cmcl | This code is for the paper Confident Multiple Choice | Machine Learning library

by chhwang Python Version: Current License: Apache-2.0

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

kandi X-RAY | cmcl Summary

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

This code is for the paper "Confident Multiple Choice Learning".

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Quality

Security

License

Reuse

Support

cmcl has a low active ecosystem.

It has 12 star(s) with 3 fork(s). There are 2 watchers for this library.

It had no major release in the last 6 months.

There are 2 open issues and 1 have been closed. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of cmcl is current.

Quality

cmcl has no bugs reported.

Security

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

License

cmcl is licensed under the Apache-2.0 License. This license is Permissive.

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

Reuse

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

cmcl 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.

Top functions reviewed by kandi - BETA

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

Define inference
Batch normalization
Computes feature shared between features
Convolutional convolution layer
Creates a fully connected layer
Build the model
Calculate loss
Create a variable schedule
Get inputs and labels
Calculate learning rate
Run training
Run the test

Get all kandi verified functions for this library.

cmcl Key Features

No Key Features are available at this moment for cmcl.

cmcl Examples and Code Snippets

No Code Snippets are available at this moment for cmcl.

Community Discussions

Trending Discussions on cmcl

Color features extraction through clustering in image searching engine

parsing tab delimited values from text file to variables

QUESTION

Color features extraction through clustering in image searching engine

Asked 2017-Dec-10 at 18:41

I'm trying to implement a perceptual-based image searching engine, that will allow users to find pictures, containing objects of relatively same or close colours to the user-specified template(object from the sample image).

The goal for now is not to match a precise object, but rather to find any significant areas that are close in color to the template. I am stuck with indexing my dataset.

I have tried some clustering algorithms, such as k-means from sklearn.cluster (as I've read from this article), to select centroids from the sample image as my features, that are eventually in CIELab color space to acquire more perceptual uniformity. But it doesn't seem to work well, as cluster centres are generated randomly and thus I've got poor metrics results even on an object and image, from which that same object was extracted!

As far as I'm concerned, a common algorithm in simple image searching programs is using distance between histograms, which is not acceptable as I try to sustain perceptually-valid colour difference, and by that I mean that I can only manage two separate colours (and maybe some additional values) to calculate metrics in CIELab colour space. I am using CMCl:c metric of my own implementation, and it produced good results so far.

Maybe someone can help me and recommend an algorithm more suitable for my purpose.

Some code that I've done so far:

...

ANSWER

Answered 2017-Dec-10 at 18:41

The usual approach would be to cluster only once, with a representative sample from all images.

This is a preprocessing step, to generate your "dictionary".

Then for feature extraction, you would map points to the fixed cluster centers, that are now shared across all images. This is a simple nearest-neighbor mapping, no clustering.

Source https://stackoverflow.com/questions/47663559

QUESTION

parsing tab delimited values from text file to variables

Asked 2017-Aug-01 at 18:03

Hello I've been struggling with this problem, I'm trying to iterate over rows and select data from them and then assign them to variables. this is the first time I'm using pandas and I'm not sure how to select the data

...

ANSWER

Answered 2017-Feb-27 at 00:49

You can use iterrows():

Source https://stackoverflow.com/questions/42476129

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

Vulnerabilities

No vulnerabilities reported

Install cmcl

You can download it from GitHub.
You can use cmcl 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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