Saravanan666/pypi-org huggingface dataset:
Package: onema-cdk.static-website
Simple static site CDK constructThis constructs creates a static website hosted on S3.RequirementsHosted zone in Route53UsageThis constructs creates:S3 bucketDNS Validated certificateCloudFront web distributionRoute53 A recordfromaws_cdk.coreimportStack,Constructfromstatic_websiteimportStaticWebsitefromaws_cdk.aws_route53importHostedZoneclassWebSiteStack(Stack):def__init__(self,scope:Construct,id:str,**kwargs):super().__init__(scope,id,**kwargs)# The code that defines your stack goes herezone=HostedZone.from_lookup(self,"HostedZone",domain_name="mydomain.com",private_zone=False)StaticWebsite(self,"serverlesslink-website",hosted_zone=zone,site_domain="blog.mydomain.com",sources="../public",website_error="404.html")Website without custom domainfromaws_cdk.coreimportStack,Constructfromstatic_websiteimportStaticWebsiteclassWebSiteStack(Stack):def__init__(self,scope:Construct,id:str,**kwargs):super().__init__(scope,id,**kwargs)# The code that defines your stack goes hereStaticWebsite(self,"serverlesslink-website",sources="../public",website_error="404.html")
Package: onemap-py
Python Wrapper for SLA's OneMap APIIntroductionThis package provides a simple way to query data using the OneMap API in Python.OneMap arranges its API endpoints by groups, and this package is organized to reflect that grouping.onemap-py allows you to make queries corresponding to the OneMap API, and returns the results to you in a standard
pythondict.Sample Usageimportonemap_pyx=onemap_py.Client("email@hostname.com","password")# Search for an address# Note: For address search, no authentication requiredgh=x.search("GRAND HYATT")gh=gh['results'][0]# Take the first result# For other queries, need to authenticatex.authenticate()# if email and password were None, interactive prompt# Get planning area for Grand Hyattplanning_area=x.PlanningAreas.find_planning_area(gh['LATITUDE'],gh['LONGITUDE'])planning_area=planning_area['pln_area_n']# Get the name# Get some population-related information for the planning areaavail_data=x.Population.available_data_types# find all available data types supported by the OneMap APIx.Population.get_population_data("age",year=2018,planning_area=planning_area)# Get route from Grand Hyatt to, say, Changi Airport Terminal 3x.Router.supported_route_types# ['walk', 'drive', 'cycle', 'pt']changi_airport=x.search("CHANGI AIRPORT TERMINAL 3")['results'][0]x.Router.route_from_postal(gh['POSTAL'],changi_airport['POSTAL'],route_type='drive')DetailsClient:Main class that encapsulates all classes defined in other modules.Clientitself is a subclass ofOneMap, but also has attributesRouter,PlanningAreas,Population, andThemes.base.OneMapProvides basic functionality such as address search, coordinate conversion, and authentication.routing.RouterProvides functionality to query routes between 2 points.
Currently supports(lat,lng)->(lat,lng)orpostal->postalplanning_areas.PlanningAreasProvides functionality to retrieve planning areas, including geospatial boundaries.population.PopulationHandles all population-related queries, including education level, economic status, work income, marital status etc.
UsePopulation.available_data_typesto see available data typesFor a full list of data provided by OneMap, you can refer to their documentation.themes.ThemesThematic information from various agencies in Singapore.
