can't understand scipy.sparse.csr_matrix example










2















I can't wrap my head around csr_matrix examples in scipy documentation: https://docs.scipy.org/doc/scipy/reference/generated/scipy.sparse.csr_matrix.html



Can someone explain how this example work?



>>> row = np.array([0, 0, 1, 2, 2, 2])
>>> col = np.array([0, 2, 2, 0, 1, 2])
>>> data = np.array([1, 2, 3, 4, 5, 6])
>>> csr_matrix((data, (row, col)), shape=(3, 3)).toarray()
array([[1, 0, 2],
[0, 0, 3],
[4, 5, 6]])


I believe this is following this format.



csr_matrix((data, (row_ind, col_ind)), [shape=(M, N)])



where data, row_ind and col_ind satisfy the relationship a[row_ind[k], col_ind[k]] = data[k].



What is a here?










share|improve this question



















  • 1





    a is the matrix.

    – Warren Weckesser
    Nov 11 '18 at 23:44















2















I can't wrap my head around csr_matrix examples in scipy documentation: https://docs.scipy.org/doc/scipy/reference/generated/scipy.sparse.csr_matrix.html



Can someone explain how this example work?



>>> row = np.array([0, 0, 1, 2, 2, 2])
>>> col = np.array([0, 2, 2, 0, 1, 2])
>>> data = np.array([1, 2, 3, 4, 5, 6])
>>> csr_matrix((data, (row, col)), shape=(3, 3)).toarray()
array([[1, 0, 2],
[0, 0, 3],
[4, 5, 6]])


I believe this is following this format.



csr_matrix((data, (row_ind, col_ind)), [shape=(M, N)])



where data, row_ind and col_ind satisfy the relationship a[row_ind[k], col_ind[k]] = data[k].



What is a here?










share|improve this question



















  • 1





    a is the matrix.

    – Warren Weckesser
    Nov 11 '18 at 23:44













2












2








2








I can't wrap my head around csr_matrix examples in scipy documentation: https://docs.scipy.org/doc/scipy/reference/generated/scipy.sparse.csr_matrix.html



Can someone explain how this example work?



>>> row = np.array([0, 0, 1, 2, 2, 2])
>>> col = np.array([0, 2, 2, 0, 1, 2])
>>> data = np.array([1, 2, 3, 4, 5, 6])
>>> csr_matrix((data, (row, col)), shape=(3, 3)).toarray()
array([[1, 0, 2],
[0, 0, 3],
[4, 5, 6]])


I believe this is following this format.



csr_matrix((data, (row_ind, col_ind)), [shape=(M, N)])



where data, row_ind and col_ind satisfy the relationship a[row_ind[k], col_ind[k]] = data[k].



What is a here?










share|improve this question
















I can't wrap my head around csr_matrix examples in scipy documentation: https://docs.scipy.org/doc/scipy/reference/generated/scipy.sparse.csr_matrix.html



Can someone explain how this example work?



>>> row = np.array([0, 0, 1, 2, 2, 2])
>>> col = np.array([0, 2, 2, 0, 1, 2])
>>> data = np.array([1, 2, 3, 4, 5, 6])
>>> csr_matrix((data, (row, col)), shape=(3, 3)).toarray()
array([[1, 0, 2],
[0, 0, 3],
[4, 5, 6]])


I believe this is following this format.



csr_matrix((data, (row_ind, col_ind)), [shape=(M, N)])



where data, row_ind and col_ind satisfy the relationship a[row_ind[k], col_ind[k]] = data[k].



What is a here?







python scipy






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share|improve this question













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edited Nov 12 '18 at 2:04







Aerin

















asked Nov 11 '18 at 23:00









AerinAerin

3,76143458




3,76143458







  • 1





    a is the matrix.

    – Warren Weckesser
    Nov 11 '18 at 23:44












  • 1





    a is the matrix.

    – Warren Weckesser
    Nov 11 '18 at 23:44







1




1





a is the matrix.

– Warren Weckesser
Nov 11 '18 at 23:44





a is the matrix.

