openmtpk 0.9.7
Overview
openMTPK is an open-source (intended) mathematics package written in C++ with a primary
focus on Numbery Theory and Cryptographic algorithms, Linear Algebra, and Machine/Deep learning concepts
as well as a range of language API's. openMTPK aims to provide options for pre-built functions, models, etc.
along with modularity for user freedom.
Look in the samples folder for examples
on how to use some of openMTPK's functionalities.
Installation
openMTPK C++ & Python is tested on the following:
OS
Architecture
Status
OSX Monterey 12.6.3
x86
RasPi OS
ARMv6/v7
bullseye 11.6
ARMv6
ubuntu 22.10
ARMv7
ubuntu 22.10
ARMv8
ubuntu 22.10
RISCV64
ubuntu 22.10
S390X
ubuntu 22.10
PPC64LE
Note Testing on Apple specific hardware (M1, M2) is in progress.
Python
To install the Python interface, use the pip package manager and run the following, pip install openmtpk. Additional hardware support is available with SWIG as a dependency for the pip
installation.
Build from source
Linux/OSX
CMake >=v3.24 (build from source for latest version)
C++20
g++12
# clone repo
$ git clone git@github.com:akielaries/openMTPK.git
$ cd openMTPK
# create build dir
$ mkdir build && cd build
# create necessary objects and static library
$ cmake -S ..
$ make
# install necessary headers and library in correct directories
$ sudo make install
Note
Keep the build directory for easy uninstallation. This process asumes your
STDLIB path is /usr/local/lib, where most 3rd-party libs are located if not,
run the following:
$ LD_LIBRARY_PATH=/usr/local/lib
To test the installation build some of the example drivers in the projects
samples directory.
# compile yourself
$ cd samples
$ g++ cipher.cpp -lopenMTPK -o cipher
$ g++ arith.cpp -lopenMTPK -o arith
# script to test all modules and their drivers
# using the projects root makefile
$ cd scripts && ./all.sh
To uninstall files related to openMTPK, simply run the following:
# enter the build dir from installation
$ cd build
$ sudo make uninstall
Modules
During early stages, modules will be developed in breadth while focusing on depth
in later stages of the PRE-V1.0.0 phase. The modules below are all in progress.
Arithmetic
Calculus
Differential
Linear Algebra
Vector Operations
Matrix Operations
Machine/Deep Learning
Regression
Cross-Validation
K-Nearest Neighbors
Neural Networks
Classifiers
Number Theory
Primes
Cryptography
Topology/Complex
Dynamical Systems
Topology
Spline
For more details view the project documentation.
Examples
View the simple examples on how to use some of the modules in different languages here.
# clone the repo and enter
$ git clone git@github.com:akielaries/openMTPK.git
$ cd openMTPK
# to run all examples
$ ./all.sh
# to remove the generated binaries
$ make clean-mods
# run unit tests and other checking methods
$ make run-tests
# clean up generated test files
$ make clean-tests
Example C++ driver file for running Caesar Cipher & Mono-Alphabetic Substitution
Keyword cipher:
#include <iostream>
#include <string>
// include the number theory module header
#include <openMTPK/number_theory.hpp>
int main() {
// declare CIPHER class obj
mtpk::Cipher cc;
/* CAESAR CIPHER */
std::string text0 = "Plaintext";
int shift_key_0 = 5;
std::string hashtext_0 = cc.caesar(text0, shift_key_0);
std::cout << "Hashtext0 = " << hashtext_0 << std::endl;
/* TESTING MONOALPHABETIC SUBSTITUION KEYWORD CIPHER */
std::string shift_key_2 = "Computer";
std::string text2 = "Password";
// encode the plaintext
std::string encoded_text = cc.keyword_encode(shift_key_2);
// call the cipher function
std::string hashtext_2 = cc.keyword(text2 , encoded_text);
std::cout << "Hashtext2 = " << hashtext_2 << std::endl;
return 0;
}
A Python example showing the same functionalities.
#!/usr/bin/python3
# import the Number Theory module
from openmtpk import nt
c = Cipher()
ciphertext_0 = c.caesar('Plaintext', 5)
print(ciphertext_0)
ciphertext_1 = c.caesar('ATTACKATONCE', 4)
print(ciphertext_1)
text = "Password"
shift = "Computer"
encoded_text = c.keyword_encode(shift);
hashtext = c.keyword(text, encoded_text);
print(hashtext)
For personal and professional use. You cannot resell or redistribute these repositories in their original state.
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