decay | Famous sorting algorithms based on vote popularity | Bot library

 by   clux JavaScript Version: 1.0.12 License: MIT

kandi X-RAY | decay Summary

kandi X-RAY | decay Summary

decay is a JavaScript library typically used in Automation, Bot applications. decay has no bugs, it has no vulnerabilities, it has a Permissive License and it has low support. You can install using 'npm i decay' or download it from GitHub, npm.

This library houses 3 popularity estimating algorithms employed by bigger news sites used to sort for best content:. Algorithms may cause scores to decay based on distance to post time.
Support
    Quality
      Security
        License
          Reuse

            kandi-support Support

              decay has a low active ecosystem.
              It has 356 star(s) with 25 fork(s). There are 13 watchers for this library.
              OutlinedDot
              It had no major release in the last 12 months.
              There are 0 open issues and 7 have been closed. On average issues are closed in 5 days. There are no pull requests.
              It has a neutral sentiment in the developer community.
              The latest version of decay is 1.0.12

            kandi-Quality Quality

              decay has 0 bugs and 0 code smells.

            kandi-Security Security

              decay has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
              decay code analysis shows 0 unresolved vulnerabilities.
              There are 0 security hotspots that need review.

            kandi-License License

              decay is licensed under the MIT License. This license is Permissive.
              Permissive licenses have the least restrictions, and you can use them in most projects.

            kandi-Reuse Reuse

              decay releases are not available. You will need to build from source code and install.
              Deployable package is available in npm.
              Installation instructions are not available. Examples and code snippets are available.

            Top functions reviewed by kandi - BETA

            kandi's functional review helps you automatically verify the functionalities of the libraries and avoid rework.
            Currently covering the most popular Java, JavaScript and Python libraries. See a Sample of decay
            Get all kandi verified functions for this library.

            decay Key Features

            No Key Features are available at this moment for decay.

            decay Examples and Code Snippets

            Examples,Live cells in red
            JavaScriptdot img1Lines of Code : 3dot img1no licencesLicense : No License
            copy iconCopy
            const decay = 0.02;
            const colourAlive = '#f41';
            const colourDead = '#ccc';
              
            A polynomial decay function .
            pythondot img2Lines of Code : 98dot img2License : Non-SPDX (Apache License 2.0)
            copy iconCopy
            def polynomial_decay(learning_rate,
                                 global_step,
                                 decay_steps,
                                 end_learning_rate=0.0001,
                                 power=1.0,
                                 cycle=False,
                                 name=None):
              
            Noisy linear cosine decay .
            pythondot img3Lines of Code : 92dot img3License : Non-SPDX (Apache License 2.0)
            copy iconCopy
            def noisy_linear_cosine_decay(learning_rate,
                                          global_step,
                                          decay_steps,
                                          initial_variance=1.0,
                                          variance_decay=0.55,
                              
            Gradient decay function .
            pythondot img4Lines of Code : 85dot img4License : Non-SPDX (Apache License 2.0)
            copy iconCopy
            def natural_exp_decay(learning_rate,
                                  global_step,
                                  decay_steps,
                                  decay_rate,
                                  staircase=False,
                                  name=None):
              """Applies natural exponential dec  

            Community Discussions

            QUESTION

            Fitting sinusoidal data in Python
            Asked 2022-Mar-20 at 13:12

            I am trying to fit experimental data

            with a function of the form:

            ...

            ANSWER

            Answered 2022-Mar-20 at 12:50

            Your fitted curve will look like this

            Source https://stackoverflow.com/questions/71546564

            QUESTION

            how can reslove : InvalidArgumentError: Graph execution error?
            Asked 2022-Mar-16 at 09:55

            Hello guys i am a biggner at computer vision and classification, i am trying to train a model using cnn method with tensorflow and keras, but i keep getting the error bellow this code , could anyone help me or give me at least a peace of advice?

            ...