Package: onemetric
onemetricInstallationInstall onemetric from PyPI (recommended):pip install onemetricInstall onemetric from the GitHub source:git clone https://github.com/SkalskiP/onemetric.gitcd onemetricpython setup.py installExampleFigure 1.Dataset sample, blue - ground-truth and red - detection.Calculate mAP@0.5>>>fromonemetric.cv.loadersimportYOLOLoader>>>fromonemetric.cv.object_detectionimportMeanAveragePrecision>>>model=load_model(...)# model-specific loading method>>>data_set=YOLOLoader(...images_dir_path=DATA_SET_IMAGES_PATH,...annotations_dir_path=DATA_SET_ANNOTATIONS_PATH...).load()>>>true_batches,detection_batches=[],[]>>>forentryindata_set:>>>detections=model(entry.get_image())# model-specific prediction method>>>true_batches.append(entry.get_annotations())>>>detection_batches.append(detections)>>>mean_average_precision=MeanAveragePrecision.from_detections(...true_batches=true_batches,...detection_batches=detection_batches,...num_classes=12,...iou_threshold=0.5...)>>>mean_average_precision.value0.61Calculate Confusion Matrix>>>confusion_matrix=ConfusionMatrix.from_detections(...true_batches=true_batches,...detection_batches=detection_batches,...num_classes=12...)>>>confusion_matrix.plot(CONFUSION_MATRIX_TARGET_PATH,class_names=CLASS_NAMES)Figure 2.Create confusion matrix chartDocumentationThe official documentation is hosted on Github Pages:https://skalskip.github.io/onemetricContributeFeel free to fileissuesorpull requests.Let us know what metrics should be part of onemetric!CitationPlease cite onemetric in your publications if this is useful for your research. Here is an example BibTeX entry:@MISC{onemetric,author={Piotr Skalski},title={{onemetric}},howpublished="\url{https://github.com/SkalskiP/onemetric/}",year={2021},}LicenseThis project is licensed under the BSD 3 - see theLICENSEfile for details.
Package: onemkl-sycl-blas
Intel® oneAPI Math Kernel Library (Intel® oneMKL) is a computing math library of highly optimized, extensively threaded routines for applications that require maximum performance. This package provides C and Data Parallel C++ (DPC++) programming language interfaces. Intel MKL C language interfaces can be called from applications written in either C or C++, as well as in any other language that can reference a C interface. Use it to optimize code for current and future generations of Intel® CPUs and GPUs.
Package: onemkl-sycl-datafitting
Intel® oneAPI Math Kernel Library (Intel® oneMKL) is a computing math library of highly optimized, extensively threaded routines for applications that require maximum performance. This package provides C and Data Parallel C++ (DPC++) programming language interfaces. Intel MKL C language interfaces can be called from applications written in either C or C++, as well as in any other language that can reference a C interface. Use it to optimize code for current and future generations of Intel® CPUs and GPUs.
Package: onemkl-sycl-dft
Intel® oneAPI Math Kernel Library (Intel® oneMKL) is a computing math library of highly optimized, extensively threaded routines for applications that require maximum performance. This package provides C and Data Parallel C++ (DPC++) programming language interfaces. Intel MKL C language interfaces can be called from applications written in either C or C++, as well as in any other language that can reference a C interface. Use it to optimize code for current and future generations of Intel® CPUs and GPUs.
Package: onemkl-sycl-lapack
Intel® oneAPI Math Kernel Library (Intel® oneMKL) is a computing math library of highly optimized, extensively threaded routines for applications that require maximum performance. This package provides C and Data Parallel C++ (DPC++) programming language interfaces. Intel MKL C language interfaces can be called from applications written in either C or C++, as well as in any other language that can reference a C interface. Use it to optimize code for current and future generations of Intel® CPUs and GPUs.
Package: onemkl-sycl-rng
Intel® oneAPI Math Kernel Library (Intel® oneMKL) is a computing math library of highly optimized, extensively threaded routines for applications that require maximum performance. This package provides C and Data Parallel C++ (DPC++) programming language interfaces. Intel MKL C language interfaces can be called from applications written in either C or C++, as well as in any other language that can reference a C interface. Use it to optimize code for current and future generations of Intel® CPUs and GPUs.
Package: onemkl-sycl-sparse
Intel® oneAPI Math Kernel Library (Intel® oneMKL) is a computing math library of highly optimized, extensively threaded routines for applications that require maximum performance. This package provides C and Data Parallel C++ (DPC++) programming language interfaces. Intel MKL C language interfaces can be called from applications written in either C or C++, as well as in any other language that can reference a C interface. Use it to optimize code for current and future generations of Intel® CPUs and GPUs.
