Saravanan666/pypi-org huggingface dataset:
Package: neural-semigroups
Neural SemigroupsThe project is abandoned.If you want to reproduce results from thepaper, please usethis
notebook.Here we try to model Cayley tables of semigroups using neural networks.This work was inspired bya sudoku
solver. A solved Sudoku puzzle
is nothing more than a Cayley table of a quasigroup from 9 items with
some well-known additional properties. So, one can imagine a puzzle made
from a Cayley table of any other magma, e.g. a semigroup, by hiding part
of its cells.There are two major differences between sudoku and puzzles based on
semigroups:it’s easy to take a glance on a table to understand whether it is a
sudoku or not. That’s why it was possible to encode numbers in a
table cells as colour intensities. Sudoku is a picture, and a
semigroup is not. It’s difficult to check a Cayley table’s
associativity with a naked eye;Sudoku puzzles are solved by humans for fun and thus catalogued. When
solving a sudoku one knows for sure that there is a unique solution.
On the contrary, nobody guesses values in a partially filled Cayley
table of a semigroup as a form of amusement. As a result, one can
create a puzzle from a full Cayley table of a semigroup but there may
be many distinct solutions.How to InstallThe best way to install this package is to usepip:pipinstallneural-semigroupsHow to useThe simplest way to get started is touse Google
Colaboratory.To look at more experimental results for different semigroups
cardinalities, you can useKaggle:cardinality
4cardinality
5There is also an experimentalnotebookcontributed byŽarko Bulić.How to ContributePull requestsare welcome. To start:gitclonehttps://github.com/inpefess/neural-semigroupscdneural-semigroups# activate python virtual environment with Python 3.6+pipinstall-Upippipinstall-Usetuptoolswheelpoetrypoetryinstall# recommended but not necessarypre-commitinstallTo check the code quality before creating a pull request, one might run
the scriptshow_report.sh.
It locally does nearly the same as the CI pipeline after the PR is
created.Reporting issues or problems with the softwareQuestions and bug reports are welcome onthe
tracker.More documentationMore documentation can be foundhere.
Package: neuralshare-node
nodethe node is a simple program that listens to the transactions on accountGAU3XW7Z5OYR7BFANSB7HGVGJRZJ5NSJ3ERWJ5HRVZRCHFSRTQ32YPEMand sends the corresponding GPT-3 response to the sender account.
The first time you start node, a configuration file called "config.json" will automatically be created in the same folder. Before restarting the node again, now that your configuration file exists, you will need to put your openai api key inside the file, inside "openai_apikey". Then simply replace"openai_apikey":nullwith"openai_apikey":"sk-xxxx". Then you can start the node which will send the response as soon as it sees a new transaction. Before sending it, it will make sure that it has not already been sent by another node.
In the configuration file you will also see a Stellar secret key which is automatically created together with the file. The account connected to it is already funded by FriendBot on FUTURENET and it is the account that will send the responses. If you wish, you can modify that secret key bearing in mind that for correct functioning the account should have XLMs available to be able to carry out transactions on Futurenet. however, it is advisable not to modify it and leave what you find
Package: neural-structured-learning
Neural Structured Learning (NSL) is a new learning paradigm to train neuralnetworks by leveraging structured signals in addition to feature inputs.Structure can be explicit as represented by a graph or implicit as inducedby adversarial perturbation.Structured signals are commonly used to represent relations or similarityamong samples that may be labeled or unlabeled. Leveraging these signalsduring neural network training harnesses both labeled and unlabeled data,which can improve model accuracy, particularly when the amount of labeleddata is relatively small. Additionally, models trained with samples that aregenerated by adversarial perturbation have been shown to be robust againstmalicious attacks, which are designed to mislead a model's prediction orclassification.NSL generalizes to Neural Graph Learning as well as to Adversarial Learning.The NSL framework in TensorFlow provides the following easy-to-use APIs andtools for developers to train models with structured signals:* Keras APIs to enable training with graphs (explicit structure) andadversarial perturbations (implicit structure).* TF ops and functions to enable training with structure when usinglower-level TensorFlow APIs.* Tools to build graphs and construct graph inputs for training.The NSL framework is designed to be flexible and can be used to train anykind of neural network. For example, feed-forward, convolution, andrecurrent neural networks can all be trained using the NSL framework. Inaddition to supervised and semi-supervised learning (a low amount ofsupervision), NSL can in theory be generalized to unsupervised learning.Incorporating structured signals is done only during training, so theperformance of the serving/inference workflow remains unchanged.