            ANSWER

            Answered 2022-Mar-16 at 09:55

            You just have to make sure your labels are zero-based starting from 0 to 2, since your output layer has 3 nodes and a softmax activation function and you are using sparse_categorical_crossentropy. Here is a working example:

            Source https://stackoverflow.com/questions/71493889

            QUESTION

            Compiled C ODE gives different results to R's using deSolve
            Asked 2022-Mar-15 at 22:08

            I have an ODE which I would like to solve using compiled C code called from R's deSolve package. The ODE in question is I an exponential decay model (y'=-d* exp(g* time)*y): But running the compiled code from within R gives different results to R's native deSolve. It's as is there they are flipped 180º. What's going on?

            C code implementation ...

            ANSWER

            Answered 2022-Mar-13 at 11:01

            Compiled code does not give different results to deSolve models implemented in R, except potential rounding errors within the limits of atoland rtol.

            The reasons of the differences in the original post where two errors in the code. One can correct it as follows:

            1. Declare static double as parms[3]; instead of parms[4]
            2. Time t in derivs is a pointer, i.e. *t

            so that the code reads as:

            Source https://stackoverflow.com/questions/71453076

            QUESTION

            Does pass-by-reference decay into pass-by-pointer in some cases?
            Asked 2022-Mar-04 at 21:18

            I've looked for an answer to this one, but I can't seem to find anything, so I'm asking here:

            Do reference parameters decay into pointers where it is logically necessary?

            Let me explain what I mean:

            If I declare a function with a reference to an int as a parameter:

            ...

            ANSWER

            Answered 2022-Mar-04 at 21:18

            The compiler can decide to implement references as pointers, or inlining or any other method it chooses to use. In terms of performance, it's irrelevant. The compiler can and will do whatever it wants to when it comes to optimization. The compiler can implement your reference as a pass-by-value if it wants to (and if it's valid to do so in the specific situation). Caching the result won't help because the compiler will do that anyways. If you want to explicitly tell the compiler that the value might change (because of another thread that has access to the same pointer), you need to use the keyword volatile (or std::atomic if you're not already using a std::mutex).
            Edit: The keyword "volatile" is never required for multithreading. std::mutex is enough.
            If you don't use the keyword volatile, the compiler will almost certainly cache the result for you (if appropriate). There are, however, at least 2 actual differences in the rules between pointers and references.

            1. Taking the address (pointer) of a temporary value (rvalue) is undefined behavior in C++.
            2. References are immutable, sometimes need to be wrapped in std::ref.

            Here I'll provide examples for both differences.

            This code using references is valid:

            Source https://stackoverflow.com/questions/70405987

            QUESTION

            Is it undefined behavior to compare a character array char u[10] with a string literal "abc"
            Asked 2022-Mar-01 at 14:44

            I came across this question on SO, and this answer to the question. The code is as follows:

            ...

            ANSWER

            Answered 2022-Mar-01 at 08:54

            The code won't do what the author presumably wanted it to do, but there's no UB. While comparing pointers to unrelated objects with < has unspecified results, checking equality is fine, and the comparison will produce false.

            Source https://stackoverflow.com/questions/71305813

            QUESTION

            Can I pass a built-in array to std::ostream_iterator without an array-to-pointer decay?
            Asked 2022-Feb-07 at 18:36

            I have overloaded operator<< to print a built-in array const int (&arr)[N]:

            ...

            ANSWER

            Answered 2022-Feb-07 at 18:36

            This has nothing to do with array-to-pointer decay and everything to do with how name lookup works.

            In this version:

            Source https://stackoverflow.com/questions/71023392

            QUESTION

            Implementation of the Metropolis-Hasting algorithm for solving gaussian integrals
            Asked 2022-Feb-02 at 20:28

            I am currently having issue with the implementation of the Metropolis-Hastings algorithm.

