Nateve compiler developed with python.

Overview

Adam

Adam is a Nateve Programming Language compiler developed using Python.

Nateve

Nateve is a new general domain programming language open source inspired by languages like Python, C++, JavaScript, and Wolfram Mathematica.

Nateve is an compiled language. Its first compiler, Adam, is fully built using Python 3.8.

Options of command line (Nateve)

  1. build: Transpile Nateve source code to Python 3.8
  2. run: Run Nateve source code
  3. compile: Compile Nateve source code to an executable file (.exe)
  4. run-init-loop: Run Nateve source code with an initial source and a loop source
  5. set-time-unit: Set Adam time unit to seconds or miliseconds (default: milisecond)
  6. -v: Activate verbose mode

Nateve Tutorial

In this tutorial, we will learn how to use Nateve step by step.

Step 1: Create a new Nateve project

$ cd my-project
$ COPY CON main.nateve

Hello World program

print("Hello, World!")

Is prime? program

def is_prime(n) {
    if n == 1 {
        return False
    }
    for i in range(2, n) {
        if n % i == 0 {
            return False
        }
    }
    return True
}

n = intput("Enter a number: ")

if is_prime(n) {
    print("It is a prime number.")
}
else {
    print("It is not a prime number.")
}

Comments

If you want to comment your code, you can use:

~ This is a single line comment ~

~
    And this a multiline comment
~

Under construction...

Let Statements

This language does not use variables. Instead of variables, you can only declare static values.

For declaring a value, you must use let and give it a value. For example:

let a = 1        -- Interger
let b = 1.0      -- Float
let c = "string" -- String
let d = true     -- Boolean
let e = [1,2,3]  -- List
let f = (1,2)    -- Tuple
...             

SigmaF allows data type as Integer, Float, Boolean, and String.

Lists

The Lists allow to use all the data types before mentioned, as well as lists and functions.

Also, they allow to get an item through the next notation:

let value_list = [1,2,3,4,5,6,7,8,9]
value_list[0]       -- Output: 1
value_list[0, 4]    -- Output: [1,2,3,4]
value_list[0, 8, 2] -- Output: [1, 3, 5, 7]

The struct of List CAll is example_list[<Start>, <End>, <Jump>]

Tuples

The tuples are data structs of length greater than 1. Unlike lists, they allow the following operations:

(1,2) + (3,4)      -- Output: (4,6)
(4,6,8) - (3,4,5)  -- Output: (1,2,3)
(0,1) == (0,1)     -- Output: true
(0,1) != (1,3)     -- Output: true

To obtain the values of a tuple, you must use the same notation of the list. But this data structure does not allow ranges like the lists (only you can get one position of a tuple).

E.g.

let t = (1,2,3,4,5,6)
t[1] -- Output: 2
t[5] -- Output: 6

And so on.

Operators

Warning: SigmaF have Static Typing, so it does not allow the operation between different data types.

These are operators:

Operator Symbol
Plus +
Minus -
Multiplication *
Division /
Modulus %
Exponential **
Equal ==
Not Equal !=
Less than <
Greater than >
Less or equal than <=
Greater or equal than >=
And &&
Or ||

The operator of negation for Boolean was not included. You can use the not() function in order to do this.

Functions

For declaring a function, you have to use the next syntax:

let example_function = fn <Name Argument>::<Argument Type> -> <Output Type> {
    => <Return Value>
}  

(For return, you have to use the => symbol)

For example:

let is_prime_number = fn x::int, i::int -> bool {
    if x <= 1 then {=> false;}
    if x == i then {=> true;}
    if (x % i) == 0 then {=> false;}
    => is_prime_number(x, i+1);
}

printLn(is_prime_number(11, 2)) -- Output: true

Conditionals

Regarding the conditionals, the syntax structure is:

if <Condition> then {
    <Consequence>
}
else{
    <Other Consequence>
}

For example:

if x <= 1 || x % i == 0 then {
    false;
}
if x == i then {
    true;
}
else {
    false;
}

Some Examples

-- Quick Sort
let qsort = fn l::list -> list {

	if (l == []) then {=> [];}
	else {
		let p = l[0];
		let xs = tail(l);
		
		let c_lesser = fn q::int -> bool {=> (q < p)}
		let c_greater = fn q::int -> bool {=> (q >= p)}

		=> qsort(filter(c_lesser, xs)) + [p] + qsort(filter(c_greater, xs));
	}
}

-- Filter
let filter = fn c::function, l::list -> list {
	if (l == []) then {=> [];} 

    => if (c(l[0])) then {[l[0]]} else {[]} +  filter(c, tail(l));
}

-- Map
let map = fn f::function, l::list -> list {
	if (l==[]) then {=> [];}
	
	=> [f(l[0])] + map(f, tail(l));
}

To know other examples of the implementations, you can go to e.g.


Feedback

I would really appreciatte your feedback. You can submit a new issue, or reach out me on Twitter.

Contribute

This is an opensource project, everyone can contribute and become a member of the community of SigmaF.

Why be a member of the SigmaF community?

