xLearn | A Lua-based framework for vision | Machine Learning library

 by   clementfarabet C Version: Current License: Non-SPDX

kandi X-RAY | xLearn Summary

kandi X-RAY | xLearn Summary

xLearn is a C library typically used in Artificial Intelligence, Machine Learning, Deep Learning applications. xLearn has no bugs, it has no vulnerabilities and it has low support. However xLearn has a Non-SPDX License. You can download it from GitHub.

what’s in this package ??. torch 5 torch5 provides a matlab-like environment for state-of-the-art machine learning algorithms. it is easy to use and provides a very efficient implementation, thanks to an easy and fast scripting language (lua) and a underlying c implementation. xlearn xlearn is an extension library for torch. it provides dozens of tools/modules for vision, image processing, and machine learning for vision. luaflow luaflow is a unified flow-graph description environment for [beta] vision / image-processing types of applications. one of its primary objectives is to abstract computing platforms, by providing a unified, high-level description flow. xflow a serializing language for luaflow, that allows algorithms to [beta] be imported/exported from/to other software frameworks. neuflow neuflow is the compiler toolkit for the neuflow processor, developped at new york university / yale university. the neuflow processor is dataflow computer optimized for vision and bio-inspired models of vision. the neuflow compiler currently converts xlearn/torch algorithms to native neuflow’s bytecode. soon to appear is a luaflow>neuflow compiler, which would simplify retargetting. it is quite important to have access to a neuflow device to be able to experiment with
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              xLearn has a low active ecosystem.
              It has 20 star(s) with 3 fork(s). There are 3 watchers for this library.
              OutlinedDot
              It had no major release in the last 6 months.
              There are 1 open issues and 1 have been closed. On average issues are closed in 1 days. There are no pull requests.
              It has a neutral sentiment in the developer community.
              The latest version of xLearn is current.

            kandi-Quality Quality

              xLearn has no bugs reported.

            kandi-Security Security

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

            kandi-License License

              xLearn has a Non-SPDX License.
              Non-SPDX licenses can be open source with a non SPDX compliant license, or non open source licenses, and you need to review them closely before use.

            kandi-Reuse Reuse

              xLearn releases are not available. You will need to build from source code and install.
              Installation instructions are not available. Examples and code snippets are available.

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            xLearn Key Features

            No Key Features are available at this moment for xLearn.

            xLearn Examples and Code Snippets

            No Code Snippets are available at this moment for xLearn.

            Community Discussions

            QUESTION

            xlearn predictions error give a different mse than output by the function
            Asked 2021-Feb-10 at 18:00

            the xlearn predict function gives a different mse than what you get by looking at the predictions and calculating it yourself. Here is code to do this; you can run it by cloning the xlearn repository and copying the below code in demo/regression/house_price in the repository

            ...

            ANSWER

            Answered 2021-Feb-09 at 23:58

            A lot of people use 1/2 MSE for the loss because it makes the derivative "easier". Given that they use the word "loss" rather than "MSE" or something like that, I'd bet this is what's going on.

            For clarity, if your loss is

            1/2n * [(y_1 - p_1)^2 + ... + (y_n - p_n)^2]

            then the derivative (wrt p) would be

            -1/n * [(y_1 - p_1) + ... + (y_n - p_n)]

            The 2 goes away because you end up multiplying by 2 for the power rule.

            pardon the formatting... I don't know how to do math stuff here.

            Source https://stackoverflow.com/questions/66026339

            QUESTION

            Custom ML function not working: undefined columns selected
            Asked 2018-Jun-18 at 14:36

            I am trying to write a custom function to do logistic regression-based ML with the caTools package, but I keep getting the error: undefined columns selected.

            I checked the input to xlearn and ylearn arguments to the logit_boost function and, as explained in the documentation, they are respectively dataframe containing feature and a vector of labels. So not sure what I am doing wrong.

            ...

            ANSWER

            Answered 2018-Jun-18 at 14:36

            In help(LogitBoost) examples section, Label = iris[, 5] results in a vector, as expected in the ylearn argument to LogitBoost().

            In your code, label_train <- train %>% dplyr::select(.data = ., !!rlang::enquo(x)) results in a data.frame. dplyr, by design, defaults to drop = FALSE (and even ignores the argument) when only one column is selected.

            We could do:

            Source https://stackoverflow.com/questions/50868808

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

            Vulnerabilities

            No vulnerabilities reported

            Install xLearn

            You can download it from GitHub.

            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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            gh repo clone clementfarabet/xLearn

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