Saravanan666/pypi-org huggingface dataset:
Package: ngpy
NGPy~~~~NGPy is a web application that enable online performing and analyzing Monte-Carlo simulations on nucleation and growth phenomena.It can be also used as a web framework to develop your own web applications.NGPy is built on top of **Flask**.NGPy is actually a distributed system since we use **ZEO server** as a database and **redis** server as a message queue.Quickstart``````````1. Install----------::$ easy_install ngpyor::$ tar -xvf ngpy-xxx.tar.gz$ cd ngpy-xxx$ python setup.py install* Make sure that the NoSQL database **redis** is installed in your system.* Other dependencies such as **ZODB3**, **WTForm**, **Numpy**, and **Matplotlib** should be taken care by *easy_install*.If not, try to install them mannually.2. Start ZEO server--------------------::$ runzeo -a 0.0.0.0:<ZEOPORT> -f /path/to/your/Data.fs* <ZEOPORT> must be an available port number, e.g. 1234* The **ZEO** server should run at a host that other hosts can connect to.3. Start redis server---------------------::$ redis-server [/path/to/redis.conf]* The **redis** server should run at a host that other hosts can connect to.4. Start the task queue daemon------------------------------::$ simd [-r <server>] [-q <qkey>]* <server> is the host where you run the **redis** server.* What **simd** does is that it just picks the messages from **redis** server and process them.So you can run **simd** at every hosts where you wish to run the task.5. Start ngpy-------------::$ run-ngpy [-c </path/to/ngpy.cfg>]The *ngpy.cfg* is the configuration file for **ngpy**.It is a Python file and will be processed by *Flask*.Only values in uppercase are actually used.More details are refered to the **Flask** documentation.The **ngpy** website should be now served at http://localhost:5000.You can visit it use any browser (Chrome, Firefox, IE, etc.) viahttp://localhost:5000if you are a local visitor, orhttp://IP.of.NGPy.run:5000if you visit remotely.Ask for Help````````````* You can directly contact me at liuyxpp@gmail.com.* You can join the mailinglist by sending an email to ngpy@librelist.com and replying to the confirmation mail.To unsubscribe, send a mail to ngpy-unsubscribe@librelist.com and reply to the confirmation mail.Links`````* `Website <http://liuyxpp.bitbucket.org>`_* `Development version <http://bitbucket.org/liuyxpp/ngpy/>`_
Package: ng.pybf
pybfpybfcontains a Python interpreter and a C transpiler/compiler for brainfuck.Installationpipinstallng.pybfUsageCLIusage: pybf [-h] [-p] [-c] [--compile] [-r] [filename]

Python interpreter and C transpiler/compiler for brainfuck

positional arguments:
  filename      path to bf file; leave blank to read from stdin