Package: onemkl-sycl-stats
Intel® oneAPI Math Kernel Library (Intel® oneMKL) is a computing math library of highly optimized, extensively threaded routines for applications that require maximum performance. This package provides C and Data Parallel C++ (DPC++) programming language interfaces. Intel MKL C language interfaces can be called from applications written in either C or C++, as well as in any other language that can reference a C interface. Use it to optimize code for current and future generations of Intel® CPUs and GPUs.
Package: onemkl-sycl-vm
Intel® oneAPI Math Kernel Library (Intel® oneMKL) is a computing math library of highly optimized, extensively threaded routines for applications that require maximum performance. This package provides C and Data Parallel C++ (DPC++) programming language interfaces. Intel MKL C language interfaces can be called from applications written in either C or C++, as well as in any other language that can reference a C interface. Use it to optimize code for current and future generations of Intel® CPUs and GPUs.
Package: onemodel
OneModelDescriptionOneModelis a Python package for defining dynamic synthetic biology models easily and efficiently.OneModel's syntax allows the definition of models with chemical reactions, ODEs and/or algebraic loops --which makes OneModel especially suitable for control theory applications where you need to combine biological processes with controllers implemented by DAEs.
OneModel focuses on code readability and modularity; and provides the user with tools to check the coherence of the generated models.
OneModel generates anSBMLmodel file as output, which can be easily converted to other language implementations (such as Matlab, Julia, OpenModelica) with sbml2dae, or you could use many of the great SBML software developed by the community.This project is under active development.Documentation:https://onemodel.readthedocs.io/en/latest/InstallationRequires Python 3.8 or greater installed.pip install onemodelCitingIf you use OneModel in your research, please use the following citations in your published works:Santos-Navarro, F. N., Navarro, J. L., Boada, Y., Vignoni, A., & Picó, J. (2022). "OneModel: an open-source SBML modeling tool focused on accessibility, simplicity, and modularity."DYCOPS.Santos-Navarro, F. N., Vignoni, A., & Picó, J. (2022). "Multi-scale host-aware modeling for analysis and tuning of synthetic gene circuits for bioproduction."PhD thesis.LicenseCopyright 2022 Fernando N. Santos-Navarro, Jose Luis Herrero, Yadira Boada, Alejandro Vignoni, and Jesús PicóLicensed under the Apache License, Version 2.0 (the "License"); you may not use this software except in compliance with the License. You may obtain a copy of the License athttp://www.apache.org/licenses/LICENSE-2.0Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.
Package: onemodel-cli
SBML2dae: a customizable SBML to Matlab parserDescriptiononemodel-cliis a command-line interface for the functionality ofOneModel.MotivationOneModel can be used as a stand-alone package within a Python project. However, foronemodel-clistreamlines using OneModel for an expert users and simplies integrating OneModel into pre-existing workflows.How to setuponemodel-clican be installed from PyPI and is compatible with Python 3.7+ for Window, Mac and Linux.The latest package can be installed with:pipinstallonemodel-cliQuick exampleTODOCitingIf you useonemodel-cliin your research, please use the following citations in your published works:Santos-Navarro, F. N., Navarro, J. L., Boada, Y., Vignoni, A., & Picó, J. (2022). "OneModel: an open-source SBML modeling tool focused on accessibility, simplicity, and modularity."DYCOPS.Santos-Navarro, F. N., Vignoni, A., & Picó, J. (2022). "Multi-scale host-aware modeling for analysis and tuning of synthetic gene circuits for bioproduction."PhD thesis.LicenseCopyright 2022 Fernando N. Santos-Navarro, Jose Luis Herrero, Yadira Boada, Alejandro Vignoni, and Jesús PicóLicensed under the Apache License, Version 2.0 (the "License"); you may not use this software except in compliance with the License. You may obtain a copy of the License athttp://www.apache.org/licenses/LICENSE-2.0Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.
