Saravanan666/pypi-org huggingface dataset:
Package: regolith-subfunctions
A Python package for easier reuseability of the Subfunctions Regolith filter for Minecraft Bedrock EditionDocumentationThis module is just a component used by the subfunctions Regolith filter and
its intended to be used by that filter only. You can find the documentation
of the filter on the page of the filter:https://github.com/Nusiq/regolith-filters/tree/master/subfunctionsInstallationpipinstallregolith-subfunctionsMIT LicenseCopyright (c) 2023 NusiqPermission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the “Software”), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.THE SOFTWARE IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
Package: regparse
regparseRead and parse exported windows registry text filesInstallInstall using pip:pip install regparseUsageTypical usage:import regparse
r=regparse.RegParse()
c=r.readfile('Computer_HKLM.reg')
r.getkey('HKEY_LOCAL_MACHINE\\SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\Policies\\System','EnableLUA','n/a-dword:00000001')Raw function to get the key:r.getconfig('HKEY_LOCAL_MACHINE\\SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\Policies\\System','"EnableLUA"','n/a-dword:00000001')Automatically parse value from the registry (note: default should be also parseable):current = reg.getval('HKEY_LOCAL_MACHINE\\SYSTEM\\Select','Current','dword:00000001')
Package: regpath
DependenciesPython 3.4, 3.5pathlib(Standard library in Python 3.4)Installpip install regpathExamplesImport:>>> from regpath import RegistryPathCreate object:>>> p = RegistryPath('HKCU')Access PurePath compaitble propertyes:>>> p
RegistryPath('HKCU')
>>> print(p)
HKCU
>>> p.drive, p.root, p.parts
('HKCU', '', ('HKCU',))List keys:>>> list(p.iterdir())
[RegistryPath('HKCU/AppEvents'), RegistryPath('HKCU/AppXBackupContentType'), RegistryPath('HKCU/Console'),
 RegistryPath('HKCU/Control Panel'), RegistryPath('HKCU/Environment'), RegistryPath('HKCU/EUDC'),
 RegistryPath('HKCU/Keyboard Layout'), RegistryPath('HKCU/Network'), RegistryPath('HKCU/Printers'),
 RegistryPath('HKCU/Software'), RegistryPath('HKCU/System'), RegistryPath('HKCU/Volatile Environment')]Join path:>>> p = p / 'Environment'
>>> p
RegistryPath('HKCU/Environment')
>>> print(p)
HKCU\Environment
>>> p.drive, p.root, p.parts
('HKCU', '\\', ('HKCU', 'Environment'))List values:>>> list(p)
['TEMP', 'TMP']
>>> p['Temp']
'%USERPROFILE%\\AppData\\Local\\Temp'Then:>>> p = RegistryPath(r'HKCU\Software\{d017c5cb-d6a6-453e-b41a-f3dc270628c0}\subkey')

>>> p.exists()
False
>>> p.mkdir(parents=True)
>>> p.exists()
True

>>> list(p)
[]
>>> p[''] = 'This is unnamed value.'
>>> list(p)
['']
>>> p['']
'This is unnamed value.'
>>> p['number'] = 0x12345678
>>> p['number']
305419896
>>> list(p)
['', 'number']
>>> del p['number']
>>> list(p)
['']
>>> del p['']
>>> list(p)
[]

>>> p['msg'] = 'hello, world'
>>> p.parent.rmdir()
Traceback (most recent call last):
...
PermissionError: [WinError 5] Access is denied.

>>> p.parent.rmtree()
>>> p.parent.exists()
False
>>> p.exists()
False
>>> list(p)
Traceback (most recent call last):
...
OSError: [WinError 1018] Illegal operation attempted on a Registry key which has been marked for deletion.
Package: regpredict
Evx regpredict mlbotThis is a simplified version ofevxpredictorpackage used to generate buy and sell signals for crypto and conventional stock markets based on the excess volume indicator(EVX). EVX is a concept where the bid-ask spread is estimated inherently from current market prices.You can read more about Evx in the whitepaperhereInstallationInstall regpredict withpython3 -m pip install regpredictUsageIn your python script simply import the module and use as follows:from regpredict.regbot import signal
print(signal(20,65,85,78,45,0.7))The above methods take an assets a,b,macd,macdsignal,grad_histogram,close_gradient,close_gradient_neg based on the time interval you have chosen. A zero classification output would instruct the user to sell, while one output means don't sell or buy if the asset is not already present in the orders.Testing an entire dataframeTesting of a dataframe for correct buy, sell signals is as simple as applying the function as follows:import pandas as pd
from regbot import signal
#from regpredict.regbot import signal
df = pd.read_csv('../path/to/your_validation.csv')

y_pred = []
def getSignal(a,b,macd,macdsignal,grad_histogram,close_gradient,close_gradient_neg):
    return signal(a,b,macd,macdsignal,grad_histogram,close_gradient,close_gradient_neg)