Package: neural-style-transfer
neuralstyletransferThis is the python library which takes two imagecontent imageandstyle imageand provide the output image looks like
a content image but painted in the style of provided reference style imageUsagepip install neural-style-transferImport NeuralStyleTransfer class from Libraryfrom neuralstyletransfer.style_transfer import NeuralStyleTransfercreate class objectnst = NeuralStyleTransfer()Load the content image and style image from local pathnst.LoadContentImage(content_img_path)
nst.LoadStyleImage(style_img_path)You can also load image from external URL by providing pathType='url' by default it is setted to 'local'nst.LoadContentImage(content_url, pathType='url')
nst.LoadStyleImage(style_url, pathType='url')Use default parameters to train the modelDefault parameters are given below we can change it based on requirement by providing values on calling .apply methodcontentWeight=1e3styleWeight=1e-2epochs=500output = nst.apply()
output.save('output.jpg')Content imageStyle imageOutput image
Package: neuralx
NeuralXNeuralX is a Python package for creating neural networks with a wide range of features, including gradient checks, regularization, dropout, mini batch, and various optimization methods. This package is designed to be beginner-friendly, making it easy for users to understand neural network structures and build powerful models.FeaturesGradient Checks:Ensure the correctness of your neural network's gradients with built-in gradient check tools.Regularization:Apply L1 and L2 regularization to prevent overfitting and improve model robustness.Dropout:Implement dropout layers for more effective regularization and enhanced generalization.Optimization Methods:Choose from different optimization methods, including the popular Adam optimizer, to train your models efficiently.InstallationYou can install NeuralX usingpip:pipinstallneuralxUsageTo get started with NeuralX, follow these steps:Import the necessary modules from NeuralX.Build your neural network architecture, specifying layers, activation functions, and other parameters.Train your model on your dataset using the chosen optimization method.Evaluate the model's performance using the provided tools, including the confusion matrix implementation.Here's a simple example of how to use NeuralX to create a basic neural network:importneuralxasnx# create layers descriptionlayers=[(training_set.shape[0],None,None,None),(5,'he','relu',1),# no_of_units, normalization_method, activation_function, dropout_keeping_probability(2,'he','relu',1),(1,'he','sigmoid',1)]# Create a neural networkmodel=nx.NeuralNetwork(layers)# Train the modelmodel.train(training_set,training_set_labels,no_of_epochs=1000,optimization={'name':'momentum','beta':0.9},is_mini_batch=True,learning_rate=0.001)# Evaluate the model using the confusion matrixcm=nx.ConfusionMatrix(model,test_set,test_set_labels)print(cm)print(cm.statistics())ContributingWe welcome contributions from the community. Feel free to submit bug reports, feature requests, or even pull requests. Together, we can make NeuralX even better!LicenseThis project is licensed under the MIT License. See theLICENSEfile for details.
Package: neuranet
🌐 NeuraNET Python API libraryThe Official NeuraNET Python Library🛠 InstallationTo get started with the library, simply run the following command:pipinstallneuranet💬 Chat UsageUsing the Chat AIs library is straightforward. Below is an example of a conversation:importneuranetapi_key="YOUR_API_KEY_HERE"mode='chat'client=neuranet.client(api_key,mode)# Set up the conversation history and modelconversation_history=[{"sender":"instruct","content":"You are a helpful assistant."},{"sender":"user","content":"Hello."},{"sender":"assistant","content":"Hello, how can I assist you?"},{"sender":"user","content":"How are you?"}]model="nlite"# options: nlite, npro, npro-vision# Get the responsetry:response=client.generate(conversation_history,model)print(response)exceptExceptionase:print(f"An error occurred:{e}")🎨 Image UsageUsing the Image generation AIs library is straightforward. Below is an example of a conversation:importneuranetapi_key="YOUR_API_KEY_HERE"mode='image'client=neuranet.client(api_key,mode)model="vinci-mini"# options: vinci-mini, vinci-maxprompt="A large oak tree"response=client.generate(prompt,model)print(response)
Package: neura-swift
Neura - SwiftInstallingUsing pipSwift is designed to be controlled through theRobotics Toolbox for Python. By installing the toolbox through PyPI, swift is installed as a dependency.Neura-Swift is an extension to work with the neura-roboticstoolbox.pip3installneura-roboticstoolboxOtherwise, Swift can be install bypip3installneura-swift
