Pure python PEMDAS expression solver without using built-in eval function

Related tags

Deep Learningpypemdas
Overview

pypemdas

Pure python PEMDAS expression solver without using built-in eval function.

Supports nested parenthesis. Supported operators: + - * / ^

Example use:

from pypemdas import pemdas
print(pemdas('1*(2-3)'))
You might also like...
 Relative Uncertainty Learning for Facial Expression Recognition
Relative Uncertainty Learning for Facial Expression Recognition

Relative Uncertainty Learning for Facial Expression Recognition The official implementation of the following paper at NeurIPS2021: Title: Relative Unc

Implementation of Enformer, Deepmind's attention network for predicting gene expression, in Pytorch
Implementation of Enformer, Deepmind's attention network for predicting gene expression, in Pytorch

Enformer - Pytorch (wip) Implementation of Enformer, Deepmind's attention network for predicting gene expression, in Pytorch. The original tensorflow

Facial detection, landmark tracking and expression transfer library for Windows, Linux and Mac

Welcome to the CSIRO Face Analysis SDK. Documentation for the SDK can be found in doc/documentation.html. All code in this SDK is provided according t

Software for Multimodalty 2D+3D Facial Expression Recognition (FER) UI
Software for Multimodalty 2D+3D Facial Expression Recognition (FER) UI

EmotionUI Software for Multimodalty 2D+3D Facial Expression Recognition (FER) UI. demo screenshot (with RealSense) required packages Python = 3.6 num

Facial Expression Detection In The Realtime
Facial Expression Detection In The Realtime

The human's facial expressions is very important to detect thier emotions and sentiment. It can be very efficient to use to make our computers make interviews. Furthermore, we have robots now can detect the human's emotions and based on thats take an action .etc. So, It will be better to provide a tool or model for this.

Facial expression detector

A tensorflow convolutional neural network model to detect facial expressions.

Implementing Graph Convolutional Networks and Information Retrieval Mechanisms using pure Python and NumPy

Implementing Graph Convolutional Networks and Information Retrieval Mechanisms using pure Python and NumPy

Creating a Linear Program Solver by Implementing the Simplex Method in Python with NumPy
Creating a Linear Program Solver by Implementing the Simplex Method in Python with NumPy

Creating a Linear Program Solver by Implementing the Simplex Method in Python with NumPy Simplex Algorithm is a popular algorithm for linear programmi

Releases(v0.4.1)
基于AlphaPose的TensorRT加速

1. Requirements CUDA 11.1 TensorRT 7.2.2 Python 3.8.5 Cython PyTorch 1.8.1 torchvision 0.9.1 numpy 1.17.4 (numpy版本过高会出报错 this issue ) python-package s

52 Dec 06, 2022
AISTATS 2019: Confidence-based Graph Convolutional Networks for Semi-Supervised Learning

Confidence-based Graph Convolutional Networks for Semi-Supervised Learning Source code for AISTATS 2019 paper: Confidence-based Graph Convolutional Ne

MALL Lab (IISc) 56 Dec 03, 2022
Code for the SIGGRAPH 2022 paper "DeltaConv: Anisotropic Operators for Geometric Deep Learning on Point Clouds."

DeltaConv [Paper] [Project page] Code for the SIGGRAPH 2022 paper "DeltaConv: Anisotropic Operators for Geometric Deep Learning on Point Clouds" by Ru

98 Nov 26, 2022
Minimal implementation of Denoised Smoothing: A Provable Defense for Pretrained Classifiers in TensorFlow.

Denoised-Smoothing-TF Minimal implementation of Denoised Smoothing: A Provable Defense for Pretrained Classifiers in TensorFlow. Denoised Smoothing is

Sayak Paul 19 Dec 11, 2022
DeepLab2: A TensorFlow Library for Deep Labeling

DeepLab2 is a TensorFlow library for deep labeling, aiming to provide a unified and state-of-the-art TensorFlow codebase for dense pixel labeling tasks.

Google Research 845 Jan 04, 2023
A list of awesome PyTorch scholarship articles, guides, blogs, courses and other resources.

