hypercube | simple hypercube/tesseract animation

 by   transcranial Go Version: Current License: MIT

kandi X-RAY | hypercube Summary

kandi X-RAY | hypercube Summary

hypercube is a Go library. hypercube has no bugs, it has no vulnerabilities, it has a Permissive License and it has low support. You can download it from GitHub.

A simple hypercube/tesseract animation. Created in Go using the awesome ln library. An older implementation in three.js also exists in the js folder.
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              hypercube has a low active ecosystem.
              It has 17 star(s) with 6 fork(s). There are 1 watchers for this library.
              OutlinedDot
              It had no major release in the last 6 months.
              hypercube has no issues reported. There are no pull requests.
              It has a neutral sentiment in the developer community.
              The latest version of hypercube is current.

            kandi-Quality Quality

              hypercube has 0 bugs and 0 code smells.

            kandi-Security Security

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

            kandi-License License

              hypercube 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

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

            Top functions reviewed by kandi - BETA

            kandi has reviewed hypercube and discovered the below as its top functions. This is intended to give you an instant insight into hypercube implemented functionality, and help decide if they suit your requirements.
            • NewHypercube creates a new Hyper cube .
            • Draws a horizontal surface
            • CalcPath computes the path relative to the given parameters .
            • Paths returns all the vertices
            Get all kandi verified functions for this library.

            hypercube Key Features

            No Key Features are available at this moment for hypercube.

            hypercube Examples and Code Snippets

            No Code Snippets are available at this moment for hypercube.

            Community Discussions

            QUESTION

            How to recursively copy arrays
            Asked 2022-Apr-15 at 04:30

            I want to recursively produce the vertices (points) of a unit n-hypercube (for the sake of clarity, just a cube here). The idea is to specify the points of the cube by recursively going through the x, y, and z components. Why not just use nested for-loops? Recursion is intrinsically swag. This is my function:

            ...

            ANSWER

            Answered 2022-Apr-15 at 04:11

            I think I solved the problem:

            Source https://stackoverflow.com/questions/71879615

            QUESTION

            How are the design variables in the SimpleGA or DifferentialEvolution drivers initialized?
            Asked 2022-Apr-10 at 23:31

            I am having trouble navigating the source code to see how the design variables in the initial population for the SimpleGA and DifferentialEvolution Drivers are set. Is there some sort of Latin Hypercube sampling of the design variable ranges? Do the initial values I set in my problem instance get used like they would for the other drivers (Scipy and pyOptSparse)?

            Many thanks, Garrett

            ...

            ANSWER

            Answered 2022-Apr-10 at 23:31

            For these two drivers, the initial value in the model is not used. Its not even clear to me what it would mean to use that value directly, since you need a stochastically generated population --- but I'm admittedly not an expert on the latest GA population initialization methods. However, I can answer the question of how they do get initialized as of OpenMDAO V3.17:

            Simple GA Driver:

            This driver does seem to use an LHS sampling like this:

            Source https://stackoverflow.com/questions/71818035

            QUESTION

            Error in tidymodels - workflowsets : The provided `grid` has the following parameter columns that have not been marked for tuning by `tune()`
            Asked 2022-Apr-02 at 21:50

            I try to use workflowsets package or approach in which I get an error. Here are the R codes (Sorry, the codes are quite long):

            ...

            ANSWER

            Answered 2022-Apr-02 at 21:50

            The neural net doesn't have a parameter called mixture, and the regularized regression model doesn't have parameters called hidden_units or epochs. You can't use the same grid of parameters for both of the models because they don't have the same hyperparameters. Instead, you will want to:

            • create separate grids for the two models
            • use option_add() to add each grid to its model via the id argument

            Also check out Chapter 15 of TMwR to see more about how to add an option to only a specific workflow. Since you are using a Latin hybercube, which is the default in tidymodels, you might want to just skip all that and use grid = 30 instead.

            Source https://stackoverflow.com/questions/71714430

            QUESTION

            Algorithm to dynamically generate m-face list for n-dimensional hypercube
            Asked 2022-Feb-25 at 12:38

            I'm attempting to design an algorithm that, given n, m, and vertices (where n = the dimension of a hypercube, m = the dimension of the faces we're trying to generate, and vertices is an ordered list of vertices in an n-dimensional hypercube), returns an array of arrays of vertices representing m-faces in an n-dimensional hypercube.

            That may have been a confusing way to word it, so here are a few examples:

            Say we wanted to get an array of edges (1-faces) in a cube (3-dimensional hypercube). If we assume vertices is an ordered array of binary representations of vertices in a cube (i.e. [[0, 0, 0], [0, 0, 1], [0, 1, 0], ..., [1, 1, 1]]), we would have:

            ...

