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.github/CODE_OF_CONDUCT.md | # Contributor Covenant Code of Conduct
## Our Pledge
In the interest of fostering an open and welcoming environment, we as contributors and maintainers pledge to making participation in our project and our community a harassment-free experience for everyone, regardless of age, body size, disability, ethnicity, gender identity and expression, level of experience, nationality, personal appearance, race, religion, or sexual identity and orientation.
## Our Standards
Examples of behavior that contributes to creating a positive environment include:
* Using welcoming and inclusive language
* Being respectful of differing viewpoints and experiences
* Gracefully accepting constructive criticism
* Focusing on what is best for the community
* Showing empathy towards other community members
Examples of unacceptable behavior by participants include:
* The use of sexualized language or imagery and unwelcome sexual attention or advances
* Trolling, insulting/derogatory comments, and personal or political attacks
* Public or private harassment
* Publishing others' private information, such as a physical or electronic address, without explicit permission
* Other conduct which could reasonably be considered inappropriate in a professional setting
## Our Responsibilities
Project maintainers are responsible for clarifying the standards of acceptable behavior and are expected to take appropriate and fair corrective action in response to any instances of unacceptable behavior.
Project maintainers have the right and responsibility to remove, edit, or reject comments, commits, code, wiki edits, issues, and other contributions that are not aligned to this Code of Conduct, or to ban temporarily or permanently any contributor for other behaviors that they deem inappropriate, threatening, offensive, or harmful.
## Scope
This Code of Conduct applies both within project spaces and in public spaces when an individual is representing the project or its community. Examples of representing a project or community include using an official project e-mail address, posting via an official social media account, or acting as an appointed representative at an online or offline event. Representation of a project may be further defined and clarified by project maintainers.
## Enforcement
Instances of abusive, harassing, or otherwise unacceptable behavior may be reported by contacting the project team at [email protected]. The project team will review and investigate all complaints, and will respond in a way that it deems appropriate to the circumstances. The project team is obligated to maintain confidentiality with regard to the reporter of an incident. Further details of specific enforcement policies may be posted separately.
Project maintainers who do not follow or enforce the Code of Conduct in good faith may face temporary or permanent repercussions as determined by other members of the project's leadership.
## Attribution
This Code of Conduct is adapted from the [Contributor Covenant][homepage], version 1.4, available at [http://contributor-covenant.org/version/1/4][version]
[homepage]: http://contributor-covenant.org
[version]: http://contributor-covenant.org/version/1/4/
|
.github/CONTRIBUTING.md | # Cosmos
Welcome to the universe of algorithms and data structures. Before you begin contributing you must understand the concept of our organisation along with our goals and inspiration.
Read all about us [here](https://github.com/OpenGenus/cosmos/wiki)
## How to Begin
`“ Getting started is the most difficult thing to do; once you file it out, they rest of the journey is as soft as the straw. Be a good beginner. ”
― Israelmore Ayivor`
1. You can start contributing by selecting any field from our high-level structure that interests you the most.
2. Once you have chosen the path, **make sure that the algorithm is not already implemented**. This step is very important because we dont't want your time and energy to be go in vain.
3. When you complete step 2, read the [style guides](https://github.com/OpenGenus/cosmos/tree/master/guides/coding_style) for the language you want to implement. The coding style for every document is required to be similar so that it can be processed quickly and precisely across all platforms.
4. Last but not the least, follow our [code of conduct](https://github.com/OpenGenus/cosmos/blob/master/.github/CODE_OF_CONDUCT.md).
So what are you waiting for? Go on and start contributing! :smile: May the code be with you :metal:
|
.github/CONTRIBUTORS.md | # Contributors
> Every work is great only because of its contributors
## Contributors
Thanks goes to these ❤️ wonderful people who made this possible:
<!-- ALL-CONTRIBUTORS-LIST:START - Do not remove or modify this section -->
| [<img src="https://avatars3.githubusercontent.com/u/10634210?v=4" width="100px;"/><br /><sub>Aditya Chatterjee</sub>](https://github.com/AdiChat)<br /> | [<img src="https://avatars2.githubusercontent.com/u/14166032?v=4" width="100px;"/><br /><sub>Vytautas Strimaitis</sub>](https://github.com/vstrimaitis)<br /> | [<img src="https://avatars3.githubusercontent.com/u/8721312?v=4" width="100px;"/><br /><sub>Lauri Välja</sub>](https://github.com/OFFLlNE)<br /> | [<img src="https://avatars1.githubusercontent.com/u/26284185?v=4" width="100px;"/><br /><sub>Keval Kale</sub>](https://github.com/jainkeval)<br /> | [<img src="https://avatars2.githubusercontent.com/u/1932305?v=4" width="100px;"/><br /><sub>Hung-Wei Chiu</sub>](https://github.com/hwchiu)<br /> | [<img src="https://avatars1.githubusercontent.com/u/13018182?v=4" width="100px;"/><br /><sub>Aakash Bhattacharya</sub>](https://github.com/abbh07)<br /> |
| :---: | :---: | :---: | :---: | :---: | :---: |
| [<img src="https://avatars3.githubusercontent.com/u/23060327?v=4" width="100px;"/><br /><sub>Navendu Gupta</sub>](https://github.com/navendu29)<br /> | [<img src="https://avatars1.githubusercontent.com/u/22820957?v=4" width="100px;"/><br /><sub>Sirjanpreet Singh Banga</sub>](https://github.com/sirjan13)<br /> | [<img src="https://avatars0.githubusercontent.com/u/11931391?v=4" width="100px;"/><br /><sub>Daniela Ruz</sub>](https://github.com/druzmieres)<br /> | [<img src="https://avatars1.githubusercontent.com/u/32430978?v=4" width="100px;"/><br /><sub>aman-mi</sub>](https://github.com/aman-mi)<br /> | [<img src="https://avatars3.githubusercontent.com/u/6378532?v=4" width="100px;"/><br /><sub>Anurag El Dorado</sub>](https://github.com/aedorado)<br /> | [<img src="https://avatars2.githubusercontent.com/u/23054280?v=4" width="100px;"/><br /><sub>charul97</sub>](https://github.com/charul97)<br /> |
