Showing results for Program Background Vector Vector
GitHub Repo
https://github.com/Awwwsboo/unix-shell-c
Awwwsboo/unix-shell-c
- Constructed a Shell program in C from scratch with fork-exec-wait pattern with self-implemented string class and C++ vector class. - Implemented Linux built-in shell commands in C: cd, !history, #<n>, ps, etc. - Programmed features: Signal Handlers, Background Processes, Redirections, Logical Operator, Command History, Optional Arguments.
GitHub Repo
https://github.com/Existawk/4309-Machine-Learning
Existawk/4309-Machine-Learning
This course offers an introduction to machine learning. Topics include naive Bayes classifiers, linear regression, linear classificiers, neural networks and backpropagation, kernel methods, decision trees, feature selection, clustering, and reinforcement learning. A strong programming background is assumed, as well as familiarity with linear algebra (vector and matrix operations), and knowledge of basic probability theory and statistics.
GitHub Repo
https://github.com/pianocry/UNIX-Shell-EE4374
pianocry/UNIX-Shell-EE4374
Unix Shell: You will develop a shell. A shell is a program that interprets commands and acts as an intermediary between the user and the operating system. There are several Unix shells available for you to use, some of them are: 1. Korn shell (ksh) 2. TC shell (tcs) 3. Bourne-again shell (bash) You will make use of the following system calls to develop a shell: a. fork(): int fork(); b. execvp(): int execvp( const char *file, char *const argv[]); c. wait() or waitpid(): int wait(status); d. close(): int close(file_descriptor); e. open():int open(const char *path, int flags); and int open(const char *path, int flags, mode_t modes); f. dup2(): int dup2(int filedes, int filedes2); g. read(): int read(file descriptor, buffer, n_to_read); h. write(): int write(file descriptor, buffer, n_to_write); To use these system calls, your program might include the following libraries: • #include <fcntl.h> • #include <sys/types.h> • #include <unistd.h> Tasks: 1) Unpack the Assignment 2 template provided by the instructor into your home directory: ‘tar zxvf student_prog2.tgz’. This will create a directory called ‘student_prog2’, please rename this directory to firstinitiallastname_prog2 using the ‘mv’ command. For example, the instructor would rename the directory by executing ‘mv student_prog2 mmcgarry_prog2’. You should replace student_argtok.c and student_argtok.h with your corresponding files from Assignment 1. Next, go into the directory and rename all of the files from ‘student_*’ to ‘firstinitiallastname_*’ much like you renamed the directory. 2) Write a main() function that prints a prompt, accepts input, tokenizes the input using the argument tokenizer from Assignment 1, and passes the argument vector to an executeCmd() function with the following prototype: int executeCmd(char **args); This function returns -1 on error, otherwise 0. Your shell should exit when an ‘x’ is entered by itself at the prompt. Your main() function will be in the file named firstinitiallastname_prog2.c and the executeCmd() function and any supporting functions will be in a file named firstinitiallastname_exec.c and the function prototype for executeCmd() should be in a file named firstinitiallastname_exec.h. To help the students, the main() function has been provided by the instructor in the Assignment 2 template. 3) Write an executeCmd() function that can execute any program in the foreground or background as well as redirect the output of any program to a file. You can write any number of functions to support executeCmd(). More specifically, executeCmd() should be able to: 2.1) Execute any program with any number of arguments, below are some examples • ls: lists your files in your current directory (format: $ ls) • ls -l: lists your files in long format (format: $ ls -l) • ls -a: lists all files (format: $ ls -a) • pwd: displays current working directory (format: $ pwd) • wc: displays the number of lines, words, and characters in a file (format: $wc filename) • mkdir: make a new directory (format: $mkdir dirName) • cat: creates, concatenates and displays text files 2.2) Same as 2.1 but the program should be run in the background, i.e., the shell will not wait for completion before displaying the prompt again. • &: runs job in background (format: $ cat prog1.c &) • Note: The instructor has provided a function in student_exec.c called execBackground() that can be used to determine when a job should run in the background. Additionally, that function will strip the ‘&’ character from the argument vector. 2.3) Allow the output of a running program to be redirected to a file • >: redirects standard output to the filename after > (format: $ grep if prog1.c > foo) 4) Use a Makefile to build your program. The Makefile provided in the Assignment 2 template only has a ‘clean’ target. You must add the other targets. 5) Submit the deliverables, indicated below, as a single tarball file named firstinitiallastname_prog2.tgz through Blackboard
