Saravanan666/pypi-org huggingface dataset:
Package: mkzbomb
# mkZbombmkZbomb is simple python script that generate ZipBomb of desired dimension.# Getting the codeDownload from repository:$ git clone https://github.com/jacopodl/mkzbomb$ cd mkzbombor install from pip:$ pip install mkzbomb# How to use_     _______                     _| |   |___  / |                   | |_ __ ___ | | __   / /| |__   ___  _ __ ___ | |__| '_ ` _ \| |/ /  / / | '_ \ / _ \| '_ ` _ \| '_ \| | | | | |   <  / /__| |_) | (_) | | | | | | |_) ||_| |_| |_|_|\_\/_____|_.__/ \___/|_| |_| |_|_.__/usage: mkzbomb.py [-h] [--layer LAYER] [--cps CPS] [--size SIZE] nameBuild ZipBomb.positional arguments:name           Output nameoptional arguments:-h, --help     show this help message and exit--layer LAYER  Set zbomb deep--cps CPS      Copies for each layer--size SIZE    Set size of 'zero' file (Size in KB)$> python mkzbomb.py SimpleBomb.zip$> python mkzbomb.py SimpleBomb.zip --layer 18# WarningBuilding(or storing) ZipBomb on Windows system with Windows Search service enabled can bring system to instability issue.# LicenseSee LICENSE
Package: ml2en
ml2en (Python, Javascript, PHP)An algorithm to transliterate Malayalam script to Roman / Latin characters (commonly 'Manglish') with reasonable phonetic fairnessKailash Nadh, October 2012Documentation:http://kailashnadh.name/code/ml2enLicensed under GNU GPL v2 license.ExampleInput:വ്യാഴത്തിന്റെ കാന്തികക്ഷേത്രം സൗരവാതത്തെ ചെറുക്കുന്ന മേഖലയാണ്‌ വ്യാഴത്തിന്റെ കാന്തമണ്ഡലം. സൂര്യനിലേക്കുള്ള ദിശയിൽ ഏതാണ്ട് എഴുപത് ലക്ഷം കിലോമീറ്ററും വിപരീത ദിശയിൽ ശനിയുടെ പരിക്രമണപഥം വരെയും ഇത് വ്യാപിച്ചുകിടക്കുന്നു. സൗരയൂഥത്തിലെ ഗ്രഹങ്ങളുടെ കാന്തമണ്ഡലങ്ങളിൽ വച്ച് ഏറ്റവും ശക്തിയേറിയതാണ്‌ വ്യാഴത്തിന്റേത്. സൗരമണ്ഡലം കഴിഞ്ഞാൽ സൗരയൂഥത്തിലെ ഏറ്റവും വലിയ ഘടനയും ഇതുതന്നെ. ഭൂമിയുടെ കാന്തമണ്ഡലത്തെക്കാൾ വീതിയേറിയതും പരന്നതുമായ വ്യാഴത്തിന്റെ കാന്തമണ്ഡലത്തിന്റെ ശക്തി ഭൂമിയൂടേതിന്റെ പത്തിരട്ടിയോളവും വ്യാപ്തം 18000 ഇരട്ടിയോളവുമാണ്‌.Output:Vyaazhatthinte kaanthikakshethram sauravaathatthe cherukkunna mekhalayaanu vyaazhatthinte kaanthamandalam. Sooryanilekkulla dishayil ethaandu ezhupathu laksham kilomeettarum vipareetha dishayil shaniyute parikramanapatham vareyum ithu vyaapicchukitakkunnu. Saurayoothatthile grahangalute kaanthamandalangalil vacchu ettavum shakthiyeriyathaanu vyaazhatthintethu. Sauramandalam kazhinjaal saurayoothatthile ettavum valiya ghatanayum ithuthanne. Bhoomiyute kaanthamandalatthekkaal veethiyeriyathum parannathumaaya vyaazhatthinte kaanthamandalatthinte shakthi bhoomiyootethinte patthirattiyolavum vyaaptham 18000 irattiyolavumaanu.Why?(Phonetic) Romanisation of Malayalam script can work decently well with phonetic search algorithms (for example, along with the Metaphone or Soundex algorithms). Could also potentially help people learning the language.UsageThe algorithm's available in three different languages, Python, Javascript, and PHP.PythonInstall withpip3 install ml2enfromml2enimportml2enprint(ml2en.transliterate(ml_str))Javascript<scriptsrc="ml2en.js"></script><script>varresult=ml2en(ml_str);</script>PHP<?phprequire'ml2en.php';$result=ml2en::transliterate($ml_str);?>
Package: ml2p
ML2P – or (ML)^2P – is the minimal lovable machine-learning pipeline and a
friendlier interface toAWS SageMaker.Design goals:support the full machine learning lifecylesupport custom feature engineeringsupport building custom models in Pythonprovide reproducible training and deployment of modelssupport the use of customised base Docker images for training and deploymentConcretely it provides a command line interface and a Python library to assist
