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Iridium - A high performance MongoDB ORM for Node.js
Iridium is designed to offer a high performance, easy to use and above all, editor friendly ODM for MongoDB on Node.js. Rather than adopting the "re-implement everything" approach often favoured by ODMs like Mongoose and friends, requiring you to learn an entirely new API and locking you into a specific coding style, Iridium tries to offer an incredibly lightweight implementation which makes your life easier where it counts and gets out of your way when you want to do anything more complex.It also means that, if you're familiar with the MongoDB CLI you should find working with Iridium very natural, with all database methods returning promises for their results and sensible, type annotated results being provided if you wish to make use of them.
mongodb typescript iridium nodejs orm odm validation preprocessingmachineJS - Automated machine learning- just give it a data file! Check out the production-ready version of this project at ClimbsRocks/auto_ml
I just built out v2 of this project that now gives you analytics info from your models, and is production-ready. machineJS is an amazing research project that clearly proved there's a hunger for automated machine learning. auto_ml tackles this exact same goal, but with more features, cleaner code, and the ability to be copy/pasted into production.
machine-learning data-science machine-learning-library machine-learning-algorithms ml data-scientists javascript-library scikit-learn kaggle numerai automated-machine-learning automl auto-ml neuralnet neural-network algorithms random-forest svm naive-bayes bagging optimization brainjs date-night sklearn ensemble data-formatting js xgboost scikit-neuralnetwork knn k-nearest-neighbors gridsearch gridsearchcv grid-search randomizedsearchcv preprocessing data-formatter kaggle-competitionfastp - An ultra-fast all-in-one FASTQ preprocessor (QC/adapters/trimming/filtering/splitting...)
A tool designed to provide fast all-in-one preprocessing for FastQ files. This tool is developed in C++ with multithreading supported to afford high performance. By default, the HTML report is saved to fastp.html (can be specified with -h option), and the JSON report is saved to fastp.json (can be specified with -j option).
fastq qc preprocessing filtering adapter overlap quality trimming splitting quality-control filter ngs bioinformatics overlapping error umi sequencing illumina polyg duplicationarraymancer-vision - Simple library for image loading, preprocessing and visualization for working with arraymancer
Simple library for image loading, preprocessing and visualization for working with arraymancer. The library operates all images as Tensor[uint8] with dimensions CxHxW, where C is in RGBA colorspace, note that other image libraries usually operates with images in HxWxC format, so remember this when using. This design choice is to optimize and facilitate operation on images in deep learning tasks.
arraymancer nim vim preprocessing imagegrunt-build-html - Build HTML templates recursively
Build HTML templates recursively. In your project's Gruntfile, add a section named buildHtml to the data object passed into grunt.initConfig().
gruntplugin grunt template preprocessing html compilepostcss-each - PostCSS plugin to iterate through values
A PostCSS plugin to iterate through values.
postcss iteration css preprocessing postcss-plugin each iteratorMODIStsp - An "R" package for automatic download and preprocessing of MODIS Land Products Time Series
MODIStsp is a “R” package devoted to automatizing the creation of time series of rasters derived from MODIS Land Products data. MODIStsp allows to perform several preprocessing steps (e.g., download, mosaicing, reprojection and resize) on MODIS data available within a given time period. Users have the ability to select which specific layers of the original MODIS HDF files they want to process. They also can select which additional Quality Indicators should be extracted from the aggregated MODIS Quality Assurance layers and, in the case of Surface Reflectance products, which Spectral Indexes should be computed from the original reflectance bands. For each output layer, outputs are saved as single-band raster filescorresponding to each available acquisition date. Virtual files allowing access to the entire time series as a single file can be also created. All processing parameters can be easily selected with a user-friendly GUI, although non-interactive execution exploiting a previously created Options File is possible. Stand-alone execution outside an “R” environment is also possible, allowing to use scheduled execution of MODIStsp to automatically update time series related to a MODIS product and extent whenever a new image is available. L. Busetto, L. Ranghetti (2016) MODIStsp: An R package for automatic preprocessing of MODIS Land Products time series, Computers & Geosciences, Volume 97, Pages 40-48, ISSN 0098-3004, http://dx.doi.org/10.1016/j.cageo.2016.08.020, URL: https://github.com/ropensci/MODIStsp.
modis gdal r preprocessing time-series remote-sensing satellite-imagery modis-data modis-land-productscpip - CPIP - a C/C++ preprocessor implemented in Python.