options:
  -h, --help    show this help message and exit
  -p, --python  use Python to interpret and run
  -c            transpile to C
  --compile     compile the transpiled C file
  -r, --run     executeYou may also call it as a Python modulepython3-mpybf[-h][-p][-c][--compile][-r][filename]
Package: ngrad
Autograd Engine & Neural Network LibraryThe repository includes an Autograd engine and a neural network library that handle an N-dimensional array.Autograd is a tool used for derivative calculation. It tracks operations on values with enabled gradients and builds a dynamic computational graph — a graph without cycles. Input values serve as the leaves of the graph, while output values act as its roots. Gradients are computed by traversing the graph from root to leaf, applying the chain rule to multiply gradients at each step.Andrej Karpathy'sMicrogradserved as inspiration for this project. But this Autograd engine will accept N-dimensional array, whereas Microgard accepts scalar values only.BlogBuilding Autograd Engine & Neural Network Library: An Interactive GuideThe article provides a comprehensive guide to building an autograd engine and a neural network library that handle an N-dimensional array. It assumes a basic understanding of Python programming, high school calculus, and neural networks but offers various teaching methods for beginners. It includes line-by-line code explanations, output visualizations, and an interactive area to explore derivatives. The guide covers the foundational concepts of neural networks, starting with derivatives and progressing to backpropagation. It explains how to perform backpropagation manually and programmatically, including implementation techniques. The article also demonstrates the building of an autograd class from scratch and its application to training a neural network on a dataset. It concludes by guiding readers through the development of a simple neural network library using the autograd class.Installationpip install ngradDevelopmentThe only library required for this to work is Numpy, which is used to handle N-dimensional array.pip install -r requirements.txtTestTo verify the accuracy of theengine.pycode and ensure that it produces the same output for the N-dimensional array, scalar value, and PyTorch APIs, execute the following command (make sure that PyTorch is installed):cd test/
pip install -r requirements.txtRun the test:python test_engine.pyTrainThisnotebookcomprises the code required for training the neural network, encompassing the code forengine.pyandlibrary.py. Within this notebook, we first set the input values, construct an MLP with a predetermined architecture, and subsequently execute forward propagation to compute and acquire the output. Subsequently, we initiate the training procedure of the neural network by generating a small dataset, followed by training the MLP model to minimize loss and enhance its prediction abilities. Finally, we present a list of the predicted outputs.NotebookThisnotebookcontains a comprehensive collection of examples and code for building, testing, and training the autograd engine and neural network library. Play with it to experiment and explore its functionalities.LicenseMIT
Package: ngram
The NGram class extends the Python ‘set’ class with efficient
fuzzy search for members by means of an N-gram similarity measure.
It also has static methods to compare a pair of strings.The N-grams are character based not word-based, and the class does not
implement a language model, merely searching for members by string similarity.See thedocumentation, which includes a tutorial and release notes.Use theGitHub issue trackerto report issues.InstallationTo install python-ngram fromPyPI:pip install ngramHow does it work?The set stores arbitrary items, but for non-string items akeyfunction
(such asstr) must be specified to provide a string represenation.  The key
function can also be used to normalise string items (e.g. lower-casing) prior
to N-gram indexing.To index a string it pads the string with a specified dummy character, then
splits it into overlapping substrings of N (default N=3) characters in length
and associates each N-gram to the items that use it.To find items similar to a query string, it splits the query into N-grams,
collects all items sharing at least one N-gram with the query,
and ranks the items by score based on the ratio of shared to unshared
N-grams between strings.HistoryIn 2007, Michel Albert (exhuma) wrote the python-ngram module based on Perl’sString::Trigrammodule by Tarek Ahmed, and committed the code for 2.0.0b2 to
a now-disusedSourceforgesubversion repo.Since late 2008, Graham Poulter has maintained python-ngram, initially refactoring
it to build on thesetclass, and also adding features, documentation, tests,
performance improvements and Python 3 support.DevelopmentDevelopment takes place onGithub.  On checking out the repo runtoxto build
the Sphinx documentation and run tests.  Runpip install -e .to install the module
in editable mode, inside a virtualenv.
Package: ngram-extration
ngram_extration概述目的用于提取语料库的最大词频情况下的最长字符串，即用于挖掘语料库中的高频长字符串，这类字符串可能是脏文本，也可能是特征文本；示例：corpus.txt中存在大量的高频字符串，如：<sprite=3>  
【坐标分享】3级土地 (777,225)    
1级码头-(372,737)通过ngram_extration可以快速挖掘这类特征字符串。安装pipinstallngram_extration用法fromngram_extrationimportNgramExtrationdefload_list_from_text(path):res=[]withopen(path)asf:forlinf:res.append(l[:-1])returnresngram_extration=NgramExtration()contents=load_list_from_text('corpus.txt')final_feature_list=ngram_extration.extract_ngram(contents,min_df=10,ngram_range=(5,30),analyzer='char',lowercase=False)
Package: ngram-graphs
Library for ngram graph comparison based on papers from George Giannakopoulos and Vangelis Karkaletsis. The library uses igraph to create directed and weighted graphs from documents and provides means to compare them using the distance metrics defined in ‘Giannakopoulos, George & Karkaletsis, Vangelis. (2009). N-gram graphs: Representing documents and document sets in summary system evaluation. ‘
Package: ngram-lm
