Gaussian-Mixture-Models | Gaussian Mixture Model and Gaussian Mixture Regression | Machine Learning library

by BatyaGG Python Version: Current License: MIT

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

kandi X-RAY | Gaussian-Mixture-Models Summary

Gaussian-Mixture-Models is a Python library typically used in Artificial Intelligence, Machine Learning, Numpy applications. Gaussian-Mixture-Models has no bugs, it has no vulnerabilities, it has a Permissive License and it has low support. However Gaussian-Mixture-Models build file is not available. You can download it from GitHub.

Gaussian Mixture Model (GMM) and Gaussian Mixture Regression (GMR) implemented purely on numpy

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Gaussian-Mixture-Models has a low active ecosystem.

It has 47 star(s) with 22 fork(s). There are 3 watchers for this library.

It had no major release in the last 6 months.

Gaussian-Mixture-Models has no issues reported. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of Gaussian-Mixture-Models is current.

Quality

Gaussian-Mixture-Models has 0 bugs and 0 code smells.

Security

Gaussian-Mixture-Models has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

Gaussian-Mixture-Models code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

Gaussian-Mixture-Models is licensed under the MIT License. This license is Permissive.

Permissive licenses have the least restrictions, and you can use them in most projects.

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Gaussian-Mixture-Models releases are not available. You will need to build from source code and install.

Gaussian-Mixture-Models has no build file. You will be need to create the build yourself to build the component from source.

Installation instructions, examples and code snippets are available.

Gaussian-Mixture-Models saves you 133 person hours of effort in developing the same functionality from scratch.

It has 333 lines of code, 11 functions and 8 files.

It has high code complexity. Code complexity directly impacts maintainability of the code.

Top functions reviewed by kandi - BETA

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

Implementation of GMR
Computes the Gaussian probability of the Gaussian distribution
Emulate the EM algorithm

Get all kandi verified functions for this library.

Gaussian-Mixture-Models Key Features

No Key Features are available at this moment for Gaussian-Mixture-Models.

Gaussian-Mixture-Models Examples and Code Snippets

No Code Snippets are available at this moment for Gaussian-Mixture-Models.

Community Discussions

Trending Discussions on Gaussian-Mixture-Models

How can implement EM-GMM in python?

Gaussian Mixture Models of an Image's Histogram

QUESTION

How can implement EM-GMM in python?

Asked 2020-Sep-27 at 02:56

I have implemented EM algorithm for GMM using this post GMMs and Maximum Likelihood Optimization Using NumPy unsuccessfully as follows:

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ANSWER

Answered 2020-Aug-19 at 07:29

As I mentioned in the comment, the critical point that I see is the means initialization. Following the default implementation of sklearn Gaussian Mixture, instead of random initialization, I switched to KMeans.

Source https://stackoverflow.com/questions/63414169

QUESTION

Gaussian Mixture Models of an Image's Histogram

Asked 2017-Aug-22 at 18:23

I am attempting to do automatic image segmentation of the different regions of a 2D MR image based on pixel intensity values. The first step is implementing a Gaussian Mixture Model on the image's histogram.

I need to plot the resulting gaussian obtained from the score_samples method onto the histogram. I have tried following the code in the answer to (Understanding Gaussian Mixture Models).

However, the resulting gaussian fails to match the histogram at all. How do I get the gaussian to match the histogram?

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ANSWER

Answered 2017-Aug-22 at 18:23

The issue was with passing the histogram rather than the array of pixel intensities to GaussianMixture.fit gmm = gmm.fit(hist). I also found that a minimum of n_components = 6 is needed to visually fit this particular histogram.

Source https://stackoverflow.com/questions/45805316

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

Vulnerabilities

No vulnerabilities reported

Install Gaussian-Mixture-Models

Clone or download the project. Install following packages: numpy <1.11.3>, matplotlib <1.5.3>,. Other versions of the packages were not tested, but higher versions are welcome. Report me to b.saduanov@gmail.com if you have any problems.

Support

There are several possibilities to improve this algorithm implementation. One of them is to make regression more flexible and work not only on one independent time series variable. Actually, this GMR implementation is able to have several independent inputs and retrieve remaining dependent outputs, but it should be tested on such data sets. At such testing stage there can be several bugs in GMR function, but only at the initialization stage with input and output Mu and Sigma declarations. Mathematical side has no any bugs, so if you try to contribute, you will have no mathematical bugs which are usually painfull to fix. If such tests will be successful and all bugs will be fixed, then 2 more parameters should be added to 'predict(np.array: input)' method which will be input and output features indexes passed as numpy arrays or python lists.

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