with shared prefixes, Tries are remarkably space-efficient. They only need to store the unique characters, rather than storing entire strings repeatedly.
+* **Dynamic Operations:**  Adding or removing strings from a Trie is a relatively simple process, making them suitable for dynamic environments where string collections can change.
+
+**Implementation:**
+
+This repository offers a C++ implementation with the following key components:
+
+* **TrieNode:**  Represents a single node in the Trie. It holds:
+    * `char data`: The character associated with the node.
+    * `std::map<char, TrieNode*> children`: A map containing references to child nodes, keyed by the characters leading to those children.
+* **Trie:**  Manages the entire Trie data structure. It provides methods like:
+    * `insert(std::string word)`: Adds a new string to the Trie.
+    * `search(std::string word)`: Determines if a string already exists in the Trie.
+    * `startsWith(std::string prefix)`: Checks if any strings in the Trie start with a given prefix.
+    * `deleteString(std::string word)`: Removes a string from the Trie.
+
+**Example Usage:**
+
+```cpp
+#include "Trie.h"
+
+int main() {
+  Trie trie;
+
+  // Insert strings
+  trie.insert("apple");
+  trie.insert("banana");
+  trie.insert("app");
+
+  // Search for strings
+  if (trie.search("apple")) {
+    std::cout << "String 'apple' exists in the Trie." << std::endl;
+  }
+
+  // Check for a prefix
+  if (trie.startsWith("ban")) {
+    std::cout << "The Trie contains a string with prefix 'ban'." << std::endl;
+  }
+
+  // Delete a string
+  trie.deleteString("banana");
+
+  return 0;
+}
+```
+
+**Beyond the Basics:**
+
+The implementation provided here offers a solid foundation.  You can expand it to incorporate additional features like:
+
+* **Counting Word Occurrences:** Modify the TrieNode to store how many times a word is inserted.
+* **Longest Common Prefix (LCP):** Implement a method to find the longest common prefix shared by two strings.
+* **Autocomplete Suggestions:** Leverage the Trie to suggest possible words as the user types.
+
+**Applications:**
+
+* **Autocomplete in Search Engines:** Efficiently suggest words as users type.
+* **Spell Checkers:** Detect misspelled words by finding close matches.
+* **Large Text Databases:** Rapidly locate words containing specific prefixes.
+* **Domain Name Registries:** Check if domain names are already taken.
+* **IP Routing Tables:** Store and search for IP address prefixes.
+
+
+
+## Trie Data Structure: Explained with Code
+
+Here's a more detailed breakdown of the Trie implementation, with code snippets and explanations:
+
+**1. TrieNode Class:**
+
+```cpp
+#include <iostream>
+#include <map>
+#include <string>
+
+class TrieNode {
+public:
+  char data; // Character stored at this node
+  std::map<char, TrieNode*> children; // Map of child nodes
+
+  TrieNode(char ch) : data(ch) {} 
+};
+```
+
+* **`data`:**  Holds the character represented by this node.
+* **`children`:** A `std::map` is used to store child nodes, with the character leading to that child as the key. This efficiently manages the branching structure of the Trie. 
+
+**2. Trie Class:**
+
+```cpp
+class Trie {
+public:
+  TrieNode* root; // Root node of the Trie
+
+  Trie() {
+    root = new TrieNode('\0'); // Root node with null character
+  }
+
+  void insert(const std::string& word) {
+    TrieNode* current = root;
+
+    for (char ch : word) {
+      if (current->children.find(ch) == current->children.end()) {
+        current->children[ch] = new TrieNode(ch); // Create child node if not found
+      }
+      current = current->children[ch]; // Move to the child node
+    }
+  }
+
+  // ... other methods (search, startsWith, deleteString) 
+};
+```
+
+* **`root`:** Points to the root node of the Trie.
+* **`insert(std::string word)`:**
+    * Starts at the `root` node.
+    * Iterates through each character (`ch`) in the word:
+        * If a child node for `ch` doesn't exist, create one.
+        * Move to the corresponding child node.
+    * After processing all characters, mark the final node as the end of a word (optional, can be done with a flag or additional data in the node).
+
+**Example Usage:**
+
+```cpp
+int main() {
+  Trie trie;
+
+  trie.insert("apple");
+  trie.insert("banana");
+  trie.insert("app");
+
+  // Search for a word
+  if (trie.search("apple")) {
+    std::cout << "'apple' found in the Trie." << std::endl;
+  } 
+
+  return 0;
+}
+```
+
+**Explanation:**
+
+1. **Initialization:** The `Trie` object is created, initializing its `root` node with a null character.
+2. **Insertion:** The `insert` method is called for each word.  The Trie structure is built as described above.
+3. **Search:** The `search` method (not shown in the code snippet) would traverse the Trie based on the word's characters. If a path leading to the end of the word is found, it indicates the word exists in the Trie.
+
+**Key Points:**
+
+* **Time Complexity:**  Prefix searches in a Trie have a time complexity of O(k), where k is the length of the prefix.
+* **Memory Efficiency:**  Tries excel at storing words with common prefixes, significantly reducing storage space compared to storing individual strings.
+
+**Example (Visual):**
+
+Let's say we insert "apple" and "app":
+
+```
+     (Root)
+      | 
+      a 
+      |
+     p
+     |
+     p 
+     |
+     l
+     |
+     e 
+     | 
+     (word end)
+    /
+   p 
+   |
+   (word end)
+```
+
+Notice that "app" only needs to store the unique portion of the string after "app" is shared with "apple".