[<img src="https://avatars1.githubusercontent.com/u/26293279?v=4&s=460" width="100px;"/><br /><sub>Manan Manwani</sub>](https://github.com/manan904)<br /> | [<img src="https://avatars3.githubusercontent.com/u/22936570?v=4&s=400" width="100px;"/><br /><sub>Bhavesh Anand</sub>](https://github.com/bhaveshAn)<br /> | [<img src="https://avatars2.githubusercontent.com/u/24618078?v=4&s=400" width="100px;"/><br /><sub>Prashant Nigam</sub>](https://github.com/prashant0598)<br /> | [<img src="https://avatars2.githubusercontent.com/u/20587669?v=4&s=460" width="100px;"/><br /><sub>Vítor Gomes Chagas</sub>](https://github.com/Vitorvgc)<br /> | [<img src="https://avatars3.githubusercontent.com/u/12906090?v=4&s=460" width="100px;"/><br /><sub>Amit Singh</sub>](https://github.com/amitsin6h)<br /> | [<img src="https://avatars0.githubusercontent.com/u/6058114?v=4&s=400" width="100px;"/><br /><sub>Alabhya Vaibhav</sub>](https://github.com/AlabhyaVaibhav)<br /> |
[<img src="https://avatars1.githubusercontent.com/u/30810402?s=460&v=4" width="100px;"/><br /><sub>Arnav Borborah</sub>](https://github.com/arnavb)<br /> | [<img src="https://avatars2.githubusercontent.com/u/28451366?s=400&v=4" width="100px;"/><br /><sub>Yatharth Shah</sub>](https://github.com/yatharthshahjpr)<br /> | [<img src="https://avatars2.githubusercontent.com/u/16862997?v=4" width="100px;"/> <br /><sub>David Wu</sub>](https://github.com/Pl4gue)<br /> | [<img src="https://avatars3.githubusercontent.com/u/16002727?v=4" width="100px;"/> <br /><sub>Mohit Khare</sub>](https://github.com/mkfeuhrer)<br /> | [<img src="https://avatars0.githubusercontent.com/u/14811383?v=4" width="100px"/><br /><sub>Saif Ahmad</sub>](https://github.com/the-saif-ahmad)<br /> | [<img src="https://avatars0.githubusercontent.com/u/22833885?v=4" width="100px"/><br /><sub>Sanchit Jalan</sub>](https://github.com/Sanchit-20)<br /> |
[<img src="https://avatars3.githubusercontent.com/u/4724181?v=4" width="100px;"/><br /><sub>Yogesh Badhe</sub>](https://github.com/warlockz)<br /> | [<img src="https://avatars2.githubusercontent.com/u/31393258?s=460&v=4" width="100px;"/><br /><sub>Garvit Kothari</sub>](https://github.com/Garvit-k)<br /> |
<!-- ALL-CONTRIBUTORS-LIST:END -->
|
.github/ISSUE_TEMPLATE.md | <!-- Thanks for filing an issue! Before submitting, please fill in the following information. -->
<!--Required Information-->
**This is a(n):**
<!-- choose one by changing [ ] to [x] -->
- [ ] New algorithm
- [ ] Update to an existing algorithm
- [ ] Error
- [ ] Proposal to the Repository
**Details:**
<!-- Details of algorithm to be added/updated -->
|
.github/PULL_REQUEST_TEMPLATE.md | **Fixes issue:**
<!-- [Mention the issue number it fixes or add the details of the changes if it doesn't has a specific issue. -->
**Changes:**
<!-- Add here what changes were made in this pull request. -->
<!-- Make sure to look at the Style Guide for your language in guides/coding_style/language_name:
https://github.com/OpenGenus/cosmos/tree/master/guides/coding_style
Note: A coding style guide may not exist for your language, since this is still in beta.
-->
<!-- Make sure to look at the Documentation Style Guide in guides/documentation.md:
https://github.com/OpenGenus/cosmos/blob/master/guides/documentation_guide.md
The document style guide may not apply for your algorithm category, you must also look at specified guide under all of the directory in the category, e.g., for project euler:
https://github.com/OpenGenus/cosmos/blob/master/code/online_challenges/src/project_euler/documentation_guide.md
-->
|
.gitignore | **/*.vscode
**/*.netrwhist
.DS_Store
.replit |
.gitmodules | [submodule "third_party/uncrustify"]
path = third_party/uncrustify
url = https://github.com/uncrustify/uncrustify.git
branch = uncrustify-0.66.1
[submodule "third_party/namanager"]
path = third_party/namanager
url = https://github.com/iattempt/namanager.git
|
.travis.yml | matrix:
include:
#C with clang
- os: linux
language: c
before_script:
- wget https://download.savannah.gnu.org/releases/libgraph/libgraph-1.0.2.tar.gz
- tar -xvf libgraph-1.0.2.tar.gz
- pushd libgraph-1.0.2 && ./configure --prefix=/usr && make && sudo make install && popd
script:
make c
addons:
apt:
sources:
- llvm-toolchain-trusty-5.0
packages:
- clang-5.0
- libsdl1.2-dev
- libsdl-image1.2 libsdl-image1.2-dev guile-1.8 guile-1.8-dev libsdl1.2debian libart-2.0-dev libaudiofile-dev libesd0-dev libdirectfb-dev libdirectfb-extra libfreetype6-dev libxext-dev x11proto-xext-dev libfreetype6 libaa1 libaa1-dev libslang2-dev libasound2 libasound2-dev
env:
- MATRIX_EVAL="CC=clang-5.0"
#C with gcc
- os: linux
language: c
before_script:
- wget https://download.savannah.gnu.org/releases/libgraph/libgraph-1.0.2.tar.gz
- tar -xvf libgraph-1.0.2.tar.gz
- pushd libgraph-1.0.2 && ./configure --prefix=/usr && make && sudo make install && popd
script:
make c
addons:
apt:
sources:
- ubuntu-toolchain-r-test
packages:
- gcc-7
- libsdl1.2-dev
- libsdl-image1.2 libsdl-image1.2-dev guile-1.8 guile-1.8-dev libsdl1.2debian libart-2.0-dev libaudiofile-dev libesd0-dev libdirectfb-dev libdirectfb-extra libfreetype6-dev libxext-dev x11proto-xext-dev libfreetype6 libaa1 libaa1-dev libslang2-dev libasound2 libasound2-dev
env:
- MATRIX_EVAL="CC=gcc-7"
#C++ with g++
- os: linux
language: cpp
script:
./scripts/build_cpp.sh
addons:
apt:
sources:
- ubuntu-toolchain-r-test
packages:
- g++-7
env:
- MATRIX_EVAL="CXX=g++-7"
# Verify file name
- os: linux
language: python
python:
- "3.6"
script:
- pip install namanager
- namanager -s third_party/namanager_settings.json --required
# Check formatting of Python code
- os: linux
language: python
python: 3.6
script:
- pip install black
- ./scripts/python_code_style_checker.sh
# Check formatting of Javascript code
- os: linux
language: node_js
node_js: node
script:
- npm install -g prettier
- ./scripts/javascript_code_style_checker.sh
before_install:
- eval "${MATRIX_EVAL}"
|
LICENSE | GNU GENERAL PUBLIC LICENSE
Version 3, 29 June 2007
Copyright (C) 2007 Free Software Foundation, Inc. <http://fsf.org/>
Everyone is permitted to copy and distribute verbatim copies
of this license document, but changing it is not allowed.
Preamble
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The licenses for most software and other practical works are designed
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the GNU General Public License is intended to guarantee your freedom to
share and change all versions of a program--to make sure it remains free
software for all its users. We, the Free Software Foundation, use the
GNU General Public License for most of our software; it applies also to
any other work released this way by its authors. You can apply it to
your programs, too.