with:S3:Managing training dataSageMaker:Launching training jobsDeploying trained modelsCreating notebook instancesOn your local machine or in a SageMaker notebook:Downloading training datasets from S3Training modelsLoading trained models from SageMaker / S3InstallingInstall ML2P with:$ pip install ml2pMailing listIf you have questions about ML2P, or would like to contribute or have
suggestions for improvements, you are welcome to join the project mailing
list athttps://groups.google.com/g/ml2pand write us a letter there.OverviewML2P helps manage a machine learning project. You’ll define your project
by writing a small YAML file namedml2p.yml:project: "ml2p-tutorial"
s3folder: "s3://your-s3-bucket/"
models:
  bob: "models.RegressorModel"
defaults:
  image: "XXXXX.dkr.ecr.REGION.amazonaws.com/your-docker-image:X.Y.Z"
  role: "arn:aws:iam::XXXXX:role/your-role"
train:
  instance_type: "ml.m5.large"
deploy:
  instance_type: "ml.t2.medium"
  record_invokes: trueThis specifies:project: the name of your projects3folder: the S3 bucket that will hold the models and data sets for your
projectmodels: a list of model names and the Python classes that will be used to
train the models and make predictionsdefaults:image: the docker image that your project will use for training and
predictionrole: the AWS role your project will run undertrain:instance_type: the AWS instance type that will be used when training
your modeldeploy:instance_type: the AWS instance type that will be used when deploying
your modelrecord_invokes: whether to record prediction requests in S3The name of your project functions as a prefix to the names of SageMaker training jobs,
models and endpoints that ML2P creates (since these names are global within a SageMaker
account).ML2P also tags all of the AWS objects it creates with your project name.TutorialSeehttps://ml2p.readthedocs.io/en/latest/tutorial/for a step-by-step tutorial.
Package: ml2rt
Machine learning utilities for model conversion, serialization, loading etcFree software: Apache Software License 2.0Installationpip install ml2rtDocumentationml2rt provides some convenient functions to convert, save & load machine learning models. It currently supports Tensorflow, PyTorch, Sklearn, Spark and ONNX but frameworks like xgboost, coreml are on the way.Saving Tensorflow modelimporttensorflowastffromml2rtimportsave_tensorflow# train your model heresess=tf.Session()save_tensorflow(sess,path,output=['output'])Saving PyTorch model# it has to be a torchscript graph made by tracing / scriptingfromml2rtimportsave_torchsave_torch(torch_script_graph,path)Saving ONNX modelfromml2rtimportsave_onnxsave_onnx(onnx_model,path)Saving sklearn modelfromml2rtimportsave_sklearnprototype=np.array(some_shape,dtype=some_dtype)# Equivalent to the input of the modelsave_sklearn(sklearn_model,path,prototype=prototype)# or# some_shape has to be a tuple and some_dtype has to be a np.dtype, np.dtype.type or str objectsave_sklearn(sklearn_model,path,shape=some_shape,dtype=some_dtype)# or# some_shape has to be a tuple and some_dtype has to be a np.dtype, np.dtype.type or str objectinital_types=utils.guess_onnx_tensortype(shape=shape,dtype=dtype)save_sklearn(sklearn_model,path,initial_types=initial_types)Saving sparkml modelfromml2rtimportsave_sparkmlprototype=np.array(some_shape,dtype=some_dtype)# Equivalent to the input of the modelsave_sparkml(spark_model,path,prototype=prototype)# or# some_shape has to be a tuple and some_dtype has to be a np.dtype, np.dtype.type or str objectsave_sparkml(spark_model,path,shape=some_shape,dtype=some_dtype)# or# some_shape has to be a tuple and some_dtype has to be a np.dtype, np.dtype.type or str objectinital_types=utils.guess_onnx_tensortype(shape=shape,dtype=dtype)save_sparkml(spark_model,path,initial_types=initial_types)Sklearn and sparkml models will be converted to ONNX first and then save to the