Awesome PyTorch Scholarship Resources A collection of awesome PyTorch and Python learning resources. Contributions are always welcome! Course Informat

Arnas Gečas 302 Dec 03, 2022
Example for AUAV 2022 with obstacle avoidance.

AUAV 2022 Sample This is a sample PX4 based quadrotor path planning framework based on Ubuntu 20.04 and ROS noetic for the IEEE Autonomous UAS 2022 co

James Goppert 11 Sep 16, 2022
MINERVA: An out-of-the-box GUI tool for offline deep reinforcement learning

MINERVA is an out-of-the-box GUI tool for offline deep reinforcement learning, designed for everyone including non-programmers to do reinforcement learning as a tool.

Takuma Seno 80 Nov 06, 2022
CMUA-Watermark: A Cross-Model Universal Adversarial Watermark for Combating Deepfakes (AAAI2022)

CMUA-Watermark The official code for CMUA-Watermark: A Cross-Model Universal Adversarial Watermark for Combating Deepfakes (AAAI2022) arxiv. It is bas

50 Nov 26, 2022
A Comprehensive Empirical Study of Vision-Language Pre-trained Model for Supervised Cross-Modal Retrieval

CLIP4CMR A Comprehensive Empirical Study of Vision-Language Pre-trained Model for Supervised Cross-Modal Retrieval The original data and pre-calculate

24 Dec 26, 2022
Repository for MDPGT

MD-PGT Repository for implementing and reproducing the results for the paper MDPGT: Momentum-based Decentralized Policy Gradient Tracking. Available E

Xian Yeow Lee 2 Dec 30, 2021
Cockpit is a visual and statistical debugger specifically designed for deep learning.

Cockpit: A Practical Debugging Tool for Training Deep Neural Networks

Felix Dangel 421 Dec 29, 2022
SoK: Vehicle Orientation Representations for Deep Rotation Estimation

SoK: Vehicle Orientation Representations for Deep Rotation Estimation Raymond H. Tu, Siyuan Peng, Valdimir Leung, Richard Gao, Jerry Lan This is the o

FIRE Capital One Machine Learning of the University of Maryland 12 Oct 07, 2022
PyTorch implementation for "Sharpness-aware Quantization for Deep Neural Networks".

Sharpness-aware Quantization for Deep Neural Networks This is the official repository for our paper: Sharpness-aware Quantization for Deep Neural Netw

Zhuang AI Group 30 Dec 19, 2022
Official implementation of FCL-taco2: Fast, Controllable and Lightweight version of Tacotron2 @ ICASSP 2021

FCL-Taco2: Towards Fast, Controllable and Lightweight Text-to-Speech synthesis (ICASSP 2021) Paper | Demo Block diagram of FCL-taco2, where the decode

Disong Wang 39 Sep 28, 2022
Denoising Normalizing Flow

Denoising Normalizing Flow Christian Horvat and Jean-Pascal Pfister 2021 We combine Normalizing Flows (NFs) and Denoising Auto Encoder (DAE) by introd

CHrvt 17 Oct 15, 2022
High-resolution networks and Segmentation Transformer for Semantic Segmentation

High-resolution networks and Segmentation Transformer for Semantic Segmentation Branches This is the implementation for HRNet + OCR. The PyTroch 1.1 v

HRNet 2.8k Jan 07, 2023
GAN example for Keras. Cuz MNIST is too small and there should be something more realistic.

Keras-GAN-Animeface-Character GAN example for Keras. Cuz MNIST is too small and there should an example on something more realistic. Some results Trai

160 Sep 20, 2022
Object detection and instance segmentation toolkit based on PaddlePaddle.

Object detection and instance segmentation toolkit based on PaddlePaddle.

9.3k Jan 02, 2023
Fuzzing the Kernel Using Unicornafl and AFL++

Unicorefuzz Fuzzing the Kernel using UnicornAFL and AFL++. For details, skim through the WOOT paper or watch this talk at CCCamp19. Is it any good? ye

Security in Telecommunications 283 Dec 26, 2022