Package: onemodel-gui
onemodel-gui: a graphical user interface for OneModelDescriptiononemodel-guiis a graphical user interface for OneModel.MotivationOneModel can be used as a Python package or as a command-line interface (onemodel-cli), but both options are aimed for expert users with Python programming knowledge.onemodel-guiis aimed for non-experts users: it is the best way to start working with OneModel.How to setuponemodel-guican be installed from PyPI and is compatible with Python 3.7+ for Window, Mac and Linux.The latest package can be installed with:pipinstallonemodel-guiQuick exampleTODOCitingIf you useonemodel-guiin your research, please use the following citations in your published works:Santos-Navarro, F. N., Navarro, J. L., Boada, Y., Vignoni, A., & Picó, J. (2022). "OneModel: an open-source SBML modeling tool focused on accessibility, simplicity, and modularity."DYCOPS.Santos-Navarro, F. N., Vignoni, A., & Picó, J. (2022). "Multi-scale host-aware modeling for analysis and tuning of synthetic gene circuits for bioproduction."PhD thesis.LicenseCopyright 2022 Fernando N. Santos-Navarro, Jose Luis Herrero, Yadira Boada, Alejandro Vignoni, and Jesús PicóLicensed under the Apache License, Version 2.0 (the "License"); you may not use this software except in compliance with the License. You may obtain a copy of the License athttp://www.apache.org/licenses/LICENSE-2.0Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.
Package: one-msg-waba-sdk
[1MSG.IO](https://1msg.io/) is the perfect WhatsApp management tool for your business. With us you get full access to the official Whatsapp API/webhooks.  Every API request must contain an Authorize HTTP header with a token.  This is your channel token, which can be found in your channel project on your profile page. Please do not give the token to anyone or post it publicly.  The authorization token must be added to each request in the GET parameter &#39;token&#39; and always passed to query string (?token&#x3D;{your_token}). Parameters in GET queries pass query string. Parameters in POST requests — through the JSON-encoded request body.   All &#39;send&#39; methods (except /sendTemplate) will only work when the dialog session with the user is open. Some of our solutions simplify and avoid such limitations, but we urge you to pay more attention to this detail
Package: oneNeuron-perceptron-pypi-guptabhishek8
oneNeuron_perceptron_pypioneNeuron_perceptron_pypiHow to Use this-fromoneNeuron.perceptronimportPerceptron## get X and y and then use below commandsmodel=Perceptron(eta=eta,epoch=epoch)model.fit(X,y)Reference -Official python docsGithub docs for github action
Package: oneNeuron-pypi-abhishekvarma12345
oneNeuron_pypioneNeuron_pypiHow to use thisIn the main file create DataFrame for any of the logic gateimport Perceptron from oneNeuron.perceptron APIimport prepare_data,save_model and save_plot from utils.all_utils APIThere are loggong modules already present in the API's. So, paste the below code as it is in the start of main filelogging_str="[%(asctime)s:%(levelname)s:%(module)s]%(message)s"logs_dir="logs"os.makedirs(logs_dir,exist_ok=True)logging.basicConfig(filename=os.path.join(logs_dir,"running_logs.log"),level=logging.INFO,format=logging_str,filemode='a')The above logging code creates logs directory and do the logging in running_logs.log fileThen train the model accordingly and include logging wherever needed in the main programfromoneNeuron.perceptronimportPerceptron## get X and y and then use below commandsmodel=Perceptron(eta=eta,epochs=epochs)model.fit(X,y)References -Official Python Docsgithub docs for github actions
Package: oneNeuron-pypi-achievehigh