disk. These models can be executed using ONNXRuntime, RedisAI etc. ONNX conversion needs to know the type of the input nodes and hence we have to pass shape & dtype or a prototype from where the utility can infer the shape & dtype or an initial_type object which is understood by the conversion utility. Frameworks like sparkml allows users to have heterogeneous inputs with more than one type. In such cases, useguess_onnx_tensortypesand create more than one initial_types which can be passed to save function as a listLoading model & scriptLoading function can load both single file models like freezed tensorflow model or torchscript model or onnx model as well as SavedModel from tensorflowmodel=ml2rt.load_model(path)script=ml2rt.load_script(script)
Package: ml4
# ML4IntroductionML4是TechYoung课程辅助工具包.## DistributionRun the following commands to
register, build and upload
the package to PYPI.python3 setup.py sdist uploadThe home page on PYPI is:https://pypi.org/project/wcc/Installsudo pip3 install ml4Usagemethods:ml4.getWindowData(data, x, y, windowSize=300, steps=300):*data:*dataframe*x:*x-axis column name , 例如 “ctime”*y:*column name, 例如 “hr”*window:*window size (s)*steps:*window steps (s)Note
Package: ml4a
Machine Learning for Artistsml4ais a Python library for making art with machine learning. It features:an API wrapping popular deep learning models with creative applications, includingStyleGAN2,SPADE,Neural Style Transfer,DeepDream, andmany others.a collection ofJupyter notebooksexplaining the basics of deep learning for beginners, and providingrecipes for using the materials creatively.Exampleml4a bundles the source code of various open source repositories asgit submodulesand contains wrappers to streamline and simplify them. For example, to generate sample images with StyleGAN2:from ml4a import image
from ml4a.models import stylegan

network_pkl = stylegan.get_pretrained_model('ffhq')
stylegan.load_model(network_pkl)

samples = stylegan.random_sample(3, labels=None, truncation=1.0)
image.display(samples)Every model inml4a.models, including thestyleganmodule above, imports all of the original repository's code into its namespace, allowing low-level access.Support ml4aBecome a sponsorYou can support ml4a bydonating through GitHub sponsors.How to contributeStart by joining theSlackor following us onTwitter. Contribute to the codebase, or help write tutorials.Licenseml4a itself islicensed MIT, but you are also bound to the licenses of anymodelsyou use.
Package: ml4bio
ml4bioThis package is a graphical interface wrapper for sklearn classification.
It is intended to be used with theML4Bio workshoptraining materials.Python environmentRequires:Python 3.5pandasnumpysklearnmatplotlibpyqt 5scipySeeconda_env.ymlfor one set of compatible package versions.
Create theml4bioconda environmentwith the commandconda env create -f conda_env.yml.
Activate the environment withconda activate ml4bio.The full Anaconda installation also provides all required dependencies.RunningThe ML4Bio workshop repository providesscriptsfor installing and launching the ml4bio GUI.
Once the ml4bio package and its dependencies have been installed, the GUI can be launched using theml4biocommand from the command line.CitationAn approachable, flexible, and practical machine learning workshop for biologists.Chris S Magnano, Fangzhou Mu, Rosemary S Russ, Milica Cvetkovic, Debora Treu, Anthony Gitter.bioRxiv, 2022. doi:10.1101/2022.02.03.479008Third party materialsThe icons in theml4bio/iconsdirectory were downloaded fromhttp://thenounproject.comunder the Creative Commons license.
Instructions on how to give credit to the creators:linkAdd this attribution where appropriate:Created by sachin modgekar from Noun Project.