When we speak of free software, we are referring to freedom, not
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To protect your rights, we need to prevent others from denying you
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|
README.md | # Cosmos
> [Join our Internship Program now](http://internship.opengenus.org/) | Try this cool [One Liner knowledge tool](https://iq.opengenus.org/one/)
>
> Participate in [Checklist project](https://github.com/OpenGenus/checklist)
> The universe of algorithm and data structures - OpenGenus Cosmos
**Cosmos** is your personal offline collection of every algorithm and data structure one will ever encounter and use in a lifetime. This provides solutions in various languages spanning `C`, `C++`, `Java`, `JavaScript`, `Swift`, `Python`, `Go` and others.
This work is maintained by a community of hundreds of people and is a _massive collaborative effort_ to bring the readily available coding knowledge **offline**.
> **Many coders ask me how to improve their own performances. I cannot say anything except "solve and review and prepare your library"** - _Uwi Tenpen_
Make your fundamentals in Algorithms and Data Structure with these free resources:
* [**50+** Linked List Problems](https://iq.opengenus.org/list-of-linked-list-problems/)
* [**50+** Array Problems](https://iq.opengenus.org/list-of-array-problems/)
* [**50+** Binary Tree Problems](https://iq.opengenus.org/list-of-binary-tree-problems/)
* [**100+** Dynamic Programming (DP) Problems](https://iq.opengenus.org/list-of-dynamic-programming-problems/)
# Cosmic Structure
Following is the high-level structure of cosmos:
* [Artificial intelligence](/code/artificial_intelligence) :robot:
* [Backtracking](/code/backtracking)
* [Bit manipulation](/code/bit_manipulation)
* [Cellular automaton](/code/cellular_automaton) 🐚
* [Compression algorithms](/code/compression) 🗜️
* [Computational geometry](/code/computational_geometry) :gear:
* [Cryptography](/code/cryptography)
* [Data structures](/code/data_structures)
* [Design pattern](/code/design_pattern)
* [Divide conquering](/code/divide_conquer) ➗
* [Dynamic programming](/code/dynamic_programming)
* [Graph algorithms](/code/graph_algorithms) 📈
* [Greedy algorithms](/code/greedy_algorithms) 💰
* [Mathematical algorithms](/code/mathematical_algorithms) :1234:
* [Networking](/code/networking) :globe_with_meridians:
* [Numerical analysis](/code/numerical_analysis) :chart_with_upwards_trend:
* [Online challenges](/code/online_challenges)
* [Operating system](/code/operating_system) 💻
* [Quantum algorithms](/code/quantum_algorithms) :cyclone:
* [Randomized algorithms](/code/randomized_algorithms) :slot_machine:
* [Searching](/code/search) 🔎
* [Selecting](/code/selection_algorithms)
* [Sorting](/code/sorting)
* [Square root decomposition](/code/square_root_decomposition)
* [String algorithms](/code/string_algorithms) 🧵
* [Unclassified](/code/unclassified) 👻
Each type has several hundreds of problems with solutions in several languages spanning `C`, `C++`, `Java`, `Python`, `Go` and others.
# Maintainers
This is a massive collaboration. Hence, to keep the quality intact and drive the vision in the proper direction, we have maintainers.
> Maintainers are your friends forever. They are vastly different from moderators.
Currently, we have **5 active maintainers** and we are expanding quickly.
The task of maintainers is to review pull requests, suggest further quality additions and keep the work up to date with the current state of the world. 🌍
[Check out our current maintainers](https://github.com/OpenGenus/cosmos/wiki/maintainers)
# Contributors
The success of our vision to bring knowledge offline depends on you. Even a small contribution helps. All forms of contributions are highly welcomed and valued.
Currently, we have over **1000+ contributors** in Cosmos and **over 2K+ contributors** in sister projects of Cosmos.
When you contribute, your name with a link (if available) is added to our [contributors list](https://github.com/OpenGenus/cosmos/wiki/contributors).
You can contribute by writing `code`, documentation in the form of `installation guides` and `style guides`, making Cosmos search friendly and many others. There are endless possibilities.
Additionally, you might want to take a look at this [contributing guidelines](https://github.com/OpenGenus/cosmos/wiki/contribute) before you make Cosmos better.
You may, also, refer to the available [style guides](/guides/coding_style) before contributing code.
# License
We believe in freedom and improvement. [GNU General Public License v3.0](https://github.com/OpenGenus/cosmos/blob/master/LICENSE)
---
OpenGenus is sponsored by DigitalOcean and Discourse.
<p style="float: left;">
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|
code/algorithm_applications/src/Cichelli's Perfect Hashing Alogorithm/Hashing.java | import java.io.File;
import java.io.FileNotFoundException;
import java.util.ArrayList;
import java.util.Collections;
import java.util.Scanner;
import java.util.Stack;
public class Hashing {
public static void main(String[] args) {
//start timer
long startTime = System.currentTimeMillis();
//list used to hold key words.
ArrayList<Key> listOfKeys = new ArrayList<Key>();
//method opens key word file then returns a scanner of the file of key words.
Scanner keyWordFile = openTextFile("files/kywrdsOdd.txt");
//method reads the key word file and populates a list of keys.
readKeys(keyWordFile, listOfKeys);
//array will be used to count frequency of letters.
int[] occurencesOfLetters = new int[26];
//this method counts the frequency of letters and stores in occurenceOfLetters array.
populateOccurencesOfLetters(listOfKeys, occurencesOfLetters);
/*this method sets the value of all keywords based off the frequency of each word's
first and last letter.*/
setValueOfAllKeys(listOfKeys, occurencesOfLetters);
//This method sorts the list of keys by value in descending order.
Collections.sort(listOfKeys);
/*this arraylist takes values from occurences array but deletes characters
* with frequencies of zero.*/
ArrayList<Character> uniqueLetters = new ArrayList<Character>();
populateUniqueLetters(uniqueLetters, occurencesOfLetters);
/*g 2 dimensional array overview:
* collumn 0 stores g-values
* collumn 1 stores ascii values of characteres
* collumn 2 stores 0 if value for g has not been set and 1 if value for g has been set.
*/
int[][] g = new int[uniqueLetters.size()][3];
populateGArray(uniqueLetters, g);
//this hashtable will have keywords mapped to it using a hash function while the cichelli's algorithm is running.
Key[] hashTable = new Key[listOfKeys.size()];
/*max and mod values initialized. max value is utilized in cichelli's algorithm
mod value is utilized in the hash function*/
int maxValue = listOfKeys.size()/2;
int modValue = listOfKeys.size();
/*creating a stack and populating it with the keywords from my list of key words.
it is easier to implement cichelli's algorithm with a stack rather than a list.*/
Stack<Key> keyWordStack = new Stack<Key>();
populateStack(listOfKeys, keyWordStack);
//Cichelli's algorithm is called to map key words to the hashTable
cichelli(keyWordStack, hashTable, g, modValue, maxValue);
//this array is used to count the frequency each keyword appears in a subsequent text file.
int[] keyWordCounter = new int[hashTable.length];
//this method initializes the counter array with zeros.
initializeCounter(keyWordCounter);
//method opens text file then returns a scanner of the file.