1. A simple and understandable code

The source code of the interpreter is made with Python 3.8, a language easy to learn, also good practices are a priority for this project.

2. A great potencial

This project has a great potential to be the next programming language of the functional paradigm, to development the AI, and to development new metaheuristics.

3. Scalable development

One of the mains approaches of this project is the implementation of TDD from the beggining and the development of new features, which allows scalability.

4. Simple and power

One of the main purposes of this programming language is to create an easy-to-learn functional language, which at the same time is capable of processing large amounts of data safely and allows concurrence and parallelism.

5. Respect for diversity

Everybody is welcome, it does not matter your genre, experience or nationality. Anyone with enthusiasm can be part of this project. Anyone from the most expert to the that is beginning to learn about programming, marketing, design, or any career.

How to start contributing?

There are multiply ways to contribute, since sharing this project, improving the brand of SigmaF, helping to solve the bugs or developing new features and making improves to the source code.

  • Share this project: You can put your star in the repository, or talk about this project. You can use the hashtag #SigmaF in Twitter, LinkedIn or any social network too.

  • Improve the brand of SigmaF: If you are a marketer, designer or writer, and you want to help, you are welcome. You can contact me on Twitter like @fabianmativeal if you are interested on doing it.

  • Help to solve the bugs: if you find one bug notify me an issue. On this we can all improve this language.

  • Developing new features: If you want to develop new features or making improvements to the project, you can do a fork to the dev branch (here are the ultimate develops) working there, and later do a pull request to dev branch in order to update SigmaF.

You might also like...
Web mining module for Python, with tools for scraping, natural language processing, machine learning, network analysis and visualization.
Web mining module for Python, with tools for scraping, natural language processing, machine learning, network analysis and visualization.

Pattern Pattern is a web mining module for Python. It has tools for: Data Mining: web services (Google, Twitter, Wikipedia), web crawler, HTML DOM par

A python framework to transform natural language questions to queries in a database query language.

__ _ _ _ ___ _ __ _ _ / _` | | | |/ _ \ '_ \| | | | | (_| | |_| | __/ |_) | |_| | \__, |\__,_|\___| .__/ \__, | |_| |_| |___/

Python library for processing Chinese text

SnowNLP: Simplified Chinese Text Processing SnowNLP是一个python写的类库,可以方便的处理中文文本内容,是受到了TextBlob的启发而写的,由于现在大部分的自然语言处理库基本都是针对英文的,于是写了一个方便处理中文的类库,并且和TextBlob

A Python package implementing a new model for text classification with visualization tools for Explainable AI :octocat:
A Python package implementing a new model for text classification with visualization tools for Explainable AI :octocat:

A Python package implementing a new model for text classification with visualization tools for Explainable AI 🍣 Online live demos: http://tworld.io/s

Python bindings to the dutch NLP tool Frog (pos tagger, lemmatiser, NER tagger, morphological analysis, shallow parser, dependency parser)

Frog for Python This is a Python binding to the Natural Language Processing suite Frog. Frog is intended for Dutch and performs part-of-speech tagging

A python wrapper around the ZPar parser for English.

NOTE This project is no longer under active development since there are now really nice pure Python parsers such as Stanza and Spacy. The repository w

💫 Industrial-strength Natural Language Processing (NLP) in Python

spaCy: Industrial-strength NLP spaCy is a library for advanced Natural Language Processing in Python and Cython. It's built on the very latest researc

Python interface for converting Penn Treebank trees to Stanford Dependencies and Universal Depenencies

PyStanfordDependencies Python interface for converting Penn Treebank trees to Universal Dependencies and Stanford Dependencies. Example usage Start by

Comments
  • [Enhancement] Nateve Vectors don't allow non-numeric datatypes

    [Enhancement] Nateve Vectors don't allow non-numeric datatypes

    Vectors just allow to use numbers (int/float) into them, because Vectors are redifinening Python Built-in lists in the middle code generation process. A possible solution is to join Vectors and Matrices into a Linear datatypes with the syntax opener tag "$", and the to make independent the python lists

    opened by eanorambuena 0
  • [Bug] Double execution of the modules in assembling process

    [Bug] Double execution of the modules in assembling process

    We need to resolve the double execution of the modules in assembling process.

    The last Non Double Execution Patch has been deprecated because it did generate bugs of type: - Code segmentation in the driver_file

    bug help wanted 
    opened by eanorambuena 0
Releases(0.0.3)
Owner
Nateve
Repositories related to the Nateve Programming Language
Nateve
Scikit-learn style model finetuning for NLP

Scikit-learn style model finetuning for NLP Finetune is a library that allows users to leverage state-of-the-art pretrained NLP models for a wide vari

indico 665 Dec 17, 2022
Extract Keywords from sentence or Replace keywords in sentences.

FlashText This module can be used to replace keywords in sentences or extract keywords from sentences. It is based on the FlashText algorithm. Install

Vikash Singh 5.3k Jan 01, 2023
Product-Review-Summarizer - Created a product review summarizer which clustered thousands of product reviews and summarized them into a maximum of 500 characters, saving precious time of customers and helping them make a wise buying decision.

