445 skills found · Page 11 of 15
sethbang / Venice AI🐍 Python client for Venice.ai. Seamlessly integrate powerful GenAI: chat 💬, image gen 🖼️, audio TTS 🔊, embeddings & more. Supports sync/async, streaming & robust error handling.
Zahabsbs / Error NarratorPython library for AI-powered explanations of Python exceptions and errors. Transforms complex tracebacks into clear, human-readable messages to speed up debugging.
RazorKenway / FixThis tool is design for resolve marshal error in python encoded script
mjcumming / PywiimPython library for controlling WiiM and LinkPlay-based audio devices. Provides async HTTP API client, UPnP event subscriptions for real-time updates, device discovery tools, and comprehensive diagnostics. Full type hints, error handling, and support for playback control, audio settings, groups, presets, and more.
octadocs / Flake8.codesA catalog of error codes for flake8 Python static analysis tool & a few of its plugins.
5hojib / TruelinkTrueLink is an asynchronous Python library that simplifies the process of extracting direct download links from a wide range of media file hosting services. It supports both individual files and folder-based downloads, offering a clean and extensible API with built-in URL validation and robust error handling.
satishchandhu97 / Chatter BotChatterBot: Machine learning in Python ChatterBot ChatterBot is a machine-learning based conversational dialog engine build in Python which makes it possible to generate responses based on collections of known conversations. The language independent design of ChatterBot allows it to be trained to speak any language. Package Version Python 3.6 Django 2.0 Requirements Status Build Status Documentation Status Coverage Status Code Climate Join the chat at https://gitter.im/chatterbot/Lobby An example of typical input would be something like this: user: Good morning! How are you doing? bot: I am doing very well, thank you for asking. user: You're welcome. bot: Do you like hats? How it works An untrained instance of ChatterBot starts off with no knowledge of how to communicate. Each time a user enters a statement, the library saves the text that they entered and the text that the statement was in response to. As ChatterBot receives more input the number of responses that it can reply and the accuracy of each response in relation to the input statement increase. The program selects the closest matching response by searching for the closest matching known statement that matches the input, it then returns the most likely response to that statement based on how frequently each response is issued by the people the bot communicates with. Installation This package can be installed from PyPi by running: pip install chatterbot Basic Usage from chatterbot import ChatBot from chatterbot.trainers import ChatterBotCorpusTrainer chatbot = ChatBot('Ron Obvious') # Create a new trainer for the chatbot trainer = ChatterBotCorpusTrainer(chatbot) # Train the chatbot based on the english corpus trainer.train("chatterbot.corpus.english") # Get a response to an input statement chatbot.get_response("Hello, how are you today?") Training data ChatterBot comes with a data utility module that can be used to train chat bots. At the moment there is training data for over a dozen languages in this module. Contributions of additional training data or training data in other languages would be greatly appreciated. Take a look at the data files in the chatterbot-corpus package if you are interested in contributing. from chatterbot.trainers import ChatterBotCorpusTrainer # Create a new trainer for the chatbot trainer = ChatterBotCorpusTrainer(chatbot) # Train based on the english corpus trainer.train("chatterbot.corpus.english") # Train based on english greetings corpus trainer.train("chatterbot.corpus.english.greetings") # Train based on the english conversations corpus trainer.train("chatterbot.corpus.english.conversations") Corpus contributions are welcome! Please make a pull request. Documentation View the documentation for ChatterBot on Read the Docs. To build the documentation yourself using Sphinx, run: sphinx-build -b html docs/ build/ Examples For examples, see the examples directory in this project's git repository. There is also an example Django project using ChatterBot, as well as an example Flask project using ChatterBot. History See release notes for changes https://github.com/gunthercox/ChatterBot/releases Development pattern for contributors Create a fork of the main ChatterBot repository on GitHub. Make your changes in a branch named something different from master, e.g. create a new branch my-pull-request. Create a pull request. Please follow the Python style guide for PEP-8. Use the projects built-in automated testing. to help make sure that your contribution is free from errors. License ChatterBot is licensed under the BSD 3-clause license.
