Logistic-Regression | Logistic Regression on the GTZAN Dataset

by vamshins Python Version: Current License: No License

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kandi X-RAY | Logistic-Regression Summary

Logistic-Regression is a Python library. Logistic-Regression has no bugs, it has no vulnerabilities and it has low support. However Logistic-Regression build file is not available. You can download it from GitHub.

cs 529 - intro to machine learning - assignment 3 - logistic regression. source code of the project is hosted on github: the executable "logisticregression.py" is provided in the assignment submission in unm learn. i have programmed in "python 2.7.9 |anaconda 2.2.0 (64-bit)|" installed in windows 8.1. i installed numpy, scikit_learn, scipy, scikits_talkbox. download all the files attached in unm learn submission and store them in "" (this may be any folder on your os) copy the data into the "/opihi.cs.uvic.ca" folder. the folder structure looks like -. execution of the program: 4.1. go to run(windows) or terminal(linux) 4.2. navigate to 4.3. run the command → python logisticregression.py - give -fft or -fft20 or -mfcc -fft → generates fft for the data and performs the logistic regression

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Logistic-Regression has a low active ecosystem.

It has 0 star(s) with 0 fork(s). There are 1 watchers for this library.

It had no major release in the last 6 months.

Logistic-Regression has no issues reported. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of Logistic-Regression is current.

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Logistic-Regression has no bugs reported.

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Logistic-Regression has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

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Logistic-Regression does not have a standard license declared.

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Logistic-Regression releases are not available. You will need to build from source code and install.

Logistic-Regression has no build file. You will be need to create the build yourself to build the component from source.

Installation instructions are not available. Examples and code snippets are available.

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Install Logistic-Regression

You can download it from GitHub.
You can use Logistic-Regression like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.

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