Where thr is a user defined threshold.


df = df[df['enter_long'] == 1]
print(df.head())

df['result'] = df.apply(lambda row: getSignal(row['a'],row['b'],row['macd'],row['macdsignal'],row['grad-histogram'],row['close-gradient']row['close-gradient-neg']), axis=1)

print(df.head())

print(len(df[df['result'] == df['enter_long']]), len(df))Your original data must already have some presumed 'buy' signal.WarningThis is not financial advise. Regpredict is entirely on its preliminary stages. Use it at your own risk.
Package: regpy
regpyregpy is a python module which will help to implement complex and basic regex in a simple way.FeaturesGrab all alphabetic dataGrab all valid mails id's.Grab or extract valid postal code.InstallationTo install regpy use this pip command :pipinstallregpyFunctionstextTo grab only alphabetic data you can use text function from regpy.importregpyregpy.text(data)emailsTo grab all valid mail id use emails function from regpy.importregpyregpy.emails(data)ipip function to grab all ipv4 ip's from list.importregpyregpy.ip(data)urlYou can use this url function in web scraping to extract https url from data.importregpyregpy.url(data)
Package: regraph
ReGraphGraph rewriting and graph-based knowledge representation library. Documentation is available athttp://dev.executableknowledge.org/ReGraph.About projectTheReGraphPython library is a generic framework for modelling graph-based systems. In this context models are viewed as graphs and graph transformations --- as a tool to describe both the system evolution and the model evolutionread more about the approach.ReGraph provides various utilities for graph rewriting which can be used for modelling the evolution of a system represented by a graph subject to rewriting. The rewriting functionality is based on the sesqui-pushout rewriting procedure [7]. In addition, the library enables a user to define a typing for models (graphs) that gives specifications for the structure of the models. This later functionality allows both to preserve the specified structure during rewriting and to propagate the changes to the specifications up to the models.ReGraphcontains a collection of utilities for rewriting graphs and hierarchies of graphs. It supports two backends:NetworkXgraph objects and onNeo4jproperty graphs stored in a graph database.InstallationIn order to install theReGraphlibrary you can either use the latest version in PyPI:pip install regraphor install from the source by cloning the repository using SSHgit clone git@github.com:Kappa-Dev/ReGraph.gitor using HTTPShttps://github.com/Kappa-Dev/ReGraph.gitInstall the library using Pythin setup tools:python setup.py installNeo4j installation and configurationIf you want to use the Neo4j-based backend of ReGraph, you need to install the Neo4j database (see installationinstructions).Moreover, ReGraph uses the APOC Neo4j plugin, currently not included in the community edition. To install the plugin see theinstructions.ReGraph usesNeo4j Bolt Driver for Python, therefore, having set up your database, you need to provide to ReGraph's API the address of the bolt server (for example,bolt://127.0.0.1:7687) and your credentials for connecting the database (i.e. user and password).Run testsNosetestsnosetests -v -s
Package: regref
regrefTo explainregrefI will walk through the following example:./regref.pytest-data/z.tab'${2}''${3}'<test-data/z.fnaz.fnais a FASTA file with mangled headers where all non-letter or number
characters have been replaced with underscores.z.tabis a table that contains the mangled names (in column 2) and the
original names (in column 3).Theregrefcommand reads the FASTA file from standard input and then replaces
every occurance of the mangled string with the original string.The arguments '${2}' and '${3}' refer to columns inz.tabby column number.regrefwill replace every occurance of each pattern. The example below
replaces the mangled string in a tree with a single-quoted orginal string../regref.pytest-data/z.tab'${2}'"'\${3}'"<test-data/z.tre
Package: regreg
RegReg is a simple multi-algorithm Python framework for prototyping and solving
regularized regression problems such as the LASSO. The goal is to enable
practitioners to quickly and easily experiment with a variety of different
models and choices of regularization.  In that spirit, the emphasis is on the
flexibility of the framework instead of computational speed for any particular