mthd98 / Project Algorithm For A Dog Identification AppProject Overview Welcome to the Convolutional Neural Networks (CNN) project in the AI Nanodegree! In this project, you will learn how to build a pipeline that can be used within a web or mobile app to process real-world, user-supplied images. Given an image of a dog, your algorithm will identify an estimate of the canine’s breed. If supplied an image of a human, the code will identify the resembling dog breed. Sample Output Along with exploring state-of-the-art CNN models for classification, you will make important design decisions about the user experience for your app. Our goal is that by completing this lab, you understand the challenges involved in piecing together a series of models designed to perform various tasks in a data processing pipeline. Each model has its strengths and weaknesses, and engineering a real-world application often involves solving many problems without a perfect answer. Your imperfect solution will nonetheless create a fun user experience! Project Instructions Instructions Clone the repository and navigate to the downloaded folder. git clone https://github.com/udacity/dog-project.git cd dog-project Download the dog dataset. Unzip the folder and place it in the repo, at location path/to/dog-project/dogImages. Download the human dataset. Unzip the folder and place it in the repo, at location path/to/dog-project/lfw. If you are using a Windows machine, you are encouraged to use 7zip to extract the folder. Download the VGG-16 bottleneck features for the dog dataset. Place it in the repo, at location path/to/dog-project/bottleneck_features. (Optional) If you plan to install TensorFlow with GPU support on your local machine, follow the guide to install the necessary NVIDIA software on your system. If you are using an EC2 GPU instance, you can skip this step. (Optional) If you are running the project on your local machine (and not using AWS), create (and activate) a new environment. Linux (to install with GPU support, change requirements/dog-linux.yml to requirements/dog-linux-gpu.yml): conda env create -f requirements/dog-linux.yml source activate dog-project Mac (to install with GPU support, change requirements/dog-mac.yml to requirements/dog-mac-gpu.yml): conda env create -f requirements/dog-mac.yml source activate dog-project NOTE: Some Mac users may need to install a different version of OpenCV conda install --channel https://conda.anaconda.org/menpo opencv3 Windows (to install with GPU support, change requirements/dog-windows.yml to requirements/dog-windows-gpu.yml): conda env create -f requirements/dog-windows.yml activate dog-project (Optional) If you are running the project on your local machine (and not using AWS) and Step 6 throws errors, try this alternative step to create your environment. Linux or Mac (to install with GPU support, change requirements/requirements.txt to requirements/requirements-gpu.txt): conda create --name dog-project python=3.5 source activate dog-project pip install -r requirements/requirements.txt NOTE: Some Mac users may need to install a different version of OpenCV conda install --channel https://conda.anaconda.org/menpo opencv3 Windows (to install with GPU support, change requirements/requirements.txt to requirements/requirements-gpu.txt): conda create --name dog-project python=3.5 activate dog-project pip install -r requirements/requirements.txt (Optional) If you are using AWS, install Tensorflow. sudo python3 -m pip install -r requirements/requirements-gpu.txt Switch Keras backend to TensorFlow. Linux or Mac: KERAS_BACKEND=tensorflow python -c "from keras import backend" Windows: set KERAS_BACKEND=tensorflow python -c "from keras import backend" (Optional) If you are running the project on your local machine (and not using AWS), create an IPython kernel for the dog-project environment. python -m ipykernel install --user --name dog-project --display-name "dog-project" Open the notebook. jupyter notebook dog_app.ipynb (Optional) If you are running the project on your local machine (and not using AWS), before running code, change the kernel to match the dog-project environment by using the drop-down menu (Kernel > Change kernel > dog-project). Then, follow the instructions in the notebook. NOTE: While some code has already been implemented to get you started, you will need to implement additional functionality to successfully answer all of the questions included in the notebook. Unless requested, do not modify code that has already been included. Evaluation Your project will be reviewed by a Udacity reviewer against the CNN project rubric. Review this rubric thoroughly, and self-evaluate your project before submission. All criteria found in the rubric must meet specifications for you to pass. Project Submission When you are ready to submit your project, collect the following files and compress them into a single archive for upload: The dog_app.ipynb file with fully functional code, all code cells executed and displaying output, and all questions answered. An HTML or PDF export of the project notebook with the name report.html or report.pdf. Any additional images used for the project that were not supplied to you for the project. Please do not include the project data sets in the dogImages/ or lfw/ folders. Likewise, please do not include the bottleneck_features/ folder.
