sparse-matrix

If you have a sparse matrix X: >> X = csr_matrix([[0,2,0,2],[0,2,0,1]]) >> print type(X) >> print X.todense() <class 'scipy.sparse.csr.csr_matrix'> [[0 2 0 2] [0 2 0 1]] And a matrix Y: >> print type(Y) >> print text_scores <class 'numpy.matrixlib.defmatrix.matrix'> [[8] [5]] ...How can you multiply each element of X by the rows of Y. For example: [[0*8 2*8 0*8 2*8] [0*5 2*5 0*5 1*5]] or: [[0 16 0 16] [0 10 0 5]] I've tired this but obviously it doesn't work as the dimensions dont match: Z = X.data * Y Unfortunatly the .multiply method of the CSR matrix seems to densify the matrix if the other

I have an MxN sparse csr_matrix , and I'd like to add a few columns with only zeroes to the right of the matrix. In principle, the arrays indptr , indices and data keep the same, so I only want to change the dimensions of the matrix. However, this seems to be not implemented. >>> A = csr_matrix(np.identity(5), dtype = int) >>> A.toarray() array([[1, 0, 0, 0, 0], [0, 1, 0, 0, 0], [0, 0, 1, 0, 0], [0, 0, 0, 1, 0], [0, 0, 0, 0, 1]]) >>> A.shape (5, 5) >>> A.shape = ((5,7)) NotImplementedError: Reshaping not implemented for csr_matrix. Also horizontally stacking a zero matrix does not seem to work

I have to create a very big 3D matrix (such as: 500000x60x60 ). Is there any way to do this in matlab? When I try omega = zeros(500000,60,60,'single'); I get an out-of-memory error. The sparse function is no option since it is only meant for 2D matrices. So is there any alternative to that for higher dimensional matrices? Matlab only has support for sparse matrices (2D). For 3D tensors/arrays, you'll have to use a workaround. I can think of two: linear indexing cell arrays Linear indexing You can create a sparse vector like so: A = spalloc(500000*60*60, 1, 100); where the last entry ( 100 )

How do I raise a scipy.sparse matrix to a power, element-wise? numpy.power should, according to its manual , do this, but it fails on sparse matrices: >>> X <1353x32100 sparse matrix of type '<type 'numpy.float64'>' with 144875 stored elements in Compressed Sparse Row format> >>> np.power(X, 2) Traceback (most recent call last): File "<stdin>", line 1, in <module> File ".../scipy/sparse/base.py", line 347, in __pow__ raise TypeError('matrix is not square') TypeError: matrix is not square Same problem with X**2 . Converting to a dense array works, but wastes precious seconds. I've had the same

Most answers only address the already-answered question about Hamming weights but ignore the point about find and dealing with the sparsity. Apparently the answer by Shai here addresses the point about find -- but I am not yet able to verify it. My answer here does not utilise the ingenuity of other answers such as the bitshifting but good enough example answer. Input >> mlf=sparse([],[],[],2^31+1,1);mlf(1)=10;mlf(10)=111;mlf(77)=1010; >> transpose(dec2bin(find(mlf))) ans = 001 000 000 011 001 010 101 Goal 1 0 0 2 1 1 2 Fast calculation for the amount of ones in binary numbers with the sparse

For example, I have a data in the following csv format: csv col0 col1 col2 col3 1 A E|A|C 3 0 B D|F 2 2 C | 2 Each column seperated by comma represent one feature. Normally, a feature is one-hot(e.g. col0, col1, col3 ), but in this case, the feature for col2 has multiple inputs(seperated by |). I'm sure tensorflow can handle one-hot feature with sparse tensor, but I'm not sure whether it could handle features with multiple inputs like col2 ? How should it be represented in Tensorflow's sparse tensor? I am using the code below (but i don't know input method of col2 ) col0 = tf.feature_column