problem, though the speed trade-off will generally not be too bad.CodeSeehttps://github.com/regreg/regregReleased under the BSD two-clause license - see the fileLICENSEin the
source distribution.The latest released version is athttps://pypi.python.org/pypi/regregSupportPlease put up issues on theregreg issue tracker.
Package: reg-resampler
Regression ReSamplingA python library for repurposing traditional classification-based resampling (undersampling and/or oversampling) techniques for regression tasks. Currently supports all resampling techniques present inimblearnWhy does this exist?While we were working on a regression task, we realized that the target variable was skewed, i.e., most samples were present in a particular range. One can easily solve the skew problem for classification tasks via a slew of resampling techniques (either under or over sampling) but this luxury is unavailable for regression tasks. We therefore decided to create an interface that can repurpose all resampling techniques for classification problems to regression problems!How to install?pip install reg_resamplerFunctions and parameters# This returns a numpy list of classes for each corresponding sample. It also automatically merges classes when requiredfit(X,target,bins=3,min_n_samples=6,balanced_binning=False,verbose=2)X- Either a pandas dataframe or numpy matrix. Complete data to be resampled.target- Either string (for pandas) or index (for numpy). The target variable to be resampled.bins=3- The number of classes that the user wants to generate. (Default: 3)min_n_samples=6- Minimum number of samples in each bin. Bins having less than this value will be merged with the closest bin. Has to be more than neighbours in imblearn. (Default: 6)balanced_binning=False- Decides whether samples are to be distributed roughly equally across all classes. (Default: False)verbose=2- 0 will disable print by package, 1 will print info about class mergers and 2 will also print class distributions.# Performs resampling and returns the resampled dataframe/numpy matrices in the form of data and target variable.resample(sampler_obj,trainX,trainY)sampler_obj- Your favourite resampling algorithm's object (currently supports imblearn)trainX- Either a pandas dataframe or numpy matrix. Data to be resampled. Also, contains the target variabletrainY- Numpy array of psuedo classes obtained from fit function.Important NoteAll functions return the same data type as provided in input.How to import?fromreg_resamplerimportresamplerUsage# Initialize the resampler objectrs=resampler()# You might recieve info about class merger for low sample classes# Generate classesY_classes=rs.fit(df_train,target=target,bins=num_bins)# Create the actual target variableY=df_train[target]# Create a smote (over-sampling) object from imblearnsmote=SMOTE(random_state=27)# Now resamplefinal_X,final_Y=rs.resample(smote,df_train,Y_classes)TutorialsYou can find furthertutorialson how to use this library for cross-validationFuture IdeasSupport for more resampling techniquesFeature RequestDrop us an email atatif.hit.hassan@gmail.comorpvsaikrithik@gmail.comif you want any particular feature
Package: regresseasy
RegressModelRegressModel is a Python package designed for streamlined regression analysis. It facilitates the process of training and evaluating multiple regression models on a dataset, providing a comprehensive comparison of their performance. This package is particularly useful for data scientists and machine learning practitioners who need to quickly assess the best regression model for their specific data.FeaturesEasy comparison of various regression models.Includes common models like Linear Regression, Lasso, Ridge, SVR, Random Forest, Decision Tree, and XGBoost.Outputs performance metrics like R2 score, RMSE, MSE, MAE, and cross-validation scores.InstallationYou can install RegressModel using pip:pipinstallregressmodelUsageHere is a simple example of how to use RegressModel:fromregresseasyimportreg_modelling
importpandasaspd
fromsklearn.model_selectionimporttrain_test_split# Sample datasetdf=pd.read_csv('your_dataset.csv')X=df.drop('target_column',axis=1)y=df['target_column']# Splitting the datasetX_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.2)# Example usage of the function# Ensure that you have defined X_train, y_train, X_test, y_test before calling this functionmodel_results=reg_modelling(X_train,y_train,X_test,y_test)# # Access specific model results, e.g., Linear Regressionprint("Linear Regression Results:",model_results["LinearRegression"])# The results variable will contain performance metrics of the modelsRequirementsPython 3.6 or higher
