a simple proof system I made to learn math without any mistakes

Overview

math_up

a simple proof system I made to learn math without any mistakes




0. Short Introduction


test yourself, enjoy your math!

math_up is an NBG-based, very simple but programmable proof verifier written in Python.
Some notable features are:

  • super-simplicity, short learning time
  • programmability
  • fully written in Python (including all the proofs!)
  • trade-off between runtime efficiency & reductionism
  • non-generic implementation (supports only 1st-order logic, NBG set theory)
  • supports on Fitch-style proofs, "let" variables and many other intuitive APIs
  • rule-based, without any AI-like things

The following sections assumes you already know basic concepts on logic and set theory.

Author : Hyunwoo Yang
  • Department of Mathematical Sciences, Seoul National University (2013 ~ 2019)
  • Modem R&D Group, Samsung Networks (2019 ~ )

1. Sentence Generation


1-1. Variables

You may use alphabets from a to z, and also from A to Z.
Just calling clear() assures all the variables are new:

clear() # clear all alphabets

1-2. Atomic Properties

YourPropertyName = make_property("its_formal_name") # required at the first time
YourPropertyName(a, b) # usage

If the property is a binary relation, you can use operator overloading.
For example:

a 

1-3. Logical Connections

~ P         # not P
P & Q       # P and Q
P | Q       # P or Q
P >> Q      # P imply Q
P == Q      # P iff Q
true        # true
false       # false

1-4. Quantifiers

All(x, y, P(x, y)) # for all x and y, P(x, y)
Exist(x, y, z, P(x, y, z)) # there exists x, y and z such that P(x, y, z)
UniquelyExist(a, P(a, t)) # a is the only one such that P(a, t) 

1-5. Functions

YourFunctionName = make_function("its_formal_name") # required at the first time
YourFunctionName(x, y, z) # usage

If the function is a binary operator, you can use operator overloading.
For example:

x 

2. Numbering or Naming Statements

(your_sentence) @ 193
(your_sentence) @ "my_theorem"

Each number allows reuses, but the string names must be unique.
Hence, please number the sentences during the proof, but name the theorem at the last.

3. Inferences

(your_sentence) @ (save_as, INFERENCE_TO_USE, *arguments, *reasons)

save_as is a number or string you'd like to save your_sentence as.
INFERENCE_TO_USE depends on the inference you want to use to deduce your_sentence.
Some arguments are required or not, depending on the INFERENCE_TO_USE
reasons are the numbers or strings corresponding to the sentences already proved, which are now being used to deduce your_sentence.

3-1. DEDUCE

with (your_assumption) @ 27 :
    # your proof ...
    (your_conclusion) @ (39, SOME_INFERENCE, argument0, argument1, reason0, reason1)
(your_assumption >> your_conclusion) @ (40, DEDUCE)

math_up supports Fitch-style proofs.
That means, you may make an assumption P, prove Q and then deduce (P implies Q).
The inference to use is DEDUCE. DEDUCE doesn't require any arguments or reasons, but it must follow right after the end of the previous with block.

3-2. TAUTOLOGY

(A >> B) @ (152, INFERENCE0, argument0, reason0, reason1)
A @ (153, INFERENCE1, argument1, argument2, reason2)
B @ (154, TAUTOLOGY, 152, 153)

When is statement is a logical conclusion of previously proved sentences, and it can be checked by simply drawing the truth table, use TAUTOLOGY.
It doesn't require any arguments.
Put the sentences needed to draw the truth table as reasons.

3-2. DEFINE_PROPERTY

(NewProperty(x, y) == definition) @ ("save_as", DEFINE_PROPERTY, "formal_name_of_the_property")

You can freely define new properties with DEFINE_PROPERTY.
It does not requires any reasoning, but requires the formal name of the new property as the only argument.

3-3. DEFINE_FUNCTION

All(x, y, some_condition(x, y) >> UniquelyExist(z, P(x, y, z))) @ (70, INFERENCE0)
All(x, y, some_condition(x, y) >> P(x, y, NewFunction(z))) @ ("save_as", DEFINE_FUNCTION, 70)

Defining new function requires a sentence with a uniquely-exist quantifier.
Using DEFINE_FUNCTION, you can convert (for all x and y, if P(x, y) there is only one z such that Q(x, y, z)) into (for all x and y, if P(x, y) then Q(x, y, f(x, y)))
It requires no arguments, but one uniquely-exist setence as the only reason.


