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
💛 Code and Dataset for our EMNLP 2021 paper: "Perspective-taking and Pragmatics for Generating Empathetic Responses Focused on Emotion Causes"

Perspective-taking and Pragmatics for Generating Empathetic Responses Focused on Emotion Causes Official PyTorch implementation and EmoCause evaluatio

Hyunwoo Kim 50 Dec 21, 2022
APEACH: Attacking Pejorative Expressions with Analysis on Crowd-generated Hate Speech Evaluation Datasets

APEACH - Korean Hate Speech Evaluation Datasets APEACH is the first crowd-generated Korean evaluation dataset for hate speech detection. Sentences of

Kevin-Yang 70 Dec 06, 2022
Différents programmes créant une interface graphique a l'aide de Tkinter pour simplifier la vie des étudiants.

GP211-Grand-Projet Ce repertoire contient tout les programmes nécessaires au bon fonctionnement de notre projet-logiciel. Cette interface graphique es

1 Dec 21, 2021
NL. The natural language programming language.

NL A Natural-Language programming language. Built using Codex. A few examples are inside the nl_projects directory. How it works Write any code in pur

2 Jan 17, 2022
This is the main repository of open-sourced speech technology by Huawei Noah's Ark Lab.

Speech-Backbones This is the main repository of open-sourced speech technology by Huawei Noah's Ark Lab. Grad-TTS Official implementation of the Grad-

HUAWEI Noah's Ark Lab 295 Jan 07, 2023
DiffSinger: Singing Voice Synthesis via Shallow Diffusion Mechanism (SVS & TTS); AAAI 2022

DiffSinger: Singing Voice Synthesis via Shallow Diffusion Mechanism This repository is the official PyTorch implementation of our AAAI-2022 paper, in

Jinglin Liu 829 Jan 07, 2023
中文医疗信息处理基准CBLUE: A Chinese Biomedical LanguageUnderstanding Evaluation Benchmark

English | 中文说明 CBLUE AI (Artificial Intelligence) is playing an indispensabe role in the biomedical field, helping improve medical technology. For fur

452 Dec 30, 2022
Twitter-Sentiment-Analysis - Twitter sentiment analysis for india's top online retailers(2019 to 2022)

Twitter-Sentiment-Analysis Twitter sentiment analysis for india's top online retailers(2019 to 2022) Project Overview : Sentiment Analysis helps us to

Balaji R 1 Jan 01, 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
基于GRU网络的句子判断程序/A program based on GRU network for judging sentences

SentencesJudger SentencesJudger 是一个基于GRU神经网络的句子判断程序,基本的功能是判断文章中的某一句话是否为一个优美的句子。 English 如何使用SentencesJudger 确认Python运行环境 安装pyTorch与LTP python3 -m pip

8 Mar 24, 2022
Fake news detector filters - Smart filter project allow to classify the quality of information and web pages

fake-news-detector-1.0 Lists, lists and more lists... Spam filter list, quality keyword list, stoplist list, top-domains urls list, news agencies webs

Memo Sim 1 Jan 04, 2022
Finally, some decent sample sentences

tts-dataset-prompts This repository aims to be a decent set of sentences for people looking to clone their own voices (e.g. using Tacotron 2). Each se

hecko 19 Dec 13, 2022
🤗🖼️ HuggingPics: Fine-tune Vision Transformers for anything using images found on the web.

🤗 🖼️ HuggingPics Fine-tune Vision Transformers for anything using images found on the web. Check out the video below for a walkthrough of this proje

Nathan Raw 185 Dec 21, 2022
Maix Speech AI lib, including ASR, chat, TTS etc.

Maix-Speech 中文 | English Brief Now only support Chinese, See 中文 Build Clone code by: git clone https://github.com/sipeed/Maix-Speech Compile x86x64 c

Sipeed 267 Dec 25, 2022
Convolutional 2D Knowledge Graph Embeddings resources

ConvE Convolutional 2D Knowledge Graph Embeddings resources. Paper: Convolutional 2D Knowledge Graph Embeddings Used in the paper, but do not use thes

Tim Dettmers 586 Dec 24, 2022
Practical Natural Language Processing Tools for Humans is build on the top of Senna Natural Language Processing (NLP)

Practical Natural Language Processing Tools for Humans is build on the top of Senna Natural Language Processing (NLP) predictions: part-of-speech (POS) tags, chunking (CHK), name entity recognition (

jawahar 20 Apr 30, 2022
A workshop with several modules to help learn Feast, an open-source feature store

Workshop: Learning Feast This workshop aims to teach users about Feast, an open-source feature store. We explain concepts & best practices by example,

Feast 52 Jan 05, 2023
An open collection of annotated voices in Japanese language

声庭 (Koniwa): オープンな日本語音声とアノテーションのコレクション Koniwa (声庭): An open collection of annotated voices in Japanese language 概要 Koniwa(声庭)は利用・修正・再配布が自由でオープンな音声とアノテ

Koniwa project 32 Dec 14, 2022
Perform sentiment analysis on textual data that people generally post on websites like social networks and movie review sites.

Sentiment Analyzer The goal of this project is to perform sentiment analysis on textual data that people generally post on websites like social networ

Madhusudan.C.S 53 Mar 01, 2022
Code to reprudece NeurIPS paper: Accelerated Sparse Neural Training: A Provable and Efficient Method to Find N:M Transposable Masks

Accelerated Sparse Neural Training: A Provable and Efficient Method to FindN:M Transposable Masks Recently, researchers proposed pruning deep neural n

itay hubara 4 Feb 23, 2022