Factorization | It's factorization. - | Video Game library

 by   purpleposeidon Java Version: Current License: Non-SPDX

kandi X-RAY | Factorization Summary

kandi X-RAY | Factorization Summary

Factorization is a Java library typically used in Gaming, Video Game, Minecraft applications. Factorization has no bugs, it has no vulnerabilities, it has build file available and it has low support. However Factorization has a Non-SPDX License. You can download it from GitHub.

It's factorization.

            kandi-support Support

              Factorization has a low active ecosystem.
              It has 25 star(s) with 20 fork(s). There are 11 watchers for this library.
              It had no major release in the last 6 months.
              There are 11 open issues and 9 have been closed. On average issues are closed in 182 days. There are no pull requests.
              It has a neutral sentiment in the developer community.
              The latest version of Factorization is current.

            kandi-Quality Quality

              Factorization has 0 bugs and 0 code smells.

            kandi-Security Security

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

            kandi-License License

              Factorization 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

              Factorization releases are not available. You will need to build from source code and install.
              Build file is available. You can build the component from source.
              Factorization saves you 55009 person hours of effort in developing the same functionality from scratch.
              It has 63330 lines of code, 5581 functions and 564 files.
              It has high code complexity. Code complexity directly impacts maintainability of the code.

            Top functions reviewed by kandi - BETA

            kandi has reviewed Factorization and discovered the below as its top functions. This is intended to give you an instant insight into Factorization implemented functionality, and help decide if they suit your requirements.
            • Update the conductor
            • Update the circuit count
            • Returns an iterable containing the members of this charge
            • Create or join the conductor set
            • Draws the boxes
            • Returns true if any of the boxes have been set
            • Draws the render data
            • Deplete the current value
            • Removes the given number of items from the list
            • Invalidate the conductor
            • Gets a mutable list
            • Increase value
            • Tests whether the given collection contains all elements of the given collection
            • Adds a charge to the conductor set
            • Removes the first element from the list
            • Serialize this ID into an internal map
            • Removes all elements in the given collection
            • Adds all elements in the specified collection to this set
            • Tries to take a value from the queue
            • Removes the element at the specified position
            • Add line
            • Removes all elements from this collection that are contained in the given collection
            • Iterate over the elements in this map
            • Return the Charge density reading from a given IChargeConductor
            • Clears all the boxes for the next tick
            • Returns the first argument in the given advancer
            Get all kandi verified functions for this library.

            Factorization Key Features

            No Key Features are available at this moment for Factorization.

            Factorization Examples and Code Snippets

            No Code Snippets are available at this moment for Factorization.

            Community Discussions


            octave nmf_bpas error: vertical dimensions mismatch (8x1 vs 1x400)
            Asked 2021-May-20 at 17:34

            I have a problem with non-negative matrix factorization in octave. I try to estimate synergies from Emg-data, but octave only lets me do this for two or more synergies, but not for one. I was able to reproduce the problem with the following code. nmf_bpas is from the linear-algebra pkg from octave-forge.



            Answered 2021-May-20 at 17:34

            This seems to be a bug in nmf_bpas.

            From what I can tell, the bug is on line 373. Change

            Source https://stackoverflow.com/questions/67618375


            LinearAlgebra.SingularException(7) while executing radial basis interpolation using Surrogates in julia?
            Asked 2021-Apr-19 at 03:17

            I am trying to execute radial basis interpolation in Julia using Surrogates package.

            I have developed the following code:



            Answered 2021-Apr-19 at 03:17

            Your training data contains only a single sampling point with multiple values. Replace with more sound data and your code works:

            Source https://stackoverflow.com/questions/67155175


            Search of max value and index in a vector
            Asked 2021-Apr-02 at 12:18

            I'm trying to parallelize this piece of code that search for a max on a column. The problem is that the parallelize version runs slower than the serial

            Probably the search of the pivot (max on a column) is slower due the syncrhonization on the maximum value and the index, right?



            Answered 2021-Mar-18 at 20:16

            There is at least two things wrong with your parallelization

            Source https://stackoverflow.com/questions/66697508


            LU-factorization with OpenMP seems to slow, need advice
            Asked 2021-Mar-10 at 19:09

            I have written a code which performs LU-factorization with OpenMP. I feel like the code is performing too slow. The computer I am working on have 16 cores and the time for 16 threads is 12.5s and for 4 threads 14.3s. This is the section of the code where the parallelization is happening. I am new to C programming and feel like I am missing something that is slowing the multithreading down.



            Answered 2021-Mar-10 at 18:18

            Based on the code that you have provided it seems to me that you do not need any lock whatsoever, try the following parallel version:

            Source https://stackoverflow.com/questions/66568428


            How to define two possible data type for property
            Asked 2021-Feb-22 at 14:22

            I am trying to define an operator overloads in C# for 2 classes: Fraction and BasicFraction. On the outside, they act the same, but internally Fraction uses PrimeFactorization (stores number as a list of prime factors) and BasicFraction uses int for storing the nominator and denominator.