ngram-lmInstallationpip install ngram-lmUsageGiven some ARPA file, a trie can be built like this:fromngram_lm.trieimportbuildlm_order=3arpa_path="/path/to/arpa-file"out_path="/the/desired/output/path/where/trie/will/be/saved"build(lm_order,arpa_path,out_path)Then, the trie can be loaded into memory and queried:fromngram_lm.trieimportTriet=Trie(out_path)context=["ele","foi"]n_predictions=5predictions=t.next_word_predictions(context,n_predictions)forpinpredictions:print(p)
Package: ngrammatcher
NGramMatcheris a module that can be used to extract n-grams, tokens, or keywords from a list of tokens.Installation$pipinstallngrammatcherUsageOverviewfromngrammatcherimportNGramMatcher# init NgramMatcher objectngm=NGramMatcher()# add ngrams and their corresponding datangm.insert_ngram(['programming','language'],'programming language')ngm.insert_ngram(['Python'],'Python')# match ngramsngm.match_ngrams(['Python','is','a','programming','language'])# ['Python', 'programming language']Adding n-gramsfromngrammatcherimportNGramMatcherngm=NGramMatcher()# You can add n-grams of any sizengm.insert_ngram(['programming','language'],'programming language')# 2-gramngm.insert_ngram(['Python'],'Python')# 1-gramngm.insert_ngram(['a']*10000,'a'*10000)# 10_000-gram# you can map any kind of data to an n-gramdata={'word':'programming language','wikipedia':'https://en.wikipedia.org/wiki/Programming_language''desc':'A programming language is any set of rules that converts...'}ngm.insert_ngram(['programming','language'],data)# you can also insert n-grams using dictionary sytaxngm[['c','plus','plus']]='c++'# or add wordsngm.insert_ngram(list('test'),'test')Finding n-gramsfromngrammatcherimportNGramMatcherngm=NGramMatcher()ngm.insert_ngram(['programming','language'],'programming language')ngm.insert_ngram(['Python'],'Python')# here we will use spacy to create tokensimportspacynlp=spacy.load('en_core_web_lg')text='Python is a programming language'tokens=[tok.textfortokinnlp(text)]# find n-gramsngm.match_ngrams(tokens)# ['Python', 'programming language']Additional FunctionalityfromngrammatcherimportNGramMatcherngm=NGramMatcher()ngm.insert_ngram(['programming','language'],'programming language')ngm.insert_ngram(['Python'],'Python')# get all n-grams in the triengm.get_all_ngrams()# [(['Python'], 'Python'), (['programming', 'language'], 'programming language')]# you can exclude the data object toongm.get_all_ngrams(keys_only=True)# [['Python'], ['programming', 'language']]# delete n-grams (returns True if deleted, False otherwise)ngm.delete_ngram(['Python'])# True# Additional Quality-of-Life functionalitylen(ngm)# get the number of n-grams in trie['programming','language']inngm# check if n-gram is in triengm[['programming','language']]='PL'# insert an n-gram into the triengm[['programming','language']]# get the data for a specific n-gramdelngm[['programming','language']]# delete an ngram using delTest$gitclonehttps://github.com/jwnz/ngrammatcher$cdngrammatcher$pipinstallpytest$pythonsetup.pytest
Package: ngram-ml
Maximum Likelihood fit for N-gramsA small library for quickly deriving the Maximum Likelihood estimates and Neural Network training for N-grams.Installationpipinstallngram-mlUsagefromngram_mlimport*ExampleMaximum Likelihood Estimator Examplemle=NGramMLEstimator(sentences=tokens,n_grams=2,label_smoothing=1)mle.calculate_cross_entropy(tokens)mle.calculate_cross_entropy([['<S>','the','cat','sat','on','the','mat','</S>']])mle.generate_sentence(30,initial_pre_seq=tuple([mle.word_to_idx['pencil']]))mle.generate_most_probable_sentence(30,initial_pre_seq=tuple([mle.word_to_idx['book']]))Neural Network Example# Neural Network Exampledataset=NGramDataset(sentences=tokens,n_grams=2)NN=NGramNeuralNet(n_grams=2,in_size=dataset.n_unique_words,embed_size=200)NN.train(dataset.x,dataset.y,n_epochs=100,lr=0.01)
Package: ngram-search
This library allows indexing many strings into a file, and then efficiently fuzzy-matching strings against what’s been indexed.Currently, the structure is built from the Rust program in memory before being written to the file, so that phase uses a lot of RAM. You cannot create/change an index from Python.String search is done from the file and requires little memory.The index is a trie structure in which trigrams can be looked up; results for each trigrams of the input are matched and sorted to get the most similar strings.Example:>>> from ngram_search import Ngrams
>>> ngrams = Ngrams(path)
>>> ngrams.search("ham", 0.24)
[(0, 1.0), (2, 0.25)]
>>> ngrams.search("spa", 0.2)
[(0, 0.375)]
Package: ngramso
stack overflow ngram analysisThis project fetches StackOverflow API get questions and make ngram analysisinstallpip install sosqpip install emekpip install ngramsofor use1- Get your key and token from stackexchange (https://stackapps.com/users/login?returnurl=/apps/oauth/register)2- sample code below.import sosq
import emek
import ngramso
df = sosq.get_result("your_search_query", "your_key", "your_access_token")
emek.process_data(df)
ngramso.process_and_generate_ngrams(df)sosq for dataframe
emek for tag analysis
ngramso for ngram analysis to compare tags with ngrams
Package: ngramtg
Ngram TextGenA simple n-gram-based random text generator written in Python.Requirements and InstallationRequirementsThis module requires Python 3.6+ due to the use of f-strings.InstallationpipinstallngramtgUsageCreate a simple script, e.g.fromngramtg.ngramimportNgramTextGenimportsysif__name__=="__main__":my_object=NgramTextGen(sys.argv[1],auto=True)print(my_object.sentence())Save it (e.g.myscript.py) and provide a corpus file to it:$ python myscript.py corpus.txt
Compassion will make you.Release History2019.01Initial releaseDevelopment and LicenseDeveloped by Paul Wicking –@DocWicking.Distributed under the MIT license. SeeLICENSEfor more information.
The source code lives onGitHub.
Package: ngraph-core
nGraph Compiler stacknGraphis an open-source graph compiler for Artificial
Neural Networks (ANNs). The nGraph Compiler stack provides an inherently
efficient graph-based compilation infrastructure designed to be compatible
