n-cube | Rules Engine Decision Tables | Rule Engine library

my problem is that i have buttons : "view" button that enable OrbitControls , "move" button that disable OrbitControls so i can use DragControls, "cube" button that add a cube (or many cubes) to the scene and everything works fine, but when i added a "remove" button so i can remove the cube, it didnt work it says that the cube is not defined. So what should i do ? `

I have a problem that when I use DragControls on an object like cubes or cylinders or spheres it works: I can drag these objects but when I use it on loaded objects with OBJLoader it doesn't work I cant drag them PS : I’m using OrbitContols also and it works fine with Geometry objects but not with loaded objects.

I am working on a large C++ framework to do some particle physics computation and it would be nice to have a method to do N-dimensional integration of a generic function with N variables. The integration happens on the N-cube [0:1]^N. The integrand is unfortunately a member function and so I am passing an std::bind object to a templated function (lambdas work well too). When N > 1, the integration method binds again the function fixing one variable and passes the new function to the method for N-1.

I have the feeling that this can be done recursively by having N as a template parameter, but this is where I get stuck: is there a way to "templetize" the binding part such that it adds the right amount of placeholders with either the arity of the function or N? Then pass the new bound method to integral, etc. I believe the problem lies in that an std::bind object has no signature. Maybe with lambdas one could exploit variadic pack expansions, but again the template has to resolve the signature somehow. Is there a way to make this work? If possible in c++11 and without boost libraries.

This is a very simplified version of what I have now without recursions, but instead different definitions of the integration method for each dimension (up to 3 now)

I want to run some integration test using Arquillian, Arquillian cube and Mongo. The desired scenario is:

1. Start the application in a managed container. Here I want to use Shrinkwrap to add just the service I want to test (for example dao service)
2. Start the database inside a docker container. Populate the db with some initial data
3. Run the test against the database

My test looks like this:

I am working to create a basic model of bubbles within a static cube of water to test the electric field generated between an ideal system of parallel plates. I am currently using the PDEToolbox included with matlab so am working with DiscreteGeometries. I have also been tinkering with importing a cube and bubbles as .stl files from CAD software and pairing them. I wish to control the location/size of the blocks such as those used in this example. I wish to create 'bubbles' so just changing the shape/size/location of the sphere used would be great! Is this feasible using matlab at all? Either of the methods stated above would be best unless there is a more applicable way to do this more simply.

I want to implement OR filter functionality in cube.js. This link says we can do this by using segments option in cubejs params.

How to use segments and what is its purpose? I tried understanding from the website with their 5 lines of documentation about it but failed to understand how to use it.

How can you hide one of the actionsheet buttons conditionally?

I have an object occupying an underlying N-dimensional square grid (represented by a numpy array) so that only 25% of the grid points are occupied. Each 1x1x1x... N-cube (i.e., hypercube) in this grid contains the same amount of this object (located only at some of the vertices of this hypercube). I have an array of the coordinates of all the occupied grid points. The task is to cycle through this array and extract the occupied coordinates for each 1x1x1... hypercube and store them in a new array for further processing.

The situation is best explained by example. Consider the 3-D case where the underlying grid has been chosen so that 1<=i,j,k<=4 giving the 2d numpy array: A = [ [1 1 1] [1 2 1] [1 3 1] [1 4 1] [2 1 1] [2 2 1] [2 3 1] [2 4 1] [3 1 1] [3 2 1] [3 3 1] [3 4 1] [4 1 1] [4 2 1] [4 3 1] [4 4 1] [1 1 2] [1 2 2] [1 3 2] [1 4 2] [2 1 2] [2 2 2] [2 3 2] [2 4 2] [3 1 2] [3 2 2] [3 3 2] [3 4 2] [4 1 2] [4 2 2] [4 3 2] [4 4 2] [1 1 3] [1 2 3] [1 3 3] [1 4 3] [2 1 3] [2 2 3] [2 3 3] [2 4 3] [3 1 3] [3 2 3] [3 3 3] [3 4 3] [4 1 3] [4 2 3] [4 3 3] [4 4 3] [1 1 4] [1 2 4] [1 3 4] [1 4 4] [2 1 4] [2 2 4] [2 3 4] [2 4 4] [3 1 4] [3 2 4] [3 3 4] [3 4 4] [4 1 4] [4 2 4] [4 3 4] [4 4 4] ]

An example of the 2d numpy array that I need to process in this case is: B = [[1,1,1],[1,2,1],[1,3,1],[1,4,1],[2,2,1],[2,3,1],[2,4,1],[3,2,1],[3,3,1],[3,4,1],[4,1,1],[4,3,1],[1,1,2],[1,4,2],[2,1,2],[2,2,2],[2,4,2],[3,1,2],[3,2,2],[3,4,2],[4,1,2],[4,2,2],[4,3,2],[4,4,2],[1,1,3],[1,4,3],[2,1,3],[2,2,3],[2,3,3],[2,4,3],[3,1,3],[3,2,3],[3,3,3],[4,1,3],[4,2,3],[4,3,3],[4,4,3],[1,2,4],[1,3,4],[1,4,4],[2,1,4],[2,2,4],[2,3,4],[3,1,4],[3,2,4],[3,3,4],[4,3,4],[4,4,4]]

B is the array of the occupied grid points only. Cycling through B, I would like to get the occupied coordinates pertaining to each of the cubes of the underlying grid. The cube edges are defined by 1<=i,j,k<=2, (3<=i<=4) & (1<=j,k<=2), etc. For instance, for the cube demarcated by 1<=i,j,k<=2, I would like to extract the array [[1,1,1],[1,1,2],[1,2,1],[2,1,2],[2,2,1],[2,2,2]] from B and store it in a new array for further processing. This is then repeated until all the 8 cubes in this example are accounted for. Note that the grids are always chosen to be even so that this type of partitioning is always possible. While I can choose the grids to be even, I have no control over the site occupations by the object.

Numpy array slicing doesn't work because, in general, the grid points are not contiguous in B. I attempted a code using "for" loops to impose the ranges for the edges of the cubes but it got too complicated quickly and it doesn't seem like the right approach. Then there is the "numpy.where" function; but the complexity of the condition makes it rather intractable. Similar challenges arise with "numpy.extract".

I want to use the Monte-Carlo integration method to find the volume of an n-sphere and compare it to the analytical result. I want to do this for 10^6 points and 10^9 points and while it works for 10^6 points somewhat (takes about a minute for n = 2 (circle) , n = 3 (sphere) and n = 12), it is awfully slow for 10^9 points.

Short explanation of MC-Method: To find the Volume of a n-Sphere with radius r = 1, I imagine an easy known Volume (say a n-cube with sides of length 2*r) which fully contains the n-sphere. Then I sample from a uniform distribution points in the n-cube and check, if the point lies in the sphere. I count all the so generated points inside the n-sphere. The ratio of V_sphere/V_cube can be approximated as N_inside/N_total and therefore, V_sphere = V_cube * N_inside/N_total

Here is the function: