Skip to main content

py4linear-regression

Linear Regression Python Library

Getting Started

This project is simply implementation of linear regression algorithm in python programming language.

Prerequisites

Numpy

Installing

The easiest way to install py4linear-regression is using pip

pip install py4linear-regression

Usage

There is 2 public method of Linear Regression class. It is learn and predict method, learn method takes 5 argument namely x_train, t_train, alpha, and epoch. It is the training data, it's label, learning rate, and number of iteration respectively. predict method takes 1 argument namely x_test. It is the data to be predicted

from py4linear_regression.regression import linear_regression
x_train = [[0,0],[0,1],[1,0],[1,1]]
t_train = [0,1,2,3]
classifier = linear_regression()
classifier.learn(x_train,t_train,0.01,250)
x_test = [[0.01,0.99],[0.99,0.01]]
y = classifier.predict(x_test)

Metadata

Release files for py4linear-regression 0.0.5

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for py4linear-regression 0.0.5
File Size Uploaded Provenance
py4linear_regression-0.0.5.tar.gz 2.2 kB Without provenance Details

Built distribution (wheel)

Table of built distributions (wheels) for py4linear-regression 0.0.5
File Interpreter ABI Platform
py4linear_regression-0.0.5-py3-none-any.whl Python 3 none any Details

Total release size: 4.9 kB

Release files / py4linear_regression-0.0.5.tar.gz

Download URL py4linear_regression-0.0.5.tar.gz
Size 2.2 kB
Tags Source
SHA-256 checksum
How to use checksums
cff520e9a65a81b72c4229ddaba58a7c906279210936aedff7cbc4a239bb2609
BLAKE2b-256 checksum
How to use checksums
d6783eb091d3162dd07c484aabe12728bbcaa63939d4aaddfdea5d21630150e7
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/3.1.1 pkginfo/1.5.0.1 requests/2.22.0 setuptools/46.1.3 requests-toolbelt/0.9.1 tqdm/4.43.0 CPython/3.7.1

Release files / py4linear_regression-0.0.5-py3-none-any.whl

Download URL py4linear_regression-0.0.5-py3-none-any.whl
Size 2.7 kB
Tags Python 3
SHA-256 checksum
How to use checksums
7d25742470cfaf8d0ab489669b7dad2c9dc862769949069d68f9a2e1ee718fd8
BLAKE2b-256 checksum
How to use checksums
2dd5b571182498748d68ff4c776c209e9e32f1f10a64259c1be479e6a648719b
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/3.1.1 pkginfo/1.5.0.1 requests/2.22.0 setuptools/46.1.3 requests-toolbelt/0.9.1 tqdm/4.43.0 CPython/3.7.1

Security for py4linear-regression 0.0.5

Provenance

Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.

No provenance found

None of the files in this release were uploaded with provenance metadata.

A note on package security

PyPI is a repository for distributing packages, not a service for auditing them. While third-party security researchers and our staff do their best to detect and remove malicious packages, PyPI cannot guarantee the quality, security or suitability of any given package.

To learn more about protecting yourself from malicious actors, visit our package security documentation.

Anthropic, PBC Visionary sponsor Bloomberg Visionary sponsor Hudson River Trading Visionary sponsor Meta Visionary sponsor NVIDIA Visionary sponsor Microsoft Sustainability sponsor Depot Continuous Integration AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page