Package: neurax
NeuraxAn ssh/sshfs wrapper to improve your mind control over remote machines.Establish anssh multiplexed
connectionUse it to mount remote directories of your choice in local path of your choices so you can access
them as if there were on your machineOpen as many shells as you wantAnd more! Coming soon! I swear! Hopefully!InstallationYou will needssh(OpenSSH >3.9 works, I have not tested anything else yet) andsshfsinstalled
and in your path to use Neurax, install them anyway you want. Then install Neurax. Vanilla pip
should work but I recommendpipxpipx install neuraxUsageCreate a config file (inTOML) for the host you want to connect to, see
examples inexamples/(optional) if you want to be able to use this config by name, add it as~/.config/neurax/configs/some_name.toml(or the equivalentXDG Base
directoryon your machine)Connect by runningneurax connect path/to/your/config.tomlorneurax connect some_namesif
you followed step 2. Running this several times will open new shells through the same multiplexed
connection, which will not close even if you exit the shell (so your mounts stay up)Disconnect by runningneurax disconnect path/to/your/config/file. This closes the
multiplexed connection. (Also works with a saved config name)
Package: neuraxle-tensorflow
Neuraxle-TensorFlowTensorFlow steps, savers, and utilities forNeuraxle.Neuraxle is a Machine Learning (ML) library for building neat pipelines, providing the right abstractions to both ease research, development, and deployment of your ML applications.Usage exampleSee also a complete exampleTensorflow 1Create a tensorflow 1 model step by giving it a graph, an optimizer, and a loss function.defcreate_graph(step:TensorflowV1ModelStep,context:ExecutionContext):tf.placeholder('float',name='data_inputs')tf.placeholder('float',name='expected_outputs')tf.Variable(np.random.rand(),name='weight')tf.Variable(np.random.rand(),name='bias')returntf.add(tf.multiply(step['data_inputs'],step['weight']),step['bias'])"""# Note: you can also return a tuple containing two elements : tensor for training (fit), tensor for inference (transform)def create_graph(step: TensorflowV1ModelStep, context: ExecutionContext)# ...decoder_outputs_training = create_training_decoder(step, encoder_state, decoder_cell)decoder_outputs_inference = create_inference_decoder(step, encoder_state, decoder_cell)return decoder_outputs_training, decoder_outputs_inference"""defcreate_loss(step:TensorflowV1ModelStep,context:ExecutionContext):returntf.reduce_sum(tf.pow(step['output']-step['expected_outputs'],2))/(2*N_SAMPLES)defcreate_optimizer(step:TensorflowV1ModelStep,context:ExecutionContext):returntf.train.GradientDescentOptimizer(step.hyperparams['learning_rate'])model_step=TensorflowV1ModelStep(create_grah=create_graph,create_loss=create_loss,create_optimizer=create_optimizer,has_expected_outputs=True).set_hyperparams(HyperparameterSamples({'learning_rate':0.01})).set_hyperparams_space(HyperparameterSpace({'learning_rate':LogUniform(0.0001,0.01)}))Tensorflow 2Create a tensorflow 2 model step by giving it a model, an optimizer, and a loss function.defcreate_model(step:Tensorflow2ModelStep,context:ExecutionContext):returnLinearModel()defcreate_optimizer(step:Tensorflow2ModelStep,context:ExecutionContext):returntf.keras.optimizers.Adam(0.1)defcreate_loss(step:Tensorflow2ModelStep,expected_outputs,predicted_outputs):returntf.reduce_mean(tf.abs(predicted_outputs-expected_outputs))model_step=Tensorflow2ModelStep(create_model=create_model,create_optimizer=create_optimizer,create_loss=create_loss,tf_model_checkpoint_folder=os.path.join(tmpdir,'tf_checkpoints'))Deep Learning Pipelinebatch_size=100epochs=3validation_size=0.15max_plotted_validation_predictions=10seq2seq_pipeline_hyperparams=HyperparameterSamples({'hidden_dim':100,'layers_stacked_count':2,'lambda_loss_amount':0.0003,'learning_rate':0.006,'window_size_future':sequence_length,'output_dim':output_dim,'input_dim':input_dim})feature_0_metric=metric_3d_to_2d_wrapper(mean_squared_error)metrics={'mse':feature_0_metric}signal_prediction_pipeline=Pipeline([TrainOnly(DataShuffler()),WindowTimeSeries(),MeanStdNormalizer(),MiniBatchSequentialPipeline([Tensorflow2ModelStep(create_model=create_model,create_loss=create_loss,create_optimizer=create_optimizer,print_loss=True).set_hyperparams(seq2seq_pipeline_hyperparams)])])pipeline,outputs=pipeline.fit_transform(data_inputs,expected_outputs)
Package: neurec