Scanner textFile = openTextFile("files/tstOdd.txt");
/*this method reads the second text file.
Other methods checkForKeyWord and printResults are called within the readOtherFile method.
The result is that the program will print number of keywords etc. present in the second text file.
The hash function is used in order compare key words with the words of the second text file. the printed
results are accurate thus proving this implementation of Cichelli's algorithm is effective.
*/
readOtherFile(textFile, g, hashTable, keyWordCounter, modValue);
//end timer
long endTime = System.currentTimeMillis();
//calculate the time it took to run the program.
long timeTaken = endTime - startTime;
System.out.println("Execution time: " + timeTaken + " milliseconds");
//System.out.println("Start time: " + startTime + " milliseconds");
//System.out.println("End time: " + endTime + " milliseconds");
}//end of main
//method opens text file then returns a scanner of the file.
public static Scanner openTextFile(String fileName) {
Scanner data;
try{
data = new Scanner(new File(fileName));
return data;
}
catch(FileNotFoundException e){
System.out.println(fileName + " did not read correctly");
}
data = null;
return data;
}
//method reads the key word file and populates a list of keys.
public static void readKeys(Scanner data, ArrayList<Key> listOfKeys) {
String currentToken;
while(data.hasNext()) {
currentToken = data.next();
currentToken = currentToken.toLowerCase();
Key currentKey = new Key(currentToken);
listOfKeys.add(currentKey);
}
}
//this method counts the frequency of letters and stores in occurenceOfLetters array.
public static void populateOccurencesOfLetters(ArrayList<Key> listOfKeys, int[] occurencesOfLetters) {
//using an array of 26 to keep track of occurences of letters
//initialize all array values to zero.
for (int i = 0; i < 26; i += 1) {
occurencesOfLetters[i] = 0;
}
// count the frequency that each letter appears as a first or last letter
for (Key e: listOfKeys) {
for (int i = 97; i < 123; i += 1) {
if (e.getFirstLetter() == (char) i) {
occurencesOfLetters[i - 97] += 1;
}
if (e.getLastLetter() == (char) i) {
occurencesOfLetters[i - 97] += 1;
}
}//end of for loop
}//end of foreach loop
}
/*this method sets the value of all keywords based off the frequency of each word's
first and last letter.*/
public static void setValueOfAllKeys(ArrayList<Key> listOfKeys, int[] occurencesOfLetters) {
//set values of keys
for(Key e : listOfKeys) {
int firstLetterValue = occurencesOfLetters[((int) e.getFirstLetter() - 97)];
e.setFirstLetterValue(firstLetterValue);
int lastLetterValue = occurencesOfLetters[((int) e.getLastLetter() - 97)];
e.setLastLetterValue(lastLetterValue);
int totalValue = firstLetterValue + lastLetterValue;
e.setTotalValue(totalValue);
}
}
//the uniqueLetters arrayList is populated with letters from the occurencesOfLetters array.
public static void populateUniqueLetters(ArrayList<Character> uniqueLetters, int[] occurencesOfLetters) {
for(int i = 0; i < occurencesOfLetters.length; i += 1) {
if (occurencesOfLetters[i] != 0) {
uniqueLetters.add((char) (i+97));
}
}
}
//this method populates 2 dimension g array
public static void populateGArray(ArrayList<Character >uniqueLetters, int [][] g) {
for (int i = 0; i < uniqueLetters.size(); i += 1) {
//collumn 0 holds g values. all initialized to zero
g[i][0] = 0;
//collumn 1 holds ascci values of letters.
g[i][1] = (int) uniqueLetters.get(i);
//collumn 2 holds 0 if g-value is not set and 1 if g-value is set. all initialized to zero.
g[i][2] = 0;
}
}
//this method populates the stack with keys from the listOfKeys.
public static void populateStack(ArrayList<Key> listOfKeys, Stack<Key> keyWordStack) {
for (int i = (listOfKeys.size() - 1); i >= 0; i -= 1) {
keyWordStack.push(listOfKeys.get(i));
}
}
//Cichelli's algorithm is called to map key words to the hashTable and is a perfect hashing algorithm.
public static boolean cichelli(Stack<Key> keyWordStack, Key [] hashTable, int[][] g, int modValue, int maxValue) {
while(keyWordStack.isEmpty() != true) {
Key word = keyWordStack.pop();
//finding first and last g values by comparing 2d gArray to first and last letters of word.
//setPositionInG refers to the row of the array where the letters was found.
for (int i = 0; i < g.length; i += 1) {
if (g[i][1] == ((int) word.getFirstLetter())) {
word.setPositionInGFirst(i);
}
if (g[i][1] == ((int) word.getLastLetter())) {
word.setpositionInGLast(i);
}
}
//if values are already assigned for both letters try to place key in table
if (g[word.getPositionInGFirst()][2] == 1 && g[word.getPositionInGLast()][2] == 1) {
//check if hash value for word is valid.
if (hashTable[hashFunction(word, g[word.getPositionInGFirst()][0], g[word.getPositionInGLast()][0], modValue)] == null) {
//assign hash value to word
hashTable[hashFunction(word, g[word.getPositionInGFirst()][0], g[word.getPositionInGLast()][0], modValue)] = word;
//recursively call cichelli method
if (cichelli(keyWordStack, hashTable, g, modValue, maxValue) == true) {
return true;
}
//detach the hash value for word
else {
hashTable[hashFunction(word, g[word.getPositionInGFirst()][0], g[word.getPositionInGLast()][0], modValue)] = null;
//set values of g array column 2 back to zero.
}
}
keyWordStack.push(word);
return false;
}
//neither letter assigned g-value AND first != last letter
else if((g[word.getPositionInGFirst()][2] == 0) && (g[word.getPositionInGLast()][2] == 0) && (word.getFirstLetter() != word.getLastLetter())) {
/*increment first g value up to maxValue times, but dont increment 1st iteration
to see if we get a hit while g value is still set to zero. Previous sentence explains
why for loop condition is (maxValue + 1)*/
for (int m = 0; m < (maxValue + 1); m += 1) {
//not incrementing g-value in first iteration
if (m > 0) {
//increment g-value
g[word.getPositionInGFirst()][0] += 1;
}
//check if hash value is valid if not we want to keep incrementing g.
if (hashTable[hashFunction(word, g[word.getPositionInGFirst()][0], g[word.getPositionInGLast()][0], modValue)] == null) {
//assign hash value to word
hashTable[hashFunction(word, g[word.getPositionInGFirst()][0], g[word.getPositionInGLast()][0], modValue)] = word;
//set values of g array column 2 to 1.
g[word.getPositionInGFirst()][2] = 1;
g[word.getPositionInGLast()][2] = 1;
//recursively call cichelli method
if (cichelli(keyWordStack, hashTable, g, modValue, maxValue) == true) {
return true;
}
//detach the hash value for word
else {
hashTable[hashFunction(word, g[word.getPositionInGFirst()][0], g[word.getPositionInGLast()][0], modValue)] = null;
//set values of g array column 2 back to zero.
g[word.getPositionInGFirst()][2] = 0;
g[word.getPositionInGLast()][2] = 0;
}
}
}//end of for loop
/*I believe this following code is only reached if hashfunction has a hit
but returns false at some point or if hash function has no hits! */
//reset g value
g[word.getPositionInGFirst()][0] = 0;
keyWordStack.push(word);
return false;
}//end of else if
// else refers to case where only one letter assigned g-value OR first = last letter
else {
//determine which letter(first or last) is assigned a g value.
