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
A PyTorch implementation of VIOLET

VIOLET: End-to-End Video-Language Transformers with Masked Visual-token Modeling A PyTorch implementation of VIOLET Overview VIOLET is an implementati

Tsu-Jui Fu 119 Dec 30, 2022
Treemap visualisation of Maya scene files

Ever wondered which nodes are responsible for that 600 mb+ Maya scene file? Features Fast, resizable UI Parsing at 50 mb/sec Dependency-free, single-f

Marcus Ottosson 76 Nov 12, 2022
Task-based datasets, preprocessing, and evaluation for sequence models.

SeqIO: Task-based datasets, preprocessing, and evaluation for sequence models. SeqIO is a library for processing sequential data to be fed into downst

Google 290 Dec 26, 2022
DomainWordsDict, Chinese words dict that contains more than 68 domains, which can be used as text classification、knowledge enhance task

DomainWordsDict, Chinese words dict that contains more than 68 domains, which can be used as text classification、knowledge enhance task。涵盖68个领域、共计916万词的专业词典知识库,可用于文本分类、知识增强、领域词汇库扩充等自然语言处理应用。

liuhuanyong 357 Dec 24, 2022
BERN2: an advanced neural biomedical namedentity recognition and normalization tool

BERN2 We present BERN2 (Advanced Biomedical Entity Recognition and Normalization), a tool that improves the previous neural network-based NER tool by

DMIS Laboratory - Korea University 99 Jan 06, 2023
SurvTRACE: Transformers for Survival Analysis with Competing Events

⭐ SurvTRACE: Transformers for Survival Analysis with Competing Events This repo provides the implementation of SurvTRACE for survival analysis. It is

Zifeng 13 Oct 06, 2022
C.J. Hutto 3.8k Dec 30, 2022
LUKE -- Language Understanding with Knowledge-based Embeddings

LUKE (Language Understanding with Knowledge-based Embeddings) is a new pre-trained contextualized representation of words and entities based on transf

Studio Ousia 587 Dec 30, 2022
This is the offline-training-pipeline for our project.

offline-training-pipeline This is the offline-training-pipeline for our project. We adopt the offline training and online prediction Machine Learning

0 Apr 22, 2022
Autoregressive Entity Retrieval

The GENRE (Generative ENtity REtrieval) system as presented in Autoregressive Entity Retrieval implemented in pytorch. @inproceedings{decao2020autoreg

Meta Research 611 Dec 16, 2022
Natural language computational chemistry command line interface.

nlcc Install pip install nlcc Must have Open-AI Codex key: export OPENAI_API_KEY=your key here then nlcc key bindings ctrl-w copy to clipboard (Note

Andrew White 37 Dec 14, 2022
Unet-TTS: Improving Unseen Speaker and Style Transfer in One-shot Voice Cloning

Unet-TTS: Improving Unseen Speaker and Style Transfer in One-shot Voice Cloning English | 中文 ❗ Now we provide inferencing code and pre-training models

164 Jan 02, 2023
A simple command line tool for text to image generation, using OpenAI's CLIP and a BigGAN

artificial intelligence cosmic love and attention fire in the sky a pyramid made of ice a lonely house in the woods marriage in the mountains lantern

Phil Wang 2.3k Jan 01, 2023
Machine translation models released by the Gourmet project

Gourmet Models Overview The Gourmet project has released several machine translation models to translate low-resource languages. This repository conta

Edinburgh NLP 5 Dec 08, 2021
A python script that will use hydra to get user and password to login to ssh, ftp, and telnet

Hydra-Auto-Hack A python script that will use hydra to get user and password to login to ssh, ftp, and telnet Project Description This python script w

2 Jan 16, 2022
基于百度的语音识别,用python实现,pyaudio+pyqt

Speech-recognition 基于百度的语音识别,python3.8(conda)+pyaudio+pyqt+baidu-aip 百度有面向python

J-L 1 Jan 03, 2022
Create a machine learning model which will predict if the mortgage will be approved or not based on 5 variables

Mortgage-Application-Analysis Create a machine learning model which will predict if the mortgage will be approved or not based on 5 variables: age, in

1 Jan 29, 2022
A Pytorch implementation of "Splitter: Learning Node Representations that Capture Multiple Social Contexts" (WWW 2019).

Splitter ⠀⠀ A PyTorch implementation of Splitter: Learning Node Representations that Capture Multiple Social Contexts (WWW 2019). Abstract Recent inte

Benedek Rozemberczki 201 Nov 09, 2022
Stand-alone language identification system

langid.py readme Introduction langid.py is a standalone Language Identification (LangID) tool. The design principles are as follows: Fast Pre-trained

2k Jan 04, 2023
Use the power of GPT3 to execute any function inside your programs just by giving some doctests

gptrun Don't feel like coding today? Use the power of GPT3 to execute any function inside your programs just by giving some doctests. How is this diff

Roberto Abdelkader Martínez Pérez 11 Nov 11, 2022