            They would follow this interface (if I use JSDoc type definitions):



            Answered 2021-Feb-21 at 14:52

            You want a discriminated union type. C# does not support them natively as a first-class language feature but you can hack-it using OneOf: https://github.com/mcintyre321/OneOf

            So you would end-up with:

            Source https://stackoverflow.com/questions/66303205


            While loop doesn't stop when number is power of 2
            Asked 2021-Feb-18 at 11:06

            I have some issues in python console app. I coded an factorization console app. It works well, but when I input a number that is power of 2 like 4, 8, 16, the while loop doesn't stop. So it doesn't print anything and the code doesn't stop. Bellow is my code.



            Answered 2021-Feb-18 at 11:06

            The problem is that you don't change n or m inside the while loop. So, for example, for input n=4, m=2, k comes out to be n/m=2 which satisfies k%m==0 and since neither n nor m changes so it runs forever.

            You can simplify the code by modifying n in the while loop to keep decreasing if it is divisible by the current divisor m. You can't do the same for k since k is reset to n again with the line k = n and it will start with the original number giving incorrect output.

            Here is a bit modified version of the code with the outer while loop:

            Source https://stackoverflow.com/questions/66257403


            issues of cuBLAS performance on batched complex linear system solver
            Asked 2021-Feb-16 at 03:03

            I am new to cuda and cuBlas, and recently I am trying to use batched cuBlas API to solve multiple systems of linear equations. Here's my code:

            The size of the matrix is N, and the number of matrices (batch size) is numOfMat.



            Answered 2021-Feb-16 at 03:03

            I think you need to look more closely at your data. If I run a modification of your code on Google Colab (Tesla T4) I get this:

            Which looks largely like your figure. But look more closely (log scales help):

            You can clearly see that up to a certain point, the runtime is largely independent of the number of matrices (around 2^8 = 64), but then scaling is linear as sizes increase. That is the transition from being able to parallelize the workload to reaching parallel capacity and having to schedule many parallel groups of operations to execute the workload. You might infer that for this particular GPU, the GPU run out of parallel capacity at between 64 and 128 concurrent operations (The T4 has 40 SM, so it might well be 80 if an SM could accommodate 2 operations per SM concurrently), after which runtime scales with multiples of that limiting size.

            This is completely normal behaviour for any parallel computation architecture I am familiar with.

            Source https://stackoverflow.com/questions/66216497


            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



            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


            How do I modularize a prime factorization Python script to take in a list as input?
            Asked 2021-Jan-17 at 22:57

            I have working Python code (below) that cracks an RSA key and times the prime factorization. How do I put this code into a module that takes a list of integers representing n for creating an n-bit prime number for range 0...2^n? I want to pass in a list of integers and return a table of the n-values and run times like this:



            Answered 2021-Jan-17 at 22:57

            There are several issues here. To start with, your factor function regenerates a new pq value, ignoring the one you produced earlier. This counts the time to get pq twice. Also, measuring time on a single execution for a process that uses a random number generator doesn't give you a good idea of the performance. You need to make several runs to get an average.

            I cleaned up your code a bit and changed the way time is measured to get an average over 100 runs:

            your code:

            Source https://stackoverflow.com/questions/65760538


            Simple addition of 1 to a large number does not work? (Python 3.9)
            Asked 2021-Jan-14 at 01:54

            Note: I am not that experienced in Python, therefore my code may not be as good as it could/should be.

            I am attempting to create a tool to facilitate calculating the algebraic factors of a certain form of number (see https://en.wikipedia.org/wiki/Aurifeuillean_factorization). This is mostly as a test/learning experience, however I have run into a problem when attempting to calculate the parameter "c", which is defined as 2^(2k+1)+1. The addition step does not work for me. I am simply getting the returned value as 2^129, instead of 2^129+1 as I am looking to get. Is this an issue with Python itself, or am I making some sort of mistake in this.




            Answered 2021-Jan-14 at 01:54

            k = (exponent - 2) / 4 makes k a float, which means you potentially introduce numerical error in computations down the line. Use integer division to stay in int world from the start:

            Source https://stackoverflow.com/questions/65712248

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


            No vulnerabilities reported

            Install Factorization

            You can download it from GitHub.
            You can use Factorization like any standard Java library. Please include the the jar files in your classpath. You can also use any IDE and you can run and debug the Factorization component as you would do with any other Java program. Best practice is to use a build tool that supports dependency management such as Maven or Gradle. For Maven installation, please refer maven.apache.org. For Gradle installation, please refer gradle.org .


            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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