Product-Review-Summarizer - Created a product review summarizer which clustered thousands of product reviews and summarized them into a maximum of 500 characters, saving precious time of customers an

Parv Bhatt 1 Jan 01, 2022
Code for paper "Role-oriented Network Embedding Based on Adversarial Learning between Higher-order and Local Features"

Role-oriented Network Embedding Based on Adversarial Learning between Higher-order and Local Features Train python main.py --dataset brazil-flights C

wang zhang 0 Jun 28, 2022
topic modeling on unstructured data in Space news articles retrieved from the Guardian (UK) newspaper using API

NLP Space News Topic Modeling Photos by nasa.gov (1, 2, 3, 4, 5) and extremetech.com Table of Contents Project Idea Data acquisition Primary data sour

edesz 1 Jan 03, 2022
Get list of common stop words in various languages in Python

Python Stop Words Table of contents Overview Available languages Installation Basic usage Python compatibility Overview Get list of common stop words

Alireza Savand 142 Dec 21, 2022
A Python script which randomly chooses and prints a file from a directory.

___ ____ ____ _ __ ___ / _ \ | _ \ | _ \ ___ _ __ | '__| / _ \ | |_| || | | || | | | / _ \| '__| | | | __/ | _ || |_| || |_| || __

yesmaybenookay 0 Aug 06, 2021
A python script to prefab your scripts/text files, and re create them with ease and not have to open your browser to copy code or write code yourself

Scriptfab - What is it? A python script to prefab your scripts/text files, and re create them with ease and not have to open your browser to copy code

DevNugget 3 Jul 28, 2021
Applied Natural Language Processing in the Enterprise - An O'Reilly Media Publication

Applied Natural Language Processing in the Enterprise This is the companion repo for Applied Natural Language Processing in the Enterprise, an O'Reill

Applied Natural Language Processing in the Enterprise 95 Jan 05, 2023
Tutorial to pretrain & fine-tune a 🤗 Flax T5 model on a TPUv3-8 with GCP

Pretrain and Fine-tune a T5 model with Flax on GCP This tutorial details how pretrain and fine-tune a FlaxT5 model from HuggingFace using a TPU VM ava

Gabriele Sarti 41 Nov 18, 2022
Code Implementation of "Learning Span-Level Interactions for Aspect Sentiment Triplet Extraction".

Span-ASTE: Learning Span-Level Interactions for Aspect Sentiment Triplet Extraction ***** New March 31th, 2022: Scikit-Style API for Easy Usage *****

Chia Yew Ken 111 Dec 23, 2022
Generating new names based on trends in data using GPT2 (Transformer network)

MLOpsNameGenerator Overall Goal The goal of the project is to develop a model that is capable of creating Pokémon names based on its description, usin

Gustav Lang Moesmand 2 Jan 10, 2022
IMS-Toucan is a toolkit to train state-of-the-art Speech Synthesis models

IMS-Toucan is a toolkit to train state-of-the-art Speech Synthesis models. Everything is pure Python and PyTorch based to keep it as simple and beginner-friendly, yet powerful as possible.

Digital Phonetics at the University of Stuttgart 247 Jan 05, 2023
C.J. Hutto 3.8k Dec 30, 2022
ttslearn: Library for Pythonで学ぶ音声合成 (Text-to-speech with Python)

ttslearn: Library for Pythonで学ぶ音声合成 (Text-to-speech with Python) 日本語は以下に続きます (Japanese follows) English: This book is written in Japanese and primaril

Ryuichi Yamamoto 189 Dec 29, 2022
PORORO: Platform Of neuRal mOdels for natuRal language prOcessing

PORORO: Platform Of neuRal mOdels for natuRal language prOcessing pororo performs Natural Language Processing and Speech-related tasks. It is easy to

Kakao Brain 1.2k Dec 21, 2022
Creating an Audiobook (mp3 file) using a Ebook (epub) using BeautifulSoup and Google Text to Speech

epub2audiobook Creating an Audiobook (mp3 file) using a Ebook (epub) using BeautifulSoup and Google Text to Speech Input examples qual a pasta do seu

7 Aug 25, 2022
text to speech toolkit. 好用的中文语音合成工具箱,包含语音编码器、语音合成器、声码器和可视化模块。

ttskit Text To Speech Toolkit: 语音合成工具箱。 安装 pip install -U ttskit 注意 可能需另外安装的依赖包:torch,版本要求torch=1.6.0,=1.7.1,根据自己的实际环境安装合适cuda或cpu版本的torch。 ttskit的

KDD 483 Jan 04, 2023
Final Project for the Intel AI Readiness Boot Camp NLP (Jan)

NLP Boot Camp (Jan) Synopsis Full Name: Prameya Mohanty Name of your School: Delhi Public School, Rourkela Class: VIII Title of the Project: iTransect

TheCodingHub 1 Feb 01, 2022
BMInf (Big Model Inference) is a low-resource inference package for large-scale pretrained language models (PLMs).

BMInf (Big Model Inference) is a low-resource inference package for large-scale pretrained language models (PLMs).

OpenBMB 377 Jan 02, 2023