NeuRec is a comprehensive and flexible Python library for recommendation models that includes a large range of state-of-the-art neural recommender models. This library aims to solve general and sequential (i.e. next-item) recommendation tasks, using the Tensorflow library to provide 26 models out of the box. NeuRec is open source and available under the MIT license. View the GitHub repository for more information:https://github.com/wubinzzu/NeuRec.git
Package: neureca
Neureca💡 for Conversational Recommender SystemsThis repository contains source code for Neureca💡 project. Neureca💡 is a framework for building conversational recommender (ConvRec) systems. It is also an open-source project that helps any ML researchers develop ConvRec system and contribute to key components easily. Neureca💡 is still under development stage.installationpip install neurecausagetrain NLU model(i.e. intent recognizer, item attribute recognizer) and Recommender model using your own datasetneureca-train intent  
neureca-train attribute 
nuereca-train recommendercreate main.py under your project directoryfrompathlibimportPathfromneurecaimportNeureca,NLU,Recommender,Explainer,Managerfromstagesimportgreeting_stage# load the latest trained version of each model if version is not speicfied in arguamentnlu=NLU()recommender=Recommender()explainer=Explainer()dialogue_manager=Manager(initial_stage=greeting_stage)#create nureca instanceneureca=Neureca(nlu=nlu,recommender=recommender,explainer=explainer,dialogue_manager=dialogue_manager,path=Path.cwd(),)if__name__=='__main__':neureca.run(env="cli")# env: cli or webrun neurecapython main.pyFor more information, please visitdocs.
Package: neurencoding
NeurencodingNeurencoding is a python package for implementing encoding models of single-unit activity for (at the moment) units recorded using electrophysiology. This package provides tools for expressing single-unit firing as a function of many external variables, such as task-related events and values.This is the package used for the International Brain Lab (IBL) brain-wide map paper.Getting startedInstallationIn order to installneurencoding, please use pip to install the package in your choice of python environment (e.g. using virtualenv or Anaconda):pip install neurencodingLoading in dataTBDSource workAll work based on code from pillowlab/neuroGLM, translated and extended.
Package: neuri
==============
Python Client Library for NeuriThis is the official Python client library for Neuri. It allows you to easily integrate Neuri into your Python applications.++++++++++++
Installation
++++++++++++To get started with the Neuri Python Client Library, you need to first install the library using pip:.. code-block:: pythonpip install neuri+++++
Import and initialize the library
+++++First you have to import the module and set up the client with your API key and service details, as shown below... code-block:: pythonimport neuri

config = {
    "service": "translate",
    "lang": "en",
    "temperature": 0.5,
    "api_key": "YOUR_API_KEY_HERE",
    "translate_to": "es" # optional
}

client = neuri.initialize_client(config)+++++
Import and initialize the library
+++++The Neuri Client Library currently supports three services:audio_file,audio_url, andtext. Each service has its own set of parameters and returns a JSON containing the results of the processing.neuri.audio_file(): Audio File.neuri.audio_url(): Audio URL.neuri.text(): Text+++++
Audio File
+++++Process audio files stored locally on your system using the audio_file service... code-block:: pythonresult = client.audio_file(file_path=[
    os.path.join(os.path.dirname(__file__), "examples/girl_phone_call.wav"),
    os.path.join(os.path.dirname(__file__), "examples/noise_man_question.wav")
])Replace the file paths in the file_path list with the actual paths to your audio files. The audio_file service will process the audio files and return the results in a JSON format.+++++
Audio URL
+++++Process audio files from a remote URL using theaudio_urlservice... code-block:: pythonresult = client.audio_url(url="https://neuri-storage.s3.amazonaws.com/public_data/girl_phone_call.wav?AWSAccessKeyId=AKIAQFECGXRQOTIJ2FUV&Signature=GjrMz1NkMtQgFd0etJUCiQg4WNI%3D&Expires=1995267608")Replace the file paths in the url list with the actual paths to your audio files. The audio_url service will process the audio files and return the results in a JSON format.+++++
Text
+++++Process text using the text service... code-block:: pythonresult = client.text(text="Hello, how are you?")Replace the text in the text parameter with the actual text you want to process. The text service will process the text and return the results in a JSON format.
Package: neurlink
NEURLINKA compact grammar for neural network definition based on PyTorch.BasicsNeurlink asks for a sequence ofnndefspecification lines to define a flexibly connected neural network.
