Sparse Matrix Multiplication With Python

25 May, 2021

Multiply other Point-wise multiplication by another matrix vector or scalar. It is implemented entirely in native python using ctypes.

Numpy Cheat Sheet Matrix Multiplication Math Operations Multiplying Matrices

The scipy sparse implementation is single-threaded at the time of writing 2020-01-03.

Sparse matrix multiplication with python. Sparse matricies are a fairly standard thing. Import scipysparse mat1 scipysparserand150e3 300e3 density1e6150e3300e3 mat2 scipysparserand300e3 300e3 density20e6150e3300e3 cmat1 scipysparsecsc_matrixmat1 cmat2 scipysparsecsc_matrixmat2 res cmat1 cmat2 Im using the latest scipy. Multiplication of two matrices X and Y is defined only if the number of columns in X is equal to the number of rows Y.

In the example Im using a 300000x4 matrix for easier printing after the multiplication. It is implemented entirely in native python using ctypes. While numpy has had the npdot mat1 mat2 function for a while I think mat1 mat2 can be a more expressive way of expressing the matrix multiplication.

PS This version uses Python 22s new feature for deriving a class from a dictionary type. Note that other entries of matrices will be zero as matrices are sparse. The first row can be selected as X 0.

The scipy sparse implementation is single-threaded at the time of writing 2020-01-03. The main advantage to MKL which motivated this is multithreaded sparse matrix multiplication. Power n dtype This function performs element-wise power.

To do a vector product between a sparse matrix and a vector simply use the matrix dot method as described in its docstring. Import time numpy scipy from scipysparse import csr_matrix import numpy as np W nprandombinomialn1 p001 size100 100 starttimetime numpymatmulWnumpytransposeW endtimetime dt_dense end - start print time taken for the dense matrix formatend - start sparse_W csr_matrixW starttimetime sparse_Wdotsparse_Wtranspose endtimetime dt_sparse end - start print time taken for the sparse matrix. Just type in python sparsepy to test some of sparses functionality.

Dot v array 1 -3 -1 dtypeint64. And the amount of RAM used by python was 3GB. For i 0.

We can treat each element as a row of the matrix. If you want a picture of your matrix using Tkinter I suggest that you also download colormap. Have you looked at scipysparseTheres no point in re-inventing the wheel here.

Return the minimum of the matrix or maximum along an axis. In Python we can implement a matrix as nested list list inside a list. The result should consist of three sparse matrices one obtained by adding the two input matrices one by multiplying the two matrices and one obtained by transpose of the first matrix.

The transpose of a matrix is calculated by changing the rows as columns and columns as rows. Multiply matrix stored with Compressed Sparse Row method with vector dN for k 0. Array 1 0 - 1 A.

Scipy is a package that builds upon Numpy but provides further mechanisms like sparse matrices which are regular matrices that do only store elements that. And the element in first row first column can be selected as X 0 0. Import numpy as np from scipysparse import csr_matrix A csr_matrix 1 2 0 0 0 3 4 0 5 v np.

This is a wrapper for the sparse matrix multiplication in the intel MKL library. A is kept sparse but A M fills a dense array if M is a dense array. I i 1 for k RowPtri.

Scipy does the matrix multiplication this means no multithreading unlike numpy. Prune Remove empty space after all non-zero elements. Minimum other Element-wise minimum between this and another matrix.

Sparse matrix multiplication shows up in many places and in Python its often handy to use a sparse matrix representation for memory purposes. This is a wrapper for the sparse matrix multiplication in the intel MKL library. The function csr_matrix is used to create a sparse matrix of c ompressed sparse row format whereas csc_matrix is used to create a sparse matrix of c ompressed sparse column format.

Numpydot handles the 2D arrays and perform matrix multiplications. Create a Sparse Matrix in Python Pythons SciPy gives tools for creating sparse matrices using multiple data structures as well as tools for converting a dense matrix to a sparse matrix. A 300000x1000 matrix shouldnt be any problem though.

To multiply them will you can make use of the numpy dot method. K k 1 resulti 0. For a matrix multiplication of the form AB we must provide in the mapper the number of rows of A referenced as row_a in the code and the number of columns of B referenced as col_b The number of columns of A and number of rows of B are always same else multiplication wont be possible.

The main advantage to MKL which motivated this is multithreaded sparse matrix multiplication. K k 1 resulti resulti ValkdColk. For example X 1 2 4 5 3 6 would represent a 3x2 matrix.

Numpydot is the dot product of matrix M1 and M2. One thing nice about the newest version of Python 3 is the operator which takes two matrices and multiplies them. This will be much faster than multiplying two dense arrays assuming you have a majority of 0 elements.

Numpy doesnt do sparse matrices.

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