Mamtapriya / Linear Regression Of Data Driven BatteryMachine-learning approach In this work, author has developed data-driven models that accurately predict the cycle life of commercial lithium iron phosphate (LFP)/ graphite cells using early-cycle data, with no prior knowledge of degradation mechanisms. To build an early-prediction model, a feature-based approach is used. Features, such as initial discharge capacity, charge time, and cell can temperature, are generated and used in a regularized linear framework and proposed from domain knowledge of lithium-ion batteries. Several features are calculated based on the discharge voltage curve to capture the electrochemical evolution of individual cells during cycling. For the Q(V), the discharge voltage curves of each cell, summary statistics such as minimum, mean, and variance are determined. The change in voltage curves between two cycles is captured by each summary statistic. Three alternative models have been studied due to the great predictive potential of features based on Q100-10(V). (1) variance of ΔQ100-10(V), (2) additional candidate features obtained during discharge and (3) features from additional data streams such as temperature and internal resistance. Data is collected from the first 100 cycles in every case. These three models are proposed to examine the cost–benefit of collecting more data streams as well as the accuracy limits of prediction. The training data (41 cells) are used to choose model features and coefficient values, while the primary testing data (43 cells) are used to evaluate model performance. The model is then tested using a secondary testing dataset (40 cells) that has been generated after the development of the model. The prediction performance is measured using two metrics: root-mean-square error (RMSE), which is measured in cycles, and average percentage error, which is explained in the ‘Machine-Learning model creation’ selection. In short, the data is first separated into training and test sets. The elastic net is then used to train the model on the training set, resulting in a linear model with downselected features and coefficients. The model is then applied to both the primary and secondary test sets. The elastic net prediction and data processing are done in MATLAB, while the classification is done in Python with the NumPy, pandas, and sklearn tools.
sola-st / PyTyAutomated program repair of Python type errors
psarkerbd / Simple CalculatorIt is a calculator software developed by python. Python version is 3.x. It is my first project on python Graphical User Interface. This project always take the two numbers and do 4 operations (Addition, Subtraction, Multiplication and Division) between that two numbers. User can only entry two numerical values otherwise it will give Error message
marceloFA / Pycee2Provide answers for python code errors
LyubomirT / Lesp📚 LESP is a lightweight, efficient spelling proofreader written in Python. It's designed to be easy to use and lightweight, while still providing a decent result when checking for spelling errors. Resource consumption is kept to a minimum, and the program is designed to be as fast as possible.
theriturajps / Gsc Url IndexerEasily index your website's URLs from a sitemap using this Python tool, featuring error handling and a sleek terminal UI.
riteshkarmakar / Bulk Email SenderPython Bulk Email Sender with Mail Merge – A powerful GUI application to send personalized emails in bulk. Features include dynamic placeholders, attachment handling, real-time preview, SMTP support, and error handling to simplify your email campaigns.
ntotorica / SMP Passivity EnforcementAuthor: Nathan Totorica Date: 5/14/2021 # Singularity Matrix Pertubation (SMP) This code was written for a class project in the course entitled ECE 504: "ST: Passive Electromagnetic Systems" taught in Spring 2021 by Dr. Ata Zadehgol at the University of Idaho in Moscow. This code was developed, in part, based on the code developed by Jennifer Houle in ECE 504 "ST: Modern Circuit Synthesis Algorithms" taught in Spring 2020 by Dr. Ata Zadehgol. Jennifer's code is available online at https://github.com/JenniferEHoule/Circuit_Synthesis. ## Overview Singular Matrix Perturbation (SMP) as introduced in [11], is a passivity enforcement algorithm for use on fitted models. This robust method is