Dirichlet | Dirichlet .NET Number Theory Library | Math library

 by   ricksladkey C# Version: Current License: Non-SPDX

kandi X-RAY | Dirichlet Summary

kandi X-RAY | Dirichlet Summary

Dirichlet is a C# library typically used in Utilities, Math applications. Dirichlet has no bugs, it has no vulnerabilities and it has low support. However Dirichlet has a Non-SPDX License. You can download it from GitHub.

Dirichlet is an number theory library for .NET. Although it is fairly complete and well tested, it lacks the organization of a released library due to its evolution and the inclusion of various experimental ideas and highly-specialized applications. In the algorithms in particular there are often many flavors of same thing in various attempts to increase performance. If there is any interest, I might separate things out and migrate more of the core to Dirichlet.Numerics.
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              Dirichlet has a low active ecosystem.
              It has 6 star(s) with 5 fork(s). There are 2 watchers for this library.
              OutlinedDot
              It had no major release in the last 6 months.
              There are 0 open issues and 1 have been closed. On average issues are closed in 17 days. There are no pull requests.
              It has a neutral sentiment in the developer community.
              The latest version of Dirichlet is current.

            kandi-Quality Quality

              Dirichlet has no bugs reported.

            kandi-Security Security

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

            kandi-License License

              Dirichlet 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

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

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            Dirichlet Key Features

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            Dirichlet Examples and Code Snippets

            K between Dirichlet distributions .
            pythondot img1Lines of Code : 74dot img1License : Non-SPDX (Apache License 2.0)
            copy iconCopy
            def _kl_dirichlet_dirichlet(d1, d2, name=None):
              """Batchwise KL divergence KL(d1 || d2) with d1 and d2 Dirichlet.
            
              Args:
                d1: instance of a Dirichlet distribution object.
                d2: instance of a Dirichlet distribution object.
                name: (optional  

            Community Discussions

            QUESTION

            Is it possible to vectorize the scipy Dirichlet PDF function?
            Asked 2021-Jun-06 at 07:52

            I know that some scipy.stats pdf functions are vectorized out of the box, as stated in the manual.

            My problem is that I've tried utilizing this vectorization with Dirichlet's pdf, scipy.stats.dirchlet.pdf(x, alpha) hopping that I could calculate an array of values for multiple x's for the same alpha parameter. Something that could get the same results as

            ...

            ANSWER

            Answered 2021-Jun-06 at 07:52

            You can obtain the exact result you want by simply transposing the first argument:

            Source https://stackoverflow.com/questions/67855329

            QUESTION

            Which hyperparameter optimization technique is used in Mallet for LDA?
            Asked 2021-May-21 at 13:47

            I am wondering which technique is used to learn the Dirichlet priors in Mallet's LDA implementation.

            Chapter 2 of Hanna Wallach's Ph.D. thesis gives a great overview and a valuable evaluation of existing and new techniques to learn the Dirichlet priors from the data.

            Tom Minka initially provided his famous fixed-point iteration approach, however without any evaluation or recommendations.

            Furthermore, Jonathan Chuang did some comparisons between previously proposed methods, including the Newton−Raphson method.

            LiangJie Hong says the following in his blog:

            A typical approach is to utilize Monte-Carlo EM approach where E-step is approximated by Gibbs sampling while M-step is to perform a gradient-based optimization approach to optimize Dirichlet parameters. Such approach is implemented in Mallet package.

            Mallet mentions the Minka's fixed-point iterations with and without histograms.

            However, the method that is actually used simply states:

            Learn Dirichlet parameters using frequency histograms

            Could someone provide any reference that describes the used technique?

            ...

            ANSWER

            Answered 2021-May-21 at 13:47

            It uses the fixed point iteration. The frequency histograms method is just an efficient way to calculate it. They provide an algebraically equivalent way to do the exact same computation. The update function consists of a sum over a large number of Digamma functions. This function by itself is difficult to compute, but the difference between two Digamma functions (where the arguments differ by an integer) is relatively easy to compute, and even better, it "telescopes" so that the answer to Digamma(a + n) - Digamma(a) is one operation away from the answer to Digamma(a + n + 1) - Digamma(a). If you work through the histogram of counts from 1 to the max, adding up the number of times you saw a count of n at each step, the calculation becomes extremely fast. Initially, we were worried that hyperparameter optimization would take so long that no one would do it. With this trick it's so fast it's not really significant compared to the Gibbs sampling.

            Source https://stackoverflow.com/questions/67622671

            QUESTION

            importing likelihoods fails
            Asked 2021-Apr-21 at 07:29

            I am trying to get in touch with Gaussian Process Classification and try to reproduce the example from https://docs.gpytorch.ai/en/stable/examples/01_Exact_GPs/GP_Regression_on_Classification_Labels.html

            Following their code, I want to import the Dirichlet Classification Likelihood by

            ...

            ANSWER

            Answered 2021-Apr-21 at 07:29

            Solved it.

            The paths of Jupyter Notebook did not include the new updated library. After updating paths to the wanted folder by

            Source https://stackoverflow.com/questions/67182490

            QUESTION

            pymc3: Truncated Normal mixture
            Asked 2021-Apr-15 at 16:01

            I want to make a mixture of two TruncatedNormal distributions in pymc3.

            I am trying to modify this piece of documentation. See example #2 for Poisson.

            ...

