CS50's Introduction to Artificial Intelligence Test Scripts

Overview

CS50's Introduction to Artificial Intelligence Test Scripts

πŸ€·β€β™‚οΈ What's this? πŸ€·β€β™€οΈ

This repository contains Python scripts to automate tests for most of the CS50’s Introduction to Artificial Intelligence with Python projects.

It does not contain any project solution/spoiler, as per the course's Academic Honesty policy.

β›” Disclaimer

This is a student-initiated project. Passing these test cases does not guarantee that you will receive a full grade from the official CS50 AI's teaching team.

πŸ“– Table of Contents

Lecture Concept Project Test Script Description
Search Breadth First Search Degrees degrees_test.py Run test cases given by problem description and this discussion
Search Minimax Tic-Tac-Toe tictactoe_test.py Let your AI play against itself for 10 rounds
Knowledge Model Checking Knights puzzle_test.py Check the correctness of the 4 puzzle results
Knowledge Knowledge Engineering Minesweeper minesweeper_test.py Check if your AI has β‰ˆ90% win rate over 1000 simulations
Uncertainty Bayesian Networks Heredity heredity_test.py Run test cases given by problem description and this discussion
Uncertainty Markov Models PageRank pagerank_test.py Compare the output of the 2 implemented functions
Optimization Constraint Satisfaction Crossword generate_test.py Generate crosswords using all 9 different structure + words combination and a special test case from this discussion
Learning Nearest-Neighbor Classification Shopping shopping_test.py Check the information is parsed correctly and result is within a reasonable range
Learning Reinforcement Learning Nim nim_test.py Check if the AI which moves second has a 100% win rate

πŸ› οΈ How to Run Tests

Guide

  1. Make sure you have Python3 installed in your machine. Anything above Python 3.4+ should work.
  2. Install pytest by running pip install pytest in a terminal. More information about pip here.
  3. Make a copy of the test file and paste it in the same folder as the project that you want to test.

    For example, if you want to test your code for degrees.py, make a copy of degrees_test.py in the same folder as your degrees.py and other files that came along with the project, like util.py, large/ and small/.

  4. Navigate to the project folder and run pytest or pytest _test.py in a terminal.

    For example, navigate to degrees/ and run pytest or pytest degrees_test.py.

Example

example

🚩 Useful pytest Flags

  • Run pytest -s to show print statements in the console
  • Run pytest -vv for verbose mode
  • Combine both flags pytest -s -vv for extra verbose mode
  • Run pytest --durations=n to see the n slowest execution time
  • Install pytest-repeat with pip and then run pytest --count n to repeat the test for n times

πŸ’» My Setup

Each test should take less than 30 seconds, depending on Python's I/O and your code efficiency.

  • Windows 10 Home Build 19042
  • Intel(R) Core(TM) i7-7700HQ CPU @ 2.80GHz
  • Python 3.9.5 64-bit
  • Visual Studio Code w/Pylance (latest release)

πŸ† Acknowledgement

Special thanks to these fellow CS50AI classmates who contributed some of the test cases on the Ed discussion site!

  • Ken Walker
  • Naveena A S
  • Ricardo L
Owner
Jet Kan
Tutor and Computer Science Undergraduate, National University of Singapore (NUS)
Jet Kan
EssentialMC2 Video Understanding

EssentialMC2 Introduction EssentialMC2 is a complete system to solve video understanding tasks including MHRL(representation learning), MECR2( relatio

Alibaba 106 Dec 11, 2022
A new test set for ImageNet

ImageNetV2 The ImageNetV2 dataset contains new test data for the ImageNet benchmark. This repository provides associated code for assembling and worki

186 Dec 18, 2022
Replication package for the manuscript "Using Personality Detection Tools for Software Engineering Research: How Far Can We Go?" submitted to TOSEM

tosem2021-personality-rep-package Replication package for the manuscript "Using Personality Detection Tools for Software Engineering Research: How Far

Collaborative Development Group 1 Dec 13, 2021
Walk with fastai

Shield: This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. Walk with fastai What is this p

Walk with fastai 124 Dec 10, 2022
Rate-limit-semaphore - Semaphore implementation with rate limit restriction for async-style (any core)

Rate Limit Semaphore Rate limit semaphore for async-style (any core) There are t

Yan Kurbatov 4 Jun 21, 2022
Cards Against Humanity AI

cah-ai This is a Cards Against Humanity AI implemented using a pre-trained Semantic Search model. How it works A player is described by a combination