//if the following condition is true than the first letter has a g value and last letter will not
if (g[word.getPositionInGFirst()][2] == 0) {
word.setUnassignedLetterPosition(word.getPositionInGFirst());
}
// reaches (else if) if the first letter does not have a gvalue. still possible neither has a g value.
else if (g[word.getPositionInGLast()][2] == 0) {
word.setUnassignedLetterPosition(word.getPositionInGLast());
}
/*reaches else. if the first letter = last letter
this case should not be reached with the given set of keywords.*/
else {
//there is no unassigned letter first = last.
//set to arbitrary value of -1.
word.setUnassignedLetterPosition(-1);
}
/*increment the unassigned letter's g value up to maxValue times, but dont increment 1st iteration
to see if we get a hit while g value is still set to zero. Previous sentence explains
why for loop condition is (maxValue + 1)*/
for (int m = 0; m < (maxValue + 1); m += 1) {
//not incrementing g-value in first iteration
if(m > 0) {
//increment g-value
g[word.getUnassignedLetterPosition()][0] += 1;
}
//check if hash value is valid if not we want to keep incrementing g.
if (hashTable[hashFunction(word, g[word.getPositionInGFirst()][0], g[word.getPositionInGLast()][0], modValue)] == null) {
//assign hash value to word
hashTable[hashFunction(word, g[word.getPositionInGFirst()][0], g[word.getPositionInGLast()][0], modValue)] = word;
//only need to change the unassigned letter's set field to 1.
g[word.getUnassignedLetterPosition()][2] = 1;
//recursively call cichelli's method
if (cichelli(keyWordStack, hashTable, g, modValue, maxValue) == true) {
return true;
}
//detach the hash value for word
else {
hashTable[hashFunction(word, g[word.getPositionInGFirst()][0], g[word.getPositionInGLast()][0], modValue)] = null;
//set unassigned letters set field back to zero.
g[word.getUnassignedLetterPosition()][2] = 0;
}
}
}//end of for loop
/*I believe this following code is only reached if hashfunction has a hit but returns false at some point
* or if hash function has no hits!
*/
//reset unassigned letters g value
g[word.getUnassignedLetterPosition()][0] = 0;
keyWordStack.push(word);
return false;
}//end of else
}//end of while
// empty stack means we have a solution
return true;
}//end of Cichelli's method
//overloaded hash function takes Key data type as an argument
public static int hashFunction(Key keyWord, int gFirst, int gLast, int modValue) {
int hashValue = (keyWord.getKeyWord().length() + gFirst + gLast) % modValue;
return hashValue;
}
//overloaded hash function takes String data type as an argument.
public static int hashFunction(String word, int gFirst, int gLast, int modValue) {
int hashValue = (word.length() + gFirst + gLast) % modValue;
return hashValue;
}
//this method initializes the keyWordCounter array with zeros.
public static void initializeCounter(int[] keyWordCounter) {
for (int i = 0; i < keyWordCounter.length; i += 1) {
keyWordCounter[i] = 0;
}
}
public static void readOtherFile(Scanner data, int g[][], Key[] hashTable, int[] keyWordCounter, int modValue) {
int lineCounter = 0, wordCounter = 0;
String x;
String []y;
while(data.hasNextLine()){
lineCounter += 1;
x = data.nextLine();
/*the following conditional statement takes care of the issue of their being an
* entirely blank line encountered before reaching the end of the text file.
*/
if(x.length() == 0) {
x = data.nextLine();
}
x = x.toLowerCase();
x = x.replaceAll("[,.()]","");
y = x.split(" ");
wordCounter += y.length;
//method compares a token to a key word to see if they are identical.
checkForKeyWord(y, g, hashTable, keyWordCounter, modValue);
}
//method prints statistical results
printResults(lineCounter, wordCounter, hashTable, keyWordCounter);
}
//method compares a token to a key word to see if they are identical.
public static void checkForKeyWord(String[] words, int[][] g, Key[] hashTable, int[] keyWordCounter, int modValue) {
for(String word: words) {
char firstLetter = word.charAt(0), lastLetter = word.charAt(word.length() - 1);
boolean firstLetterBool = false, lastLetterBool = false;
int firstGvalue = 0, lastGvalue = 0;
for(int i = 0; i < g.length; i += 1) {
if(firstLetter == (char) g[i][1]) {
firstLetterBool = true;
firstGvalue = g[i][0];
}
if (lastLetter == (char) g[i][1]) {
lastLetterBool = true;
lastGvalue = g[i][0];
}
}
if (firstLetterBool == true && lastLetterBool == true) {
int index = hashFunction(word, firstGvalue, lastGvalue, modValue);
if (word.equals(hashTable[index].getKeyWord())) {
keyWordCounter[index] += 1;
}
}
}
}
//method prints statistical results
public static void printResults(int lineCounter, int wordCounter, Key[] hashTable, int[] keyWordCounter) {
System.out.println("Total Lines Read " + lineCounter);
System.out.println("Total Words Read " + wordCounter);
System.out.println("Break down by key word");
for(int i = 0; i < hashTable.length; i += 1) {
System.out.println(" " + hashTable[i].getKeyWord() + ": " + keyWordCounter[i]);
}
int totalKeys = 0;
for(int e: keyWordCounter) {
totalKeys += e;
}
System.out.println("Total Keywords: " + totalKeys);
}
}//end of class
|
code/algorithm_applications/src/Cichelli's Perfect Hashing Alogorithm/Key.java | public class Key implements Comparable<Key> {
//fields
private String keyWord;
private char firstLetter, lastLetter;
//unnassignedLetter is used if only one letter(first or last) is not set.
private int totalValue, firstLetterValue, lastLetterValue, positionInGFirst, positionInGLast, unassignedLetterPosition;
//constructor
public Key(String keyWord) {
this.keyWord = keyWord;
this.firstLetter = keyWord.charAt(0);
this.lastLetter = keyWord.charAt(keyWord.length() - 1);
this.totalValue = 0; //updated in setValue method
this.firstLetterValue = 0; //updated in setValue method
this.lastLetterValue = 0; //updated in setValue method
this.positionInGFirst = 0; //updated in Cichelli's alogrithm
this.positionInGLast = 0; //updated in Cichelli's alogrithm
//unassignedLetterPosition is set to -1 as an arbitrary value.
this.unassignedLetterPosition = -1; //can updated in Cichelli's alogrithm under certain conditions.