            ANSWER

            Answered 2021-Apr-15 at 16:01

            You don't need to give the distribution a name. The string tn1 passed as first the argument to pm.TruncatedNormal.dist is interpreted as mu (as you pass a named mu also, you get the exception). Try

            Source https://stackoverflow.com/questions/67111703

            QUESTION

            storing value of ImplicitSourceTerm
            Asked 2021-Apr-08 at 20:43

            I am working with fipy and I wish to simulate a flow with a free flux BC on some selected faces. Following other examples, I tried 2 different technics:

            ...

            ANSWER

            Answered 2021-Apr-08 at 20:43
            1. why are the values of phi and phi2 slightly different?

            phi and phi2 are different because eq2 doesn't converge as rapidly as eq1. This is because eq1 is more implicit than eq2. If you change the tolerance for the residual, e.g., res > 1e-10, you'll see the two solutions are in much closer agreement.

            1. how could I extract the outflow term for each cell (when a more complex grid will be used) while keeping 'ImplicitSourceTerm', which is more efficient?

            You can still evaluate the flux phi2 * extCoef * phi2.faceGrad, even when you use the ImplicitSourceTerm.

            In general, it's not easy to extract what each Term is doing physically (see issue #461). You can use the FIPY_DISPLAY_MATRIX environment variable to see how each Term contributes to the solution matrix, but this may or may not give you much physical intuition for what's going on.

            Source https://stackoverflow.com/questions/67009597

            QUESTION

            Really weird and erratic behavior when running a simple Julia code. Commenting out inconsequential print (and other statements) removes these errors
            Asked 2021-Apr-06 at 19:34

            I am new to Julia and I am really surprised by the behavior of the following code.

            ...

            ANSWER

            Answered 2021-Apr-06 at 19:34

            Moving to Julia 1.5 fixed the issue.

            Source https://stackoverflow.com/questions/65870410

            QUESTION

            Solving PDE on 1D cylindrical coordinates with FiPy
            Asked 2021-Apr-02 at 14:26

            Let me start by saying that I have found similar problems to mine on the NARKIVE FiPy mailing list archive but since the equations won't load, they are not very useful. For example Convection-diffusion problem on a 1D cylindrical grid, or on another mailing list archive Re: FiPy Heat Transfer Solution. In the second linked mail Daniel says:

            There are two ways to solve on a cylindrical domain in FiPy. You can either use the standard diffusion equation in Cartesian coordinates (2nd equation below) and with a mesh that is actually cylindrical in shape or you can use the diffusion equation formulated on a cylindrical coordinate system (1st equation below) and use a standard 2D / 1D grid mesh.

            And the equations are not there. In this case it is actually fine because I understand the first solution and I want to use that.

            I want to solve the following equation on a 1D cylindrical grid (sorry I don't have 10 reputation yet so I cannot post the nice rendered equations):

            with boundary conditions:

            where rho_core is the left side of the mesh, and rho_edge is the right side of the mesh. rho is the normalized radius, J is the Jacobian:

            R is the real radius in meters, so the dimension of the Jacobian is distance. The initial conditions doesn't really matter, but in my code example I will use a numerical Dirac-delta at R=0.8.

            I have a working example without(!) the Jacobian, but it's quite long, and it doesn't use FiPy's Viewers so I'll link a gist: https://gist.github.com/leferi99/142b90bb686cdf5116ef5aee425a4736

            The main part in question is the following:

            ...

            ANSWER

            Answered 2021-Apr-02 at 14:26

            [cobbling an answer from the discussion in the comments]

            The results are similar between a Grid1D and a CylindricalGrid1D, particularly in the early steps, but they are not the same. They are quite different as the problem evolves.

            FiPy doesn't like things outside the divergence, but you should be able to multiply the equation by J and put it in the coefficient of the TransientTerm, e.g.,

            or

            Source https://stackoverflow.com/questions/66890330

            QUESTION

            'numpy.int64' object is not iterable when using latent dirichlet allocation
            Asked 2021-Mar-23 at 15:34

            I am trying to apply the latent dirichlet allocation algorithm to a .csv file retrieved from twitter data.

            Currently I run across the error:

            ...

            ANSWER

            Answered 2021-Feb-24 at 20:45

            I believe you want to select the top 10 words and you are using a wrong syntax. You are only selecting the word ranked 10 which is not iterable. Change line 261 to this to select the top 10 instead of only selecting the 10th:

            Source https://stackoverflow.com/questions/66358528

            QUESTION

            How to decompose a mixed distribution using MCMC
            Asked 2021-Mar-07 at 03:45

            I have data that is a 50:50 mix of a normal distribution and a constant value:

            ...

            ANSWER

            Answered 2021-Mar-07 at 03:45
            Incommensurability of Probability Density and Mass

            The issue here is that the likelihood of coming from each model involves probability density for the Gaussian and mass for the discrete, which are not commensurate. Specifically, the computation for comparing where a zero observation came from, will involve likelihoods

            Source https://stackoverflow.com/questions/66407576

            QUESTION

            CuPy and Dirichlet gives me TypeError: unsupported operand type(s) for +=: 'int' and 'tuple'
            Asked 2021-Feb-28 at 15:33

            I simply want to create a random matrix A whose vectors are drawn from the Dirichlet distribution. The function works fine with numpy:

            ...

            ANSWER

            Answered 2021-Feb-28 at 15:33

            This is a bug in cupy which you should report on their GitHub.

            They do not properly handle the case of an integer argument, despite the documentation. They require that you provide either a tuple or None. This is why you see the behavior you’re seeing. (If you provided a tuple (a, b), then the resulting shape would properly be (a, b, n).

            The workaround here is to provide the shape you want as a length-1 tuple: (n,). Note that the comma is necessary.

            Source https://stackoverflow.com/questions/66405325

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

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            Install Dirichlet

            You can download it from GitHub.

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