CPIP is a C/C++ Preprocessor implemented in Python. It faithfully records all aspects of preprocessing and can produce visualisations that make debugging preprocessing far easier. There are other installation methods including directly from source.
c-plus-plus preprocessor preprocessing pre-processor pre-processingSeqTools - A python library to manipulate and transform sequences
SeqTools facilitates the manipulation of datasets and the evaluation of a transformation pipeline. Some of the provided functionnalities include: mapping element-wise operations, reordering, reindexing, concatenation, joining, slicing, minibatching, etc... To improve ease of use, SeqTools assumes that dataset are objects that implement a list-like sequence interface: a container object with a length and its elements accessible via indexing or slicing. All SeqTools functions take and return objects compatible with this simple and convenient interface.
on-the-fly lazy-evaluation preprocessing pipeline delayedcall machine-learning mapping librarygreenglas - Machine Intelligence Preprocessing Framework
Greenglas tries to provide a smart and customizable pipeline for preprocessing data for machine learning tasks. Clean preprocessing methods for the most common type of data, makes preprocessing easy. Greenglas offers a pipeline of Modifiers and Transformers to turn non-numeric data into a safe and consistent numeric output in the form of Coaster's SharedTensor. For putting your preprocessed data to use, you might like to use the Machine Learning Framework Leaf. For more information see the Documentation.
machine-learning preprocessing frameworkreceipt-scanner - Receipt scanner extracts information from your PDF or image receipts - built in NodeJS
Receipt scanner extracts information from your PDF or image receipts. These dependencies are only necessary if you're going to use imagemagick or graphicsmagick image preprocessor.
ocr receipt-scanner receipts optical-character-recognition extract-data extract-information computer-vision graphicsmagick imagemagick invoice opencv preprocessing receipt scanner sharp tesseractITU-Turkish-NLP-Pipeline-Caller - A Python3 wrapper tool to help using ITU Turkish NLP Pipeline API
As I no longer have time to maintain this project I am looking for collaborators to help to maintain. You can sign up by sending a pull request which fixes a bug or adds a feature. For details of the pipeline, please check the pipeline page and the sources below.
nlp natural-language-processing preprocessing natural-language-understanding cli itu morphological-analyser named-entity-recognition pipeline python3-wrapper turkish-languagexam - :dart: Personal data science and machine learning toolbox
xam is my personal data science and machine learning toolbox. It is written in Python 3 and stands on the shoulders of giants (mainly pandas and scikit-learn). It loosely follows scikit-learn's fit/transform/predict convention. ⚠️ Because xam is a personal toolkit, the --upgrade flag will install the latest releases of each dependency (scipy, pandas etc.). I like to stay up-to-date with the latest library versions.
machine-learning data-science preprocessing stackingdata-formatter - Takes raw csv input and formats it to be ready for neural networks
Formats and cleans your data to get it ready for machine learning!
neural-network machine-learning data-formatting normalization min-max-normalization min-max-normalizing brain.js automated-machine-learning bestbrain data-science kaggle scikit-learn sklearn scikit-neuralnetworks lasagne nolearn nolearn.lasagne data-cleaning data-munging data-preparation imputing-missing-values filling-in-missing-values dataset data-set training testing random-forest vectorization categorization one-hot-encoding dictvectorizer preprocessing feature-selection feature-engineering
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