scikit-learn
numpy
xgboostContributingContributions to RegressModel are welcome! Feel free to fork the repository and submit pull requests.LicenseRegressModel is licensed under the MIT License - see the LICENSE file for details.AuthorSaiKoushikGandikota
Package: regresser
Regresser - Visual Regression LibraryWhat is it?A visual regression tool for comparing web apps and websites to visually find differences between versions.Usage:WebdriverFirst we need to create a baseline, your main version. Any other version will be compared to this one.fromregresserimportwebdriverwebdriver.Driver(url='https://www.nytimes.com/',background=True,timeout=60)Now we're able to create another version. Eg.:fromregresserimportwebdriverwebdriver.Driver(url='https://www.nytimes.com/',background=True,timeout=60)webdriver.Driver(url='https://www.nytimes.com/',version='stg',background=False,timeout=60)```Options:version='staging'#version namedriver='firefox'#defaults to firefox. Use 'chrome' to enable chromedrivertimeout=10#timeout loading a pagebackground=TrueorFalse#Loads the website in background (headless)width=None#force a width, otherwise will be automatic.height=None#force a height, otherwise will be automatic.Analyzerfromregresserimportanalyzeranalyzer.Compare(version='stg')ResultsThere are currently two outputs: alpha_diff.png and highlights.png.
The alpha_diff will hide everything except differences, and highlights will show differences in red color after comparing
versions.
Package: regressio
Regressio is a python module for univariate regression, interpolation, and smoothing.The available models are:Linear regressionRidge regressionLinear splineIsotonic regressionBin regressionCubic splineNatural cubic splineExponential moving averageKernel functions (Gaussian, KNN, Weighted average)There are also functions implemented to generate data samples.The available data generators are:Random walkIsotonic sampleInstallationRegressio is supported in Python 3.8+ and requires only NumPy and Matplotlib.pipinstallregressio--upgradeorpipinstallgit+https://github.com/brendanartley/RegressioExample UsageCubic spline.# Import modules + classesfromregressio.modelsimportcubic_splinefromregressio.datagenimportgenerate_random_walkimportnumpyasnpimportmatplotlib.pyplotasplt# Set figsize and seedplt.rcParams['figure.figsize']=(10,5)np.random.seed(0)# Generate data samplex,y=generate_random_walk(150)# Fit model and plot resultmodel=cubic_spline(pieces=15)model.fit(x,y,plot=True,confidence_interval=0.99)Linear regression.# Import modules + classesfromregressio.modelsimportlinear_regressionfromregressio.datagenimportgenerate_random_walkimportnumpyasnpimportmatplotlib.pyplotasplt# Set figsize and seedplt.rcParams['figure.figsize']=(10,5)np.random.seed(1)# Generate data samplex,y=generate_random_walk(100)# Fit model and plot resultmodel=linear_regression(degree=5)model.fit(x,y,plot=True,confidence_interval=0.95)Exponential moving average.# Import modules + classesfromregressio.modelsimportexp_moving_averagefromregressio.datagenimportgenerate_isotonic_sampleimportnumpyasnpimportmatplotlib.pyplotasplt# Set figsize and seedplt.rcParams['figure.figsize']=(10,5)np.random.seed(6)# Generate data samplex,y=generate_isotonic_sample(100)# Fit model and plot resultmodel=exp_moving_average(alpha=0.2)model.fit(x,y,plot=True,confidence_interval=0.90)For more examples, navigate to theexamples.ipynbfile in this repository.ContributionsWe welcome all to contribute their expertise to the Regressio library. If you are new to open source contributions,this guidegives some great tips on how to get started.If you have a complex feature in mind or find a large bug in the code, please create a detailed issue and we will get to work on it.ReferencesHyndman, R.J., & Athanasopoulos, G. (2021) Forecasting: principles and practice, 3rd edition, OTexts: Melbourne, Australia. OTexts.com/fpp3. Accessed July 2022.Kong, Qingkai, et al. Python Programming and Numerical Methods: A Guide for Engineers and Scientists. Academic Press, an Imprint of Elsevier, pythonnumericalmethods.berkeley.edu, Accessed July 2022.Li, Bao, (2022). Stat 508: Applied Data Mining, Statistical Learning: Stat Online. PennState: Statistics Online Courses, online.stat.psu.edu/stat508, Accessed July 2022.Brett, M. (2014, October 26). An introduction to smoothing. Tutorials on imaging, computing and mathematics. matthew-brett.github.io/teaching, Accessed July 2022.