Alex Nichol 2 Aug 22, 2022
PyTorch code for ICPR 2020 paper Future Urban Scene Generation Through Vehicle Synthesis

Future urban scene generation through vehicle synthesis This repository contains Pytorch code for the ICPR2020 paper "Future Urban Scene Generation Th

Alessandro Simoni 4 Oct 11, 2021
Time Series Forecasting with Temporal Fusion Transformer in Pytorch

Forecasting with the Temporal Fusion Transformer Multi-horizon forecasting often contains a complex mix of inputs – including static (i.e. time-invari

NicolΓ‘s Fornasari 6 Jan 24, 2022
DFFNet: An IoT-perceptive Dual Feature Fusion Network for General Real-time Semantic Segmentation

DFFNet Paper DFFNet: An IoT-perceptive Dual Feature Fusion Network for General Real-time Semantic Segmentation. Xiangyan Tang, Wenxuan Tu, Keqiu Li, J

4 Sep 23, 2022
A deep learning based semantic search platform that computes similarity scores between provided query and documents

semanticsearch This is a deep learning based semantic search platform that computes similarity scores between provided query and documents. Documents

1 Nov 30, 2021
Using Machine Learning to Test Causal Hypotheses in Conjoint Analysis

Readme File for "Using Machine Learning to Test Causal Hypotheses in Conjoint Analysis" by Ham, Imai, and Janson. (2022) All scripts were written and

0 Jan 27, 2022
JAX + dataclasses

jax_dataclasses jax_dataclasses provides a wrapper around dataclasses.dataclass for use in JAX, which enables automatic support for: Pytree registrati

Brent Yi 35 Dec 21, 2022
This repository for project that can Automate Number Plate Recognition (ANPR) in Morocco Licensed Vehicles. πŸ’» + πŸš™ + πŸ‡²πŸ‡¦ = πŸ€– πŸ•΅πŸ»β€β™‚οΈ

MoroccoAI Data Challenge (Edition #001) This Reposotory is result of our work in the comepetiton organized by MoroccoAI in the context of the first Mo

SAFOINE EL KHABICH 14 Oct 31, 2022
DeFMO: Deblurring and Shape Recovery of Fast Moving Objects (CVPR 2021)

Evaluation, Training, Demo, and Inference of DeFMO DeFMO: Deblurring and Shape Recovery of Fast Moving Objects (CVPR 2021) Denys Rozumnyi, Martin R. O

Denys Rozumnyi 139 Dec 26, 2022
T2F: text to face generation using Deep Learning

⭐ [NEW] ⭐ T2F - 2.0 Teaser (coming soon ...) Please note that all the faces in the above samples are generated ones. The T2F 2.0 will be using MSG-GAN

Animesh Karnewar 533 Dec 22, 2022
PyTorch implementation of our Adam-NSCL algorithm from our CVPR2021 (oral) paper "Training Networks in Null Space for Continual Learning"

Adam-NSCL This is a PyTorch implementation of Adam-NSCL algorithm for continual learning from our CVPR2021 (oral) paper: Title: Training Networks in N

Shipeng Wang 34 Dec 21, 2022
Deep Markov Factor Analysis (NeurIPS2021)

Deep Markov Factor Analysis (DMFA) Codes and experiments for deep Markov factor analysis (DMFA) model accepted for publication at NeurIPS2021: A. Farn

Sarah Ostadabbas 2 Dec 16, 2022
Learning with Subset Stacking

Learning with Subset Stacking (LESS) LESS is a new supervised learning algorithm that is based on training many local estimators on subsets of a given

S. Ilker Birbil 19 Oct 04, 2022
PyTorch code for EMNLP 2021 paper: Don't be Contradicted with Anything! CI-ToD: Towards Benchmarking Consistency for Task-oriented Dialogue System

Don’t be Contradicted with Anything!CI-ToD: Towards Benchmarking Consistency for Task-oriented Dialogue System This repository contains the PyTorch im

Libo Qin 25 Sep 06, 2022
PINN Burgers - 1D Burgers equation simulated by PINN

PINN(s): Physics-Informed Neural Network(s) for Burgers equation This is an impl

ShotaDEGUCHI 1 Feb 12, 2022