GitHub Repo
https://github.com/fluxion9/A-GENTLE-INTRODUCTION-TO-QUANTUM-COMPUTING-
fluxion9/A-GENTLE-INTRODUCTION-TO-QUANTUM-COMPUTING-
This is a repository that gently introduces Quantum Computing to anyone with no background in math, physics and computer science. This repo assumes no knowledge at all about Quantum Mechanics and would start by introducing the basic concepts of linear algebra, Basic Probability theories, Vector spaces and Hilbert spaces, Matrix Operations and Matrix Mechanics, Introduction to the postulates of Quantum Mechanics, Spintronics and Angular Momentum, Classical logic gates operations and Concepts of gate-based Quantum Programming using QISKIT. This repo would also feature relevant articles, Research papers, podcasts, textbook references, linkdln posts, youtube links and blog posts in areas where they would accelerate your understanding of these Quantum Computing. Also, it will discuss the various use cases and Applications of Quantum Technology. So right now, let us start with the history of Quantum Mechanics :)
GitHub Repo
https://github.com/DimaOshchepkov/vyatsu_applicant_bot
DimaOshchepkov/vyatsu_applicant_bot
Telegram bot to support VyatSU applicants. Answers frequently asked questions, provides information about programs and exams. Built with aiogram 3, clean architecture, vector search, and background task support.
GitHub Repo
https://github.com/KimmoPyyhtia/XRS_spectra_classifier
KimmoPyyhtia/XRS_spectra_classifier
My Master's thesis code and data. This program creates a training set for XRS-spectra and uses a Support Vector Machine and a Neural Network to classify the spectra that have scattered from the sample instead of the background.
GitHub Repo
https://github.com/ahmetcik/ML-and-LA-from-scratch
ahmetcik/ML-and-LA-from-scratch
Some machine learning (ML) methods plus related numerical linear algebra (LA) and also quadratic programming algorithms to solve the ML optimization problems, i.e. no implementation from external libraries is used except for numpy arrays and basic numpy operations on arrays, such as algebraic operations, matrix multiplication, etc. The algorithms are described in theoretical-background.pdf. Note that, neither the implementations are optimized nor are the chosen (especially LA) algorithms to solve the ML problems optimal. The considered LA algorithms are: QR-decomposition based on Gram-Schmidt proces and Housholder reflections, QR algorithm to determine eigenvalues (and vectors for symmetric matrices), singular-value decompostion, Cholesky decomposition, and forward and backward substitution. Quadratic programming is performed via the primal-dual interior-point method. The included machine-learning methods are: linear (least-squares and ridge) regression, non-negative least squares regression, orthogonal matching pursuit, kernel ridge regression, support vector machines, logistic regression, and principal component analysis.
GitHub Repo
https://github.com/MyUsernamee/vector-fields
MyUsernamee/vector-fields
A simple background program
GitHub Repo
https://github.com/TheShubhamArya/Machine-Learning
TheShubhamArya/Machine-Learning
This is for my Machine Learning (CSE 4309) course at UTA for Fall 2021. This course offers an introduction to machine learning. Topics include Bayes classifiers, linear regression, neural networks and backpropagation, decision trees, clustering, and reinforcement learning. A strong programming background is assumed, as well as familiarity with linear algebra (vector and matrix operations), and knowledge of basic probability theory and statistics. This repository contains the assignments I had to do through this course.
GitHub Repo
https://github.com/FilitsaK/Master-Thesis-2019-Durham-Univesity