}
/*this method is necessary in sorting the list of keys by value.
* I created this compareTo method based on info from the following link:
* https://beginnersbook.com/2013/12/java-arraylist-of-object-sort-example-comparable-and-comparator/
*/
@Override
public int compareTo(Key comparesTo) {
int compareValue = ((Key)comparesTo).getTotalValue();
return compareValue-this.totalValue;
}
//getter and setter methods
public String getKeyWord() {
return keyWord;
}
public char getFirstLetter() {
return firstLetter;
}
public char getLastLetter() {
return lastLetter;
}
public int getFirstLetterValue() {
return firstLetterValue;
}
public void setFirstLetterValue(int value) {
this.firstLetterValue = value;
}
public int getLastLetterValue() {
return lastLetterValue;
}
public void setLastLetterValue(int value) {
this.lastLetterValue = value;
}
public void setTotalValue(int value) {
this.totalValue = value;
}
public int getTotalValue() {
return totalValue;
}
public void setPositionInGFirst(int position) {
this.positionInGFirst = position;
}
public int getPositionInGFirst() {
return positionInGFirst;
}
public void setpositionInGLast(int position) {
this.positionInGLast = position;
}
public int getPositionInGLast() {
return positionInGLast;
}
public void setUnassignedLetterPosition(int position) {
this.unassignedLetterPosition = position;
}
public int getUnassignedLetterPosition() {
return unassignedLetterPosition;
}
}
|
code/algorithm_applications/src/Cichelli's Perfect Hashing Alogorithm/files/kywrdsOdd.txt | jitter
frequency
Chapter
camera
step
in
range
the
ToF
influence |
code/algorithm_applications/src/Cichelli's Perfect Hashing Alogorithm/files/tstOdd.txt | During the last decade, the ToF range imaging cameras have significantly
improved and have been used in various applications from agricultural sectors
to engineering industries. With the improvement of the technology to build
the depth sensor, novel applications of ToF range imaging will increase. At
present, more researchers are mainly focused on the noise investigation in
these cameras. However, from this thesis, we found that the jitter significantly
influences range measurements therefore it is worth further investigating jitter
in ToF range imaging systems. Several investigations could be extended to
further perform the ToF range imaging systems due to the jitter influence on
measurements as follows.
The proposed methodology in Chapters 4 and 5 could be extended to correct
range measurements after extracting and measuring the RJ and PJ in the
light signal of the camera. This will be very useful since the available jitter can
be compensated before the measurement. Since for a particular configuration
of the AMCW camera (e.g., with default modulation frequency and integration
period), the periodic and random jitter in the illumination source is fixed
amounts which can be computed with the proposed methodology. Then, considering
the benchmark numerical results in Chapters 7 and 8 for the influence
of the periodic and random jitter on range measurements, respectively, can
be applied for compensating of the measurement. In addition, the proposed
algorithm may be applied to extract the jitter in other types of ToF range
imaging cameras such as pulse based and pseudo-noise based modulation cameras.
This methodology can be used for jitter measurement in any kind of
application where the reference clock signal cannot be accessed.
A cheaper SDR USB dongle with the proposed algorithm for jitter extraction
in ToF cameras at lower frequencies in the RF signal was investigated in
Chapter 6. The high sample rates and high quality of SDRs can be used to
explore the relatively large range of frequencies of the PJ and RJ in the light
source of the AMCW ToF cameras by setting a suitable local oscillator frequency
to get a large intermediate frequency (less than the Nyquist frequency)
in the dongle. Also, this concept may be used to find the jitter in the light
source of the aforementioned two modulation types of ToF range imaging cameras
as well as in any other relevant applications. In addition, it may possible
to modify the DSP engine in the dongle which is a software based controller in
order to get maximum benefit but this may not be a straightforward process.
In Chapter 7, the influence of the periodic jitter on range measurements
was investigated. Can we compensate the PJ amount in the first phase step
(by determining and correcting it) before it proceeds to the second phase step
and so on, in the correlation function within the integration period? With
the influence of the PJ in each phase step a constant (a fixed amount), then
it can be further analysed with the crosstalking between phase steps, during
the integration period. But if it is not a constant (i.e., random behaviour),
then it will be difficult to compensate for the influence of the PJ on range
measurements. Thus, this is a more challenging task for future investigation
that arises from Chapter 7. On the other hand, here the periodic jitter at a
single frequency was considered. But in practice, the ToF cameras may have
periodic jitter at multiple frequencies. Then, it is very interesting to know how
the analytical model will change accordingly. Will the influence be linear or
non-linear and will it be partially or fully cancel on range measurements? So,
some interesting questions arise from this future investigation.
A finite number of evaluations of the Monte Carlo simulation were used to
investigate the influence of random jitter on range measurements in Chapter 8.
With increasing the number of simulations, it is surprisingly effective to estimate
the better result than the current one. In order to understand the
impact of uncertainty of the results obtained, it is necessary to analyse the
stochastic model for the full range of possible outcomes, to get much more