            I am trying to use the algorithm to calculate integrals of the form

            In using this algorithm, we can obtain a long chain of configurations ( in this case, each configuration is just a single numbers) such that in the tail-end of the chain the probability of having a particular configuration follows (or rather tends to) a gaussian distribution.

            My code seems to be messing up with obtaining the said gaussian distributions. There is a strange dependence on the transition probablity (the probablity of picking a new candidate configuration depending on the previous configuration in the chain). However, if this transition probability is symmetric, there should be no dependence on this function at all (it only affects speed at which phase space [space of potential configurations] is explored and how quickly the chain converges to the desired distribution)!

            In my case I am using a normal distribution transition function (which satisfies the need to be symmetric), with width d. For each d I use I do indeed get a gaussian distribution however the standard deviation, sigma, depends on my choice of d. The resulting gaussian should have a sigma of roughly 0.701 but I find that the value I actually get depends on the parameter d, when it shouldn't.

            I am not sure where the error in this code is, any help would be greatly appreciated!

            ...

            ANSWER

            Answered 2022-Feb-02 at 20:28

            You need to save x even when it doesn't change. Otherwise the center values are under-counted, and more so as d increases, which increases the variance.

            Source https://stackoverflow.com/questions/70958350

            QUESTION

            Create std::string from std::span of unsigned char
            Asked 2022-Jan-23 at 16:19

            I am using a C library which uses various fixed-sized unsigned char arrays with no null terminator as strings.

            I've been converting them to std::string using the following function:

            ...

            ANSWER

            Answered 2022-Jan-22 at 22:33

            QUESTION

            Passing a C-style array to `span`
            Asked 2021-Nov-27 at 02:27

            C++20 introduced std::span, which is a view-like object that can take in a continuous sequence, such as a C-style array, std::array, and std::vector. A common problem with a C-style array is it will decay to a pointer when passing to a function. Such a problem can be solved by using std::span:

            ...

            ANSWER

            Answered 2021-Nov-27 at 02:27

            The question is not why this fails for int[], but why it works for all the other types! Unfortunately, you have fallen prey to ADL which is actually calling std::size instead of the size function you have written. This is because all overloads of your function fail, and so it looks in the namespace of the first argument for a matching function, where it finds std::size. Rerun your program with the function renamed to something else:

            Source https://stackoverflow.com/questions/70117922

            QUESTION

            How can I check a confusion_matrix after fine-tuning with custom datasets?
            Asked 2021-Nov-24 at 13:26

            This question is the same with How can I check a confusion_matrix after fine-tuning with custom datasets?, on Data Science Stack Exchange.

            Background

            I would like to check a confusion_matrix, including precision, recall, and f1-score like below after fine-tuning with custom datasets.

            Fine tuning process and the task are Sequence Classification with IMDb Reviews on the Fine-tuning with custom datasets tutorial on Hugging face.

            After finishing the fine-tune with Trainer, how can I check a confusion_matrix in this case?

            An image of confusion_matrix, including precision, recall, and f1-score original site: just for example output image

            ...

            ANSWER

            Answered 2021-Nov-24 at 13:26

            What you could do in this situation is to iterate on the validation set(or on the test set for that matter) and manually create a list of y_true and y_pred.

            Source https://stackoverflow.com/questions/68691450

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

            Vulnerabilities

            No vulnerabilities reported

            Install decay

            You can install using 'npm i decay' or download it from GitHub, npm.

            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 .
            Find more information at:

            Find, review, and download reusable Libraries, Code Snippets, Cloud APIs from over 650 million Knowledge Items

            Find more libraries
            Install
          • npm

            npm i decay

          • CLONE
          • HTTPS

            https://github.com/clux/decay.git

          • CLI

            gh repo clone clux/decay

          • sshUrl

            git@github.com:clux/decay.git

          • Stay Updated

            Subscribe to our newsletter for trending solutions and developer bootcamps

            Agree to Sign up and Terms & Conditions

            Share this Page

            share link