3-4. DUAL

(~All(x, P(x)) == Exist(x, ~P(x))) @ (32, DUAL)
((~Exist(y, Q(z, y))) == All(y, ~Q(z, y))) @ (33, DUAL)

not all == exist not, while not exist == all not.
To use these equivalences, use DUAL.
It requires no arguments or reasons.

3-5. FOUND

P(f(c)) @ (5, INFERENCE0, argument0)
Exist(x, P(x)) @ (6, FOUND, f(c), 5)

If you found a term satisfying the desired property, you can deduce the existence by FOUND.
It requires the term you actually found as an argument, and a sentence showing the desired property as the only reason.

3-6. LET

Exist(x, P(x)) @ (6, INFERENCE0, argument0, 5)
P(c) @ (7, LET, c, 6)

This one is the inverse of FOUND- i.e. it gives a real example from the existence.
It requires a fresh variable(i.e. never been used after the last clean()) to use as an existential bound variable as an argument.
Also, of course, an existential statement is required as the only reason.

3-7. PUT

All(x, P(x, z)) @ (13, INFERENCE0, 7, 9)
P(f(u), z) @ (14, PUT, f(u), 13)

PUT is used to deduce a specific example from the universally quantifiered sentence.
The term you want to put is an argument, and of course, the universally quantifiered sentence is the only reason.

3-8. REPLACE

P(x, a, a)
(a == f(c)) @ (8, INFERENCE0, argument0, 7)
P(x, a, f(c)) @ (9, REPLACE, 8)

When the two terms s and t are shown to be equal to each other, and the sentence Q is obtained from a previously proved P by interchainging s and t several times, REPLACE deduces Q from the two reasoning, i.e. s == t and P.
No arguments are needed.

3-9. AXIOM

any_sentence @ ("save_as", AXIOM)

AXIOM requires no inputs, but simply makes a sentence to be proved.

3-10. BY_UNIQUE

UniquelyExist(x, P(x)) @ (0, INFERENCE0)
P(a) @ (1, INFERENCE1, argument0)
P(f(b)) @ (2, INFERENCE2, argument1)
(a == f(b)) @ (3, BY_UNIQUE, 0, 1, 2)

BY_UNIQUE requires no arguments, but requires three reasons.
The first one is about unique existence, and the last two are specifications.
You can conclude two terms used for specifications respectively are equal to each other.
3-10. CLAIM_UNIQUE

Exist(x, P(x)) @ (6, INFERENCE0, argument0, 5)
P(c) @ (7, LET, c, 6)
P(d) @ (8, LET, d, 6)
# your proof ...
(c == d) @ (13, INFERENCE0, 12)
UniquelyExist(x, P(x)) @ (14, CLAIM_UNIQUE, 13)

CLAIM_UNIQUE upgrades an existence statement to a unique-existence statement.
Before you use it, you have to apply LET two times, and show the result variables are the same.
No arguments are required, but the equality is consumed as the only reason.

3-11. DEFINE_CLASS

UniquelyExist(C, All(x, (x in C) == UniquelyExist(a, UniquelyExist(b, (x == Tuple(a,b)) and Set(a) & Set(b) & P(a, b))))) @ (17, DEFINE_CLASS, C, x, [a, b], P(a, b))

This implements the class existence theorem of the NBG set theory.
No reasoning is required, but there are four arguments:
C : a fresh variable, to be a newly defined class
x : a fresh variable, to indicate the elements of C
[a, b, ...] : list of the components of x
P(a, b) : a condition satisfied by the components


4. Remarks


4-1. Trade-Off : Runtime Efficiency vs. Reductionism

The class existence theorem is actually not an axiom, but is PROVABLE, due to Goedel
However, proving it requires recursively break down all the higher-level definitions to the primitive ones
I'm afraid our computers would not have enough resourse to do such tedious computation...
Similarly, meta-theorems such as deduction theorem, RULE-C, function definition are NOT reduced by this program.


4-2. Trade-Off : Readability vs. Completeness

Actually, we need one more axiom: All(x, P and Q(x)) >> (P and All(x, Q(x)))
But I'll not implement this here... it may not, and should not be needed for readable proofs.
For the similar reasons, the program doesn't allow weird sentences like All(x, All(x, P(x))) or All(x, P(y)).
Strictly speaking, math_up is an incomplete system to restrict the proofs more readable.


4-3. Acknowledgement

Thanks to everyone taught me math & CS.
Mendelson's excellent book, Introduction to Mathematical Logic was extremely helpful.
Jech's Set Theory was hard to read but great.