accurate results. On the other hand, all simulations were done at fixed integration
period T = 1 ms and it is interesting to investigate the behaviour of the
random jitter for various T, especially for less than 1 ms integration period
since now the depth sensor manufacturers are trying to develop the AMCW
ToF cameras with short integration periods. |
code/algorithm_applications/src/Strassens algorithm/strassens algorithm.cpp | #include <bits/stdc++.h>
using namespace std;
#define ff(i, a, b) for (int i = int(a); i < int(b); i++)
#define ffn(i, n) ff(i, 0, n)
static bool comp(vector<int> &v1, vector<int> &v2)
{
return (v1[1] < v2[1]);
}
vector<vector<int>> aloc_ary(vector<vector<int>> a, int l, int m, int n)
{
int i, k, o, b;
vector<vector<int>> c(n, vector<int>(n, 0));
for (k = 0, i = l; k < n; k++, i++)
for (o = 0, b = m; o < n; o++, b++)
c[k][o] = a[i][b];
return c;
}
vector<vector<int>> adn_ary(vector<vector<int>> a, vector<vector<int>> b, int n)
{
vector<vector<int>> c(n, vector<int>(n, 0));
int i, j;
ffn(i, n)
ffn(j, n)
c[i][j] = a[i][j] + b[i][j];
return c;
}
vector<vector<int>> sbn_ary(vector<vector<int>> a, vector<vector<int>> b, int n)
{
vector<vector<int>> c(n, vector<int>(n, 0));
int i, j;
ffn(i, n)
ffn(j, n)
c[i][j] = a[i][j] - b[i][j];
return c;
}
void crt(vector<int> g[], int n, int st)
{
queue<int> q;
bool v[n + 1];
memset(v, 0, sizeof(v));
q.push(st);
v[st] = 1;
while (!q.empty())
{
int a = q.front();
q.pop();
ffn(j, g[a].size())
{
if (!v[g[a][j]])
{
q.push(g[a][j]);
v[g[a][j]] = 1;
}
}
}
}
void cbn_ary(vector<vector<int>> &c, vector<vector<int>> c11, int i, int j, int n)
{
int a, b, x, y;
for (a = 0, x = i; a < n; a++, x++)
for (b = 0, y = j; b < n; b++, y++)
c[x][y] = c11[a][b];
}
vector<vector<int>> mul_ary(vector<vector<int>> ary1, vector<vector<int>> ary2, int n)
{
if (n == 1)
{
vector<vector<int>> res(n, vector<int>(n, 0));
res[0][0] = ary1[0][0] * ary2[0][0];
return res;
}
vector<vector<int>> ary1_11 = aloc_ary(ary1, 0, 0, n / 2);
vector<vector<int>> ary1_12 = aloc_ary(ary1, 0, n / 2, n / 2);
vector<vector<int>> ary1_21 = aloc_ary(ary1, n / 2, 0, n / 2);
vector<vector<int>> ary1_22 = aloc_ary(ary1, n / 2, n / 2, n / 2);
vector<vector<int>> ary2_11 = aloc_ary(ary2, 0, 0, n / 2);
vector<vector<int>> ary2_12 = aloc_ary(ary2, 0, n / 2, n / 2);
vector<vector<int>> ary2_21 = aloc_ary(ary2, n / 2, 0, n / 2);
vector<vector<int>> ary2_22 = aloc_ary(ary2, n / 2, n / 2, n / 2);
vector<vector<int>> ary_tmp1 = mul_ary(adn_ary(ary1_11, ary1_22, n / 2), adn_ary(ary2_11, ary2_22, n / 2), n / 2);
vector<vector<int>> ary_tmp2 = mul_ary(adn_ary(ary1_21, ary1_22, n / 2), ary2_11, n / 2);
vector<vector<int>> ary_tmp3 = mul_ary(ary1_11, sbn_ary(ary2_12, ary2_22, n / 2), n / 2);
vector<vector<int>> ary_tmp4 = mul_ary(ary1_22, sbn_ary(ary2_21, ary2_11, n / 2), n / 2);
vector<vector<int>> ary_tmp5 = mul_ary(adn_ary(ary1_11, ary1_12, n / 2), ary2_22, n / 2);
vector<vector<int>> ary_tmp6 = mul_ary(sbn_ary(ary1_21, ary1_11, n / 2), adn_ary(ary2_11, ary2_12, n / 2), n / 2);
vector<vector<int>> ary_tmp7 = mul_ary(sbn_ary(ary1_12, ary1_22, n / 2), adn_ary(ary2_21, ary2_22, n / 2), n / 2);
vector<vector<int>> res11 = adn_ary(sbn_ary(adn_ary(ary_tmp1, ary_tmp4, n / 2), ary_tmp5, n / 2), ary_tmp7, n / 2);
vector<vector<int>> res12 = adn_ary(ary_tmp3, ary_tmp5, n / 2);
vector<vector<int>> res21 = adn_ary(ary_tmp2, ary_tmp4, n / 2);
vector<vector<int>> res22 = adn_ary(sbn_ary(adn_ary(ary_tmp1, ary_tmp3, n / 2), ary_tmp2, n / 2), ary_tmp6, n / 2);
vector<vector<int>> res(n, vector<int>(n, 0));
cbn_ary(res, res11, 0, 0, n / 2);
cbn_ary(res, res12, 0, n / 2, n / 2);
cbn_ary(res, res21, n / 2, 0, n / 2);
cbn_ary(res, res22, n / 2, n / 2, n / 2);
return res;
}
int main()
{
int n, i, j, x;
cin >> n;
if (log2(n) != floor(log2(n)))
x = pow(2, ceil(log2(n)));
else
x = n;
vector<vector<int>> ary1(n, vector<int>(n,0));
vector<vector<int>> ary2(n, vector<int>(n,0));
for (i = 0; i < n; ++i)
for (j = 0; j < n; ++j)
scanf("%d", &ary1[i][j]);
for (i = 0; i < n; ++i)
for (j = 0; j < n; ++j)
scanf("%d", &ary2[i][j]);
vector<vector<int>> mul1(x + 1, vector<int>(x + 1, 0));
vector<vector<int>> mul2(x + 1, vector<int>(x + 1, 0));
for (i = 0; i < n; ++i) {
for (j = 0; j < n; ++j) {
mul1[i][j] = ary1[i][j];
mul2[i][j] = ary2[i][j];
}
}
vector<vector<int>> res_arr(n, vector<int>(n, 0));
res_arr = mul_ary(mul1, mul2, x);
ffn(i, n)
{
ffn(j, n)
cout << res_arr[i][j] << " ";
cout << "\n";
}
return 0;
} |
code/algorithm_applications/src/binary_search/ceil_of_element/ceil_of_element_in_sorted_array.cpp | #include<bits/stdc++.h>
using namespace std;
int solve(int arr[],int n, int ele){
int ans=-1;
int low=0;
int high=n-1;
while(low<=high){
int mid=low+(high-low)/2;
if(ele==arr[mid]){
return ele;
}
else if(ele>arr[mid]){
ans=mid;
high=mid-1;
}
else{
low=mid+1;
}
}
return arr[ans];
}
int main(){
int n;
cin>>n;
int arr[n];
for(int i=0;i<n;i++){
cin>>arr[i];
}
int ele;
cin>>ele;
int ans=solve(arr,n,ele);
cout<<"Ceil of "<<ele<<" is "<<ans<<endl;
return 0;
}
|
code/algorithm_applications/src/binary_search/distributing_candies/CandyDistribution.java | import java.io.BufferedReader;
import java.io.IOException;
import java.io.InputStreamReader;
class CandyDistribution {
public static void main(String[] args) throws IOException {
BufferedReader br = new BufferedReader(new InputStreamReader(System.in));
//testcases
int t = Integer.parseInt(br.readLine());
while (t-- > 0){
String[] input = br.readLine().split(" ");
//n: number of candy boxes
int n = Integer.parseInt(input[0]);
//k: number of friends
long k = Long.parseLong(input[1]);
String[] input1 = br.readLine().split(" ");
long[] arr = new long[n];
long max = Integer.MIN_VALUE;
for(int i=0; i<n; i++){
arr[i] = Long.parseLong(input1[i]);
//we need to find max for the upper bound in the binary search.
