anjapago / logisiticregressionclassifier Goto Github PK
View Code? Open in Web Editor NEWImplementation of logistic regression with gradient descent.
Implementation of logistic regression with gradient descent.
For explanations, please view the report pdf, or the python notebook. This project contains three files that have the logistic regression implementation. 1. "logisticregression.py": This can be run from the terminal with python 3 and the desired data file. For example to run this, the command would be: python3.6 logisticregression.py "data.mat" (using one of the .mat files in the data folder) This file will run both the training and testing algorithms, and output results of the accuracy. Also it will display loglikelihoods as it iterates. 2. "Logistic-Regression-Classifier.ipynb" This can be opened as a jupyter notebook and run step by step. Running each box in order will result in running the full logistic regression with gradient descent. It requires python 3 as the kernel, and all the necessary imports. It requires the data files and the classifier.py file. 3. "classifier.py" This is a simplified version of "logisticregression.py" containing code meant to be used from the jupyter notebook. Note: first unzip and move out the data files.
A declarative, efficient, and flexible JavaScript library for building user interfaces.
๐ Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
An Open Source Machine Learning Framework for Everyone
The Web framework for perfectionists with deadlines.
A PHP framework for web artisans
Bring data to life with SVG, Canvas and HTML. ๐๐๐
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
Some thing interesting about web. New door for the world.
A server is a program made to process requests and deliver data to clients.
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
Some thing interesting about visualization, use data art
Some thing interesting about game, make everyone happy.
We are working to build community through open source technology. NB: members must have two-factor auth.
Open source projects and samples from Microsoft.
Google โค๏ธ Open Source for everyone.
Alibaba Open Source for everyone
Data-Driven Documents codes.
China tencent open source team.