with the many upcoming processors, like the Intel Nervana™ Neural Network
Processor (Intel® Nervana™ NNP), while also unlocking a massive performance
boost on any existing hardware targets in your neural network: both GPUs
and CPUs. Using its flexible infrastructure, you will find it becomes
much easier to create Deep Learning (DL) models that can adhere to the
"write once, run anywhere" mantra that enables your AI solutions to easily
go from concept to production to scale.Frameworks using nGraph to execute workloads have shownup to 45Xperformance
boost compared to native implementations.Using the Python APInGraph can be used directly with thePython APIdescribed here, or
with theC++ APIdescribed in thecore documentation. Alternatively,
its performance benefits can be realized through frontends such asTensorFlow,PaddlePaddleandONNX.
You can also create your own custom framework to integrate directly with thenGraph Opsfor highly-targeted graph execution.InstallationnGraph is available as binary wheels you can install from PyPI. nGraph binary
wheels are currently tested on Ubuntu 16.04. To build and test on other
systems, you may want to trybuildingfrom sources.Installing nGraph Python API from PyPI is easy:pip install ngraph-coreUsage exampleUsing nGraph's Python API to construct a computation graph and execute a
computation is simple. The following example shows how to create a minimal(A + B) * Ccomputation graph and calculate a result using 3 numpy arrays
as input.importnumpyasnpimportngraphasngA=ng.parameter(shape=[2,2],name='A',dtype=np.float32)B=ng.parameter(shape=[2,2],name='B',dtype=np.float32)C=ng.parameter(shape=[2,2],name='C',dtype=np.float32)# >>> print(A)# <Parameter: 'A' ([2, 2], float)>model=(A+B)*C# >>> print(model)# <Multiply: 'Multiply_14' ([2, 2])>runtime=ng.runtime(backend_name='CPU')# >>> print(runtime)# <Runtime: Backend='CPU'>computation=runtime.computation(model,A,B,C)# >>> print(computation)# <Computation: Multiply_14(A, B, C)>value_a=np.array([[1,2],[3,4]],dtype=np.float32)value_b=np.array([[5,6],[7,8]],dtype=np.float32)value_c=np.array([[9,10],[11,12]],dtype=np.float32)result=computation(value_a,value_b,value_c)# >>> print(result)# [[ 54.  80.]#  [110. 144.]]print('Result = ',result)
Package: ngraph-mxnet
====================This directory and nested files contain the nGraph-MXNet Python package and language binding.## InstallationTo install nGraph-MXNet Python package, visit the [nGraph-MXNET github page](http://www.github.com/NervanaSystems/ngraph-mxnet)Home-page: https://github.com/NervanaSystems/ngraph-mxnetAuthor: UNKNOWNAuthor-email: UNKNOWNLicense: UNKNOWNDescription: UNKNOWNPlatform: UNKNOWNClassifier: Development Status :: 5 - Production/StableClassifier: Intended Audience :: DevelopersClassifier: Intended Audience :: EducationClassifier: Intended Audience :: Science/ResearchClassifier: License :: OSI Approved :: Apache Software LicenseClassifier: Programming Language :: C++Classifier: Programming Language :: CythonClassifier: Programming Language :: OtherClassifier: Programming Language :: PerlClassifier: Programming Language :: PythonClassifier: Programming Language :: Python :: 2.7Classifier: Programming Language :: Python :: 3.4Classifier: Programming Language :: Python :: 3.5Classifier: Programming Language :: Python :: 3.6Classifier: Programming Language :: Python :: Implementation :: CPythonClassifier: Topic :: Scientific/EngineeringClassifier: Topic :: Scientific/Engineering :: Artificial IntelligenceClassifier: Topic :: Scientific/Engineering :: MathematicsClassifier: Topic :: Software DevelopmentClassifier: Topic :: Software Development :: LibrariesClassifier: Topic :: Software Development :: Libraries :: Python Modules
Package: ngraph-onnx
ngraph-onnxnGraph Backend for ONNX.This repository contains tools to runONNXmodels using theIntel nGraph libraryas a backend.InstallationFollow ourbuildinstructions to install nGraph-ONNX from sources.Usage exampleImporting an ONNX modelYou can download models from theONNX model zoo. For example ResNet-50:$ wget https://s3.amazonaws.com/download.onnx/models/opset_8/resnet50.tar.gz
$ tar -xzvf resnet50.tar.gzUse the following Python commands to convert the downloaded model to an nGraph model:# Import ONNX and load an ONNX file from disk>>>importonnx>>>onnx_protobuf=onnx.load('resnet50/model.onnx')# Convert ONNX model to an ngraph model>>>fromngraph_onnx.onnx_importer.importerimportimport_onnx_model>>>ng_function=import_onnx_model(onnx_protobuf)# The importer returns a list of ngraph models for every ONNX graph output:>>>print(ng_function)<Function:'resnet50'([1,1000])>This creates an nGraphFunctionobject, which can be used to execute a computation on a chosen backend.Running a computationAfter importing an ONNX model, you will have an nGraphFunctionobject.
Now you can create an nGraphRuntimebackend and use it to compile yourFunctionto a backend-specificComputationobject.
Finally, you can execute your model by calling the createdComputationobject with input data.# Using an ngraph runtime (CPU backend) create a callable computation object>>>importngraphasng>>>runtime=ng.runtime(backend_name='CPU')>>>resnet_on_cpu=runtime.computation(ng_function)# Load an image (or create a mock as in this example)>>>importnumpyasnp>>>picture=np.ones([1,3,224,224],dtype=np.float32)# Run computation on the picture:>>>resnet_on_cpu(picture)[array([[2.16105007e-04,5.58412226e-04,9.70510227e-05,5.76671446e-05,7.45318757e-05,4.80892748e-04,5.67404088e-04,9.48728994e-05,...
Package: ngrok-api
ngrok API client library for PythonThis library wraps thengrok HTTP APIto make it
easier to consume in Python.InstallationThis library is published onPyPi:pip install ngrok-apiSupportThe best place to get support using this library is through thengrok Slack Community. If you find any bugs, please contribute by opening a new GitHub issue.DocumentationA quickstart guide and a full API reference are included in thengrok python API documentation.QuickstartPlease consult thedocumentationfor additional examples.import ngrok