Package: ml4chem
AboutML4Chem is a package to deploy machine learning for chemistry and materials
science. It is written in Python 3, and intends to offer modern and rich
features to perform machine learning (ML) workflows for chemical physics.A list of features and ML algorithms are shown below.PyTorch backend.Completely modular. You can use any part of this package in your project.Free software <3. No secrets! Pull requests and additions are more than
welcome!Documentation (work in progress).Explicit and idiomatic:ml4chem.get_me_a_coffee().Distributed training in a data parallel paradigm aka mini-batches.Scalability and distributed computations are powered by Dask.Real-time tools to track status of your computations.Messagepack serialization.CitingIf you find this software useful, please use this DOI to cite it:DOI: 10.5281/zenodo.3471761DocumentationTo get started,  read the documentation athttps://ml4chem.dev. It is arranged in a way that you
can go through the theory as well as some code snippets to understand how to
use this software. Additionally, you can dive through themodule
indexto get more information about
different classes and functions of ML4Chem.VisualizationsNote: This package is under development.CopyrightML4Chem: Machine Learning for Chemistry and Materials (ML4Chem) Copyright (c) 2019, The
Regents of the University of California, through Lawrence Berkeley National
Laboratory (subject to receipt of any required approvals from the U.S.
Dept. of Energy).  All rights reserved.If you have questions about your rights to use or distribute this software,
please contact Berkeley Lab's Intellectual Property Office atIPO@lbl.gov.NOTICE.  This Software was developed under funding from the U.S. Department
of Energy and the U.S. Government consequently retains certain rights.  As
such, the U.S. Government has been granted for itself and others acting on
its behalf a paid-up, nonexclusive, irrevocable, worldwide license in the
Software to reproduce, distribute copies to the public, prepare derivative
works, and perform publicly and display publicly, and to permit other to do
so.
Package: ml4data
ML4Data Python SDK v1Homepage:https://ml4data.comInstallationFrom setuppython3setup.pyinstallUsageNLPfromml4dataimportNLPClientclient=NLPClient('YOUR API KEY HERE!')client.analyze_sentiment("Este cine no esta bueno porque era muy sucio")Visionfromml4dataimportVisionClientclient=VisionClient('YOUR API KEY HERE!')client.detect_objects('examples/restaurant.png')
Package: ml4floods
ML4Floods is an end-to-end ML pipeline for flood extent estimation: from data preprocessing, model training, model deployment to visualization.InstallInstall frompip:pipinstallml4floodsInstall the latest version from GitHub:pipinstallgit+https://github.com/spaceml-org/ml4floods#egg=ml4floodsDocsspaceml-org.github.io/ml4floodsThese tutorials may help you explore the datasets and models:Run the clouds-aware flood segmentation model in Sentinel-2 and Landsat and vectorise the flood mapsRun the model on time series of Sentinel-2 imagesIngest data from Copernicus EMSML-models step by stepTrainingInference on new data(a Sentinel-2 image)Perf metricsTheWorldFloodsdatabaseTheWorldFloodsdatabasecontains 509 pairs of Sentinel-2 images and flood segmentation masks.
It requires approximately 300GB of hard-disk storage.TheWorldFloodsdatabase and all pre-trained models are released under aCreative Commons non-commercial licenceTo download theWorldFloodsdatabase or the pretrained flood segmentation models seethe instructions to download the database.CiteIf you find this work useful please cite:@article{portales-julia_global_2023,
	title = {Global flood extent segmentation in optical satellite images},
	volume = {13},
	issn = {2045-2322},
	doi = {10.1038/s41598-023-47595-7},
	number = {1},
	urldate = {2023-11-30},
	journal = {Scientific Reports},
	author = {Portalés-Julià, Enrique and Mateo-García, Gonzalo and Purcell, Cormac and Gómez-Chova, Luis},
	month = nov,
	year = {2023},
	pages = {20316},
}
@article{mateo-garcia_towards_2021,
	title = {Towards global flood mapping onboard low cost satellites with machine learning},
	volume = {11},
	issn = {2045-2322},
	doi = {10.1038/s41598-021-86650-z},
	number = {1},
	urldate = {2021-04-01},
	journal = {Scientific Reports},
	author = {Mateo-Garcia, Gonzalo and Veitch-Michaelis, Joshua and Smith, Lewis and Oprea, Silviu Vlad and Schumann, Guy and Gal, Yarin and Baydin, Atılım Güneş and Backes, Dietmar},
	month = mar,
	year = {2021},
	pages = {7249},
}AboutML4Floods has been funded by the United Kingdom Space Agency (UKSA) and led byTrillium Technologies. It has also been partially supported by the Spanish Ministry of Science and Innovation project PID2019-109026RB-I00 (MINECO-ERDF MCIN/AEI/10.13039/501100011033).