– Warren Weckesser
Nov 11 '18 at 23:44












2 Answers
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oldest

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2














This is a sparse matrix. So, it stores the explicit indices and values at those indices. So for example, since row=0 and col=0 corresponds to 1 (the first entries of all three arrays in your example). Hence, the [0,0] entry of the matrix is 1. And so on.






share|improve this answer






























    0














    As far as I understand, in row and col arrays we have indices which corrensponds to non-zero values in matrix. a[0, 0] = 1, a[0, 2] = 2, a[1, 2] = 3 and so on. As we have no indices for a[0, 1], a[1, 0], a[1, 1] so appropriate values in matrix are equal to 0.



    Also, maybe this little intro will be helpful for you:
    https://www.youtube.com/watch?v=Lhef_jxzqCg






    share|improve this answer
























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      2 Answers
      2






      active

      oldest

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      2 Answers
      2






      active

      oldest

      votes









      active

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      votes






      active

      oldest

      votes









      2














      This is a sparse matrix. So, it stores the explicit indices and values at those indices. So for example, since row=0 and col=0 corresponds to 1 (the first entries of all three arrays in your example). Hence, the [0,0] entry of the matrix is 1. And so on.






      share|improve this answer



























        2














        This is a sparse matrix. So, it stores the explicit indices and values at those indices. So for example, since row=0 and col=0 corresponds to 1 (the first entries of all three arrays in your example). Hence, the [0,0] entry of the matrix is 1. And so on.






        share|improve this answer

























          2












          2








          2







          This is a sparse matrix. So, it stores the explicit indices and values at those indices. So for example, since row=0 and col=0 corresponds to 1 (the first entries of all three arrays in your example). Hence, the [0,0] entry of the matrix is 1. And so on.






          share|improve this answer













          This is a sparse matrix. So, it stores the explicit indices and values at those indices. So for example, since row=0 and col=0 corresponds to 1 (the first entries of all three arrays in your example). Hence, the [0,0] entry of the matrix is 1. And so on.







          share|improve this answer












          share|improve this answer



          share|improve this answer










          answered Nov 12 '18 at 2:14









          Rohit PandeyRohit Pandey

          98151639




          98151639























              0














              As far as I understand, in row and col arrays we have indices which corrensponds to non-zero values in matrix. a[0, 0] = 1, a[0, 2] = 2, a[1, 2] = 3 and so on. As we have no indices for a[0, 1], a[1, 0], a[1, 1] so appropriate values in matrix are equal to 0.



              Also, maybe this little intro will be helpful for you:
              https://www.youtube.com/watch?v=Lhef_jxzqCg






              share|improve this answer





























                0














                As far as I understand, in row and col arrays we have indices which corrensponds to non-zero values in matrix. a[0, 0] = 1, a[0, 2] = 2, a[1, 2] = 3 and so on. As we have no indices for a[0, 1], a[1, 0], a[1, 1] so appropriate values in matrix are equal to 0.



                Also, maybe this little intro will be helpful for you:
                https://www.youtube.com/watch?v=Lhef_jxzqCg






                share|improve this answer



























                  0












                  0








                  0







                  As far as I understand, in row and col arrays we have indices which corrensponds to non-zero values in matrix. a[0, 0] = 1, a[0, 2] = 2, a[1, 2] = 3 and so on. As we have no indices for a[0, 1], a[1, 0], a[1, 1] so appropriate values in matrix are equal to 0.



                  Also, maybe this little intro will be helpful for you:
                  https://www.youtube.com/watch?v=Lhef_jxzqCg






                  share|improve this answer















                  As far as I understand, in row and col arrays we have indices which corrensponds to non-zero values in matrix. a[0, 0] = 1, a[0, 2] = 2, a[1, 2] = 3 and so on. As we have no indices for a[0, 1], a[1, 0], a[1, 1] so appropriate values in matrix are equal to 0.



                  Also, maybe this little intro will be helpful for you:
                  https://www.youtube.com/watch?v=Lhef_jxzqCg







                  share|improve this answer














                  share|improve this answer



                  share|improve this answer








                  edited Jan 24 at 10:25

























                  answered Jan 24 at 9:55









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