hypercube | simple hypercube/tesseract animation
kandi X-RAY | hypercube Summary
kandi X-RAY | hypercube Summary
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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Top functions reviewed by kandi - BETA
- NewHypercube creates a new Hyper cube .
- Draws a horizontal surface
- CalcPath computes the path relative to the given parameters .
- Paths returns all the vertices
hypercube Key Features
hypercube Examples and Code Snippets
Community Discussions
Trending Discussions on hypercube
QUESTION
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 04:11I think I solved the problem:
QUESTION
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 23:31For 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:
This driver does seem to use an LHS sampling like this:
QUESTION
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 21:50The 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 theid
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.
QUESTION
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 18:53Every 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.
- Count in binary using the fixed dimensions to find all the face positions
QUESTION
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 16:27Notice 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.
QUESTION
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 16:19It 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:
QUESTION
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 08:56I 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 ofp
random vectors in dimensionn
; - Generate coordinates
t
foramount
points in the basiscoeffs
- Return the coordinates of the
amount
points in R^n
, which is the matrix product oft
andcoeffs
.
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?
QUESTION
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.
C code is less strict on types, so I had to use
std::bitset
In addition to the bug mentioned by @PaulChernoch in the aforementioned SE post, the next
for
loop segfaults, too.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
QUESTION
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 15:12I am not exactly sure if this is what you are looking for, but here it goes:
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).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.
Then isolate those cars with the biggest distances (here, we want 3 cars)
Finally subset your dataframe to only include those cars:
QUESTION
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 16:44John, 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.
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