# Construct the API client
client = ngrok.Client("<API KEY>")

# List all online tunnels
for t in client.tunnels.list():
    print(t)

# Create an IP policy that allows traffic from some subnets
policy = client.ip_policies.create()
for cidr in ["24.0.0.0/8", "12.0.0.0/8"]:
    client.ip_policy_rules.create(cidr=cidr, ip_policy_id=policy.id, action="allow")
Package: ngrok-flask-cart
flask-ngrok-CARTAn offshoot offlask-ngrok(see alsoflask-ngrok2,flask-ngrok3) for making demo Flask apps from a personal machine.Installationpipinstallngrok-flask-cartapp is the Flask appdomain is the static domain provided byngrokauth_token is the authentication token provided byngrokExamplefrom flask import Flask
from ngrok_flask_cart import run_with_ngrok

app = Flask(__name__)
run_with_ngrok(app=app, domain='--domain=<domain here>', auth_token='<token here>')

@app.route("/")
def hello():
    return "Hello World!"

if __name__ == '__main__':
    app.run()will runngrok                                                           (Ctrl+C to quit)

Session Status                online                                            
Account                       <your account> (Plan: Free)                              
Version                       3.6.0                                             
Region                        United States (us)                                
Latency                       -                                                 
Web Interface                 http://127.0.0.1:4040                             
Forwarding                    <domain here> -> 
                                                                                
Connections                   ttl     opn     rt1     rt5     p50     p90       
                              0       0       0.00    0.00    0.00    0.00     * Running on http://<your domain>                       
 * Traffic stats available on http://127.0.0.1:4040