Machine-Learning-Samples

by qqiang00 Python Version: Current License: No License

X-Ray Key Features Code Snippets Community Discussions(1)Vulnerabilities Install Support

kandi X-RAY | Machine-Learning-Samples Summary

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

Machine-Learning-Samples

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Quality

Security

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Machine-Learning-Samples has a low active ecosystem.

It has 20 star(s) with 11 fork(s). There are 3 watchers for this library.

It had no major release in the last 6 months.

Machine-Learning-Samples has no issues reported. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of Machine-Learning-Samples is current.

Quality

Machine-Learning-Samples has 0 bugs and 0 code smells.

Security

Machine-Learning-Samples has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

Machine-Learning-Samples code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

Machine-Learning-Samples does not have a standard license declared.

Check the repository for any license declaration and review the terms closely.

Without a license, all rights are reserved, and you cannot use the library in your applications.

Reuse

Machine-Learning-Samples releases are not available. You will need to build from source code and install.

Machine-Learning-Samples has no build file. You will be need to create the build yourself to build the component from source.

Machine-Learning-Samples saves you 1214 person hours of effort in developing the same functionality from scratch.

It has 2734 lines of code, 180 functions and 28 files.

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

Top functions reviewed by kandi - BETA

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

Train the model .
Segment CNStr
Check the gradient of the batch gradient .
Performs a batch of sequential matches .
Forward pass with LSTM .
takes a string
Test the RNN function .
Check the gradient of the test .
Segment a file .
Train softmax regressor

Get all kandi verified functions for this library.

Machine-Learning-Samples Key Features

No Key Features are available at this moment for Machine-Learning-Samples.

Machine-Learning-Samples Examples and Code Snippets

No Code Snippets are available at this moment for Machine-Learning-Samples.

Community Discussions

Trending Discussions on Machine-Learning-Samples

How solve .MPS file using the latest IBM Waston Studio APIs

QUESTION

How solve .MPS file using the latest IBM Waston Studio APIs

Asked 2020-Nov-16 at 16:46

I'm trying to migrate a ,currently broken because of breaking changes, utility that solves a .mps using IBM's APIs.
The original code, uses an empty model.tar.gz file, creates a deployment and passes the .mps file tp a new job.
The (python)code looks like this :

...

ANSWER

Answered 2020-Nov-16 at 16:46

The answer was provided by Alex Fleischer on another forum.

A full example can be found here:
https://medium.com/@AlainChabrier/solve-lp-problems-from-do-experiments-9afd4d53aaf5
The above link(which is similar to the code in my question) shows an example with a ".lp" file but it's exactly the same for a ".mps" file too. (no note that the model type is do-cplex_12.10 , not do-docplex_12.10)

My problem was that I was using an empty model.tar.gz file.
Once you have the .lp/.mps file in the archive, everything works as expected

Source https://stackoverflow.com/questions/64524702

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

Vulnerabilities

No vulnerabilities reported

Install Machine-Learning-Samples

You can download it from GitHub.
You can use Machine-Learning-Samples 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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