fast, computationally inexpensive, and accurate in enforcing passivity. This implementation using Python can easily be used with Vector Fitting algorithm implemented in [12]. Example cases to demonstrate passivity enforcmment were taken from [7][15], as well as custom examples designed in simulation software based off of multiport circuit synthesis as described in [6]. ## I/O and Flow Description - Instructions for input and output and flow description can be found in flow_diagram.pdf. ## Files - SMP.py: Singularity matrix perturbation implementation of [11] - s_compare.py: Compare ADS simulated S matrices. Calculate RMS error and plot - eig_plot.py: Plotting functions. Eigenvalue plots based off of plot.py in [12] - smp_ex.py: Example of how to pull .sp file in, parse parameters, send to vector fitting, enforce passivity, and generate new passive circuit. Imported from [12] - Ex2_y.py - vectfit3.py - intercheig.py - rot.py - pass_check.py - fitcalc.py - FRPY.py - intercheig.py - violextrema.py - plots.py - quadprog.py - pr2ss.py - utils.py ## Licensing GPL-3.0 License In addition to licensing: Embedding any of (or parts from) the routines of the Matrix Fitting Toolbox in a commercial software, or a software requiring licensing, is strictly prohibited. This applies to all routines, see Section 2.1. If the code is used in a scientific work, then reference should me made as follows: VFdriver.m and/or vectfit3.m: References [1],[2],[3] RPdriver.m and/or FRPY.m applied to Y-parameters: [8],[9] ## References [1] B. Gustavsen and A. Semlyen, "Rational approximation of frequency domain responses by Vector Fitting", IEEE Trans. Power Delivery, vol. 14, no. 3, pp. 1052-1061, July 1999. [2] B. Gustavsen, "Improving the pole relocating properties of vector fitting", IEEE Trans. Power Delivery, vol. 21, no. 3, pp. 1587-1592, July 2006. [3] D. Deschrijver, M. Mrozowski, T. Dhaene, and D. De Zutter, "Macromodeling of Multiport Systems Using a Fast Implementation of the Vector Fitting Method", IEEE Microwave and Wireless Components Letters, vol. 18, no. 6, pp. 383-385, June 2008. [4] B. Gustavsen, VFIT3, The Vector Fitting Website. March 20, 2013. Accessed on: Jan. 21, 2020. [Online]. Available: https://www.sintef.no/projectweb/vectfit/downloads/vfit3/. [5] A. Zadehgol, "A semi-analytic and cellular approach to rational system characterization through equivalent circuits", Wiley IJNM, 2015. [Online]. https://doi.org/10.1002/jnm.2119 [6] V. Avula and A. Zadehgol, "A Novel Method for Equivalent Circuit Synthesis from Frequency Response of Multi-port Networks", EMC EUR, pp. 79-84, 2016. [Online]. Available: ://WOS:000392194100012. [7] B. Gustavsen, Matrix Fitting Toolbox, The Vector Fitting Website. March 20, 2013. Accessed on: Feb. 25, 2020. [Online]. Available: https://www.sintef.no/projectweb/vectorfitting/downloads/matrix-fitting-toolbox/. [8] B. Gustavsen, "Fast passivity enforcement for S-parameter models by perturbation of residue matrix eigenvalues", IEEE Trans. Advanced Packaging, vol. 33, no. 1, pp. 257-265, Feb. 2010. [9] B. Gustavsen, "Fast Passivity Enforcement for Pole-Residue Models by Perturbation of Residue Matrix Eigenvalues", IEEE Trans. Power Delivery, vol. 23, no. 4, pp. 2278-2285, Oct. 2008. [10] A. Semlyen, B. Gustavsen, "A Half-Size Singularity Test Matrix for Fast and Reliable Passivity Assessment of Rational Models," IEEE Trans. Power Delivery, vol. 24, no. 1, pp. 345-351, Jan. 2009. [11] E. Medina, A. Ramirez, J. Morales and K. Sheshyekani, "Passivity Enforcement of FDNEs via Perturbation of Singularity Test Matrix," in IEEE Transactions on Power Delivery, vol. 35, no. 4, pp. 1648-1655, Aug. 2020, doi: 10.1109/TPWRD.2019.2949216. [12] Houle, Jennifer, GitHub. May 10, 2020. Accessed on: February 3, 2021. [Online]. Available: https://github.com/jenniferEhoule/circuit_synthesis
amirhessam88 / Regression Error Characteristic CurveRegression Error Characteristic (REC) Curves in Python
dhesse / Py UwerrPython implementation of Monte Carlo error analysis a la Wolff.
CUMTstu / GNSS Pseudo Range PositioningThe pseudo range single point positioning and error visualization of GPS are realized by python. The intention is to finish college course assignments and python learning.
QuantumSavory / PyQDecoders.jlA Julia interface to popular python packages for decoding quantum error correcting codes.
