sparse-matrix

I have got an output using sparse matrix in python, i need to store this sparse matrix in my hard disk, how can i do it? if i should create a database then how should i do?? this is my code: import nltk import cPickle import numpy from scipy.sparse import lil_matrix from nltk.corpus import wordnet as wn from nltk.corpus import brown f = open('spmatrix.pkl','wb') def markov(L): count=0 c=len(text1) for i in range(0,c-2): h=L.index(text1[i]) k=L.index(text1[i+1]) mat[h,k]=mat[h,k]+1//matrix cPickle.dump(mat,f,-1) text = [w for g in brown.categories() for w in brown.words(categories=g)] text1

I am looking for a library for dealing with sparse matrices in fortran 90/95. I only need very basic operations like matrix-vector multiplication. What do you suggest I use? I have searched around and an extension(?) to BLAS called "sparse blas", documented in chapter 3 of the blast technical forum specification: http://www.netlib.org/blas/blast-forum/ , seems ideal. According to that document, there should be a fortran 95 interface to the library. However, I haven't been able to find an actual implementation of this specification anywhere that I was able to download. I am kind of getting the

I have been playing around with Conway's Game of life and recently discovered some amazingly fast implementations such as Hashlife and Golly. (download Golly here - http://golly.sourceforge.net/ ) One thing that I cant get my head around is how do coders implement the infinite grid? We can't keep an infinite array of anything, if you run golly and get a few gliders to fly off past the edges, wait for a few mins and zoom right out, you will see the gliders still there out in space running away, so how in gods name is this concept of infinity dealt with programmatically? Is there a well

I would appreciate any help, to understand following behavior when slicing a lil_matrix (A) from the scipy.sparse package. Actually, I would like to extract a submatrix based on an arbitrary index list for both rows and columns. When I used this two lines of code: x1 = A[list 1,:] x2 = x1[:,list 2] Everything was fine and I could extract the right submatrix. When I tried to do this in one line, it failed (The returning matrix was empty) x=A[list 1,list 2] Why is this so? Overall, I have used a similar command in matlab and there it works. So, why not use the first, since it works? It seems to

We have tried using tf.nn.embedding_lookup and it works. But it needs dense input data and now we need tf.nn.embedding_lookup_sparse for sparse input. I have written the following code but get some errors. import tensorflow as tf import numpy as np example1 = tf.SparseTensor(indices=[[4], [7]], values=[1, 1], shape=[10]) example2 = tf.SparseTensor(indices=[[3], [6], [9]], values=[1, 1, 1], shape=[10]) vocabulary_size = 10 embedding_size = 1 var = np.array([0.0, 1.0, 4.0, 9.0, 16.0, 25.0, 36.0, 49.0, 64.0, 81.0]) #embeddings = tf.Variable(tf.ones([vocabulary_size, embedding_size])) embeddings =

I have a regular matrix (non-sparse) that I would like to convert to a sparseMatrix (using the Matrix package). Is there a function to do this or do I need to do a bunch of loops? ex. > regMat <- matrix(0, nrow=10, ncol=10) > regMat[3,5] <- round(runif(1),2)*100 > regMat[2,8] <- round(runif(1),2)*100 > regMat[8,4] <- round(runif(1),2)*100 > regMat[1,6] <- round(runif(1),2)*100 > regMat[7,4] <- round(runif(1),2)*100 > regMat [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [1,] 0 0 0 0 0 49 0 0 0 0 [2,] 0 0 0 0 0 0 0 93 0 0 [3,] 0 0 0 0 20 0 0 0 0 0 [4,] 0 0 0 0 0 0 0 0 0 0 [5,] 0 0 0 0 0 0 0

My data can be viewed as a matrix of 10B entries (100M x 100), which is very sparse (< 1/100 * 1/100 of entries are non-zero). I would like to feed the data into into a Keras Neural Network model which I have made, using a Tensorflow backend. My first thought was to expand the data to be dense, that is, write out all 10B entries into a series of CSVs, with most entries zero. However, this is quickly overwhelming my resources (even doing the ETL overwhelmed pandas and is causing postgres to struggle). So I need to use true sparse matrices. How can I do that with Keras (and Tensorflow)? While