Owner
양현우
양현우
VirtualBox Power Driver for MAAS (Metal as a Service)

vboxpower VirtualBox Power Driver for MAAS (Metal as a Service) A way to manage the power of VirtualBox virtual machines via the MAAS webhook driver.

Saeid Bostandoust 131 Dec 17, 2022
Solve various integral equations using numerical methods in Python

Solve Volterra and Fredholm integral equations This Python package estimates Volterra and Fredholm integral equations using known techniques. Installa

Matthew Wildrick Thomas 18 Nov 28, 2022
A python script developed to process Windows memory images based on triage type.

Overview A python script developed to process Windows memory images based on triage type. Requirements Python3 Bulk Extractor Volatility2 with Communi

CrowdStrike 245 Nov 24, 2022
Cvdl-hw2 - Find Contour, Camera Calibration, Augmented Reality and Stereo Disparity Map

opevcvdl-hw2 This project uses openCV and Qt to achieve the requirements. Version Python 3.7 opencv-contrib-python 3.4.2.17 Matplotlib 3.1.1 pyqt5 5.1

Kenny Cheng 3 Aug 17, 2022
ToDo - A simple bot to keep track of things you need to do

ToDo A simple bot to keep track of things you need to do. Installation You will

3 Sep 18, 2022
Grade 8 Version of Space Invaders

Space-Invaders Grade 8 Version of Space Invaders Compatability This program is Python 3 Compatable, and not Python 2 Compatable because i haven't test

Space64 0 Feb 16, 2022
TinyBar - Tiny MacOS menu bar utility to track price dynamics for assets on TinyMan.org

📃 About A simple MacOS menu bar app to display current coins from most popular Liquidity Pools on TinyMan.org

Al 8 Dec 23, 2022
Companion Web site for Fluent Python, Second Edition

Fluent Python, the site Source code and content for fluentpython.com. The site complements Fluent Python, Second Edition with extra content that did n

Fluent Python 49 Dec 08, 2022
A git extension for seeing your Cloud Build deployment

A git extension for seeing your Cloud Build deployment

Katie McLaughlin 13 May 10, 2022
Web3 Solidity Connector

With this project, you can compile your sol files and create new transactions including creating contract and calling the state changer functions. You can integrate integrate your sol files with Pyth

Fethi Tekyaygil 3 Oct 09, 2022
An application to see if your Ethereum staking validator(s) are members of the current or next post-Altair sync committees.

eth_sync_committee.py Since the Altair upgrade, 512 validators are randomly chosen every 256 epochs (~27 hours) to form a sync committee. Validators i

4 Oct 27, 2022
Percolation simulation using python

PythonPercolation Percolation simulation using python Exemple de percolation : Etude statistique sur le pourcentage de remplissage jusqu'à percolation

Tony Chouteau 1 Sep 08, 2022
A python mathematics module

A python mathematics module

Fayas Noushad 4 Nov 28, 2021
This repository contains a lot of short scripting programs implemented both in Python (Flask) and TypeScript (NodeJS).

fast-scripts This repository contains a lot of short scripting programs implemented both in Python (Flask) and TypeScript (NodeJS). In python These wi

Nahum Maurice 3 Dec 10, 2022
Get a list of content on your Netflix My List that is expiring in the next month or two.

Netflix My List Expiring Movies Annoyed at Netflix for taking away your movies? Now you don't have to be! Installation instructions Install selenium C

24 Aug 06, 2022
Medical appointments No-Show classifier

Medical Appointments No-shows Why do 20% of patients miss their scheduled appointments? A person makes a doctor appointment, receives all the instruct

4 Apr 20, 2022
Binjago - Set of tools aiding in analysis of stripped Golang binaries with Binary Ninja

Binjago 🥷 Set of tools aiding in analysis of stripped Golang binaries with Bina

W3ndige 2 Jul 23, 2022
Python implementation of an automatic parallel parking system in a virtual environment, including path planning, path tracking, and parallel parking

Automatic Parallel Parking: Path Planning, Path Tracking & Control This repository contains a python implementation of an automatic parallel parking s

134 Jan 09, 2023
Cash in on Expressed Barcode Tags (EBTs) from NGS Sequencing Data with Python

Cash in on Expressed Barcode Tags (EBTs) from NGS Sequencing Data with Python Cashier is a tool developed by Russell Durrett for the analysis and extr

3 Sep 11, 2022
PaintPrint - This module can colorize any text in your terminal

PaintPrint This module can colorize any text in your terminal Author: tankalxat3

Alexander Podstrechnyy 2 Feb 17, 2022