            ANSWER

            Answered 2022-Feb-24 at 18:53

            Every m-sized subset of the n dimensions is an "orientation" with those m dimensions "free". For each orientation, you can generate a face by generating all 2m combinations of the m coordinates in the m free dimensions, while holding the coordinates for the other dimensions fixed. Each of the 2n-m combinations of coordinates for the other dimensions is the "position" of a different face.

            The number of orientations is C(n,m) = n!/m!(n-m)!, so you should end up with C(n,m) * 2n-m faces overall.

            The number of edges in a cube, for example = C(3,1) * 22 = 3 * 4 = 12.

            The number of faces on a cube is C(3,2) * 2 = 3 * 2 = 6.

            It's not too difficult to generate all faces:

            • For each orientation, determine the free and fixed dimensions
              • Count in binary using the fixed dimensions to find all the face positions
                • For each face, count in binary over the free dimensions to generate its vertices.

            Source https://stackoverflow.com/questions/71256623

            QUESTION

            How to get the distribution of a parameter using Latin Hypercube Sampling that has bounds in different scales using Python?
            Asked 2022-Feb-22 at 16:27

            I have an equation with three parameters namely a, b, and c. I am minimizing the parameters of this equation by comparing it to a measured behaviour. For this purpose, I am trying to generate a Latin Hypercube Sampling of three-dimensional parameter space (namely for a, b, and c) and want to use different samples as an initial guess for the minimization. Also, I want to generate these samples within bounds ((1e-15,1e-05), (1,5), (0,1)) for each parameter respectively.

            I have the following code for the generation of scaled parameter space:

            ...

            ANSWER

            Answered 2022-Feb-22 at 16:27

            Notice that the interval (1e-15-1e-8) about 1000 times smaller than the interval (1e-8-1e-5). If you want something to span over multiple orders of magnitude probably you want a logarithmic scale.

            Source https://stackoverflow.com/questions/71224554

            QUESTION

            Recipe for XGBoost tidymodels. Error: unused argument (values)
            Asked 2021-Nov-03 at 15:09

            Currently I am doing some experiments with hyperparameter tuning for XGBoost regression on time series, using a latin hypercube sampling strategy. When running the code below, all the models fail during the tune_grid operation. The cause seems to be the recipe object. I used step_dummy() to transform the value column of my univariate time series In the .notes object appears the Error message: preprocessor 1/1: Error: unused argument (values)

            I found some other post where this issue popped up, but none of the solutions helped in my case.

            ...

            ANSWER

            Answered 2021-Oct-27 at 16:19

            It looks like the problem is that those date predictors aren't getting converted to numeric values, which xgboost needs. You did use step_dummy() but dates are not factor/nominal variables so they are not getting chosen by all_nominal(). If you explicitly choose them, this is what happens:

            Source https://stackoverflow.com/questions/69690336

            QUESTION

            Sampling random points from linear subspaces of a given radius in arbitary dimensions
            Asked 2021-Sep-06 at 17:13

            For a project, I need to be able to sample random points uniformly from linear subspaces (ie. lines and hyperplanes) within a certain radius. Since these are linear subspaces, they must go through the origin. This should work for any dimension n from which we draw our subspaces for in Rn.

            I want my range of values to be from -0.5 to 0.5 (ie, all the points should fall within a hypercube whose center is at the origin and length is 1). I have tried to do the following to generate random subspaces and then points from those subspaces but I don't think it's exactly correct (I think I'm missing some form of normalization for the points):

            ...

            ANSWER

            Answered 2021-Sep-03 at 08:56

            I think I'm missing some form of normalization for the points

            Yes, you identified the issue correctly. Let me sum up your algorithm as it stands:

            • Generate a random subspace basis coeffs made of p random vectors in dimension n;
            • Generate coordinates t for amount points in the basis coeffs
            • Return the coordinates of the amount points in R^n, which is the matrix product of t and coeffs.

            This works, except for one detail: the basis coeffs is not an orthonormal basis. The vectors of coeffs do not define a hypercube of side length 1; instead, they define a random parallelepiped.

            To fix your code, you need to generate a random orthonormal basis instead of coeffs. You can do that using scipy.stats.ortho_group.rvs, or if you don't want to import scipy.stats, refer to the accepted answer to that question: How to create a random orthonormal matrix in python numpy?

