sparse-matrix

问题 Assume I have a matrix like: 4 0 3 5 0 2 6 0 7 0 1 0 I want it binarized as: 0 0 0 0 0 1 0 0 0 0 1 0 That is set threshold equal to 2, any element greater than the threshold is set to 0, any element less or equal than the threshold(except 0) is set to 1. Can we do this on python's csr_matrix or any other sparse matrix? I know scikit-learn offer Binarizer to replace values below or equal to the threshold by 0, above it by 1. 回答1: When dealing with a sparse matrix, s , avoid inequalities that

问题 I have an assignment where Im supposed to finish the implementation on a generic sparse matrix. Im stuck on the addition part. The matrix is only going to support numbers so I had it extend Number hoping I could then add the numbers, thats wrong. The data structure is NOT an array, it is essentially 2 linked lists. (one for rows and one for columns) Here is the code in question: public MatrixSparse<? extends Number> addition(MatrixSparse<? extends Number> A, MatrixSparse<? extends Number> B,

问题 I want to perform a large matrix multiplication C = A * B.T and then filter C by applying a stringent threshold, collecting a list of the form (row index, column index, value). A and B are sparse, with mostly zero entries. They are initially represented as sparse scipy csr matrices. Sizes of the matrices (when they are in dense format): A: 9G (900,000 x 1200) B: 6.75G (700,000 x 1200) C, before thresholding: 5000G C, after thresholding: 0.5G Using pyspark, what strategy would you expect to be

问题 I need the fastest-possible solution to this problem as it will be applied to a huge data set: Given this master list: m=['abc','bcd','cde','def'] ...and this reference list of lists: r=[['abc','def'],['bcd','cde'],['abc','def','bcd']] I'd like to compare each list within r to the master list (m) and generate a new list of lists. This new object will have a 1 for matches based on the order in m and 0 for non-matches. So the new object (list of lists) will always have the lists of the same

问题 Why is adding a sparse and a full matrix slower than adding two full matrices? The following code demonstrates consistent superior performance for hFullAddFull . I_FULL = 600; J_FULL = 10000; FULL_COUNT = I_FULL*J_FULL; NON_ZERO_ELEMENT_COUNT = 1000; nonZeroIdxs = randsample(FULL_COUNT, NON_ZERO_ELEMENT_COUNT); mat_Sp = spalloc(I_FULL, J_FULL, NON_ZERO_ELEMENT_COUNT); mat_Sp(nonZeroIdxs) = 0.5; mat_Full = full(mat_Sp); otherMat_Full = rand(I_FULL, J_FULL); hFullAddSp = @()otherMat_Full+mat_Sp

问题 I'm thinking of using Boost's Sparse Matrix for a computation where minimal memory usage is the goal. Unfortunately, the documentation page didn't include a discussion of the sparse matrix implementation's memory usage when I looked through it. Nor am I sure how to determine how much memory the sparse matrix is using at any given time. How much memory will the sparse matrix use? Can you quote a source? How can I find out how much memory the matrix is using at a given time t ? 回答1: I cannot

问题 I am computing the cosine similarity between matrix of vectors, and I get the result in a sparse matrix like this: (0, 26) 0.359171459261 (0, 25) 0.121145761751 (0, 24) 0.316922015914 (0, 23) 0.157622038039 (0, 22) 0.636466644041 (0, 21) 0.136216495731 (0, 20) 0.243164535496 (0, 19) 0.348272617805 (0, 18) 0.636466644041 (0, 17) 1.0 But there are duplicates for example: (0, 24) 0.316922015914 and (24, 0) 0.316922015914 What I want to do is to remove them by indice and be (if I have (0,24) then

问题 I have a very large csv file (about 91 million rows so a for loop takes too long in R) of similarities between keywords (about 50,000 unique keywords) that when I read into a data.frame looks like: > df kwd1 kwd2 similarity a b 1 b a 1 c a 2 a c 2 It is a sparse list and I can convert it into a sparse matrix using sparseMatrix(): > myMatrix a b c a . 1 2 b 1 . . c 2 . . However, now I would like to convert this into a dist object. I tried as.dist(myMatrix) but I was given the error that the

问题 I have downloaded and included UJMP (Universal Java Matrix Package) library to my project for generating sparse matrix. But I could not find any documentation about functions of the library, how to create a sparse matrix, adding element to matrix etc. Is there anyone experienced about it or have a documentation about the library? Thank you for all. 回答1: There is a la4j library that supports sparse matrices and vectors. Follow the examples given at official site. la4j supports CRS (Compressed

问题 I've connected the Kafka Stream to the Spark. As well as I've trained Apache Spark Mlib model to prediction based on a streamed text. My problem is, get a prediction I need to pass a DataFramework. //kafka stream val stream = KafkaUtils.createDirectStream[String, String]( ssc, PreferConsistent, Subscribe[String, String](topics, kafkaParams) ) //load mlib model val model = PipelineModel.load(modelPath) stream.foreachRDD { rdd => rdd.foreach { record => //to get a prediction need to pass DF val