Package: ml4k
# Python wrapper for the machinelearningforkids.co.uk APIThis library was designed for use with [machinelearningforkids.co.uk](https://machinelearningforkids.co.uk).## InstallationUse pip to install the `ml4k` package:sudo pip install ml4k## UsageIn order to use your machine learning model, create a `Model` objectwith your project's API key:import ml4kAPI_KEY = "PASTE-API-KEY-HERE"model = ml4k.Model(API_KEY)The following methods are available:### `model.classify(data)`Classifies the given data and returns a dictionary with the result. Theresult dictionary contains the `"label"` and `"confidence"` values.### `model.add_training_data(label, data)`Adds training data to the given label "bucket". You can pass text,images, or a list of numbers.## Examples### Recognizing Text# Get input from user and pass it to our modelcommand = input("Type a command: ")result = model.classify("Turn on the lamp")if result["label"] == "lamp_on":print("Turning on the lamp")elif result["label"] == "lamp_off":print("Turning off the lamp")### Recognizing ImagesRecognizing images is similar, but you need to pass binary image data.There are many was to get image data in python, such as opening andreading a file, or capturing from the webcam using a the[SimpleCV](http://simplecv.org/) library.Images will be automatically downsized before sending to the API.# Assuming you have image data stored as a binary string...result = model.classify(image)if result["label"] == "dog":print("That's a dog")elif result["label"] == "cat":print("That's a cat")
Package: ml4pd
ml4pdProcess design with Machine Learning.How to Install for Userspip install ml4pd git+https://github.com/NREL/ml4pd_models.git@v1How to Set Up for Developmentgit clone https://github.com/NREL/ml4pd.git
git clone https://github.com/NREL/ml4pd_models.git
mamba env create -f ml4pd/environment.yml
conda develop ml4pd
conda develop ml4pd_modelsAdditional GitHub repositories for docs, tests and trainingml4pd_utils: code base for generating & preparing data for training.autoaspen: database for data obtained by aspen & python.To debug docsUsemkdocs servewithinml4pddirectory, then go tolocalhost:8000.generate_site.pygets docstrings (written in makrdown) from classes and put them in the right directory.To make changes to notebooks, addml4pdto path withsys.path.append()orconda develop.Relationship with ml4pd_modelsTo minimize manual work, ml4pd dependends on a specific ml4pd_models github branch. When changes are made to
either ml4pd or ml4pd_models that will break compatibility, create new branch in ml4pd_models, and apply change
to workflow .yml files, this README and index.md in docs.
Package: ml4proflow-mods-io
ml4proflow-mods-ioThis package provides basic input/output modules for ml4proflow.InstallationActivate your virtual environment (optionally) and
install the package with pip:pipinstallml4proflow-mods-ioContributionFor development, install this repository in editable mode with pip:gitclonehttps://gitlab.ub.uni-bielefeld.de/ml4proflow/ml4proflow-mods-io.gitcdml4proflow-mods-io
pipinstall-e.version: 1.1
Package: ml4s
ML4Science🌐Homepage•📖Documentation•🔗API•▶ Videos•ExamplesML4Science provides a unified math and neural network API for Jax, PyTorch, TensorFlow and NumPy.See theinstallation Instructionson how to compile the optional custom CUDA operations.fromjaximportnumpyasjnpimporttorchimporttensorflowastfimportnumpyasnpfromml4simportmathmath.sin(1.)math.sin(jnp.asarray([1.]))math.sin(torch.tensor([1.]))math.sin(tf.constant([1.]))math.sin(np.asarray([1.]))CompatibilityWriting code that works with PyTorch, Jax, and TensorFlow makes it easier to share code with other people and collaborate.Your published research code will reach a broader audience.When you run into a bug / roadblock with one library, you can simply switch to another.ML4Science can efficientlyconvert tensors between ML librarieson-the-fly, so you can even mix the different ecosystems.Fewer mistakesNo more data type troubles: ML4Scienceautomatically converts data typeswhere needed and lets you specify theFP precision globally or by context!No more reshaping troubles: ML4Science performsreshaping under-the-hood.Isneighbor_idx.at[jnp.reshape(idx, (-1,))].set(jnp.reshape(cell_idx, (-1,) + cell_idx.shape[-2:]))correct?: ML4Science provides a custom Tensor class that lets you writeeasy-to-read, more concise, more explicit, less error-prone code.Unique featuresn-dimensional operations: With ML4Science, you can write code thatautomatically works in 1D, 2D and 3D, choosing the corresponding operations based on the input dimensions.Preconditioned linear solves: ML4Science canbuild sparse matrices from your Python functionsand run linear solverswith preconditioners.Flexible neural network architectures:ML4Science provides various configurable neural network architectures, from MLPs to U-Nets.Non-uniform tensors: ML4Science allows you tostack tensors of different sizes and keeps track of the resulting shapes.
Package: ml4teens
ML4TeensMachine Learning for Teens(Aprendizaje Automático paraadolescentes)Paquete Python (ml4teensen pip) que permite creararquitecturas basadas en bloquesque lleven a cabo un proceso de ML.Cada bloque hace algo concreto, en modocaja negra; cada uno de ellos generasignals y poseeslots en donde recibir las señales.Un objeto (singleton) se encarga de emparejar los signals con slots (con control de tipos) y lanzar la red.[!NOTE]
Versión actual: 0.1.34 en pip.[!NOTE]
El submóduloblocks/imgestá en estado estable.El submódulocoreestá en estado estable.El código que sigue, muestra un ejemplo -básico- de lo que puede hacer el paquete (en la rama 'main').importml4teensasml;context=ml.core.Context.instance;context.reset();# no es necesario, pero...imagen=ml.blocks.img.ImageSource();img2text=ml.blocks.img.ImageToText(caption="A photo of an");terminal=ml.blocks.Terminal();salida=ml.blocks.Display(width=300);imagen("image")>>img2text("image");imagen("image")>>salida("image");img2text("text")>>terminal("stdout");source="https://img.freepik.com/foto-gratis/mujer-tiro-completo-bicicleta-al-aire-libre_23-2149413735.jpg?w=1380&t=st=1704297833~exp=1704298433~hmac=433c68f72fc841cbb094d598521f8b72dad100a383f59b39de5f490cce7c7b99";context.emit(target=imagen,slot_name="source",data=source);context.wait();Por otro lado, hacer un bloque es sencillo, uno básico que -por ejemplo- que se queda con sólo un canal de una imagen:fromPIL.ImageimportImage;fromPIL.Imageimportfromarray;fromml4teens.coreimportBlock;classSingleChannel(Block):#-------------------------------------------------------------------------# channeldef__init__(self,**kwargs):super().__init__(**kwargs);asserttype(self.params.channel)isint,"El parámetro 'channel' debe ser el número del canal (0, ...)";#------------------------------------------------------------------------- slots@Block.slot("image",{Image})defslot_image(self,slot,data):c=len(data.getbands());n=self.params.channel;assertninrange(0,c),f"El canal{n}no puede ser extraído de una imagen de{c}canales (recuerda: empieza a contar en 0)";imagen=data.getchannel(n);self.signal_image(imagen);self.reset("image");#------------------------------------------------------------------------- signals@Block.signal("image",Image)defsignal_image(self,data):returndata;Este bloque (de tipopassthru), puede recibir imágenes en forma de imagen
PIL por medio delslotllamadoimagey reenvía el canal indicado de
dicha imagen por medio de una señal, igualmente llamadaimage.Observar:El constructor de la clase puede recibir parámetros de usuario.El slot se define mediante un decorador (@Block.slot).La señal (signal) se define igualmente mediante un decorador (@Block.signal).El slot, una vez hecha la conversión, pasa la imagen a la señal, invocando al métodosignal_image.Los métodos decorados por @Block.signal no tienen que hacernecesariamentealgo, salvo devolver el dato que finalmente se envíe.