MLFromScratch | Machine Learning algorithms , with Numpy | Machine Learning library

by lowener Python Version: Current License: GPL-3.0

X-Ray Key Features Code Snippets Community Discussions(2)Vulnerabilities Install Support

kandi X-RAY | MLFromScratch Summary

MLFromScratch is a Python library typically used in Artificial Intelligence, Machine Learning, Numpy applications. MLFromScratch has no bugs, it has no vulnerabilities, it has build file available, it has a Strong Copyleft License and it has low support. You can download it from GitHub.

Implementation in Python of popular Machine Learning algorithms, with Numpy only. Tests are available, using scikit-learns Datasets.

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Quality

Security

License

Reuse

Support

MLFromScratch has a low active ecosystem.

It has 8 star(s) with 0 fork(s). There are 1 watchers for this library.

It had no major release in the last 6 months.

There are 3 open issues and 6 have been closed. On average issues are closed in 22 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of MLFromScratch is current.

Quality

MLFromScratch has no bugs reported.

Security

MLFromScratch has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

License

MLFromScratch is licensed under the GPL-3.0 License. This license is Strong Copyleft.

Strong Copyleft licenses enforce sharing, and you can use them when creating open source projects.

Reuse

MLFromScratch releases are not available. You will need to build from source code and install.

Build file is available. You can build the component from source.

Top functions reviewed by kandi - BETA

kandi has reviewed MLFromScratch and discovered the below as its top functions. This is intended to give you an instant insight into MLFromScratch implemented functionality, and help decide if they suit your requirements.

Test for a GMM matrix
Fit the gradient descent function
Linear gradient descent
Scale the data
Softmax
Fit the model
Compute the cross entropy of the target and preds
Perform forward computation
Backward computation
Perform clustering
Performs e - step clustering
Performs the centers clustering
Compute the model for the classification
Quick test function
Predict the covariance matrix
Fit the kernel to the given data
Compute weak learner using weak learner method
Creates a weak learn
Finds the best problem for a given test
Estimate the House test for a test
Return the predictions for X
Fit the model to X
Predicts the predicted features
Test for a dummy test
Fit the gradient estimator
Predict the predictions for X

Get all kandi verified functions for this library.

MLFromScratch Key Features

No Key Features are available at this moment for MLFromScratch.

MLFromScratch Examples and Code Snippets

No Code Snippets are available at this moment for MLFromScratch.

Community Discussions

Trending Discussions on MLFromScratch

Implementation of Principal Component Analysis from Scratch Orients the Data Differently than scikit-learn

How to apply KMeans to get the centroid using dataframe with multiple features

QUESTION

Implementation of Principal Component Analysis from Scratch Orients the Data Differently than scikit-learn

Asked 2021-Jun-11 at 14:09

Based on the guide Implementing PCA in Python, by Sebastian Raschka I am building the PCA algorithm from scratch for my research purpose. The class definition is:

...

ANSWER

Answered 2021-Jun-11 at 12:52

When calculating an eigenvector you may change its sign and the solution will also be a valid one.

So any PCA axis can be reversed and the solution will be valid.

Nevertheless, you may wish to impose a positive correlation of a PCA axis with one of the original variables in the dataset, inverting the axis if needed.

Source https://stackoverflow.com/questions/67932137

QUESTION

How to apply KMeans to get the centroid using dataframe with multiple features

Asked 2020-Nov-28 at 10:37

I am following this detailed KMeans tutorial: https://github.com/python-engineer/MLfromscratch/blob/master/mlfromscratch/kmeans.py which uses dataset with 2 features.

But I have a dataframe with 5 features (columns), so instead of using the def euclidean_distance(x1, x2): function in the tutorial, I compute the euclidean distance as below.

...

ANSWER

Answered 2020-Nov-18 at 22:57

Reading the data and clustering it should not throw any errors, even when you increase the number of features in the dataset. In fact, you only get an error in that part of the code when you redefine the euclidean_distance function.

This asnwer addresses the actual error of the plotting function that you are getting.

Source https://stackoverflow.com/questions/64805951

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install MLFromScratch

You can download it from GitHub.
You can use MLFromScratch 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.

Support

For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

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