            Source https://stackoverflow.com/questions/69036765

            QUESTION

            Mapping points to and from a Hilbert curve
            Asked 2021-Jul-19 at 02:29

            I have been trying to write a function for the Hilbert curve map and inverse map. Fortunately there was another SE post on it, and the accepted answer was highly upvoted, and featured code based on a paper in a peer-reviewed academic journal.

            Unfortunately, I played around with the code above and looked at the paper, and I'm not sure how to get this to work. What appears to be broken is that my code drawing the second half of a 2-bit 2-dimensional Hilbert Curve backwards. If you draw out the 2-d coordinates in the last column, you'll see the second half of the curve (position 8 and on) backwards.

            I don't think I'm allowed to post the original C code, but the C++ version below is only lightly edited. A few things that are different in my code.

            1. C code is less strict on types, so I had to use std::bitset

            2. In addition to the bug mentioned by @PaulChernoch in the aforementioned SE post, the next for loop segfaults, too.

            3. The paper represents one-dimensional coordinates weirdly. They call it the number's "Transpose." I wrote a function that produces a "Transpose" from a regular integer.

            Another thing about this algorithm: it doesn't produce a map between unit intervals and unit hypercubes. Rather, it stretches the problem out and maps between intervals and cubes with unit spacing.

            ...

            ANSWER

            Answered 2021-Jul-19 at 02:29

            "In addition to the bug mentioned by @PaulChernoch in the above mentioned SE post, the next for loop segfaults, too." Actually it was a bug in my code--I was having a hard time iterating over a container backwards. I started looking at myself after I realized there were other Python packages (e.g. this and this) that use the same underlying C code. Neither of them were complaining about the other for loop.

            In short, I changed

            Source https://stackoverflow.com/questions/68365065

            QUESTION

            How to sample a data.frame to minimise correlation between selected columns?
            Asked 2021-Mar-20 at 15:12

            I am trying to subsample a data.frame in a way that the sample would have observations that capture as much variation as possible among a set of columns of the original data.frame.

            An example with the mtcars dataset: I'd like to find 3 cars that are the most different from each other by mpg, vs and carb. Looking at the data visually, it would probably be Toyota Corolla (high mpg, vs 1, low carb), Cadillac Fleetwood (low mpg, vs 0, medium carb) and either Maserati Bora (low-med mpg, vs 0, high carb) or Ferrari Dino (medium mpg, vs 0, med-high carb):

            ...

            ANSWER

            Answered 2021-Mar-20 at 15:12

            I am not exactly sure if this is what you are looking for, but here it goes:

            1. calculate a distance matrix, giving you information about how "far away" each car is from all other cars, based on all the attributes they have (the default for dist() is eucledian, which you can change).

            2. Then take the rowsums or colsums (same thing) from that matrix, which just sums up for each car what the combined distance to all other cars is.

            3. Then isolate those cars with the biggest distances (here, we want 3 cars)

            4. Finally subset your dataframe to only include those cars:

            Source https://stackoverflow.com/questions/66675917

            QUESTION

            Custom Python Monte Carlo integration function is underestimating multi-dimensional integrals
            Asked 2021-Mar-01 at 16:44

            I need to create a custom Monte Carlo integration function to adapt to custom multi-dimensional distribution objects using NumPy. I need it to integrate over the same values in each dimension. It works correctly for a single dimension, but underestimates in multiple dimensions, which gets worse with higher dimensions. I am using this paper (equation 5) as a guide. Is my equation of Volume * Mean Density incorrect? Is my sampling method incorrect? I'm really at a loss for what the error is.

            ...

            ANSWER

            Answered 2021-Mar-01 at 16:44

            John, looks good overall but it looks to me that you're figuring the expected result incorrectly. I think the expected result should be (F(2) - F(-2)^3 where F is the Gaussian cdf for mean 0 and variance 1. For F(2) - F(-2), I get erf(sqrt(2)) which is approximately 0.9545, and then (F(2) - F(-2))^3 is 0.8696, which agrees pretty well with your results.

            I don't know what mvn.cdf is supposed to return, but the concept of "cdf" is a little fishy in more than one dimension, so maybe you can steer away from that.

            About multidimensional integration in general, you mention using Halton sequences. I think that's an interesting idea too. My experience with computing integrals is to use quadrature rules in 1 or 2 dimensions, low-discrepancy sequences in 3 to several (5? 7? I dunno), and MC in more than that. Oh, and also my advice is to work pretty hard for exact results before resorting to numerical approximations.

            I would be interested to hear about what you're working on.

            Source https://stackoverflow.com/questions/66404952

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