Each line ofnndefsyntax follows the pattern((C1, S2), ..., (Cn, Sn), NerveClass[input_selector, tag](...))that says anervetakes inputs determined byinput_selectorand outputntensors, each of which has channel sizeCand spatial shapeS.input selectorEverynndefnode can select its input from the set of all previous lines. By default,input_selectorcan be omitted and means outputs of previousnndef; it can also be a integer index, aslice, or alist, so that one can specify multiple input nodes. The implementation of each layer is a subclass ofNerveand can get informations of the input specifications by attributeself.input_links.tagcan be optionally specified to allow you refer to anndefspecification by a string alias.output dimensionsOutput dimensions specify channels and spatial shapes seperately. The spatial shape written as a (tuple of) integer/float number is arelativedown sampling ratio of thebase_shape(specified bynv.Input). It can also be written as a string so that it evaluates to absolute shape. The dimensions are written out to give a straightforward impression of the computation flow of the entire network, and shape transformation is done automatically so that you don't bother to derive how many stride or padding you might want.Example (resnet)defresnet50(num_classes:int=1000,**block_keywords):block=BottleNeck(**block_keywords,expansion=4)expansion=4returnbuild([((3,1),nv.Input()),((64,2),Conv2d_ReLU_BN(7)),# 7x7 conv, stride 2((64,4),MaxPool2d(3)),# 3x3 maxpool, stride 2[((64*expansion,4),block)]*3,# 3 layers of residual blocks, w/o downsampling[((128*expansion,8),block)]*4,# 4 layers of residual blocks, downsampling (x2) happens at the first block[((256*expansion,16),block)]*6,# 256 * expansion is the actual output channel, the bottleneck shape is interpreted inside of the block.[((512*expansion,32),block)]*3,# finally downsamped to 1/32 of origin, you may have noted that you can easily figure out the global downsample ratio.((512*expansion,"(1, 1)"),AvgPool2d()),# a average pooling layer downsamples to an absolute shape.((num_classes,"(1, 1)"),Conv2d(1)),# final linear layer.])# in tests/test_build_models.pyimporttorchimportneurlinkmodel=neurlink.resnet18()x=torch.randn((2,3,224,224))out=model(x,output_intermediate=True)foryinout:print(y.shape)$pythontests/test_build_models.py

torch.Size([2,3,224,224])torch.Size([2,64,112,112])torch.Size([2,64,56,56])torch.Size([2,64,56,56])torch.Size([2,64,56,56])torch.Size([2,128,28,28])torch.Size([2,128,28,28])torch.Size([2,256,14,14])torch.Size([2,256,14,14])torch.Size([2,512,7,7])torch.Size([2,512,7,7])torch.Size([2,512,1,1])torch.Size([2,1000,1,1])
Package: neuro
NPU Python Client PackageTo install, run:pip install npuTo use in code:import npuThis library has four core functions.npu.api, npu.compile, npu.train, npu.predictHere is an example script of them in use.import npu
from npu.vision.models import resnet18
from npu.vision.datasets import CIFAR10


npu.api(API_KEY)

model = npu.train(resnet18(pretrained=True),
                         train_data=CIFAR10.train,
                         val_data=CIFAR10.val,
                         loss=npu.loss.CrossEntropyLoss,
                         optim=npu.optim.SGD(lr=0.01),
                         batch_size=128,
                         epochs=2)

output = npu.predict(model, data)Full documentation ishere
Package: neuro3d
neuro3dBlender framework for visualization of neurons and simulation data.InstallationMake sure thatpythonandblenderare valid commands. Most likely you have to make them available on your PATH.Linuxcurl -Ls https://github.com/Helveg/neuro3d/releases/latest/download/install | pythonWindowsExecute the following snippet from the command line or PowerShell:powershell.exe -command PowerShell -ExecutionPolicy Bypass -noprofile -command Invoke-WebRequest -Uri https://github.com/Helveg/neuro3d/releases/latest/download/install -OutFile _tmp_n3d_install.py; Invoke-Expression "python _tmp_n3d_install.py"; Remove-Item -Path "_tmp_n3d_install.py"
Package: neuroanalysis
NeuroanalysisModular and interactive tools for analysis of neurophysiology data, with emphasis on
patch-clamp electrophysiology.Functions for running common analysis algorithmsSynaptic/calcium event detection and characterizationSynaptic release modelingVC and CC spike detection and characterizationVC and CC test pulse analysisBasic signal processing (filtering, baseline removal, etc.)Data abstraction layer to allow adapting new data formats (see neuroanalysis/data.py)Re-usable user interface elements for implementing common analysis tasksStatusThis project is early in development and does not yet have a stable API.
Issues and pull requests are accepted (see CONTRIBUTING.md), but may not be
reviewed or accepted on any fixed schedule.