3D sparse matrix implementation?
I've found a quite good sparse matrix implementation for c# over http://www.blackbeltcoder.com/Articles/algorithms/creating-a-sparse-matrix-in-net.
But as i work in 3d coordinate-system, i need a sparse-matrix implementation that i can use to map the 3d-coordinate system.
Details: I'm storing large amounts of primitive shapes data in memory like cubes. I do have large amounts of them (around 30 million) and i've lots of null (zero) entries around. Given that my each entry costs 1-bytes of entry, i'd like to implement a sparse-matrix so that i can fairly save memory space.
Note: Fast access to matrix cells is a fairly important factor for me, so i'd be trading speed over memory consumption.
I've been wondering about this question for quite a while but cannot find a reference: How does Matlab transpose a sparse matrix so fast, given that it is stored in CSC (compressed sparse column) form
Are there any storage optimized Sparse Matrix implementations in C#?
I see 2 implementations of sparse matrix in this package. OpenMapRealMatrix SparseFieldMatrix Both are documented as Sparse matrix implementation based on an open addressed map. Do you know what a
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I have a 3D matrix (MxNxK) and want to resize it to (M'xN'xK') (like imresize in matlab). I am using image pyramid, but its result is not very accurate and need a better one. Any solution?
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I'm trying to use large 10^5x10^5 sparse matrices but seem to be running up against scipy: n = 10 ** 5 x = scipy.sparse.rand(n, n, .001) gets ValueError: Trying to generate a random sparse matrix su
To my understanding, numpy.sparse.csr_sparse.dot(other) does multiply other to my sparse matrix from the right: A = numpy.sparse.csr_sparse(something) B = numpy.matrix(something) C = A.dot(B) # C = A*
I have a 3d mxnxt matrix , I want to be able to extract t 2d nxm matrices. In my case I have a 1024x1024x10 matrix and I want to have 10 images showing it to me. This is not reshaping, I want just par
If I have a 3D matrix, X that is 4 x 10 x 50. The matrix consists of positions and velocities in the first dimension, different particles (or boats or whatever) indexes in the second and lastly the d
I can define a sparse Matrix using a vector for i, j, and x: i <- c(1,3:8) j <- c(2,9,6:10) x <- 7 * (1:7) (A <- sparseMatrix(i, j, x = x)) I want to extract the i, j, and x elements from
I am building a simple model of the Milky Way and one of the things I need to store is a 3D grid of mass densities. The problem is that if I put a rectangular box around the galaxy, most of the grid c
In Matlab, if I were to have a 3D Matrix as follows:- >> T = rand(4,4,3) T(:,:,1) = 0.3214 0.0986 0.4552 0.4033 0.2283 0.8989 0.7460 0.8627 0.9535 0.5170 0.6831 0.6013 0.1657 0.7017 0.9876 0.944
I have a very big and sparse matrix, represented as a CSV file (67 GB). Is it possible to load and work with this matrix in Matlab? I can use a 64bit version on a MAC OS computer, 8GB RAM. I have read
I was trying to iterate over the non zero elements of a row major sparse matrix, such as shown below: Eigen::SparseMatrix<double,Eigen::RowMajor> Test(2, 3); Test.insert(0, 1) = 34; Test.insert
I'm using Python, Numpy and Scipy packages to do matrix computations. I am attempting to perform the calculation X.transpose() * W * X where X is a 2x3 dense matrix and W is a sparse diagonal matrix.
So I have a large 3D matrix (Matrix1=round(rand(100,100,3)*100);) and I need to use the Find option to pick out all the values <16 and replace them with 0. I know it's easier to use other ways, but
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In a scipy program I'm creating a dia_matrix (sparse matrix type) with 5 diagonals. The centre diagonal the +1 & -1 diagonals and the +4 & -4 diagonals (usually >> 4, but the principle i
In MATLAB I have calculated the Fundamental matrix (of two images) using the normalized Eight point algorithm. From that I need to triangulate the corresponding image points in 3D space. From what I u
I need to create a matrix with values from a numpy array. The values should be distributed over the matrix lines according to an array of indices. Like this: >>> values array([ 0.73620381, 0.
We have a matlab program in which we want to calculate the following expression: sum( (M*x) .* x) Here, M is a small dense matrix (say 100 by 100) and x is a sparse fat matrix (say of size 100 by 1
I have a 3D large Matrix, the first index (x) represents frequency and the second and third indexes (y and z) are the indexes of the data. I want to print the data for each index for all the frequenci
Using the Eigen library in C++, given a sparse matrix A, what is the most efficient way (row-wise operations? how to?) to compute a sparse matrix B such that B(i, j) = A(i, j) / A(i, i) ? That is, div
I have a sparse matrix A, generated as an output of glmnet function. When I print the matrix A, it shows all the entries and at the top it reads - 1897 x 100 sparse Matrix of class dgCMatrix [[ su
i am now having a single date vector A (362 rows) and i have a 3D matrix B (dimensions 360*180*3620) > str(ssta_date) POSIXlt[1:362], format: 1981-11-15 1981-12-15 1982-01-15 1982-02-15 19
I want to use sparse matrices for BOW feature representation. I have experimented with coo_matrix from scipy, but it doesn't seem to support what I want to do: I would like to initialize a matrix of a
I'm writing a program that will convert a sparse matrix to Blocked Compressed Row Storage BCRS. I know how to acquire Rowptr, Colind(although not in the code) and A_f. Code: p = 0; for (i = 0; i <
I don`t know how to solve this problem in Fundamentals of data structure in C ed.2nd ch2.5 On a computer with w bits per word, how much storage is needed to represent a sparse matrix, A, with t nonzer
I need to smooth a 3D matrix M. The output of smoothing is S. The matlab code can be like this: S = smooth3(M, 'box', 3); The problem is only some parts in the matrix M should be considered during th
I have a 3d array like this datamonth <- array(0, dim = c(length(LONG),length(LATG),length(YEAR))) >dim(datamonth)  361 181 30 where the first two dimensions are Longitude and Latitude (I ha