oneNeuron_pypioneNeuron_pypi##How to use thisfromoneNeuron.perceptronimport##get X and Y and then use below commandsmodel_OR=Perceptron(eta=eta,epochs=epochs)model_OR.fit(X,y)#Referenceofficial python docs[github docs for github actions](https://docs.github.com/en/actions/automating-builds-and-tests/building-and-testing-python#publishing-to-package-registries)
Package: oneNeuron-pypi-BihanBanerjee
oneNeuron_pypioneNeuron_pypiHow to use thisfromoneNeuron.perceptronimportPerceptron## first have X and y and then use below commandsmodel=Perceptron(eta=eta,epochs=epochs)model.fit(X,y)Reference -Official Python docsReference -Github docs for Github actionsDatasetsx1x2y000010100111
Package: oneNeuron-pypi-DhyeySherasia
oneNeuron_pypioneNeuron_pypiHow to use:fromoneNeuron_pypi.perceptronimportPerceptron# Get X and y first and then use below mentioned commandsmodel=Perceptron(eta=ETA,epochs=EPOCHS)model.fit(X,y)ReferencesOfficial python docsOther references
Package: oneNeuron-pypi-dipdregan
oneNeuron_pypianother -:Dipendra Pratapcreate direcotry inside directorymkdirssrc/oneNeuroncreate direcotry inside directory the directory create another directorymkdirs-psrc/oneNeuroncreate file inside directorytouchsrc/oneNeuron/perceptron.pyoneNeuron_pypioneNeuron_pypiHow to use thisfromoneNeuron.perceptronimportPerceptron## get X and y and then use below commandsmodel=Perceptron(eta=eta,epochs=epochs)model.fit(X,y)Reference -official python docsgithub docs for github actions
Package: oneNeuron-pypi-isaiyamudhugit
oneNeuron.pypioneNeuron.pypihow to usefromoneNeuron.perceptronimportPerceptron## get X and y and then use below commandsmodel=Perceptron(eta=eta,epochs=epochs)model.fit(X,y)reference -[official python doc]  (https://packaging.python.org/tutorials/packaging-projects/)[official docs for github action] (https://docs.github.com/en/actions/automating-builds-and-tests/building-and-testing-python#publishing-to-package-registries)
Package: oneNeuron-pypi-jaiyesh
oneNeuron_pypioneNeuron_pypihOW TO USEfromoneNeuron.perceptronimportPerceptron## get x and ymodel=Perceptron(eta,epochs)#MAking an object model from class perceptronmodel.fit(X,y)#Fit method of Perceptron classReference-official python packaging documentationgithub actions
Package: oneNeuron-pypi-jhasid
oneNeuron_pypioneNeuron_pypiReferencePackaging Referencegit hub docs for git hub actionsHow to usefromoneNeuron.pereceptronimportPerceptron#src file## get X,Y and then use as belowmodel=Perceptron(eta=eta,epochs=epochs)model.fit(x,y)
Package: oneNeuron-pypi-kkkumar2
SINGLE LAYER PERCEPTRON :The perceptron is a single processing unit of any neural network. Frank Rosenblatt first proposed in 1958 is a simple neuron which is used to classify its input into one or two categories. Perceptron is a linear classifier, and is used in supervised learning. It helps to organize the given input data.A perceptron is a neural network unit that does a precise computation to detect features in the input data. Perceptron is mainly used to classify the data into two parts. Therefore, it is also known as Linear Binary Classifier.The perceptron consists of 4 parts.Input value or One input layer: The input layer of the perceptron is made of artificial input neurons and takes the initial data into the system for further processing.Weights and Bias:
Weight: It represents the dimension or strength of the connection between units. If the weight to node 1 to node 2 has a higher quantity, then neuron 1 has a more considerable influence on the neuron.Bias: It is the same as the intercept added in a linear equation. It is an additional parameter which task is to modify the output along with the weighted sum of the input to the other neuron.
Net sum: It calculates the total sum.Activation Function: A neuron can be activated or not, is determined by an activation function. The activation function calculates a weighted sum and further adding bias with it to give the result.how to use this Package :Import these libraries :fromoneNeuron.perceptronimportPerceptronfromoneNeuron.allutilsimportprepare_datafromoneNeuron.allutilsimportsave_modelhow to call Perceptron class :model=Perceptron(eta,epochs)model.fit(X,y)A glance of the code :classPerceptron:def__init__(self,eta,epochs):self.weights=np.random.randn(3)*1e-4# SMALL WEIGHT INITlogging.info(f"initial weights before training:\n{self.weights}")self.eta=eta# LEARNING RATEself.epochs=epochsdefactivationFunction(self,inputs,weights):z=np.dot(inputs,weights)# z = W * Xreturnnp.where(z>0,1,0)# CONDITION, IF TRUE, ELSEdeffit(self,X,y):self.X=Xself.y=yX_with_bias=np.c_[self.X,-np.ones((len(self.X),1))]# CONCATINATIONlogging.info(f"X with bias:\n{X_with_bias}")forepochintqdm(range(self.epochs),total=self.epochs,desc="Training the Model"):logging.info("--"*10)logging.info(f"for epoch:{epoch}")logging.info("--"*10)y_hat=self.activationFunction(X_with_bias,self.weights)# foward propagationlogging.info(f"predicted value after forward pass:\n{y_hat}")self.error=self.y-y_hatlogging.info(f"error:\n{self.error}")self.weights=self.weights+self.eta*np.dot(X_with_bias.T,self.error)# backward propagationlogging.info(f"updated weights after epoch:\n{epoch}/{self.epochs}:\n{self.weights}")logging.info("#####"*10)defpredict(self,X):X_with_bias=np.c_[X,-np.ones((len(X),1))]returnself.activationFunction(X_with_bias,self.weights)deftotal_loss(self):total_loss=np.sum(self.error)logging.info(f"total loss:{total_loss}")returntotal_loss
Package: oneNeuron-pypi-kokila0602
oneNeuron_pypioneNeuron_pypiHow to use thisfromoneNeuron.perceptronimportPerceptron## get x and y and then use this commandsmodel=Perceptron(eta=eta,epochs=epochs)model.fit(X,y)# Reference[officalpythondocs](https://packaging.python.org/tutorials/packaging-projects/)[githubdocsforgithubactions](https://docs.github.com/en/actions/automating-builds-and-tests/building-and-testing-python#publishing-to-package-registries)
Package: one-Neuron-pypi-LijiAlex
oneNeuron_pypione-Neuron-pypi-LijiAlexDescriptionA package to implement the functionality of a PerceptronPackage descriptiononeNeuron.perceptron [A Perceptron class]utils.all_utils.createModel [Creates a perceptron model]utils.all_utils.prepare_data [Prepares a pandas data frame from numpy array data]utils.all_utils.save_model [Saves the perceptron model created]utils.all_utils.save_plot [Saves the plot of the input vs output]Installationpip install one-Neuron-pypi-LijiAlexHow to import the PackagefromoneNeuron.perceptronimportperceptronfromutils.all_utilsimportcreateModelSample code to create an AND modelfromutils.all_utilsimportcreateModeldefmain(data,eta,epoch,file_name,plot_name):createModel(data,eta,epoch,file_name,plot_name)if__name__=='__main__':##entry pointAND={"x1":[0,0,1,1],"x2":[0,1,0,1],"y":[0,0,0,1]}#dataETA=0.3# learning rate between 0 and 1 (assume)EPOCHS=10try:main(data=AND,eta=ETA,epoch=EPOCHS,file_name="and.model",plot_name="and.png")exceptExceptionase:raisee# raise exception in terminalTips to create other modelsCopy the sample code for AND.Replace data with desired data.Give appropriate value for data,file_name and plot_name while calling main.Sample Project using the packagehttps://github.com/LijiAlex/oneNeuronWhat worksAND, OR, NOT, NOT, NANDWhat doesn't workXOR, XNOR