max = Math.max(max,arr[i]);
}
System.out.println(binarySearch(n, k, arr, max));
}
}
public static long binarySearch(int n, long k, long[] arr, long max){
long low = 1;
long high = max;
long mid;
long res = 0;
while (high>=low){
mid = low + (high-low)/2;
if(canDistribute(mid, n, k, arr)){
low = mid+1;
res = mid;
}
else{
high = mid-1;
}
}
return res;
}
public static boolean canDistribute(long val, int n, long k, long[] arr){
if(k==1){
return true;
}
long peopleServed = 0;
for (int i=n-1; i>=0; i--){
//this is the number of people who can get candy
peopleServed += arr[i]/val;
}
if(peopleServed>=k){
return true;
}
return false;
}
}
|
code/algorithm_applications/src/binary_search/first_and_last_position_in_sorted_array/firstAndLastPosInSortedArray.cpp | #include <bits/stdc++.h>
using namespace std;
int first(int arr[], int x, int n)
{
int low = 0, high = n - 1, res = -1;
while (low <= high)
{
int mid = (low + high) / 2;
if (arr[mid] > x)
high = mid - 1;
else if (arr[mid] < x)
low = mid + 1;
else
{
res = mid;
high = mid - 1;
}
}
return res;
}
int last(int arr[], int x, int n)
{
int low = 0, high = n - 1, res = -1;
while (low <= high)
{
int mid = (low + high) / 2;
if (arr[mid] > x)
high = mid - 1;
else if (arr[mid] < x)
low = mid + 1;
else
{
res = mid;
low = mid + 1;
}
}
return res;
}
int main()
{
int n;
cin >> n;
int arr[n];
for (int i = 0; i < n; i++)
{
cin >> arr[i];
}
int q;
cin >> q;
while (q--)
{
int a;
cin >> a;
cout << first(arr, a, n) << " " << last(arr, a, n) << endl;
}
} |
code/algorithm_applications/src/binary_search/floor_of_element/floor_of_element_in_sorted_array.cpp | #include<bits/stdc++.h>
using namespace std;
int solve(int arr[],int n,int ele){
int ans=-1;
int low=0;
int high=n-1;
while(low<=high){
int mid=low+(high-low)/2;
if(arr[mid]==ele){
return ele;
}
else if(arr[mid]<ele){
ans=mid;
low=mid+1;
}
else{
high=mid-1;
}
}
return arr[ans];
}
int main(){
int n;
cin>>n;
int arr[n];
for(int i=0;i<n;i++){
cin>>arr[i];
}
int ele;
cin>>ele;
int ans=solve(arr,n,ele);
cout<<"floor of "<<ele<<" is "<<ans<<endl;
return 0;
}
|
code/algorithm_applications/src/bubble_sort/bubble_sort_implementation.cpp | #include<iostream>
using namespace std;
int main() {
int n;
cin >> n;
int arr[n];
for(int i = 0; i < n; i++) {
cin >> arr[i];
}
// Bubble sort algorithm code
int counter = 1;
while(counter < n) {
for(int i = 0; i < n-counter; i++){
if(arr[i] > arr[i+1]) {
// if arr[i] > arr[i+1] then we swap those two element
// because they are not in sorting form
int temp = arr[i];
arr[i] = arr[i+1];
arr[i+1] = temp;
}
}
counter++;
}
cout << '\n';
return 0;
}
|
code/algorithm_applications/src/merge_arrays/merge_two_arrays.c | #include <stdio.h>
int main(){
int m;
printf("Enter the number of elements in first array\n");
scanf("%d",&m);
int arr1[m];
int n;
printf("\nEnter the number of elements in second array\n");
scanf("%d",&n);
int arr2[n];
printf("\nEnter the elements of first array\n");
for(int i=0;i<m;i++){
scanf("%d",&arr1[i]);
}
printf("\nEnter the elements of second array\n");
for(int i=0;i<n;i++){
scanf("%d",&arr2[i]);
}
int arr[m+n];
int i=0;
for(i=0;i<m;i++){
arr[i]=arr1[i];
}
for(int j=0;j<n;j++){
arr[i+j]=arr2[j];
}
printf("\nFirst array is\n");
for(int i=0;i<m;i++){
printf("%d ",arr1[i]);
}
printf("\nSecond array is\n");
for(int i=0;i<n;i++){
printf("%d ",arr2[i]);
}
printf("\nMerged array is\n");
for(int i=0;i<m+n;i++){
printf("%d ",arr[i]);
}
}
|
code/artificial_intelligence/src/Inception_Pre-trained_Model/Inception_model.md | The architecture of the Inception model is heavily engineered and quite complex to understand as it is huge both in terms of depth and width.
The visual representations below make it simpler to understand the entire architecture and thus make it easier to understand the subsequent versions
Learn more: https://iq.opengenus.org/inception-pre-trained-cnn-model/
|
code/artificial_intelligence/src/Inception_Pre-trained_Model/README.md | This article at opengenus about the Inception Pre-Trained cnn model gives an entire overview of the following :
* Inception model
* It's architecture
* The various versions of this model
* Their evolution history
The Inception model , introduced in 2014, is a deep CNN architecture. It has also won the ImageNet Large-Scale Visual Recognition Challenge (ILSVRC14). These networks are useful in computer vision and tasks where convolutional filters are used.
Heretofore, inception has evolved through its four versions namely, **Inception v1** ,**Inception v2 and v3**, **Inception v4 and Resnet**.
These versions have showed iterative improvement over each other which has helped in pushing its speed and accuracy.
All of these, along with their architectures, have been discussed in detail in the above article.
|
code/artificial_intelligence/src/VGG-11/README.md | The article at OpenGenus elaborted about VGG-11, its architecture, number of layers it comprise of along with its application and importnance.
It is a pre-trained model, on a dataset and contains the weights that represent the features of whichever dataset it was trained on.
VGG-11, it attributes to 11 weighted layers, Weights represent the strength of connections between units
in adjacent network layers,are used to connect the each neurons in one layer to the every neurons in the next layer,
weights near zero mean changing this input will not change the output.
VGG incorporates 1x1 convolutional layers to make the decision function more non-linear without changing the receptive fields.
Labeled as deep CNN, VGG surpasses datasets outside of ImageNet.
The architecture of VGG-11 and the ctivation functions insight to different applications and importance whose
detialed account has ben provided.
Learn more: https://iq.opengenus.org/vgg-11/
|
code/artificial_intelligence/src/VGG-11/vgg11.md | 11 Layers f VGG-11 are as follows:
The detialed explanation has been provided in order to get better insight of how the model should be incorporated.
Convolution using 64 filters + Max Pooling
Convolution using 128 filters + Max Pooling
Convolution using 256 filters
Convolution using 256 filters + Max Pooling
Convolution using 512 filters
Convolution using 512 filters + Max Pooling
Convolution using 512 filters
Convolution using 512 filters + Max Pooling
Fully connected with 4096 nodes
Fully connected with 4096 nodes
Output layer with Softmax activation with 1000 nodes.
Note: Here the max-pooling layer that is shown in the image is not counted among the 11 layers of VGG,
the reason for which is listed above. In accordance with the Softmax Activation function, it can be easily verified
from the VGG-11 architecture.
One major conclusion that can be drawn from the whole study is VGG-11 prospers with an appreciable number of layers due to small size
of convolution filter. This accounts for good accuracy of the model and decent performance outcomes
Learn more: https://iq.opengenus.org/vgg-11/
|
code/artificial_intelligence/src/Vaccum_Cleaner/VC.md | This is the code for simple reflex, goal based agent for vaccum cleaner.
|
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