Package: regressionAlgorithm
POLINOMIAL REGRESSION ALGORITHMpip3installregressionAlgorithmPOLYNOMIAL ORDER 1fromregressionAlgorithmimportPolynomialOrderOneimportmatplotlib.pyplotasplt# valuex=[1,2,3,4,5]y=[5,4,3,2,1]# intialrl=PolynomialOrderOne()rl.fit(x,y)rl.start()# get dataprint(rl.plot())print(rl.line())# data visualizationplt.figure(figsize=(10,7))plt.scatter(x,y)plt.plot(x,rl.line())plt.savefig("visual.png")# r squaredprint(rl.r_squared())# result 1.0# r valueprint(rl.r_value())# result -1.0PREDICT Y VALUE...x=10# formula (y = a + b * x) = (y = a + b * 10)print(rl.predict(x))# result -4.0POLYNOMIAL ORDER 2fromregressionAlgorithmimportPolynomialOrderTwoimportmatplotlib.pyplotasplt# valuex=[1,2,3,4,5]y=[5,4,3,2,1]# intialq=PolynomialOrderTwo()q.fit(x,y)q.start()# resultprint(q.plot())print(q.r_square())# data visualizationplt.figure(figsize=(10,7))plt.scatter(x,y,color="blue")plt.plot(x,k.draw_line(),color="red")plt.savefig("quadratic.png")PREDICT Y VALUE...x_pred=5# formula y = a + b * x + c * square(x) -> y = a + b * x_red + c * square(x_pred)print(q.predict(x_pred))# result 0.9999999999997522
Package: regression-enrichment-surface
Regression Enrichment SurfaceUnder dev.How to use
Simple case:
To produced average plots over some stratification (useful for dose response like data to produce results over types of cells for instance)trues,preds=get_predicition_data()rds_model=rds.RegressionEnrichmentSurface(percent_min=-3)rds_model.compute(trues,preds,samples=30)rds_model.plot(save_file=args.metric_plot_prefix+"rds_on_cell.png",title='Regression Enrichment Surface (Avg over Unique Cells)')To produced average plots over some stratification (useful for dose response like data to produce results over types of cells for instance)trues,preds,labels=get_predicition_data()rds_model=rds.RegressionEnrichmentSurface(percent_min=-3)rds_model.compute(trues,preds,stratify=labels,samples=30)rds_model.plot(save_file=args.metric_plot_prefix+"rds_on_cell.png",title='Regression Enrichment Surface (Avg over Unique Cells)')Code for producing regression enrichment analysisFree software: MIT licenseDocumentation:https://regression-enrichment-surface.readthedocs.io.FeaturesTODOCreditsThis package was created withCookiecutterand theaudreyr/cookiecutter-pypackageproject template.History0.1.0 (2020-01-01)First release on PyPI.
Package: regressioninc
RegressionInCA package focussing on regression for complex-valued variablesTerminologyy = X * coefy are called the observationsX are the predictorscoef are the coefficientsOther formulations and terminology includeFittingWhen fitting the linear problem, the aim is to:Find the coefficients, coef, given observations y and predictors XPredictingWhen predicting the linear problem, the aim is to:Calculate new observations, y, given predictors X and coefficients coef
Package: regressionmodels
RegressionmodelsA package which can be used to fit linear- as well as polynomial Regression Models to n-dimensional data.InstallationThe installation requires a Python Version of 3.6+.You can install the package via:pip install regressionmodelsIf you already installed the package you can update it via:pip install --upgrade sdllib#Installation for DevelopersLocally, the package and its development-dependencies can be installed via:pip install .[dev]and in the main directory of the package viapython setup.py sdist bdistwheelthe package can be built as Source- and Binary-Distribution.UnittestsYou can run Unittests withpytest. You can conduct them locally viapytest test.pyOverview of the Modulesmodel.pyIncludes a class Regression, which can be initialized viaRegression(deg=1)with the parameter deg (default =1) to determine which degree of Regression function should be fit on the data.The beta vector can be estimated using the method:.fit(X,y)After that, the fitted beta can be used to predict unseen data via:.predict(X)
Package: regressive-imagery-dictionary
“The Regressive Imagery Dictionary (RID) is a coding scheme for text analysis
that is designed to measure ‘primordial’ and conceptual content. Primordial
thought is the kind of free-form, associative thinking involved in fantasy
and dreams. Like Freud’s id, I guess. Conceptual (or secondary) thought is
logical, reality-based and focused on problem solving.”via: John Wiseman (http://lemonodor.com/archives/001511.html)